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6 Commits
ns/flamegr
...
nv/5122
| Author | SHA1 | Date | |
|---|---|---|---|
|
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7dc2836208 | ||
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22acc0feb9 | ||
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bb5a062ef3 | ||
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b06525bac2 | ||
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31fda2861a | ||
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d3ffefd15a |
4
.github/workflows/integrationci.yaml
vendored
4
.github/workflows/integrationci.yaml
vendored
@@ -39,12 +39,10 @@ jobs:
|
||||
matrix:
|
||||
suite:
|
||||
- alerts
|
||||
- basepath
|
||||
- callbackauthn
|
||||
- cloudintegrations
|
||||
- dashboard
|
||||
- ingestionkeys
|
||||
- inframonitoring
|
||||
- logspipelines
|
||||
- passwordauthn
|
||||
- preference
|
||||
@@ -85,7 +83,7 @@ jobs:
|
||||
run: |
|
||||
cd tests && uv sync
|
||||
- name: webdriver
|
||||
if: matrix.suite == 'callbackauthn' || matrix.suite == 'basepath'
|
||||
if: matrix.suite == 'callbackauthn'
|
||||
run: |
|
||||
wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | sudo apt-key add -
|
||||
echo "deb http://dl.google.com/linux/chrome/deb/ stable main" | sudo tee -a /etc/apt/sources.list.d/google-chrome.list
|
||||
|
||||
@@ -91,7 +91,7 @@ func runServer(ctx context.Context, config signoz.Config, logger *slog.Logger) e
|
||||
sqlstoreProviderFactories(),
|
||||
signoz.NewTelemetryStoreProviderFactories(),
|
||||
func(ctx context.Context, providerSettings factory.ProviderSettings, store authtypes.AuthNStore, licensing licensing.Licensing) (map[authtypes.AuthNProvider]authn.AuthN, error) {
|
||||
return signoz.NewAuthNs(ctx, providerSettings, store, licensing, config.Global)
|
||||
return signoz.NewAuthNs(ctx, providerSettings, store, licensing)
|
||||
},
|
||||
func(ctx context.Context, sqlstore sqlstore.SQLStore, config authz.Config, _ licensing.Licensing, _ []authz.OnBeforeRoleDelete) (factory.ProviderFactory[authz.AuthZ, authz.Config], error) {
|
||||
openfgaDataStore, err := openfgaserver.NewSQLStore(sqlstore, config)
|
||||
|
||||
@@ -107,17 +107,17 @@ func runServer(ctx context.Context, config signoz.Config, logger *slog.Logger) e
|
||||
sqlstoreProviderFactories(),
|
||||
signoz.NewTelemetryStoreProviderFactories(),
|
||||
func(ctx context.Context, providerSettings factory.ProviderSettings, store authtypes.AuthNStore, licensing licensing.Licensing) (map[authtypes.AuthNProvider]authn.AuthN, error) {
|
||||
samlCallbackAuthN, err := samlcallbackauthn.New(ctx, store, licensing, config.Global)
|
||||
samlCallbackAuthN, err := samlcallbackauthn.New(ctx, store, licensing)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
oidcCallbackAuthN, err := oidccallbackauthn.New(store, licensing, providerSettings, config.Global)
|
||||
oidcCallbackAuthN, err := oidccallbackauthn.New(store, licensing, providerSettings)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
authNs, err := signoz.NewAuthNs(ctx, providerSettings, store, licensing, config.Global)
|
||||
authNs, err := signoz.NewAuthNs(ctx, providerSettings, store, licensing)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
@@ -440,17 +440,6 @@ traces:
|
||||
max_depth_to_auto_expand: 5
|
||||
# Threshold below which all spans are returned without windowing.
|
||||
max_limit_to_select_all_spans: 10000
|
||||
flamegraph:
|
||||
# Maximum number of BFS depth levels included in a windowed response.
|
||||
max_selected_levels: 50
|
||||
# Maximum spans per level before sampling is applied.
|
||||
max_spans_per_level: 100
|
||||
# Number of highest-latency spans always included when sampling a level.
|
||||
sampling_top_latency_count: 5
|
||||
# Number of timestamp buckets used for uniform sampling within a level.
|
||||
sampling_bucket_count: 50
|
||||
# Threshold below which all spans are returned without windowing or sampling.
|
||||
select_all_spans_limit: 100000
|
||||
|
||||
##################### Authz #################################
|
||||
authz:
|
||||
|
||||
@@ -1360,8 +1360,6 @@ components:
|
||||
- sqs
|
||||
- storageaccountsblob
|
||||
- cdnprofile
|
||||
- containerapp
|
||||
- aks
|
||||
type: string
|
||||
CloudintegrationtypesServiceMetadata:
|
||||
properties:
|
||||
@@ -6640,70 +6638,6 @@ components:
|
||||
- attribute
|
||||
- resource
|
||||
type: string
|
||||
SpantypesFlamegraphSpan:
|
||||
properties:
|
||||
attributes:
|
||||
additionalProperties: {}
|
||||
type: object
|
||||
durationNano:
|
||||
minimum: 0
|
||||
type: integer
|
||||
event:
|
||||
items:
|
||||
$ref: '#/components/schemas/SpantypesEvent'
|
||||
type: array
|
||||
hasError:
|
||||
type: boolean
|
||||
level:
|
||||
format: int64
|
||||
type: integer
|
||||
name:
|
||||
type: string
|
||||
parentSpanId:
|
||||
type: string
|
||||
resource:
|
||||
additionalProperties:
|
||||
type: string
|
||||
type: object
|
||||
spanId:
|
||||
type: string
|
||||
timestamp:
|
||||
minimum: 0
|
||||
type: integer
|
||||
required:
|
||||
- spanId
|
||||
- parentSpanId
|
||||
- timestamp
|
||||
- durationNano
|
||||
- hasError
|
||||
- name
|
||||
- level
|
||||
- event
|
||||
- attributes
|
||||
- resource
|
||||
type: object
|
||||
SpantypesGettableFlamegraphTrace:
|
||||
properties:
|
||||
endTimestampMillis:
|
||||
format: int64
|
||||
type: integer
|
||||
hasMore:
|
||||
type: boolean
|
||||
spans:
|
||||
items:
|
||||
items:
|
||||
$ref: '#/components/schemas/SpantypesFlamegraphSpan'
|
||||
type: array
|
||||
type: array
|
||||
startTimestampMillis:
|
||||
format: int64
|
||||
type: integer
|
||||
required:
|
||||
- spans
|
||||
- startTimestampMillis
|
||||
- endTimestampMillis
|
||||
- hasMore
|
||||
type: object
|
||||
SpantypesGettableSpanMapperGroups:
|
||||
properties:
|
||||
items:
|
||||
@@ -6724,6 +6658,11 @@ components:
|
||||
type: object
|
||||
SpantypesGettableWaterfallTrace:
|
||||
properties:
|
||||
aggregations:
|
||||
items:
|
||||
$ref: '#/components/schemas/SpantypesSpanAggregationResult'
|
||||
nullable: true
|
||||
type: array
|
||||
endTimestampMillis:
|
||||
minimum: 0
|
||||
type: integer
|
||||
@@ -6764,15 +6703,6 @@ components:
|
||||
traceId:
|
||||
type: string
|
||||
type: object
|
||||
SpantypesPostableFlamegraph:
|
||||
properties:
|
||||
selectFields:
|
||||
items:
|
||||
$ref: '#/components/schemas/TelemetrytypesTelemetryFieldKey'
|
||||
type: array
|
||||
selectedSpanId:
|
||||
type: string
|
||||
type: object
|
||||
SpantypesPostableSpanMapper:
|
||||
properties:
|
||||
config:
|
||||
@@ -6811,6 +6741,14 @@ components:
|
||||
type: object
|
||||
SpantypesPostableWaterfall:
|
||||
properties:
|
||||
aggregations:
|
||||
items:
|
||||
$ref: '#/components/schemas/SpantypesSpanAggregation'
|
||||
nullable: true
|
||||
type: array
|
||||
limit:
|
||||
minimum: 0
|
||||
type: integer
|
||||
selectedSpanId:
|
||||
type: string
|
||||
uncollapsedSpans:
|
||||
@@ -20597,11 +20535,12 @@ paths:
|
||||
summary: Put profile in Zeus for a deployment.
|
||||
tags:
|
||||
- zeus
|
||||
/api/v3/traces/{traceID}/flamegraph:
|
||||
/api/v3/traces/{traceID}/waterfall:
|
||||
post:
|
||||
deprecated: false
|
||||
description: Returns the flamegraph view of spans for a given trace ID.
|
||||
operationId: GetFlamegraph
|
||||
description: Returns the waterfall view of spans for a given trace ID with tree
|
||||
structure, metadata, and windowed pagination
|
||||
operationId: GetWaterfall
|
||||
parameters:
|
||||
- in: path
|
||||
name: traceID
|
||||
@@ -20612,7 +20551,7 @@ paths:
|
||||
content:
|
||||
application/json:
|
||||
schema:
|
||||
$ref: '#/components/schemas/SpantypesPostableFlamegraph'
|
||||
$ref: '#/components/schemas/SpantypesPostableWaterfall'
|
||||
responses:
|
||||
"200":
|
||||
content:
|
||||
@@ -20620,7 +20559,7 @@ paths:
|
||||
schema:
|
||||
properties:
|
||||
data:
|
||||
$ref: '#/components/schemas/SpantypesGettableFlamegraphTrace'
|
||||
$ref: '#/components/schemas/SpantypesGettableWaterfallTrace'
|
||||
status:
|
||||
type: string
|
||||
required:
|
||||
@@ -20663,7 +20602,7 @@ paths:
|
||||
- VIEWER
|
||||
- tokenizer:
|
||||
- VIEWER
|
||||
summary: Get flamegraph view for a trace
|
||||
summary: Get waterfall view for a trace
|
||||
tags:
|
||||
- tracedetail
|
||||
/api/v4/traces/{traceID}/waterfall:
|
||||
|
||||
@@ -5,12 +5,10 @@ import (
|
||||
"fmt"
|
||||
"log/slog"
|
||||
"net/url"
|
||||
"path"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/authn"
|
||||
"github.com/SigNoz/signoz/pkg/errors"
|
||||
"github.com/SigNoz/signoz/pkg/factory"
|
||||
"github.com/SigNoz/signoz/pkg/global"
|
||||
"github.com/SigNoz/signoz/pkg/http/client"
|
||||
"github.com/SigNoz/signoz/pkg/licensing"
|
||||
"github.com/SigNoz/signoz/pkg/types/authtypes"
|
||||
@@ -28,14 +26,13 @@ var defaultScopes []string = []string{"email", "profile", oidc.ScopeOpenID}
|
||||
var _ authn.CallbackAuthN = (*AuthN)(nil)
|
||||
|
||||
type AuthN struct {
|
||||
settings factory.ScopedProviderSettings
|
||||
store authtypes.AuthNStore
|
||||
licensing licensing.Licensing
|
||||
httpClient *client.Client
|
||||
globalConfig global.Config
|
||||
settings factory.ScopedProviderSettings
|
||||
store authtypes.AuthNStore
|
||||
licensing licensing.Licensing
|
||||
httpClient *client.Client
|
||||
}
|
||||
|
||||
func New(store authtypes.AuthNStore, licensing licensing.Licensing, providerSettings factory.ProviderSettings, globalConfig global.Config) (*AuthN, error) {
|
||||
func New(store authtypes.AuthNStore, licensing licensing.Licensing, providerSettings factory.ProviderSettings) (*AuthN, error) {
|
||||
settings := factory.NewScopedProviderSettings(providerSettings, "github.com/SigNoz/signoz/ee/authn/callbackauthn/oidccallbackauthn")
|
||||
|
||||
httpClient, err := client.New(providerSettings.Logger, providerSettings.TracerProvider, providerSettings.MeterProvider)
|
||||
@@ -44,11 +41,10 @@ func New(store authtypes.AuthNStore, licensing licensing.Licensing, providerSett
|
||||
}
|
||||
|
||||
return &AuthN{
|
||||
settings: settings,
|
||||
store: store,
|
||||
licensing: licensing,
|
||||
httpClient: httpClient,
|
||||
globalConfig: globalConfig,
|
||||
settings: settings,
|
||||
store: store,
|
||||
licensing: licensing,
|
||||
httpClient: httpClient,
|
||||
}, nil
|
||||
}
|
||||
|
||||
@@ -201,7 +197,7 @@ func (a *AuthN) oidcProviderAndoauth2Config(ctx context.Context, siteURL *url.UR
|
||||
RedirectURL: (&url.URL{
|
||||
Scheme: siteURL.Scheme,
|
||||
Host: siteURL.Host,
|
||||
Path: path.Join(a.globalConfig.ExternalPath(), redirectPath),
|
||||
Path: redirectPath,
|
||||
}).String(),
|
||||
}, nil
|
||||
}
|
||||
|
||||
@@ -6,12 +6,10 @@ import (
|
||||
"encoding/base64"
|
||||
"encoding/pem"
|
||||
"net/url"
|
||||
"path"
|
||||
"strings"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/authn"
|
||||
"github.com/SigNoz/signoz/pkg/errors"
|
||||
"github.com/SigNoz/signoz/pkg/global"
|
||||
"github.com/SigNoz/signoz/pkg/licensing"
|
||||
"github.com/SigNoz/signoz/pkg/types/authtypes"
|
||||
"github.com/SigNoz/signoz/pkg/valuer"
|
||||
@@ -26,16 +24,14 @@ const (
|
||||
var _ authn.CallbackAuthN = (*AuthN)(nil)
|
||||
|
||||
type AuthN struct {
|
||||
store authtypes.AuthNStore
|
||||
licensing licensing.Licensing
|
||||
globalConfig global.Config
|
||||
store authtypes.AuthNStore
|
||||
licensing licensing.Licensing
|
||||
}
|
||||
|
||||
func New(ctx context.Context, store authtypes.AuthNStore, licensing licensing.Licensing, globalConfig global.Config) (*AuthN, error) {
|
||||
func New(ctx context.Context, store authtypes.AuthNStore, licensing licensing.Licensing) (*AuthN, error) {
|
||||
return &AuthN{
|
||||
store: store,
|
||||
licensing: licensing,
|
||||
globalConfig: globalConfig,
|
||||
store: store,
|
||||
licensing: licensing,
|
||||
}, nil
|
||||
}
|
||||
|
||||
@@ -136,7 +132,7 @@ func (a *AuthN) serviceProvider(siteURL *url.URL, authDomain *authtypes.AuthDoma
|
||||
return nil, err
|
||||
}
|
||||
|
||||
acsURL := &url.URL{Scheme: siteURL.Scheme, Host: siteURL.Host, Path: path.Join(a.globalConfig.ExternalPath(), redirectPath)}
|
||||
acsURL := &url.URL{Scheme: siteURL.Scheme, Host: siteURL.Host, Path: redirectPath}
|
||||
|
||||
// Note:
|
||||
// The ServiceProviderIssuer is the client id in case of keycloak. Since we set it to the host here, we need to set the client id == host in keycloak.
|
||||
|
||||
@@ -41,15 +41,6 @@ if (typeof window.IntersectionObserver === 'undefined') {
|
||||
(window as any).IntersectionObserver = IntersectionObserverMock;
|
||||
}
|
||||
|
||||
if (typeof window.ResizeObserver === 'undefined') {
|
||||
class ResizeObserverMock {
|
||||
observe(): void {}
|
||||
unobserve(): void {}
|
||||
disconnect(): void {}
|
||||
}
|
||||
(window as any).ResizeObserver = ResizeObserverMock;
|
||||
}
|
||||
|
||||
// Patch getComputedStyle to handle CSS parsing errors from @signozhq/* packages.
|
||||
// These packages inject CSS at import time via style-inject / vite-plugin-css-injected-by-js.
|
||||
// jsdom's nwsapi cannot parse some of the injected selectors (e.g. Tailwind's :animate-in),
|
||||
|
||||
@@ -2651,8 +2651,6 @@ export enum CloudintegrationtypesServiceIDDTO {
|
||||
sqs = 'sqs',
|
||||
storageaccountsblob = 'storageaccountsblob',
|
||||
cdnprofile = 'cdnprofile',
|
||||
containerapp = 'containerapp',
|
||||
aks = 'aks',
|
||||
}
|
||||
export type CloudintegrationtypesCloudIntegrationServiceDTOAnyOf = {
|
||||
/**
|
||||
@@ -7771,77 +7769,6 @@ export enum SpantypesFieldContextDTO {
|
||||
attribute = 'attribute',
|
||||
resource = 'resource',
|
||||
}
|
||||
export type SpantypesFlamegraphSpanDTOAttributes = { [key: string]: unknown };
|
||||
|
||||
export type SpantypesFlamegraphSpanDTOResource = { [key: string]: string };
|
||||
|
||||
export interface SpantypesFlamegraphSpanDTO {
|
||||
/**
|
||||
* @type object
|
||||
*/
|
||||
attributes: SpantypesFlamegraphSpanDTOAttributes;
|
||||
/**
|
||||
* @type integer
|
||||
* @minimum 0
|
||||
*/
|
||||
durationNano: number;
|
||||
/**
|
||||
* @type array
|
||||
*/
|
||||
event: SpantypesEventDTO[];
|
||||
/**
|
||||
* @type boolean
|
||||
*/
|
||||
hasError: boolean;
|
||||
/**
|
||||
* @type integer
|
||||
* @format int64
|
||||
*/
|
||||
level: number;
|
||||
/**
|
||||
* @type string
|
||||
*/
|
||||
name: string;
|
||||
/**
|
||||
* @type string
|
||||
*/
|
||||
parentSpanId: string;
|
||||
/**
|
||||
* @type object
|
||||
*/
|
||||
resource: SpantypesFlamegraphSpanDTOResource;
|
||||
/**
|
||||
* @type string
|
||||
*/
|
||||
spanId: string;
|
||||
/**
|
||||
* @type integer
|
||||
* @minimum 0
|
||||
*/
|
||||
timestamp: number;
|
||||
}
|
||||
|
||||
export interface SpantypesGettableFlamegraphTraceDTO {
|
||||
/**
|
||||
* @type integer
|
||||
* @format int64
|
||||
*/
|
||||
endTimestampMillis: number;
|
||||
/**
|
||||
* @type boolean
|
||||
*/
|
||||
hasMore: boolean;
|
||||
/**
|
||||
* @type array
|
||||
*/
|
||||
spans: SpantypesFlamegraphSpanDTO[][];
|
||||
/**
|
||||
* @type integer
|
||||
* @format int64
|
||||
*/
|
||||
startTimestampMillis: number;
|
||||
}
|
||||
|
||||
export type SpantypesSpanMapperGroupConditionDTOAnyOf = {
|
||||
/**
|
||||
* @type array,null
|
||||
@@ -8093,6 +8020,10 @@ export interface SpantypesWaterfallSpanDTO {
|
||||
}
|
||||
|
||||
export interface SpantypesGettableWaterfallTraceDTO {
|
||||
/**
|
||||
* @type array,null
|
||||
*/
|
||||
aggregations?: SpantypesSpanAggregationResultDTO[] | null;
|
||||
/**
|
||||
* @type integer
|
||||
* @minimum 0
|
||||
@@ -8139,17 +8070,6 @@ export interface SpantypesGettableWaterfallTraceDTO {
|
||||
uncollapsedSpans?: string[] | null;
|
||||
}
|
||||
|
||||
export interface SpantypesPostableFlamegraphDTO {
|
||||
/**
|
||||
* @type array
|
||||
*/
|
||||
selectFields?: TelemetrytypesTelemetryFieldKeyDTO[];
|
||||
/**
|
||||
* @type string
|
||||
*/
|
||||
selectedSpanId?: string;
|
||||
}
|
||||
|
||||
export enum SpantypesSpanMapperOperationDTO {
|
||||
move = 'move',
|
||||
copy = 'copy',
|
||||
@@ -8212,6 +8132,15 @@ export interface SpantypesPostableTraceAggregationsDTO {
|
||||
}
|
||||
|
||||
export interface SpantypesPostableWaterfallDTO {
|
||||
/**
|
||||
* @type array,null
|
||||
*/
|
||||
aggregations?: SpantypesSpanAggregationDTO[] | null;
|
||||
/**
|
||||
* @type integer
|
||||
* @minimum 0
|
||||
*/
|
||||
limit?: number;
|
||||
/**
|
||||
* @type string
|
||||
*/
|
||||
@@ -10495,11 +10424,11 @@ export type GetHosts200 = {
|
||||
status: string;
|
||||
};
|
||||
|
||||
export type GetFlamegraphPathParameters = {
|
||||
export type GetWaterfallPathParameters = {
|
||||
traceID: string;
|
||||
};
|
||||
export type GetFlamegraph200 = {
|
||||
data: SpantypesGettableFlamegraphTraceDTO;
|
||||
export type GetWaterfall200 = {
|
||||
data: SpantypesGettableWaterfallTraceDTO;
|
||||
/**
|
||||
* @type string
|
||||
*/
|
||||
|
||||
@@ -12,14 +12,13 @@ import type {
|
||||
} from 'react-query';
|
||||
|
||||
import type {
|
||||
GetFlamegraph200,
|
||||
GetFlamegraphPathParameters,
|
||||
GetTraceAggregations200,
|
||||
GetTraceAggregationsPathParameters,
|
||||
GetWaterfall200,
|
||||
GetWaterfallPathParameters,
|
||||
GetWaterfallV4200,
|
||||
GetWaterfallV4PathParameters,
|
||||
RenderErrorResponseDTO,
|
||||
SpantypesPostableFlamegraphDTO,
|
||||
SpantypesPostableTraceAggregationsDTO,
|
||||
SpantypesPostableWaterfallDTO,
|
||||
} from '../sigNoz.schemas';
|
||||
@@ -128,46 +127,46 @@ export const useGetTraceAggregations = <
|
||||
return useMutation(getGetTraceAggregationsMutationOptions(options));
|
||||
};
|
||||
/**
|
||||
* Returns the flamegraph view of spans for a given trace ID.
|
||||
* @summary Get flamegraph view for a trace
|
||||
* Returns the waterfall view of spans for a given trace ID with tree structure, metadata, and windowed pagination
|
||||
* @summary Get waterfall view for a trace
|
||||
*/
|
||||
export const getFlamegraph = (
|
||||
{ traceID }: GetFlamegraphPathParameters,
|
||||
spantypesPostableFlamegraphDTO?: BodyType<SpantypesPostableFlamegraphDTO>,
|
||||
export const getWaterfall = (
|
||||
{ traceID }: GetWaterfallPathParameters,
|
||||
spantypesPostableWaterfallDTO?: BodyType<SpantypesPostableWaterfallDTO>,
|
||||
signal?: AbortSignal,
|
||||
) => {
|
||||
return GeneratedAPIInstance<GetFlamegraph200>({
|
||||
url: `/api/v3/traces/${traceID}/flamegraph`,
|
||||
return GeneratedAPIInstance<GetWaterfall200>({
|
||||
url: `/api/v3/traces/${traceID}/waterfall`,
|
||||
method: 'POST',
|
||||
headers: { 'Content-Type': 'application/json' },
|
||||
data: spantypesPostableFlamegraphDTO,
|
||||
data: spantypesPostableWaterfallDTO,
|
||||
signal,
|
||||
});
|
||||
};
|
||||
|
||||
export const getGetFlamegraphMutationOptions = <
|
||||
export const getGetWaterfallMutationOptions = <
|
||||
TError = ErrorType<RenderErrorResponseDTO>,
|
||||
TContext = unknown,
|
||||
>(options?: {
|
||||
mutation?: UseMutationOptions<
|
||||
Awaited<ReturnType<typeof getFlamegraph>>,
|
||||
Awaited<ReturnType<typeof getWaterfall>>,
|
||||
TError,
|
||||
{
|
||||
pathParams: GetFlamegraphPathParameters;
|
||||
data?: BodyType<SpantypesPostableFlamegraphDTO>;
|
||||
pathParams: GetWaterfallPathParameters;
|
||||
data?: BodyType<SpantypesPostableWaterfallDTO>;
|
||||
},
|
||||
TContext
|
||||
>;
|
||||
}): UseMutationOptions<
|
||||
Awaited<ReturnType<typeof getFlamegraph>>,
|
||||
Awaited<ReturnType<typeof getWaterfall>>,
|
||||
TError,
|
||||
{
|
||||
pathParams: GetFlamegraphPathParameters;
|
||||
data?: BodyType<SpantypesPostableFlamegraphDTO>;
|
||||
pathParams: GetWaterfallPathParameters;
|
||||
data?: BodyType<SpantypesPostableWaterfallDTO>;
|
||||
},
|
||||
TContext
|
||||
> => {
|
||||
const mutationKey = ['getFlamegraph'];
|
||||
const mutationKey = ['getWaterfall'];
|
||||
const { mutation: mutationOptions } = options
|
||||
? options.mutation &&
|
||||
'mutationKey' in options.mutation &&
|
||||
@@ -177,54 +176,54 @@ export const getGetFlamegraphMutationOptions = <
|
||||
: { mutation: { mutationKey } };
|
||||
|
||||
const mutationFn: MutationFunction<
|
||||
Awaited<ReturnType<typeof getFlamegraph>>,
|
||||
Awaited<ReturnType<typeof getWaterfall>>,
|
||||
{
|
||||
pathParams: GetFlamegraphPathParameters;
|
||||
data?: BodyType<SpantypesPostableFlamegraphDTO>;
|
||||
pathParams: GetWaterfallPathParameters;
|
||||
data?: BodyType<SpantypesPostableWaterfallDTO>;
|
||||
}
|
||||
> = (props) => {
|
||||
const { pathParams, data } = props ?? {};
|
||||
|
||||
return getFlamegraph(pathParams, data);
|
||||
return getWaterfall(pathParams, data);
|
||||
};
|
||||
|
||||
return { mutationFn, ...mutationOptions };
|
||||
};
|
||||
|
||||
export type GetFlamegraphMutationResult = NonNullable<
|
||||
Awaited<ReturnType<typeof getFlamegraph>>
|
||||
export type GetWaterfallMutationResult = NonNullable<
|
||||
Awaited<ReturnType<typeof getWaterfall>>
|
||||
>;
|
||||
export type GetFlamegraphMutationBody =
|
||||
| BodyType<SpantypesPostableFlamegraphDTO>
|
||||
export type GetWaterfallMutationBody =
|
||||
| BodyType<SpantypesPostableWaterfallDTO>
|
||||
| undefined;
|
||||
export type GetFlamegraphMutationError = ErrorType<RenderErrorResponseDTO>;
|
||||
export type GetWaterfallMutationError = ErrorType<RenderErrorResponseDTO>;
|
||||
|
||||
/**
|
||||
* @summary Get flamegraph view for a trace
|
||||
* @summary Get waterfall view for a trace
|
||||
*/
|
||||
export const useGetFlamegraph = <
|
||||
export const useGetWaterfall = <
|
||||
TError = ErrorType<RenderErrorResponseDTO>,
|
||||
TContext = unknown,
|
||||
>(options?: {
|
||||
mutation?: UseMutationOptions<
|
||||
Awaited<ReturnType<typeof getFlamegraph>>,
|
||||
Awaited<ReturnType<typeof getWaterfall>>,
|
||||
TError,
|
||||
{
|
||||
pathParams: GetFlamegraphPathParameters;
|
||||
data?: BodyType<SpantypesPostableFlamegraphDTO>;
|
||||
pathParams: GetWaterfallPathParameters;
|
||||
data?: BodyType<SpantypesPostableWaterfallDTO>;
|
||||
},
|
||||
TContext
|
||||
>;
|
||||
}): UseMutationResult<
|
||||
Awaited<ReturnType<typeof getFlamegraph>>,
|
||||
Awaited<ReturnType<typeof getWaterfall>>,
|
||||
TError,
|
||||
{
|
||||
pathParams: GetFlamegraphPathParameters;
|
||||
data?: BodyType<SpantypesPostableFlamegraphDTO>;
|
||||
pathParams: GetWaterfallPathParameters;
|
||||
data?: BodyType<SpantypesPostableWaterfallDTO>;
|
||||
},
|
||||
TContext
|
||||
> => {
|
||||
return useMutation(getGetFlamegraphMutationOptions(options));
|
||||
return useMutation(getGetWaterfallMutationOptions(options));
|
||||
};
|
||||
/**
|
||||
* Returns the waterfall view of spans including all spans if total spans are under a limit, a max count otherwise. Aggregations are dropped compared to v3
|
||||
|
||||
@@ -27,6 +27,7 @@ const getTraceV4 = async (
|
||||
{
|
||||
selectedSpanId: props.selectedSpanId,
|
||||
uncollapsedSpans,
|
||||
limit: 10000,
|
||||
},
|
||||
);
|
||||
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
import { useCallback, useMemo, useRef } from 'react';
|
||||
import ChartLayout from 'container/DashboardContainer/visualization/layout/ChartLayout/ChartLayout';
|
||||
import UPlotLegend from 'lib/uPlotV2/components/Legend/UPlotLegend';
|
||||
import Legend from 'lib/uPlotV2/components/Legend/Legend';
|
||||
import {
|
||||
LegendPosition,
|
||||
TooltipRenderArgs,
|
||||
@@ -47,7 +47,7 @@ export default function ChartWrapper({
|
||||
return null;
|
||||
}
|
||||
return (
|
||||
<UPlotLegend
|
||||
<Legend
|
||||
config={config}
|
||||
position={legendConfig.position}
|
||||
averageLegendWidth={averageLegendWidth}
|
||||
|
||||
@@ -1,67 +0,0 @@
|
||||
.pieChartWrapper {
|
||||
display: flex;
|
||||
flex-direction: column;
|
||||
height: 100%;
|
||||
width: 100%;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.pieChartNoData {
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
width: 100%;
|
||||
height: 100%;
|
||||
font-size: 14px;
|
||||
}
|
||||
|
||||
// Size is set inline from the computed chart dimensions (mirrors the uPlot
|
||||
// chart/legend split); this just centres the donut within that box.
|
||||
.pieChartContainer {
|
||||
flex: 0 0 auto;
|
||||
position: relative;
|
||||
display: flex;
|
||||
justify-content: center;
|
||||
align-items: center;
|
||||
}
|
||||
|
||||
.pieChartTooltip {
|
||||
padding: 8px 12px;
|
||||
border-radius: 4px;
|
||||
box-shadow: 0 2px 10px rgba(0, 0, 0, 0.2);
|
||||
background-color: var(--l2-background) !important;
|
||||
border: 1px solid var(--l2-border) !important;
|
||||
z-index: 9999;
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
}
|
||||
.pieChartTooltipContent {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
gap: 8px;
|
||||
}
|
||||
|
||||
.pieChartIndicator {
|
||||
width: 12px;
|
||||
height: 12px;
|
||||
border-radius: 2px;
|
||||
margin-right: 8px;
|
||||
display: inline-block;
|
||||
}
|
||||
|
||||
.pieChartTooltipValue {
|
||||
font-weight: bold;
|
||||
margin-top: 4px;
|
||||
}
|
||||
|
||||
// Wraps the shared chart Legend. Its width/height are set inline from the
|
||||
// computed chart dimensions, so the VirtuosoGrid inside gets the same bounded
|
||||
// box (right column / bottom rows) the uPlot charts use.
|
||||
.pieChartLegend {
|
||||
flex: 0 0 auto;
|
||||
min-height: 0;
|
||||
min-width: 0;
|
||||
padding: 8px;
|
||||
}
|
||||
@@ -1,235 +0,0 @@
|
||||
import { useCallback, useMemo, useRef } from 'react';
|
||||
import { Color } from '@signozhq/design-tokens';
|
||||
import { Group } from '@visx/group';
|
||||
import { Pie as VisxPie } from '@visx/shape';
|
||||
import { defaultStyles, useTooltip, useTooltipInPortal } from '@visx/tooltip';
|
||||
import { getYAxisFormattedValue } from 'components/Graph/yAxisConfig';
|
||||
import { useResizeObserver } from 'hooks/useDimensions';
|
||||
import Legend from 'lib/uPlotV2/components/Legend/Legend';
|
||||
import { LegendPosition } from 'lib/uPlotV2/components/types';
|
||||
|
||||
import { PieChartProps, PieSlice } from '../types';
|
||||
import { calculateChartDimensions } from '../utils';
|
||||
|
||||
import { usePieInteractions } from '../../hooks/usePieInteractions';
|
||||
import PieArc from './PieArc';
|
||||
import PieCenterLabel from './PieCenterLabel';
|
||||
import styles from './Pie.module.scss';
|
||||
import { PieTooltipData } from './types';
|
||||
import { getFillColor } from './utils';
|
||||
|
||||
/**
|
||||
* Donut chart rendered with @visx. Splits its area into chart + legend with the
|
||||
* same `calculateChartDimensions` logic as the uPlot charts (right column /
|
||||
* up-to-two bottom rows), renders the shared chart Legend, and delegates the
|
||||
* arcs, centre total and interaction state to PieArc / PieCenterLabel /
|
||||
* usePieInteractions. Pure presentation — slices are pre-resolved by the caller.
|
||||
*/
|
||||
export default function Pie({
|
||||
data,
|
||||
yAxisUnit,
|
||||
decimalPrecision,
|
||||
isDarkMode,
|
||||
position = LegendPosition.BOTTOM,
|
||||
id,
|
||||
onSliceClick,
|
||||
'data-testid': testId,
|
||||
}: PieChartProps): JSX.Element {
|
||||
const {
|
||||
active,
|
||||
setActive,
|
||||
visibleData,
|
||||
legendItems,
|
||||
focusedSeriesIndex,
|
||||
onLegendClick,
|
||||
onLegendMouseMove,
|
||||
onLegendMouseLeave,
|
||||
} = usePieInteractions(data, id);
|
||||
|
||||
const {
|
||||
tooltipOpen,
|
||||
tooltipLeft,
|
||||
tooltipTop,
|
||||
tooltipData,
|
||||
hideTooltip,
|
||||
showTooltip,
|
||||
} = useTooltip<PieTooltipData>();
|
||||
|
||||
const { containerRef, TooltipInPortal } = useTooltipInPortal({
|
||||
scroll: true,
|
||||
detectBounds: true,
|
||||
});
|
||||
|
||||
const wrapperRef = useRef<HTMLDivElement>(null);
|
||||
const { width: containerWidth, height: containerHeight } =
|
||||
useResizeObserver(wrapperRef);
|
||||
|
||||
// Reuse the uPlot chart/legend split so the donut + legend get the same area
|
||||
// allocation (right column, or up-to-two bottom rows) as every other panel.
|
||||
const { width, height, legendWidth, legendHeight, averageLegendWidth } =
|
||||
useMemo(
|
||||
() =>
|
||||
calculateChartDimensions({
|
||||
containerWidth,
|
||||
containerHeight,
|
||||
legendConfig: { position },
|
||||
seriesLabels: data.map((slice) => slice.label),
|
||||
}),
|
||||
[containerWidth, containerHeight, position, data],
|
||||
);
|
||||
|
||||
// Donut geometry derived from the allocated chart box.
|
||||
const { size, radius, innerRadius } = useMemo(() => {
|
||||
const nextSize = Math.min(width, height);
|
||||
const nextRadius = nextSize * 0.35;
|
||||
return {
|
||||
size: nextSize,
|
||||
radius: nextRadius,
|
||||
innerRadius: nextRadius * 0.6,
|
||||
};
|
||||
}, [width, height]);
|
||||
|
||||
const totalValue = useMemo(
|
||||
() => visibleData.reduce((sum, slice) => sum + slice.value, 0),
|
||||
[visibleData],
|
||||
);
|
||||
|
||||
const labelColor = isDarkMode ? Color.BG_VANILLA_100 : Color.BG_INK_400;
|
||||
const activeColor = active?.color ?? null;
|
||||
|
||||
const handleSliceEnter = useCallback(
|
||||
(slice: PieSlice, centroidX: number, centroidY: number): void => {
|
||||
showTooltip({
|
||||
tooltipData: {
|
||||
label: slice.label,
|
||||
value: getYAxisFormattedValue(
|
||||
slice.value.toString(),
|
||||
yAxisUnit || 'none',
|
||||
decimalPrecision,
|
||||
),
|
||||
color: slice.color,
|
||||
},
|
||||
tooltipTop: centroidY + height / 2,
|
||||
tooltipLeft: centroidX + width / 2,
|
||||
});
|
||||
setActive(slice);
|
||||
},
|
||||
[showTooltip, setActive, yAxisUnit, decimalPrecision, height, width],
|
||||
);
|
||||
|
||||
const handleSliceLeave = useCallback((): void => {
|
||||
hideTooltip();
|
||||
setActive(null);
|
||||
}, [hideTooltip, setActive]);
|
||||
|
||||
if (!data.length) {
|
||||
return (
|
||||
<div
|
||||
ref={wrapperRef}
|
||||
className={styles.pieChartWrapper}
|
||||
data-testid={testId}
|
||||
>
|
||||
<div className={styles.pieChartNoData}>No data</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
|
||||
const isRightLegend = position === LegendPosition.RIGHT;
|
||||
|
||||
return (
|
||||
<div
|
||||
ref={wrapperRef}
|
||||
className={styles.pieChartWrapper}
|
||||
style={{ flexDirection: isRightLegend ? 'row' : 'column' }}
|
||||
data-testid={testId}
|
||||
>
|
||||
<div className={styles.pieChartContainer} style={{ width, height }}>
|
||||
{size > 0 && (
|
||||
<svg width={width} height={height} ref={containerRef}>
|
||||
<Group top={height / 2} left={width / 2}>
|
||||
<VisxPie
|
||||
data={visibleData}
|
||||
pieValue={(slice: PieSlice): number => slice.value}
|
||||
outerRadius={radius}
|
||||
innerRadius={innerRadius}
|
||||
padAngle={0.01}
|
||||
cornerRadius={3}
|
||||
width={size}
|
||||
height={size}
|
||||
>
|
||||
{(pie): JSX.Element[] =>
|
||||
pie.arcs.map((arc) => (
|
||||
<PieArc
|
||||
key={`arc-${arc.data.label}-${arc.data.value}-${arc.startAngle.toFixed(
|
||||
6,
|
||||
)}`}
|
||||
slice={arc.data}
|
||||
arcPath={pie.path(arc) || ''}
|
||||
centroid={pie.path.centroid(arc)}
|
||||
startAngle={arc.startAngle}
|
||||
endAngle={arc.endAngle}
|
||||
radius={radius}
|
||||
totalValue={totalValue}
|
||||
yAxisUnit={yAxisUnit}
|
||||
decimalPrecision={decimalPrecision}
|
||||
labelColor={labelColor}
|
||||
fill={getFillColor(arc.data.color, activeColor)}
|
||||
onEnter={handleSliceEnter}
|
||||
onLeave={handleSliceLeave}
|
||||
onClick={onSliceClick}
|
||||
/>
|
||||
))
|
||||
}
|
||||
</VisxPie>
|
||||
<PieCenterLabel
|
||||
total={totalValue}
|
||||
yAxisUnit={yAxisUnit}
|
||||
decimalPrecision={decimalPrecision}
|
||||
radius={radius}
|
||||
innerRadius={innerRadius}
|
||||
color={labelColor}
|
||||
/>
|
||||
</Group>
|
||||
</svg>
|
||||
)}
|
||||
{tooltipOpen && tooltipData && (
|
||||
<TooltipInPortal
|
||||
top={tooltipTop}
|
||||
left={tooltipLeft}
|
||||
className={styles.pieChartTooltip}
|
||||
style={{
|
||||
...defaultStyles,
|
||||
color: labelColor,
|
||||
}}
|
||||
>
|
||||
<div
|
||||
className={styles.pieChartIndicator}
|
||||
style={{ background: tooltipData.color }}
|
||||
/>
|
||||
<div className={styles.pieChartTooltipContent}>
|
||||
<span>{tooltipData.label}</span>
|
||||
<span className={styles.pieChartTooltipValue}>{tooltipData.value}</span>
|
||||
</div>
|
||||
</TooltipInPortal>
|
||||
)}
|
||||
</div>
|
||||
<div
|
||||
className={styles.pieChartLegend}
|
||||
style={{
|
||||
width: legendWidth,
|
||||
height: legendHeight,
|
||||
}}
|
||||
>
|
||||
<Legend
|
||||
items={legendItems}
|
||||
position={position}
|
||||
averageLegendWidth={averageLegendWidth}
|
||||
focusedSeriesIndex={focusedSeriesIndex}
|
||||
onClick={onLegendClick}
|
||||
onMouseMove={onLegendMouseMove}
|
||||
onMouseLeave={onLegendMouseLeave}
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -1,123 +0,0 @@
|
||||
import type { PrecisionOption } from 'components/Graph/types';
|
||||
import { getYAxisFormattedValue } from 'components/Graph/yAxisConfig';
|
||||
|
||||
import { PieSlice } from '../types';
|
||||
|
||||
import { getArcGeometry } from './utils';
|
||||
|
||||
// Slices below this share of the total don't get a leader label (too cramped).
|
||||
const MIN_LABEL_SHARE = 0.03;
|
||||
const MAX_LABEL_LENGTH = 15;
|
||||
|
||||
interface PieArcProps {
|
||||
slice: PieSlice;
|
||||
/** SVG path `d` for the arc, from the visx pie generator. */
|
||||
arcPath: string;
|
||||
/** Arc centroid `[x, y]`, used to anchor the leader line and tooltip. */
|
||||
centroid: [number, number];
|
||||
startAngle: number;
|
||||
endAngle: number;
|
||||
radius: number;
|
||||
/** Sum of visible slice values — drives the show-label threshold. */
|
||||
totalValue: number;
|
||||
yAxisUnit?: string;
|
||||
decimalPrecision?: PrecisionOption;
|
||||
labelColor: string;
|
||||
/** Resolved fill (already dimmed if another slice is active). */
|
||||
fill: string;
|
||||
onEnter: (slice: PieSlice, centroidX: number, centroidY: number) => void;
|
||||
onLeave: () => void;
|
||||
onClick?: (slice: PieSlice) => void;
|
||||
}
|
||||
|
||||
/**
|
||||
* A single donut slice: the arc path plus, for non-tiny slices, a leader line
|
||||
* out to an external label + value. Pure presentation — interaction is
|
||||
* delegated to the `onEnter`/`onLeave`/`onClick` callbacks.
|
||||
*/
|
||||
export default function PieArc({
|
||||
slice,
|
||||
arcPath,
|
||||
centroid,
|
||||
startAngle,
|
||||
endAngle,
|
||||
radius,
|
||||
totalValue,
|
||||
yAxisUnit,
|
||||
decimalPrecision,
|
||||
labelColor,
|
||||
fill,
|
||||
onEnter,
|
||||
onLeave,
|
||||
onClick,
|
||||
}: PieArcProps): JSX.Element {
|
||||
const { label, value } = slice;
|
||||
const [centroidX, centroidY] = centroid;
|
||||
const { labelX, labelY, lineEndX, lineEndY, textAnchor } = getArcGeometry(
|
||||
startAngle,
|
||||
endAngle,
|
||||
radius,
|
||||
);
|
||||
|
||||
const displayValue = getYAxisFormattedValue(
|
||||
value.toString(),
|
||||
yAxisUnit || 'none',
|
||||
decimalPrecision,
|
||||
);
|
||||
const shortenedLabel =
|
||||
label.length > MAX_LABEL_LENGTH ? `${label.substring(0, 12)}...` : label;
|
||||
const shouldShowLabel = value / totalValue > MIN_LABEL_SHARE;
|
||||
|
||||
return (
|
||||
<g
|
||||
onMouseEnter={(): void => onEnter(slice, centroidX, centroidY)}
|
||||
onMouseLeave={onLeave}
|
||||
onClick={(): void => onClick?.(slice)}
|
||||
>
|
||||
<path d={arcPath} fill={fill} />
|
||||
{shouldShowLabel && (
|
||||
<>
|
||||
<line
|
||||
x1={centroidX}
|
||||
y1={centroidY}
|
||||
x2={lineEndX}
|
||||
y2={lineEndY}
|
||||
stroke={labelColor}
|
||||
strokeWidth={1}
|
||||
/>
|
||||
<line
|
||||
x1={lineEndX}
|
||||
y1={lineEndY}
|
||||
x2={labelX}
|
||||
y2={labelY}
|
||||
stroke={labelColor}
|
||||
strokeWidth={1}
|
||||
/>
|
||||
<text
|
||||
x={labelX}
|
||||
y={labelY - 8}
|
||||
dy=".33em"
|
||||
fill={labelColor}
|
||||
fontSize={10}
|
||||
textAnchor={textAnchor}
|
||||
pointerEvents="none"
|
||||
>
|
||||
{shortenedLabel}
|
||||
</text>
|
||||
<text
|
||||
x={labelX}
|
||||
y={labelY + 8}
|
||||
dy=".33em"
|
||||
fill={labelColor}
|
||||
fontSize={10}
|
||||
fontWeight="bold"
|
||||
textAnchor={textAnchor}
|
||||
pointerEvents="none"
|
||||
>
|
||||
{displayValue}
|
||||
</text>
|
||||
</>
|
||||
)}
|
||||
</g>
|
||||
);
|
||||
}
|
||||
@@ -1,57 +0,0 @@
|
||||
import type { PrecisionOption } from 'components/Graph/types';
|
||||
import { getYAxisFormattedValue } from 'components/Graph/yAxisConfig';
|
||||
|
||||
import { getScaledFontSize } from './utils';
|
||||
|
||||
interface PieCenterLabelProps {
|
||||
/** Sum of the visible slice values, shown in the donut hole. */
|
||||
total: number;
|
||||
yAxisUnit?: string;
|
||||
decimalPrecision?: PrecisionOption;
|
||||
radius: number;
|
||||
innerRadius: number;
|
||||
color: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* The total shown in the centre of the donut. Splits the formatted value into
|
||||
* its numeric part and unit so each can be sized independently, and scales the
|
||||
* numeric font down for long values so it never overflows the hole.
|
||||
*/
|
||||
export default function PieCenterLabel({
|
||||
total,
|
||||
yAxisUnit,
|
||||
decimalPrecision,
|
||||
radius,
|
||||
innerRadius,
|
||||
color,
|
||||
}: PieCenterLabelProps): JSX.Element {
|
||||
const formattedTotal = getYAxisFormattedValue(
|
||||
total.toString(),
|
||||
yAxisUnit || 'none',
|
||||
decimalPrecision,
|
||||
);
|
||||
const matches = formattedTotal.match(/([\d.]+[KMB]?)(.*)$/);
|
||||
const numericTotal = matches?.[1] || formattedTotal;
|
||||
const unitTotal = matches?.[2]?.trim() || '';
|
||||
|
||||
const numericFontSize = getScaledFontSize({
|
||||
text: numericTotal,
|
||||
baseSize: radius * 0.3,
|
||||
innerRadius,
|
||||
});
|
||||
const unitFontSize = numericFontSize * 0.5;
|
||||
|
||||
return (
|
||||
<text textAnchor="middle" dominantBaseline="central" fill={color}>
|
||||
<tspan fontSize={numericFontSize} fontWeight="bold">
|
||||
{numericTotal}
|
||||
</tspan>
|
||||
{unitTotal && (
|
||||
<tspan fontSize={unitFontSize} opacity={0.9} dx={2}>
|
||||
{unitTotal}
|
||||
</tspan>
|
||||
)}
|
||||
</text>
|
||||
);
|
||||
}
|
||||
@@ -1,116 +0,0 @@
|
||||
import React from 'react';
|
||||
import { fireEvent, render, screen, within } from '@testing-library/react';
|
||||
import { TooltipProvider } from '@signozhq/ui/tooltip';
|
||||
import { LegendPosition } from 'lib/uPlotV2/components/types';
|
||||
import { LegendItem } from 'lib/uPlotV2/config/types';
|
||||
|
||||
import { PieSlice } from '../../types';
|
||||
import Pie from '../Pie';
|
||||
|
||||
jest.mock('hooks/useDimensions', () => ({
|
||||
useResizeObserver: jest.fn().mockReturnValue({ width: 400, height: 300 }),
|
||||
}));
|
||||
|
||||
jest.mock('components/Graph/yAxisConfig', () => ({
|
||||
getYAxisFormattedValue: jest.fn((value: string) => value),
|
||||
}));
|
||||
|
||||
// VirtuosoGrid only renders a window in jsdom; render every item so we can
|
||||
// assert on legend entries.
|
||||
jest.mock('react-virtuoso', () => ({
|
||||
VirtuosoGrid: ({
|
||||
data,
|
||||
itemContent,
|
||||
}: {
|
||||
data: LegendItem[];
|
||||
itemContent: (index: number, item: LegendItem) => React.ReactNode;
|
||||
}): JSX.Element => (
|
||||
<div data-testid="virtuoso-grid">
|
||||
{data.map((item, index) => (
|
||||
<div key={item.seriesIndex ?? index}>{itemContent(index, item)}</div>
|
||||
))}
|
||||
</div>
|
||||
),
|
||||
}));
|
||||
|
||||
const DATA: PieSlice[] = [
|
||||
{ label: 'frontend', value: 100, color: '#aa0000' },
|
||||
{ label: 'cart', value: 60, color: '#00aa00' },
|
||||
{ label: 'checkout', value: 40, color: '#0000aa' },
|
||||
];
|
||||
|
||||
function renderPie(
|
||||
props: Partial<React.ComponentProps<typeof Pie>> = {},
|
||||
): void {
|
||||
render(
|
||||
<TooltipProvider>
|
||||
<Pie data={DATA} isDarkMode={false} data-testid="pie" {...props} />
|
||||
</TooltipProvider>,
|
||||
);
|
||||
}
|
||||
|
||||
describe('Pie', () => {
|
||||
it('renders the "No data" state for empty data', () => {
|
||||
render(
|
||||
<TooltipProvider>
|
||||
<Pie data={[]} isDarkMode={false} data-testid="pie" />
|
||||
</TooltipProvider>,
|
||||
);
|
||||
expect(screen.getByText('No data')).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('renders one arc per slice plus the legend entries and centre total', () => {
|
||||
renderPie();
|
||||
|
||||
const svg = screen.getByTestId('pie').querySelector('svg') as SVGElement;
|
||||
expect(svg.querySelectorAll('path')).toHaveLength(DATA.length);
|
||||
|
||||
const legend = screen.getByTestId('virtuoso-grid');
|
||||
expect(within(legend).getByText('frontend')).toBeInTheDocument();
|
||||
expect(within(legend).getByText('cart')).toBeInTheDocument();
|
||||
expect(within(legend).getByText('checkout')).toBeInTheDocument();
|
||||
|
||||
// Centre total = 100 + 60 + 40 (formatter mocked to echo the value).
|
||||
expect(screen.getByText('200')).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('lays the legend out in a row for the right position and a column for bottom', () => {
|
||||
const { rerender } = render(
|
||||
<TooltipProvider>
|
||||
<Pie
|
||||
data={DATA}
|
||||
isDarkMode={false}
|
||||
position={LegendPosition.RIGHT}
|
||||
data-testid="pie"
|
||||
/>
|
||||
</TooltipProvider>,
|
||||
);
|
||||
expect(screen.getByTestId('pie')).toHaveStyle({ flexDirection: 'row' });
|
||||
|
||||
rerender(
|
||||
<TooltipProvider>
|
||||
<Pie
|
||||
data={DATA}
|
||||
isDarkMode={false}
|
||||
position={LegendPosition.BOTTOM}
|
||||
data-testid="pie"
|
||||
/>
|
||||
</TooltipProvider>,
|
||||
);
|
||||
expect(screen.getByTestId('pie')).toHaveStyle({ flexDirection: 'column' });
|
||||
});
|
||||
|
||||
it('hides a slice when its legend marker is clicked', () => {
|
||||
renderPie();
|
||||
const svg = screen.getByTestId('pie').querySelector('svg') as SVGElement;
|
||||
expect(svg.querySelectorAll('path')).toHaveLength(3);
|
||||
|
||||
const marker = document.querySelector(
|
||||
'[data-legend-item-id="1"] [data-is-legend-marker="true"]',
|
||||
) as HTMLElement;
|
||||
fireEvent.click(marker);
|
||||
|
||||
// One slice hidden → one fewer arc drawn.
|
||||
expect(svg.querySelectorAll('path')).toHaveLength(2);
|
||||
});
|
||||
});
|
||||
@@ -1,85 +0,0 @@
|
||||
import { fireEvent, render, screen } from '@testing-library/react';
|
||||
|
||||
import { PieSlice } from '../../types';
|
||||
import PieArc from '../PieArc';
|
||||
|
||||
jest.mock('components/Graph/yAxisConfig', () => ({
|
||||
// Echo the raw value so assertions are deterministic.
|
||||
getYAxisFormattedValue: jest.fn((value: string) => value),
|
||||
}));
|
||||
|
||||
const SLICE: PieSlice = { label: 'frontend', value: 50, color: '#f00' };
|
||||
|
||||
function renderArc(props: Partial<React.ComponentProps<typeof PieArc>> = {}): {
|
||||
onEnter: jest.Mock;
|
||||
onLeave: jest.Mock;
|
||||
onClick: jest.Mock;
|
||||
container: HTMLElement;
|
||||
} {
|
||||
const onEnter = jest.fn();
|
||||
const onLeave = jest.fn();
|
||||
const onClick = jest.fn();
|
||||
const { container } = render(
|
||||
<svg>
|
||||
<PieArc
|
||||
slice={SLICE}
|
||||
arcPath="M0,0L1,1"
|
||||
centroid={[10, 20]}
|
||||
startAngle={0}
|
||||
endAngle={Math.PI}
|
||||
radius={100}
|
||||
totalValue={100}
|
||||
labelColor="#fff"
|
||||
fill="#f00"
|
||||
onEnter={onEnter}
|
||||
onLeave={onLeave}
|
||||
onClick={onClick}
|
||||
{...props}
|
||||
/>
|
||||
</svg>,
|
||||
);
|
||||
return { onEnter, onLeave, onClick, container };
|
||||
}
|
||||
|
||||
describe('PieArc', () => {
|
||||
it('renders the arc path with the resolved fill', () => {
|
||||
const { container } = renderArc();
|
||||
const path = container.querySelector('path');
|
||||
expect(path).toHaveAttribute('d', 'M0,0L1,1');
|
||||
expect(path).toHaveAttribute('fill', '#f00');
|
||||
});
|
||||
|
||||
it('shows the leader label + value for a slice above the threshold', () => {
|
||||
renderArc(); // 50 / 100 = 0.5
|
||||
expect(screen.getByText('frontend')).toBeInTheDocument();
|
||||
expect(screen.getByText('50')).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('hides the leader label for a slice below the 3% threshold', () => {
|
||||
renderArc({ totalValue: 10000 }); // 50 / 10000 = 0.005
|
||||
expect(screen.queryByText('frontend')).not.toBeInTheDocument();
|
||||
// the arc path itself still renders
|
||||
expect(screen.queryByText('50')).not.toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('truncates labels longer than 15 chars', () => {
|
||||
renderArc({
|
||||
slice: { label: 'a-really-long-service-name', value: 50, color: '#f00' },
|
||||
});
|
||||
expect(screen.getByText('a-really-lon...')).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('fires onEnter with the slice + centroid, and onLeave / onClick', () => {
|
||||
const { onEnter, onLeave, onClick, container } = renderArc();
|
||||
const g = container.querySelector('g') as SVGGElement;
|
||||
|
||||
fireEvent.mouseEnter(g);
|
||||
expect(onEnter).toHaveBeenCalledWith(SLICE, 10, 20);
|
||||
|
||||
fireEvent.mouseLeave(g);
|
||||
expect(onLeave).toHaveBeenCalledTimes(1);
|
||||
|
||||
fireEvent.click(g);
|
||||
expect(onClick).toHaveBeenCalledWith(SLICE);
|
||||
});
|
||||
});
|
||||
@@ -1,45 +0,0 @@
|
||||
import { render, screen } from '@testing-library/react';
|
||||
import { getYAxisFormattedValue } from 'components/Graph/yAxisConfig';
|
||||
|
||||
import PieCenterLabel from '../PieCenterLabel';
|
||||
|
||||
jest.mock('components/Graph/yAxisConfig', () => ({
|
||||
getYAxisFormattedValue: jest.fn(),
|
||||
}));
|
||||
|
||||
const mockFormat = getYAxisFormattedValue as jest.MockedFunction<
|
||||
typeof getYAxisFormattedValue
|
||||
>;
|
||||
|
||||
function renderInSvg(node: JSX.Element): ReturnType<typeof render> {
|
||||
// PieCenterLabel returns an SVG <text>, so it needs an <svg> host.
|
||||
return render(<svg>{node}</svg>);
|
||||
}
|
||||
|
||||
describe('PieCenterLabel', () => {
|
||||
const baseProps = {
|
||||
total: 3700,
|
||||
radius: 100,
|
||||
innerRadius: 60,
|
||||
color: '#fff',
|
||||
};
|
||||
|
||||
it('renders the formatted total (numeric + unit suffix) as one numeric tspan when there is no separate unit', () => {
|
||||
mockFormat.mockReturnValue('3.7K');
|
||||
renderInSvg(<PieCenterLabel {...baseProps} />);
|
||||
expect(screen.getByText('3.7K')).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('splits the numeric part and the trailing unit into separate tspans', () => {
|
||||
mockFormat.mockReturnValue('1.2 MB');
|
||||
renderInSvg(<PieCenterLabel {...baseProps} />);
|
||||
expect(screen.getByText('1.2')).toBeInTheDocument();
|
||||
expect(screen.getByText('MB')).toBeInTheDocument();
|
||||
});
|
||||
|
||||
it('passes the unit + precision through to the formatter', () => {
|
||||
mockFormat.mockReturnValue('100');
|
||||
renderInSvg(<PieCenterLabel {...baseProps} total={100} yAxisUnit="bytes" />);
|
||||
expect(mockFormat).toHaveBeenCalledWith('100', 'bytes', undefined);
|
||||
});
|
||||
});
|
||||
@@ -1,101 +0,0 @@
|
||||
import {
|
||||
getArcGeometry,
|
||||
getFillColor,
|
||||
getScaledFontSize,
|
||||
lightenColor,
|
||||
} from '../utils';
|
||||
|
||||
describe('Pie utils', () => {
|
||||
describe('getScaledFontSize', () => {
|
||||
it('returns the base size for empty text', () => {
|
||||
expect(getScaledFontSize({ text: '', baseSize: 30, innerRadius: 100 })).toBe(
|
||||
30,
|
||||
);
|
||||
});
|
||||
|
||||
it('does not scale short text (length <= 3)', () => {
|
||||
// scaleFactor = max(0.3, 1) = 1 → baseSize, capped by innerRadius * 0.9.
|
||||
expect(
|
||||
getScaledFontSize({ text: '3.7', baseSize: 30, innerRadius: 100 }),
|
||||
).toBe(30);
|
||||
});
|
||||
|
||||
it('scales longer text down', () => {
|
||||
// length 8 → scaleFactor = max(0.3, 1 - 5 * 0.09) = 0.55 → 30 * 0.55.
|
||||
expect(
|
||||
getScaledFontSize({ text: '12345678', baseSize: 30, innerRadius: 100 }),
|
||||
).toBeCloseTo(16.5);
|
||||
});
|
||||
|
||||
it('floors the scale factor at 0.3 for very long text', () => {
|
||||
// length 20 → 1 - 17 * 0.09 < 0.3 → floored to 0.3 → 100 * 0.3.
|
||||
expect(
|
||||
getScaledFontSize({
|
||||
text: '12345678901234567890',
|
||||
baseSize: 100,
|
||||
innerRadius: 1000,
|
||||
}),
|
||||
).toBeCloseTo(30);
|
||||
});
|
||||
|
||||
it('caps the size at 90% of the inner radius', () => {
|
||||
expect(
|
||||
getScaledFontSize({ text: '3.7', baseSize: 200, innerRadius: 10 }),
|
||||
).toBeCloseTo(9);
|
||||
});
|
||||
});
|
||||
|
||||
describe('getArcGeometry', () => {
|
||||
it('places the label below for a slice centred at the top (angle 0)', () => {
|
||||
const g = getArcGeometry(0, 0, 100);
|
||||
expect(g.labelX).toBeCloseTo(0);
|
||||
expect(g.labelY).toBeCloseTo(-130);
|
||||
expect(g.lineEndX).toBeCloseTo(0);
|
||||
expect(g.lineEndY).toBeCloseTo(-110);
|
||||
// sin(0) is not > 0 → anchor end.
|
||||
expect(g.textAnchor).toBe('end');
|
||||
});
|
||||
|
||||
it('anchors to the start on the right half (angle pi/2)', () => {
|
||||
const g = getArcGeometry(0, Math.PI, 100);
|
||||
expect(g.labelX).toBeCloseTo(130);
|
||||
expect(g.labelY).toBeCloseTo(0);
|
||||
expect(g.textAnchor).toBe('start');
|
||||
});
|
||||
|
||||
it('anchors to the end on the left half (angle 3pi/2)', () => {
|
||||
const g = getArcGeometry(Math.PI, 2 * Math.PI, 100);
|
||||
expect(g.labelX).toBeCloseTo(-130);
|
||||
expect(g.textAnchor).toBe('end');
|
||||
});
|
||||
});
|
||||
|
||||
describe('lightenColor', () => {
|
||||
it('converts a #rrggbb hex to rgba at the given opacity', () => {
|
||||
expect(lightenColor('#ff0000', 0.4)).toBe('rgba(255, 0, 0, 0.4)');
|
||||
});
|
||||
|
||||
it('accepts hex without a leading #', () => {
|
||||
expect(lightenColor('00ff00', 0.4)).toBe('rgba(0, 255, 0, 0.4)');
|
||||
});
|
||||
|
||||
it('returns the original colour when it is not parseable hex', () => {
|
||||
expect(lightenColor('rgba(0,0,0,1)', 0.4)).toBe('rgba(0,0,0,1)');
|
||||
expect(lightenColor('red', 0.4)).toBe('red');
|
||||
});
|
||||
});
|
||||
|
||||
describe('getFillColor', () => {
|
||||
it('returns the colour unchanged when nothing is active', () => {
|
||||
expect(getFillColor('#ff0000', null)).toBe('#ff0000');
|
||||
});
|
||||
|
||||
it('returns the colour unchanged for the active slice', () => {
|
||||
expect(getFillColor('#ff0000', '#ff0000')).toBe('#ff0000');
|
||||
});
|
||||
|
||||
it('dims non-active slices to 40% opacity', () => {
|
||||
expect(getFillColor('#00ff00', '#ff0000')).toBe('rgba(0, 255, 0, 0.4)');
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -1,35 +0,0 @@
|
||||
/**
|
||||
* Pie-local types. Kept out of the component / util files so each stays focused
|
||||
* (per the one-component-per-file + dedicated-types rules). Shared chart types
|
||||
* (PieSlice, PieChartProps) live in the parent charts/types.ts.
|
||||
*/
|
||||
|
||||
export interface ScaledFontSizeArgs {
|
||||
text: string;
|
||||
baseSize: number;
|
||||
innerRadius: number;
|
||||
}
|
||||
|
||||
export interface ArcGeometry {
|
||||
/** Outer point where the leader label sits. */
|
||||
labelX: number;
|
||||
labelY: number;
|
||||
/** Elbow point where the leader line bends toward the label. */
|
||||
lineEndX: number;
|
||||
lineEndY: number;
|
||||
/** Anchor the label left/right depending on which half of the circle it's in. */
|
||||
textAnchor: 'start' | 'end';
|
||||
}
|
||||
|
||||
export interface ParsedRgb {
|
||||
r: number;
|
||||
g: number;
|
||||
b: number;
|
||||
}
|
||||
|
||||
/** Resolved tooltip payload shown when a slice is hovered. */
|
||||
export interface PieTooltipData {
|
||||
label: string;
|
||||
value: string;
|
||||
color: string;
|
||||
}
|
||||
@@ -1,89 +0,0 @@
|
||||
/**
|
||||
* Pure presentation helpers for the Pie chart. Kept out of the component file
|
||||
* so the renderer stays declarative (per the one-component-per-file rule).
|
||||
*/
|
||||
|
||||
import { ArcGeometry, ParsedRgb, ScaledFontSizeArgs } from './types';
|
||||
|
||||
/**
|
||||
* Shrinks the centre-total font as the text gets longer so it never overflows
|
||||
* the donut hole. Ported from the V1 PiePanelWrapper.
|
||||
*/
|
||||
export function getScaledFontSize({
|
||||
text,
|
||||
baseSize,
|
||||
innerRadius,
|
||||
}: ScaledFontSizeArgs): number {
|
||||
if (!text) {
|
||||
return baseSize;
|
||||
}
|
||||
|
||||
const { length } = text;
|
||||
// More aggressive scaling for very long numbers.
|
||||
const scaleFactor = Math.max(0.3, 1 - (length - 3) * 0.09);
|
||||
// Don't use more than 90% of the inner radius.
|
||||
const maxSize = innerRadius * 0.9;
|
||||
|
||||
return Math.min(baseSize * scaleFactor, maxSize);
|
||||
}
|
||||
|
||||
/**
|
||||
* Computes the leader-line / label geometry for one arc from its angular span.
|
||||
* Pulled out of the render prop so the SVG markup stays declarative.
|
||||
*/
|
||||
export function getArcGeometry(
|
||||
startAngle: number,
|
||||
endAngle: number,
|
||||
radius: number,
|
||||
): ArcGeometry {
|
||||
const angle = (startAngle + endAngle) / 2;
|
||||
const labelRadius = radius * 1.3;
|
||||
const lineEndRadius = radius * 1.1;
|
||||
return {
|
||||
labelX: Math.sin(angle) * labelRadius,
|
||||
labelY: -Math.cos(angle) * labelRadius,
|
||||
lineEndX: Math.sin(angle) * lineEndRadius,
|
||||
lineEndY: -Math.cos(angle) * lineEndRadius,
|
||||
textAnchor: Math.sin(angle) > 0 ? 'start' : 'end',
|
||||
};
|
||||
}
|
||||
|
||||
// Parses `#rrggbb` into its components. Returns null for anything else (e.g. an
|
||||
// already-rgba string), letting callers fall back to the original colour.
|
||||
function hexToRgb(color: string): ParsedRgb | null {
|
||||
const result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})$/i.exec(color);
|
||||
return result
|
||||
? {
|
||||
r: parseInt(result[1], 16),
|
||||
g: parseInt(result[2], 16),
|
||||
b: parseInt(result[3], 16),
|
||||
}
|
||||
: null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Returns an rgba() string for `color` at the given opacity. Used to dim the
|
||||
* non-hovered slices. Falls back to the original colour if it can't be parsed.
|
||||
*/
|
||||
export function lightenColor(color: string, opacity: number): string {
|
||||
const rgb = hexToRgb(color);
|
||||
if (!rgb) {
|
||||
return color;
|
||||
}
|
||||
return `rgba(${rgb.r}, ${rgb.g}, ${rgb.b}, ${opacity})`;
|
||||
}
|
||||
|
||||
/**
|
||||
* Resolves the fill for a slice given the currently-hovered slice colour:
|
||||
* everything but the active slice dims to 40% opacity. With nothing hovered
|
||||
* (`activeColor === null`) every slice keeps its full colour.
|
||||
*/
|
||||
export function getFillColor(
|
||||
color: string,
|
||||
activeColor: string | null,
|
||||
): string {
|
||||
if (activeColor === null) {
|
||||
return color;
|
||||
}
|
||||
return activeColor === color ? color : lightenColor(color, 0.4);
|
||||
}
|
||||
@@ -3,14 +3,13 @@ import { PrecisionOption } from 'components/Graph/types';
|
||||
import {
|
||||
IRenderTooltipFooterArgs,
|
||||
LegendConfig,
|
||||
LegendPosition,
|
||||
TooltipRenderArgs,
|
||||
} from 'lib/uPlotV2/components/types';
|
||||
import { UPlotConfigBuilder } from 'lib/uPlotV2/config/UPlotConfigBuilder';
|
||||
import {
|
||||
DashboardCursorSync,
|
||||
SyncTooltipFilterMode,
|
||||
ChartClickData,
|
||||
TooltipClickData,
|
||||
} from 'lib/uPlotV2/plugins/TooltipPlugin/types';
|
||||
import { BaseAutocompleteData } from 'types/api/queryBuilder/queryAutocompleteResponse';
|
||||
|
||||
@@ -23,10 +22,10 @@ interface BaseChartProps {
|
||||
/** Key that pins the tooltip while hovering. Defaults to DEFAULT_PIN_TOOLTIP_KEY ('l'). */
|
||||
pinKey?: string;
|
||||
/** Called when the user clicks the uPlot overlay. Receives resolved click data. */
|
||||
onClick?: (clickData: ChartClickData) => void;
|
||||
onClick?: (clickData: TooltipClickData) => void;
|
||||
yAxisUnit?: string;
|
||||
decimalPrecision?: PrecisionOption;
|
||||
pinnedTooltipElement?: (clickData: ChartClickData) => React.ReactNode;
|
||||
pinnedTooltipElement?: (clickData: TooltipClickData) => React.ReactNode;
|
||||
renderTooltipFooter?: (args: IRenderTooltipFooterArgs) => React.ReactNode;
|
||||
customTooltip?: (props: TooltipRenderArgs) => React.ReactNode;
|
||||
'data-testid'?: string;
|
||||
@@ -70,36 +69,3 @@ export type ChartProps =
|
||||
| TimeSeriesChartProps
|
||||
| BarChartProps
|
||||
| HistogramChartProps;
|
||||
|
||||
/**
|
||||
* One resolved pie/donut slice: a display label, its (already parsed) positive
|
||||
* numeric value, and the colour used for the arc + legend swatch.
|
||||
*/
|
||||
export interface PieSlice {
|
||||
label: string;
|
||||
value: number;
|
||||
color: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* Props for the Pie chart. Unlike the others above, Pie is NOT uPlot-based
|
||||
* (it renders with @visx), so it deliberately does not extend BaseChartProps /
|
||||
* UPlotBasedChartProps — it takes pre-resolved slices and self-measures its
|
||||
* draw area rather than receiving a uPlot config + aligned data.
|
||||
*/
|
||||
export interface PieChartProps {
|
||||
data: PieSlice[];
|
||||
yAxisUnit?: string;
|
||||
decimalPrecision?: PrecisionOption;
|
||||
isDarkMode: boolean;
|
||||
/** Legend placement. Drives the chart-vs-legend layout. Default BOTTOM. */
|
||||
position?: LegendPosition;
|
||||
/**
|
||||
* Widget id used to persist per-slice hide/unhide state to localStorage
|
||||
* (shared GRAPH_VISIBILITY_STATES, keyed by label). Omit to disable persistence.
|
||||
*/
|
||||
id?: string;
|
||||
/** Fired when a slice (or its legend entry) is clicked. */
|
||||
onSliceClick?: (slice: PieSlice) => void;
|
||||
'data-testid'?: string;
|
||||
}
|
||||
|
||||
@@ -1,147 +0,0 @@
|
||||
import { act, renderHook } from '@testing-library/react';
|
||||
import {
|
||||
getStoredSeriesVisibility,
|
||||
updateSeriesVisibilityToLocalStorage,
|
||||
} from 'container/DashboardContainer/visualization/panels/utils/legendVisibilityUtils';
|
||||
import type { MouseEvent } from 'react';
|
||||
|
||||
import { PieSlice } from '../../charts/types';
|
||||
import { usePieInteractions } from '../usePieInteractions';
|
||||
|
||||
jest.mock(
|
||||
'container/DashboardContainer/visualization/panels/utils/legendVisibilityUtils',
|
||||
);
|
||||
|
||||
const mockGetStored = getStoredSeriesVisibility as jest.MockedFunction<
|
||||
typeof getStoredSeriesVisibility
|
||||
>;
|
||||
const mockUpdateStored =
|
||||
updateSeriesVisibilityToLocalStorage as jest.MockedFunction<
|
||||
typeof updateSeriesVisibilityToLocalStorage
|
||||
>;
|
||||
|
||||
const DATA: PieSlice[] = [
|
||||
{ label: 'frontend', value: 100, color: '#a' },
|
||||
{ label: 'cart', value: 60, color: '#b' },
|
||||
{ label: 'checkout', value: 40, color: '#c' },
|
||||
];
|
||||
|
||||
// Builds a fake legend click/move event: `e.target.closest('[data-legend-item-id]')`
|
||||
// resolves to the item at `index`, and `e.target.dataset.isLegendMarker` flags marker clicks.
|
||||
function legendEvent(
|
||||
index: number | null,
|
||||
isMarker = false,
|
||||
): MouseEvent<HTMLDivElement> {
|
||||
const itemEl =
|
||||
index == null ? null : { dataset: { legendItemId: String(index) } };
|
||||
return {
|
||||
target: {
|
||||
closest: (): unknown => itemEl,
|
||||
dataset: { isLegendMarker: isMarker ? 'true' : undefined },
|
||||
},
|
||||
} as unknown as MouseEvent<HTMLDivElement>;
|
||||
}
|
||||
|
||||
describe('usePieInteractions', () => {
|
||||
beforeEach(() => {
|
||||
mockGetStored.mockReturnValue(null);
|
||||
mockUpdateStored.mockReset();
|
||||
});
|
||||
|
||||
it('starts with everything visible and nothing focused', () => {
|
||||
const { result } = renderHook(() => usePieInteractions(DATA));
|
||||
|
||||
expect(result.current.visibleData).toStrictEqual(DATA);
|
||||
expect(result.current.legendItems.map((i) => i.show)).toStrictEqual([
|
||||
true,
|
||||
true,
|
||||
true,
|
||||
]);
|
||||
expect(result.current.focusedSeriesIndex).toBeNull();
|
||||
expect(result.current.active).toBeNull();
|
||||
});
|
||||
|
||||
describe('marker click (toggle one)', () => {
|
||||
it('hides then unhides the clicked slice', () => {
|
||||
const { result } = renderHook(() => usePieInteractions(DATA, 'panel-1'));
|
||||
|
||||
act(() => result.current.onLegendClick(legendEvent(1, true)));
|
||||
|
||||
expect(result.current.visibleData).toStrictEqual([DATA[0], DATA[2]]);
|
||||
expect(result.current.legendItems[1].show).toBe(false);
|
||||
expect(mockUpdateStored).toHaveBeenLastCalledWith('panel-1', [
|
||||
{ label: 'frontend', show: true },
|
||||
{ label: 'cart', show: false },
|
||||
{ label: 'checkout', show: true },
|
||||
]);
|
||||
|
||||
act(() => result.current.onLegendClick(legendEvent(1, true)));
|
||||
|
||||
expect(result.current.visibleData).toStrictEqual(DATA);
|
||||
expect(result.current.legendItems[1].show).toBe(true);
|
||||
});
|
||||
});
|
||||
|
||||
describe('label click (isolate / reset)', () => {
|
||||
it('isolates the clicked slice, then resets on a second click', () => {
|
||||
const { result } = renderHook(() => usePieInteractions(DATA));
|
||||
|
||||
act(() => result.current.onLegendClick(legendEvent(0, false)));
|
||||
|
||||
expect(result.current.visibleData).toStrictEqual([DATA[0]]);
|
||||
expect(result.current.legendItems.map((i) => i.show)).toStrictEqual([
|
||||
true,
|
||||
false,
|
||||
false,
|
||||
]);
|
||||
|
||||
act(() => result.current.onLegendClick(legendEvent(0, false)));
|
||||
|
||||
expect(result.current.visibleData).toStrictEqual(DATA);
|
||||
});
|
||||
});
|
||||
|
||||
describe('hover', () => {
|
||||
it('focuses the hovered slice and clears on leave', () => {
|
||||
const { result } = renderHook(() => usePieInteractions(DATA));
|
||||
|
||||
act(() => result.current.onLegendMouseMove(legendEvent(2)));
|
||||
expect(result.current.active).toStrictEqual(DATA[2]);
|
||||
expect(result.current.focusedSeriesIndex).toBe(2);
|
||||
|
||||
act(() => result.current.onLegendMouseLeave());
|
||||
expect(result.current.active).toBeNull();
|
||||
expect(result.current.focusedSeriesIndex).toBeNull();
|
||||
});
|
||||
|
||||
it('does not focus a hidden slice', () => {
|
||||
const { result } = renderHook(() => usePieInteractions(DATA));
|
||||
|
||||
act(() => result.current.onLegendClick(legendEvent(1, true))); // hide cart
|
||||
act(() => result.current.onLegendMouseMove(legendEvent(1)));
|
||||
|
||||
expect(result.current.active).toBeNull();
|
||||
});
|
||||
});
|
||||
|
||||
describe('persistence', () => {
|
||||
it('does not write to storage when no id is provided', () => {
|
||||
const { result } = renderHook(() => usePieInteractions(DATA));
|
||||
act(() => result.current.onLegendClick(legendEvent(0, true)));
|
||||
expect(mockUpdateStored).not.toHaveBeenCalled();
|
||||
});
|
||||
|
||||
it('rehydrates hidden slices from storage on mount (matched by label)', () => {
|
||||
mockGetStored.mockReturnValue([
|
||||
{ label: 'frontend', show: true },
|
||||
{ label: 'cart', show: false },
|
||||
{ label: 'checkout', show: true },
|
||||
]);
|
||||
|
||||
const { result } = renderHook(() => usePieInteractions(DATA, 'panel-1'));
|
||||
|
||||
expect(result.current.visibleData).toStrictEqual([DATA[0], DATA[2]]);
|
||||
expect(result.current.legendItems[1].show).toBe(false);
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -1,168 +0,0 @@
|
||||
import { LegendItem } from 'lib/uPlotV2/config/types';
|
||||
import type { Dispatch, MouseEvent, SetStateAction } from 'react';
|
||||
import { useCallback, useEffect, useMemo, useRef, useState } from 'react';
|
||||
|
||||
import {
|
||||
getStoredSeriesVisibility,
|
||||
updateSeriesVisibilityToLocalStorage,
|
||||
} from '../panels/utils/legendVisibilityUtils';
|
||||
import { PieSlice } from '../charts/types';
|
||||
|
||||
export interface UsePieInteractionsResult {
|
||||
/** The hovered/focused slice (drives donut dimming + tooltip). */
|
||||
active: PieSlice | null;
|
||||
setActive: Dispatch<SetStateAction<PieSlice | null>>;
|
||||
/** Slices currently shown (hidden ones removed). */
|
||||
visibleData: PieSlice[];
|
||||
/** Legend item per slice (`show` reflects hide state). */
|
||||
legendItems: LegendItem[];
|
||||
/** Index of the active slice for the legend's focus highlight, or null. */
|
||||
focusedSeriesIndex: number | null;
|
||||
onLegendClick: (e: MouseEvent<HTMLDivElement>) => void;
|
||||
onLegendMouseMove: (e: MouseEvent<HTMLDivElement>) => void;
|
||||
onLegendMouseLeave: () => void;
|
||||
}
|
||||
|
||||
// Reads the slice index off the nearest `[data-legend-item-id]` ancestor of the
|
||||
// event target (the shared Legend tags each item with its seriesIndex).
|
||||
function getLegendIndex(e: MouseEvent<HTMLDivElement>): number | null {
|
||||
const el = (e.target as HTMLElement | null)?.closest<HTMLElement>(
|
||||
'[data-legend-item-id]',
|
||||
);
|
||||
const id = el?.dataset.legendItemId;
|
||||
return id != null ? Number(id) : null;
|
||||
}
|
||||
|
||||
/**
|
||||
* Pie interaction + derived state: hover/focus, slice hide/unhide (mirroring the
|
||||
* uPlot legend — marker toggles one, label isolates), and persistence of the
|
||||
* hidden set to localStorage (keyed by `id`, matched by label) so it survives
|
||||
* reloads. Returns the visible slices, legend items, focus index, and the
|
||||
* legend container handlers.
|
||||
*/
|
||||
export function usePieInteractions(
|
||||
data: PieSlice[],
|
||||
id?: string,
|
||||
): UsePieInteractionsResult {
|
||||
const [active, setActive] = useState<PieSlice | null>(null);
|
||||
const [hiddenIndices, setHiddenIndices] = useState<Set<number>>(
|
||||
() => new Set(),
|
||||
);
|
||||
const isolatedIndexRef = useRef<number | null>(null);
|
||||
|
||||
const legendItems = useMemo<LegendItem[]>(
|
||||
() =>
|
||||
data.map((slice, index) => ({
|
||||
seriesIndex: index,
|
||||
label: slice.label,
|
||||
color: slice.color,
|
||||
show: !hiddenIndices.has(index),
|
||||
})),
|
||||
[data, hiddenIndices],
|
||||
);
|
||||
|
||||
// Hidden slices drop out so the remaining arcs + centre total recompute.
|
||||
const visibleData = useMemo(
|
||||
() => data.filter((_, index) => !hiddenIndices.has(index)),
|
||||
[data, hiddenIndices],
|
||||
);
|
||||
|
||||
// Rehydrate hide/unhide from localStorage (matched by label) whenever the
|
||||
// data set changes — including first load and every refetch, since the store
|
||||
// is the source of truth and toggles write back to it.
|
||||
useEffect(() => {
|
||||
if (!id || !data.length) {
|
||||
return;
|
||||
}
|
||||
const stored = getStoredSeriesVisibility(id);
|
||||
if (!stored) {
|
||||
return;
|
||||
}
|
||||
const hidden = new Set<number>();
|
||||
data.forEach((slice, index) => {
|
||||
if (stored.find((s) => s.label === slice.label)?.show === false) {
|
||||
hidden.add(index);
|
||||
}
|
||||
});
|
||||
setHiddenIndices(hidden);
|
||||
}, [id, data]);
|
||||
|
||||
// Apply a new hidden set and persist it (label + show) to localStorage.
|
||||
const applyHidden = useCallback(
|
||||
(hidden: Set<number>): void => {
|
||||
setHiddenIndices(hidden);
|
||||
if (id) {
|
||||
updateSeriesVisibilityToLocalStorage(
|
||||
id,
|
||||
data.map((slice, index) => ({
|
||||
label: slice.label,
|
||||
show: !hidden.has(index),
|
||||
})),
|
||||
);
|
||||
}
|
||||
},
|
||||
[id, data],
|
||||
);
|
||||
|
||||
const onLegendMouseMove = useCallback(
|
||||
(e: MouseEvent<HTMLDivElement>): void => {
|
||||
const index = getLegendIndex(e);
|
||||
// Don't focus/dim for hidden slices — they aren't on the donut.
|
||||
setActive(index != null && !hiddenIndices.has(index) ? data[index] : null);
|
||||
},
|
||||
[data, hiddenIndices],
|
||||
);
|
||||
|
||||
// Marker click toggles just that slice on/off; label click isolates it
|
||||
// (clicking the isolated one again resets to all) — mirrors the uPlot legend.
|
||||
const onLegendClick = useCallback(
|
||||
(e: MouseEvent<HTMLDivElement>): void => {
|
||||
const index = getLegendIndex(e);
|
||||
if (index == null) {
|
||||
return;
|
||||
}
|
||||
const isMarker = (e.target as HTMLElement).dataset.isLegendMarker;
|
||||
|
||||
if (isMarker) {
|
||||
const next = new Set(hiddenIndices);
|
||||
if (next.has(index)) {
|
||||
next.delete(index);
|
||||
} else {
|
||||
next.add(index);
|
||||
}
|
||||
applyHidden(next);
|
||||
return;
|
||||
}
|
||||
|
||||
const isReset = isolatedIndexRef.current === index;
|
||||
isolatedIndexRef.current = isReset ? null : index;
|
||||
if (isReset) {
|
||||
applyHidden(new Set());
|
||||
return;
|
||||
}
|
||||
const next = new Set<number>();
|
||||
data.forEach((_, i) => {
|
||||
if (i !== index) {
|
||||
next.add(i);
|
||||
}
|
||||
});
|
||||
applyHidden(next);
|
||||
},
|
||||
[data, hiddenIndices, applyHidden],
|
||||
);
|
||||
|
||||
const onLegendMouseLeave = useCallback((): void => setActive(null), []);
|
||||
|
||||
const focusedIndex = active ? data.indexOf(active) : -1;
|
||||
|
||||
return {
|
||||
active,
|
||||
setActive,
|
||||
visibleData,
|
||||
legendItems,
|
||||
focusedSeriesIndex: focusedIndex >= 0 ? focusedIndex : null,
|
||||
onLegendClick,
|
||||
onLegendMouseMove,
|
||||
onLegendMouseLeave,
|
||||
};
|
||||
}
|
||||
@@ -72,7 +72,7 @@ export const deploymentWidgetInfo = [
|
||||
yAxisUnit: '',
|
||||
},
|
||||
{
|
||||
title: 'Memory usage, request, limits',
|
||||
title: 'Memory usage, request, limits)',
|
||||
yAxisUnit: 'bytes',
|
||||
},
|
||||
{
|
||||
|
||||
@@ -69,7 +69,7 @@ export const jobWidgetInfo = [
|
||||
yAxisUnit: '',
|
||||
},
|
||||
{
|
||||
title: 'Memory Usage',
|
||||
title: 'Memory usage, request, limits',
|
||||
yAxisUnit: 'bytes',
|
||||
},
|
||||
{
|
||||
|
||||
@@ -703,7 +703,7 @@ export const getNamespaceMetricsQueryPayload = (
|
||||
],
|
||||
having: [],
|
||||
legend: `{{${k8sPodNameKey}}}`,
|
||||
limit: 10,
|
||||
limit: 20,
|
||||
orderBy: [],
|
||||
queryName: 'A',
|
||||
reduceTo: ReduceOperators.AVG,
|
||||
@@ -1014,8 +1014,8 @@ export const getNamespaceMetricsQueryPayload = (
|
||||
id: '5f2a55c5',
|
||||
key: {
|
||||
dataType: DataTypes.String,
|
||||
id: k8sNamespaceNameKey,
|
||||
key: k8sNamespaceNameKey,
|
||||
id: k8sStatefulsetNameKey,
|
||||
key: k8sStatefulsetNameKey,
|
||||
type: 'tag',
|
||||
},
|
||||
op: '=',
|
||||
|
||||
@@ -317,9 +317,9 @@ export const getVolumeMetricsQueryPayload = (
|
||||
{
|
||||
aggregateAttribute: {
|
||||
dataType: DataTypes.Float64,
|
||||
id: 'k8s_volume_inodes_used--float64--Gauge--true',
|
||||
id: 'k8s_volume_inodes_used--float64----true',
|
||||
key: k8sVolumeInodesUsedKey,
|
||||
type: 'Gauge',
|
||||
type: '',
|
||||
},
|
||||
aggregateOperator: 'avg',
|
||||
dataSource: DataSource.METRICS,
|
||||
@@ -409,9 +409,9 @@ export const getVolumeMetricsQueryPayload = (
|
||||
{
|
||||
aggregateAttribute: {
|
||||
dataType: DataTypes.Float64,
|
||||
id: 'k8s_volume_inodes--float64--Gauge--true',
|
||||
id: 'k8s_volume_inodes--float64----true',
|
||||
key: k8sVolumeInodesKey,
|
||||
type: 'Gauge',
|
||||
type: '',
|
||||
},
|
||||
aggregateOperator: 'avg',
|
||||
dataSource: DataSource.METRICS,
|
||||
@@ -501,9 +501,9 @@ export const getVolumeMetricsQueryPayload = (
|
||||
{
|
||||
aggregateAttribute: {
|
||||
dataType: DataTypes.Float64,
|
||||
id: 'k8s_volume_inodes_free--float64--Gauge--true',
|
||||
id: 'k8s_volume_inodes_free--float64----true',
|
||||
key: k8sVolumeInodesFreeKey,
|
||||
type: 'Gauge',
|
||||
type: '',
|
||||
},
|
||||
aggregateOperator: 'avg',
|
||||
dataSource: DataSource.METRICS,
|
||||
|
||||
@@ -1619,9 +1619,6 @@ export const getHostQueryPayload = (
|
||||
const diskOpTimeKey = dotMetricsEnabled
|
||||
? 'system.disk.operation_time'
|
||||
: 'system_disk_operation_time';
|
||||
const diskOpsKey = dotMetricsEnabled
|
||||
? 'system.disk.operations'
|
||||
: 'system_disk_operations';
|
||||
const diskPendingKey = dotMetricsEnabled
|
||||
? 'system.disk.pending_operations'
|
||||
: 'system_disk_pending_operations';
|
||||
@@ -2378,24 +2375,9 @@ export const getHostQueryPayload = (
|
||||
op: 'AND',
|
||||
},
|
||||
functions: [],
|
||||
groupBy: [
|
||||
{
|
||||
dataType: DataTypes.String,
|
||||
id: 'direction--string--tag--false',
|
||||
|
||||
key: 'direction',
|
||||
type: 'tag',
|
||||
},
|
||||
{
|
||||
dataType: DataTypes.String,
|
||||
id: 'device--string--tag--false',
|
||||
|
||||
key: 'device',
|
||||
type: 'tag',
|
||||
},
|
||||
],
|
||||
groupBy: [],
|
||||
having: [],
|
||||
legend: '{{device}}::{{direction}}',
|
||||
legend: 'system disk io',
|
||||
limit: null,
|
||||
orderBy: [],
|
||||
queryName: 'A',
|
||||
@@ -2427,9 +2409,9 @@ export const getHostQueryPayload = (
|
||||
{
|
||||
aggregateAttribute: {
|
||||
dataType: DataTypes.Float64,
|
||||
id: 'system_disk_operations--float64--Sum--true',
|
||||
id: 'system_disk_operation_time--float64--Sum--true',
|
||||
|
||||
key: diskOpsKey,
|
||||
key: diskOpTimeKey,
|
||||
type: 'Sum',
|
||||
},
|
||||
aggregateOperator: 'rate',
|
||||
@@ -2439,7 +2421,7 @@ export const getHostQueryPayload = (
|
||||
filters: {
|
||||
items: [
|
||||
{
|
||||
id: 'diskops_f1',
|
||||
id: 'diskop_f1',
|
||||
key: {
|
||||
dataType: DataTypes.String,
|
||||
id: 'host_name--string--tag--false',
|
||||
@@ -2472,7 +2454,7 @@ export const getHostQueryPayload = (
|
||||
],
|
||||
having: [
|
||||
{
|
||||
columnName: `SUM(${diskOpsKey})`,
|
||||
columnName: `SUM(${diskOpTimeKey})`,
|
||||
op: '>',
|
||||
value: 0,
|
||||
},
|
||||
@@ -2575,88 +2557,6 @@ export const getHostQueryPayload = (
|
||||
start,
|
||||
end,
|
||||
},
|
||||
{
|
||||
selectedTime: 'GLOBAL_TIME',
|
||||
graphType: PANEL_TYPES.TIME_SERIES,
|
||||
query: {
|
||||
builder: {
|
||||
queryData: [
|
||||
{
|
||||
aggregateAttribute: {
|
||||
dataType: DataTypes.Float64,
|
||||
id: 'system_disk_operation_time--float64--Sum--true',
|
||||
|
||||
key: diskOpTimeKey,
|
||||
type: 'Sum',
|
||||
},
|
||||
aggregateOperator: 'rate',
|
||||
dataSource: DataSource.METRICS,
|
||||
disabled: false,
|
||||
expression: 'A',
|
||||
filters: {
|
||||
items: [
|
||||
{
|
||||
id: 'diskoptime_f1',
|
||||
key: {
|
||||
dataType: DataTypes.String,
|
||||
id: 'host_name--string--tag--false',
|
||||
|
||||
key: hostNameKey,
|
||||
type: 'tag',
|
||||
},
|
||||
op: '=',
|
||||
value: hostName,
|
||||
},
|
||||
],
|
||||
op: 'AND',
|
||||
},
|
||||
functions: [],
|
||||
groupBy: [
|
||||
{
|
||||
dataType: DataTypes.String,
|
||||
id: 'device--string--tag--false',
|
||||
|
||||
key: 'device',
|
||||
type: 'tag',
|
||||
},
|
||||
{
|
||||
dataType: DataTypes.String,
|
||||
id: 'direction--string--tag--false',
|
||||
|
||||
key: 'direction',
|
||||
type: 'tag',
|
||||
},
|
||||
],
|
||||
having: [
|
||||
{
|
||||
columnName: `SUM(${diskOpTimeKey})`,
|
||||
op: '>',
|
||||
value: 0,
|
||||
},
|
||||
],
|
||||
legend: '{{device}}::{{direction}}',
|
||||
limit: null,
|
||||
orderBy: [],
|
||||
queryName: 'A',
|
||||
reduceTo: ReduceOperators.AVG,
|
||||
spaceAggregation: 'sum',
|
||||
stepInterval: 60,
|
||||
timeAggregation: 'rate',
|
||||
},
|
||||
],
|
||||
queryFormulas: [],
|
||||
queryTraceOperator: [],
|
||||
},
|
||||
clickhouse_sql: [{ disabled: false, legend: '', name: 'A', query: '' }],
|
||||
id: 'a8b3d2e1-4f5c-4a6b-9c8d-7e2f1a0b3c4f',
|
||||
promql: [{ disabled: false, legend: '', name: 'A', query: '' }],
|
||||
queryType: EQueryType.QUERY_BUILDER,
|
||||
},
|
||||
variables: {},
|
||||
formatForWeb: false,
|
||||
start,
|
||||
end,
|
||||
},
|
||||
];
|
||||
};
|
||||
|
||||
@@ -2731,5 +2631,5 @@ export const hostWidgetInfo = [
|
||||
{ title: 'System disk io (bytes transferred)', yAxisUnit: 'bytes' },
|
||||
{ title: 'System disk operations/s', yAxisUnit: 'short' },
|
||||
{ title: 'Queue size', yAxisUnit: 'short' },
|
||||
{ title: 'System disk operation time/s', yAxisUnit: 's' },
|
||||
{ title: 'Disk operations time', yAxisUnit: 's' },
|
||||
];
|
||||
|
||||
@@ -96,28 +96,14 @@ function CreateOrEdit(props: CreateOrEditProps): JSX.Element {
|
||||
return undefined;
|
||||
}
|
||||
|
||||
const {
|
||||
domainToAdminEmailList,
|
||||
allowedGroups,
|
||||
serviceAccountJson,
|
||||
domainToAdminEmail: _domainToAdminEmail,
|
||||
fetchTransitiveGroupMembership,
|
||||
...rest
|
||||
} = config;
|
||||
const { domainToAdminEmailList, ...rest } = config;
|
||||
const domainToAdminEmail = convertDomainMappingsToRecord(
|
||||
domainToAdminEmailList,
|
||||
);
|
||||
|
||||
return {
|
||||
...rest,
|
||||
...(rest.fetchGroups
|
||||
? {
|
||||
allowedGroups,
|
||||
serviceAccountJson,
|
||||
domainToAdminEmail: domainToAdminEmail ?? {},
|
||||
fetchTransitiveGroupMembership,
|
||||
}
|
||||
: { domainToAdminEmail: {} }),
|
||||
domainToAdminEmail: domainToAdminEmail ?? {},
|
||||
};
|
||||
}, [form]);
|
||||
|
||||
@@ -143,7 +129,7 @@ function CreateOrEdit(props: CreateOrEditProps): JSX.Element {
|
||||
|
||||
return {
|
||||
...rest,
|
||||
groupMappings: rest.useRoleAttribute ? undefined : (groupMappings ?? {}),
|
||||
groupMappings: groupMappings ?? {},
|
||||
};
|
||||
}, [form]);
|
||||
|
||||
|
||||
@@ -1,195 +0,0 @@
|
||||
import { fireEvent, render, screen, waitFor } from 'tests/test-utils';
|
||||
import { rest, server } from 'mocks-server/server';
|
||||
import { AuthtypesGettableAuthDomainDTO } from 'api/generated/services/sigNoz.schemas';
|
||||
|
||||
import CreateEdit from '../CreateEdit/CreateEdit';
|
||||
import {
|
||||
AUTH_DOMAINS_UPDATE_ENDPOINT,
|
||||
mockDomainWithRoleMapping,
|
||||
mockGoogleAuthDomain,
|
||||
mockGoogleAuthWithWorkspaceGroups,
|
||||
mockOidcWithClaimMapping,
|
||||
mockSamlWithAttributeMapping,
|
||||
mockUpdateSuccessResponse,
|
||||
} from './mocks';
|
||||
|
||||
// @signozhq/ui/button internal effects block form.validateFields() in tests
|
||||
jest.mock('@signozhq/ui/button', () => ({
|
||||
...jest.requireActual('@signozhq/ui/button'),
|
||||
Button: ({
|
||||
children,
|
||||
onClick,
|
||||
loading,
|
||||
disabled,
|
||||
'aria-label': ariaLabel,
|
||||
prefix,
|
||||
suffix,
|
||||
}: {
|
||||
children?: React.ReactNode;
|
||||
onClick?: React.MouseEventHandler<HTMLButtonElement>;
|
||||
loading?: boolean;
|
||||
disabled?: boolean;
|
||||
'aria-label'?: string;
|
||||
prefix?: React.ReactNode;
|
||||
suffix?: React.ReactNode;
|
||||
}) => (
|
||||
<button
|
||||
type="button"
|
||||
onClick={onClick}
|
||||
disabled={disabled || loading}
|
||||
aria-label={ariaLabel}
|
||||
>
|
||||
{prefix}
|
||||
{children}
|
||||
{suffix}
|
||||
</button>
|
||||
),
|
||||
}));
|
||||
|
||||
type SavedPayload = {
|
||||
config: {
|
||||
googleAuthConfig?: Record<string, unknown>;
|
||||
samlConfig?: Record<string, unknown>;
|
||||
oidcConfig?: Record<string, unknown>;
|
||||
roleMapping?: Record<string, unknown>;
|
||||
};
|
||||
};
|
||||
|
||||
async function submitForm(
|
||||
record: AuthtypesGettableAuthDomainDTO,
|
||||
): Promise<SavedPayload> {
|
||||
const requests: SavedPayload[] = [];
|
||||
|
||||
server.use(
|
||||
rest.put(AUTH_DOMAINS_UPDATE_ENDPOINT, async (req, res, ctx) => {
|
||||
requests.push((await req.json()) as SavedPayload);
|
||||
return res(ctx.status(200), ctx.json(mockUpdateSuccessResponse));
|
||||
}),
|
||||
);
|
||||
|
||||
render(<CreateEdit isCreate={false} record={record} onClose={jest.fn()} />);
|
||||
fireEvent.click(screen.getByRole('button', { name: /save changes/i }));
|
||||
await waitFor(() => expect(requests).toHaveLength(1));
|
||||
|
||||
return requests[0];
|
||||
}
|
||||
|
||||
describe('CreateEdit — payload sanitization', () => {
|
||||
afterEach(() => server.resetHandlers());
|
||||
|
||||
describe('Google Auth', () => {
|
||||
it('sends core fields and omits workspace fields when fetchGroups is not set', async () => {
|
||||
const payload = await submitForm(mockGoogleAuthDomain);
|
||||
|
||||
const g = payload.config.googleAuthConfig;
|
||||
expect(g?.clientId).toBe('test-client-id');
|
||||
expect(g?.clientSecret).toBe('test-client-secret');
|
||||
expect(g?.allowedGroups).toBeUndefined();
|
||||
expect(g?.serviceAccountJson).toBeUndefined();
|
||||
expect(g?.fetchTransitiveGroupMembership).toBeUndefined();
|
||||
expect(g?.domainToAdminEmail).toStrictEqual({});
|
||||
});
|
||||
|
||||
it('strips workspace fields when fetchGroups is false', async () => {
|
||||
const payload = await submitForm({
|
||||
...mockGoogleAuthWithWorkspaceGroups,
|
||||
config: {
|
||||
...mockGoogleAuthWithWorkspaceGroups.config,
|
||||
googleAuthConfig: {
|
||||
...mockGoogleAuthWithWorkspaceGroups.config?.googleAuthConfig,
|
||||
fetchGroups: false,
|
||||
},
|
||||
},
|
||||
});
|
||||
|
||||
const g = payload.config.googleAuthConfig;
|
||||
expect(g?.fetchGroups).toBe(false);
|
||||
expect(g?.allowedGroups).toBeUndefined();
|
||||
expect(g?.serviceAccountJson).toBeUndefined();
|
||||
expect(g?.fetchTransitiveGroupMembership).toBeUndefined();
|
||||
expect(g?.domainToAdminEmail).toStrictEqual({});
|
||||
});
|
||||
|
||||
it('includes all workspace fields when fetchGroups is true', async () => {
|
||||
const payload = await submitForm(mockGoogleAuthWithWorkspaceGroups);
|
||||
|
||||
const g = payload.config.googleAuthConfig;
|
||||
expect(g?.fetchGroups).toBe(true);
|
||||
expect(g?.serviceAccountJson).toBe('{"type": "service_account"}');
|
||||
expect(g?.fetchTransitiveGroupMembership).toBe(true);
|
||||
expect(g?.allowedGroups).toStrictEqual([
|
||||
'allowed-group-1',
|
||||
'allowed-group-2',
|
||||
]);
|
||||
expect(g?.domainToAdminEmail).toStrictEqual({
|
||||
'google-groups.com': 'admin@google-groups.com',
|
||||
});
|
||||
});
|
||||
});
|
||||
|
||||
describe('SAML', () => {
|
||||
it('sends core and attributeMapping fields', async () => {
|
||||
const payload = await submitForm(mockSamlWithAttributeMapping);
|
||||
|
||||
const s = payload.config.samlConfig;
|
||||
expect(s?.samlIdp).toBe('https://idp.saml-attrs.com/sso');
|
||||
expect(s?.samlEntity).toBe('urn:saml-attrs:idp');
|
||||
expect(s?.samlCert).toBe('MOCK_CERTIFICATE_ATTRS');
|
||||
expect(s?.insecureSkipAuthNRequestsSigned).toBe(true);
|
||||
|
||||
const attr = s?.attributeMapping as Record<string, unknown>;
|
||||
expect(attr?.name).toBe('user_display_name');
|
||||
expect(attr?.groups).toBe('member_of');
|
||||
expect(attr?.role).toBe('signoz_role');
|
||||
});
|
||||
});
|
||||
|
||||
describe('OIDC', () => {
|
||||
it('sends all fields including claimMapping', async () => {
|
||||
const payload = await submitForm(mockOidcWithClaimMapping);
|
||||
|
||||
const o = payload.config.oidcConfig;
|
||||
expect(o?.issuer).toBe('https://oidc.claims.com');
|
||||
expect(o?.issuerAlias).toBe('https://alias.claims.com');
|
||||
expect(o?.clientId).toBe('claims-client-id');
|
||||
expect(o?.clientSecret).toBe('claims-client-secret');
|
||||
expect(o?.insecureSkipEmailVerified).toBe(true);
|
||||
expect(o?.getUserInfo).toBe(true);
|
||||
|
||||
const claim = o?.claimMapping as Record<string, unknown>;
|
||||
expect(claim?.email).toBe('user_email');
|
||||
expect(claim?.name).toBe('display_name');
|
||||
expect(claim?.groups).toBe('user_groups');
|
||||
expect(claim?.role).toBe('user_role');
|
||||
});
|
||||
});
|
||||
|
||||
describe('Role Mapping', () => {
|
||||
it('strips groupMappings when useRoleAttribute is true', async () => {
|
||||
const payload = await submitForm({
|
||||
...mockDomainWithRoleMapping,
|
||||
config: {
|
||||
...mockDomainWithRoleMapping.config,
|
||||
roleMapping: {
|
||||
...mockDomainWithRoleMapping.config?.roleMapping,
|
||||
useRoleAttribute: true,
|
||||
},
|
||||
},
|
||||
});
|
||||
|
||||
expect(payload.config.roleMapping?.useRoleAttribute).toBe(true);
|
||||
expect(payload.config.roleMapping?.groupMappings).toBeUndefined();
|
||||
});
|
||||
|
||||
it('sends groupMappings when useRoleAttribute is false', async () => {
|
||||
const payload = await submitForm(mockDomainWithRoleMapping);
|
||||
|
||||
expect(payload.config.roleMapping?.useRoleAttribute).toBe(false);
|
||||
expect(payload.config.roleMapping?.groupMappings).toStrictEqual({
|
||||
'admin-group': 'ADMIN',
|
||||
'dev-team': 'EDITOR',
|
||||
viewers: 'VIEWER',
|
||||
});
|
||||
});
|
||||
});
|
||||
});
|
||||
@@ -22,12 +22,11 @@ export const StyledCheckOutlined = styled(Check)`
|
||||
float: right;
|
||||
`;
|
||||
|
||||
export const TagContainer = styled(Badge).attrs({
|
||||
color: 'secondary',
|
||||
variant: 'outline',
|
||||
})`
|
||||
export const TagContainer = styled(Badge)`
|
||||
&&& {
|
||||
display: flex;
|
||||
border-radius: 3px;
|
||||
padding: 0.1rem 0.2rem;
|
||||
font-weight: 300;
|
||||
font-size: 0.6rem;
|
||||
}
|
||||
@@ -39,5 +38,4 @@ export const TagLabel = styled.span`
|
||||
|
||||
export const TagValue = styled.span`
|
||||
text-transform: capitalize;
|
||||
font-weight: 400;
|
||||
`;
|
||||
|
||||
@@ -171,18 +171,17 @@
|
||||
}
|
||||
|
||||
.legend-copy-button {
|
||||
// Always laid out (space reserved) but transparent, so revealing it on
|
||||
// hover fades the icon in without reflowing the row / shifting the label.
|
||||
// Shrink the shared icon Button (defaults to a 2rem square) to the
|
||||
// compact legend row via its size tokens.
|
||||
--button-height: auto;
|
||||
--button-width: auto;
|
||||
--button-padding: 2px;
|
||||
|
||||
opacity: 0;
|
||||
display: none;
|
||||
align-items: center;
|
||||
justify-content: center;
|
||||
flex-shrink: 0;
|
||||
padding: 2px;
|
||||
margin: 0;
|
||||
border: none;
|
||||
color: var(--l2-foreground);
|
||||
cursor: pointer;
|
||||
border-radius: 4px;
|
||||
opacity: 1;
|
||||
transition:
|
||||
opacity 0.15s ease,
|
||||
color 0.15s ease;
|
||||
@@ -193,8 +192,9 @@
|
||||
}
|
||||
|
||||
&:hover {
|
||||
background: var(--l3-background);
|
||||
background: color-mix(in srgb, var(--l1-foreground) 5%, transparent);
|
||||
.legend-copy-button {
|
||||
display: flex;
|
||||
opacity: 1;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,41 +1,39 @@
|
||||
import { useCallback, useMemo, useRef, useState } from 'react';
|
||||
import { VirtuosoGrid } from 'react-virtuoso';
|
||||
import { Input } from 'antd';
|
||||
import { Button } from '@signozhq/ui/button';
|
||||
import { TooltipSimple } from '@signozhq/ui/tooltip';
|
||||
import { Input, Tooltip as AntdTooltip } from 'antd';
|
||||
import cx from 'classnames';
|
||||
import { useCopyToClipboard } from 'hooks/useCopyToClipboard';
|
||||
import { LegendItem } from 'lib/uPlotV2/config/types';
|
||||
import useLegendsSync from 'lib/uPlotV2/hooks/useLegendsSync';
|
||||
import { Check, Copy } from '@signozhq/icons';
|
||||
|
||||
import { useLegendActions } from '../../hooks/useLegendActions';
|
||||
import { LegendPosition, LegendProps } from '../types';
|
||||
|
||||
import './Legend.styles.scss';
|
||||
|
||||
export const MAX_LEGEND_WIDTH = 240;
|
||||
|
||||
/**
|
||||
* Presentational legend. Renders the supplied `items` (markers + labels, an
|
||||
* optional copy button, and a search box for the RIGHT position) and delegates
|
||||
* all interaction to the container handlers. Source-agnostic — the uPlot
|
||||
* charts feed it via UPlotLegend; Pie feeds it directly.
|
||||
*/
|
||||
export default function Legend({
|
||||
items,
|
||||
position,
|
||||
position = LegendPosition.BOTTOM,
|
||||
config,
|
||||
averageLegendWidth = MAX_LEGEND_WIDTH,
|
||||
focusedSeriesIndex,
|
||||
onClick,
|
||||
onMouseMove,
|
||||
onMouseLeave,
|
||||
showCopy = true,
|
||||
}: LegendProps): JSX.Element {
|
||||
const { legendItemsMap, focusedSeriesIndex, setFocusedSeriesIndex } =
|
||||
useLegendsSync({ config });
|
||||
const { onLegendClick, onLegendMouseMove, onLegendMouseLeave } =
|
||||
useLegendActions({
|
||||
setFocusedSeriesIndex,
|
||||
focusedSeriesIndex,
|
||||
});
|
||||
const legendContainerRef = useRef<HTMLDivElement | null>(null);
|
||||
const [legendSearchQuery, setLegendSearchQuery] = useState('');
|
||||
const { copyToClipboard, id: copiedId } = useCopyToClipboard();
|
||||
|
||||
// Search is intrinsic to the right-positioned legend.
|
||||
const searchEnabled = position === LegendPosition.RIGHT;
|
||||
const legendItems = useMemo(
|
||||
() => Object.values(legendItemsMap),
|
||||
[legendItemsMap],
|
||||
);
|
||||
|
||||
const isSingleRow = useMemo(() => {
|
||||
if (!legendContainerRef.current || position !== LegendPosition.BOTTOM) {
|
||||
@@ -43,19 +41,21 @@ export default function Legend({
|
||||
}
|
||||
const containerWidth = legendContainerRef.current.clientWidth;
|
||||
|
||||
const totalLegendWidth = items.length * (averageLegendWidth + 16);
|
||||
const totalLegendWidth = legendItems.length * (averageLegendWidth + 16);
|
||||
const totalRows = Math.ceil(totalLegendWidth / containerWidth);
|
||||
return totalRows <= 1;
|
||||
}, [averageLegendWidth, items.length, position]);
|
||||
}, [averageLegendWidth, legendContainerRef, legendItems.length, position]);
|
||||
|
||||
const visibleLegendItems = useMemo(() => {
|
||||
if (!searchEnabled || !legendSearchQuery.trim()) {
|
||||
return items;
|
||||
if (position !== LegendPosition.RIGHT || !legendSearchQuery.trim()) {
|
||||
return legendItems;
|
||||
}
|
||||
|
||||
const query = legendSearchQuery.trim().toLowerCase();
|
||||
return items.filter((item) => item.label?.toLowerCase().includes(query));
|
||||
}, [searchEnabled, legendSearchQuery, items]);
|
||||
return legendItems.filter((item) =>
|
||||
item.label?.toLowerCase().includes(query),
|
||||
);
|
||||
}, [position, legendSearchQuery, legendItems]);
|
||||
|
||||
const handleCopyLegendItem = useCallback(
|
||||
(e: React.MouseEvent, seriesIndex: number, label: string): void => {
|
||||
@@ -68,9 +68,6 @@ export default function Legend({
|
||||
const renderLegendItem = useCallback(
|
||||
(item: LegendItem): JSX.Element => {
|
||||
const isCopied = copiedId === item.seriesIndex;
|
||||
// `color` is uPlot's stroke union (string | fn | gradient); only a string
|
||||
// is a usable CSS colour for the marker.
|
||||
const markerColor = typeof item.color === 'string' ? item.color : undefined;
|
||||
return (
|
||||
<div
|
||||
key={item.seriesIndex}
|
||||
@@ -80,68 +77,54 @@ export default function Legend({
|
||||
'legend-item-focused': focusedSeriesIndex === item.seriesIndex,
|
||||
})}
|
||||
>
|
||||
<TooltipSimple title={item.label} arrow side="top" disableHoverableContent>
|
||||
<AntdTooltip title={item.label}>
|
||||
<div className="legend-item-label-trigger">
|
||||
<div
|
||||
className="legend-marker"
|
||||
style={{ borderColor: markerColor }}
|
||||
style={{ borderColor: String(item.color) }}
|
||||
data-is-legend-marker={true}
|
||||
/>
|
||||
<span className="legend-label">{item.label}</span>
|
||||
</div>
|
||||
</TooltipSimple>
|
||||
{showCopy && (
|
||||
<TooltipSimple
|
||||
title={isCopied ? 'Copied' : 'Copy'}
|
||||
arrow
|
||||
side="top"
|
||||
disableHoverableContent
|
||||
</AntdTooltip>
|
||||
<AntdTooltip title={isCopied ? 'Copied' : 'Copy'}>
|
||||
<button
|
||||
type="button"
|
||||
className="legend-copy-button"
|
||||
onClick={(e): void =>
|
||||
handleCopyLegendItem(e, item.seriesIndex, item.label ?? '')
|
||||
}
|
||||
aria-label={`Copy ${item.label}`}
|
||||
data-testid="legend-copy"
|
||||
>
|
||||
<Button
|
||||
type="button"
|
||||
size="icon"
|
||||
variant="ghost"
|
||||
color="secondary"
|
||||
className="legend-copy-button"
|
||||
onClick={(e): void =>
|
||||
handleCopyLegendItem(e, item.seriesIndex, item.label ?? '')
|
||||
}
|
||||
aria-label={`Copy ${item.label}`}
|
||||
// data-testid (not testId): TooltipSimple's trigger injects
|
||||
// data-testid:undefined via Radix Slot, and Button spreads
|
||||
// incoming props after its own testId — so set it as a prop
|
||||
// that wins the Slot merge and survives the spread.
|
||||
data-testid="legend-copy"
|
||||
>
|
||||
{isCopied ? <Check size={12} /> : <Copy size={12} />}
|
||||
</Button>
|
||||
</TooltipSimple>
|
||||
)}
|
||||
{isCopied ? <Check size={12} /> : <Copy size={12} />}
|
||||
</button>
|
||||
</AntdTooltip>
|
||||
</div>
|
||||
);
|
||||
},
|
||||
[copiedId, focusedSeriesIndex, handleCopyLegendItem, position, showCopy],
|
||||
[copiedId, focusedSeriesIndex, handleCopyLegendItem, position],
|
||||
);
|
||||
|
||||
const isEmptyState = useMemo(() => {
|
||||
if (!searchEnabled || !legendSearchQuery.trim()) {
|
||||
if (position !== LegendPosition.RIGHT || !legendSearchQuery.trim()) {
|
||||
return false;
|
||||
}
|
||||
return visibleLegendItems.length === 0;
|
||||
}, [searchEnabled, legendSearchQuery, visibleLegendItems]);
|
||||
}, [position, legendSearchQuery, visibleLegendItems]);
|
||||
|
||||
return (
|
||||
<div
|
||||
ref={legendContainerRef}
|
||||
className="legend-container"
|
||||
onClick={onClick}
|
||||
onMouseMove={onMouseMove}
|
||||
onMouseLeave={onMouseLeave}
|
||||
onClick={onLegendClick}
|
||||
onMouseMove={onLegendMouseMove}
|
||||
onMouseLeave={onLegendMouseLeave}
|
||||
style={{
|
||||
['--legend-average-width' as string]: `${averageLegendWidth + 16}px`, // 16px is the marker width
|
||||
}}
|
||||
>
|
||||
{searchEnabled && (
|
||||
{position === LegendPosition.RIGHT && (
|
||||
<div className="legend-search-container">
|
||||
<Input
|
||||
allowClear
|
||||
|
||||
@@ -1,41 +0,0 @@
|
||||
import { useMemo } from 'react';
|
||||
import useLegendsSync from 'lib/uPlotV2/hooks/useLegendsSync';
|
||||
|
||||
import { useLegendActions } from '../../hooks/useLegendActions';
|
||||
import { LegendPosition, UPlotLegendProps } from '../types';
|
||||
|
||||
import Legend from './Legend';
|
||||
|
||||
/**
|
||||
* uPlot legend controller. Derives the legend items + focus/visibility state
|
||||
* from the chart config (useLegendsSync) and the toggle/focus interactions from
|
||||
* the plot context (useLegendActions), then renders the presentational Legend.
|
||||
* Must be rendered inside a PlotContextProvider.
|
||||
*/
|
||||
export default function UPlotLegend({
|
||||
position = LegendPosition.BOTTOM,
|
||||
config,
|
||||
averageLegendWidth,
|
||||
}: UPlotLegendProps): JSX.Element {
|
||||
const { legendItemsMap, focusedSeriesIndex, setFocusedSeriesIndex } =
|
||||
useLegendsSync({ config });
|
||||
const { onLegendClick, onLegendMouseMove, onLegendMouseLeave } =
|
||||
useLegendActions({
|
||||
setFocusedSeriesIndex,
|
||||
focusedSeriesIndex,
|
||||
});
|
||||
|
||||
const items = useMemo(() => Object.values(legendItemsMap), [legendItemsMap]);
|
||||
|
||||
return (
|
||||
<Legend
|
||||
items={items}
|
||||
position={position}
|
||||
averageLegendWidth={averageLegendWidth}
|
||||
focusedSeriesIndex={focusedSeriesIndex}
|
||||
onClick={onLegendClick}
|
||||
onMouseMove={onLegendMouseMove}
|
||||
onMouseLeave={onLegendMouseLeave}
|
||||
/>
|
||||
);
|
||||
}
|
||||
@@ -7,12 +7,11 @@ import {
|
||||
within,
|
||||
} from '@testing-library/react';
|
||||
import userEvent from '@testing-library/user-event';
|
||||
import { TooltipProvider } from '@signozhq/ui/tooltip';
|
||||
import { LegendItem } from 'lib/uPlotV2/config/types';
|
||||
import useLegendsSync from 'lib/uPlotV2/hooks/useLegendsSync';
|
||||
|
||||
import { useLegendActions } from '../../hooks/useLegendActions';
|
||||
import UPlotLegend from '../Legend/UPlotLegend';
|
||||
import Legend from '../Legend/Legend';
|
||||
import { LegendPosition } from '../types';
|
||||
|
||||
const mockWriteText = jest.fn().mockResolvedValue(undefined);
|
||||
@@ -48,7 +47,7 @@ const mockUseLegendActions = useLegendActions as jest.MockedFunction<
|
||||
typeof useLegendActions
|
||||
>;
|
||||
|
||||
describe('UPlotLegend', () => {
|
||||
describe('Legend', () => {
|
||||
beforeAll(() => {
|
||||
// JSDOM does not define navigator.clipboard; add it so we can spy on writeText
|
||||
Object.defineProperty(navigator, 'clipboard', {
|
||||
@@ -116,13 +115,11 @@ describe('UPlotLegend', () => {
|
||||
|
||||
const renderLegend = (position?: LegendPosition): RenderResult =>
|
||||
render(
|
||||
<TooltipProvider>
|
||||
<UPlotLegend
|
||||
position={position}
|
||||
// config is consumed by the mocked useLegendsSync hook, not directly
|
||||
config={{} as any}
|
||||
/>
|
||||
</TooltipProvider>,
|
||||
<Legend
|
||||
position={position}
|
||||
// config is not used directly in the component, it's consumed by the mocked hook
|
||||
config={{} as any}
|
||||
/>,
|
||||
);
|
||||
|
||||
describe('layout and position', () => {
|
||||
@@ -1,10 +1,9 @@
|
||||
import { MouseEventHandler, ReactNode } from 'react';
|
||||
import { ReactNode } from 'react';
|
||||
import { Timezone } from 'components/CustomTimePicker/timezoneUtils';
|
||||
import { PrecisionOption } from 'components/Graph/types';
|
||||
import uPlot from 'uplot';
|
||||
|
||||
import { UPlotConfigBuilder } from '../config/UPlotConfigBuilder';
|
||||
import { LegendItem } from '../config/types';
|
||||
import { SyncTooltipFilterMode } from '../plugins/TooltipPlugin/types';
|
||||
|
||||
/**
|
||||
@@ -110,33 +109,7 @@ export enum LegendPosition {
|
||||
export interface LegendConfig {
|
||||
position: LegendPosition;
|
||||
}
|
||||
/**
|
||||
* Presentational legend props. Source-agnostic: it renders whatever `items`
|
||||
* it's given and delegates interaction to the container handlers, so it serves
|
||||
* both uPlot charts (via UPlotLegend) and non-uPlot charts (Pie). The search
|
||||
* box is intrinsic to the RIGHT position (derived from `position`, not a flag).
|
||||
*/
|
||||
export interface LegendProps {
|
||||
items: LegendItem[];
|
||||
/** Legend placement; always supplied by the container. */
|
||||
position: LegendPosition;
|
||||
averageLegendWidth?: number;
|
||||
/** Series index to highlight (hovered/focused). */
|
||||
focusedSeriesIndex: number | null;
|
||||
/**
|
||||
* Container-delegated handlers. Items carry `data-legend-item-id`, so the
|
||||
* handler reads the target's id rather than binding per item.
|
||||
*/
|
||||
onClick: MouseEventHandler<HTMLDivElement>;
|
||||
onMouseMove: MouseEventHandler<HTMLDivElement>;
|
||||
onMouseLeave: () => void;
|
||||
/** Show the per-item copy button. Default true. */
|
||||
showCopy?: boolean;
|
||||
}
|
||||
|
||||
/** Props for the uPlot legend controller, which derives items + interaction
|
||||
* from the chart config and renders the presentational Legend. */
|
||||
export interface UPlotLegendProps {
|
||||
position?: LegendPosition;
|
||||
config: UPlotConfigBuilder;
|
||||
averageLegendWidth?: number;
|
||||
|
||||
@@ -37,7 +37,7 @@ export interface TooltipViewState {
|
||||
isHovering: boolean;
|
||||
isPinned: boolean;
|
||||
dismiss: () => void;
|
||||
clickData: ChartClickData | null;
|
||||
clickData: TooltipClickData | null;
|
||||
contents?: ReactNode;
|
||||
}
|
||||
|
||||
@@ -59,17 +59,17 @@ export interface TooltipPluginProps {
|
||||
/** Key that pins the tooltip while hovering. Defaults to DEFAULT_PIN_TOOLTIP_KEY ('l'). */
|
||||
pinKey?: string;
|
||||
/** Called when the user clicks the uPlot overlay. Receives resolved click data. */
|
||||
onClick?: (clickData: ChartClickData) => void;
|
||||
onClick?: (clickData: TooltipClickData) => void;
|
||||
syncMode?: DashboardCursorSync;
|
||||
syncKey?: string;
|
||||
syncMetadata?: TooltipSyncMetadata;
|
||||
render: (args: TooltipRenderArgs) => ReactNode;
|
||||
pinnedTooltipElement?: (clickData: ChartClickData) => ReactNode;
|
||||
pinnedTooltipElement?: (clickData: TooltipClickData) => ReactNode;
|
||||
maxWidth?: number;
|
||||
maxHeight?: number;
|
||||
}
|
||||
|
||||
export interface ChartClickData {
|
||||
export interface TooltipClickData {
|
||||
xValue: number;
|
||||
yValue: number;
|
||||
focusedSeries: {
|
||||
@@ -101,7 +101,7 @@ export interface TooltipControllerState {
|
||||
hoverActive: boolean;
|
||||
isAnySeriesActive: boolean;
|
||||
pinned: boolean;
|
||||
clickData: ChartClickData | null;
|
||||
clickData: TooltipClickData | null;
|
||||
style: TooltipViewState['style'];
|
||||
horizontalOffset: number;
|
||||
verticalOffset: number;
|
||||
|
||||
@@ -2,7 +2,7 @@ import { getFocusedSeriesAtPosition } from 'lib/uPlotLib/plugins/onClickPlugin';
|
||||
|
||||
import {
|
||||
TOOLTIP_OFFSET,
|
||||
ChartClickData,
|
||||
TooltipClickData,
|
||||
TooltipLayoutInfo,
|
||||
TooltipViewState,
|
||||
} from './types';
|
||||
@@ -167,11 +167,14 @@ export function createLayoutObserver(
|
||||
}
|
||||
|
||||
/**
|
||||
* Resolves a ChartClickData snapshot from a MouseEvent (real or synthetic)
|
||||
* Resolves a TooltipClickData snapshot from a MouseEvent (real or synthetic)
|
||||
* and the current uPlot instance. Shared by the overlay click handler and the
|
||||
* keyboard-pin handler (which synthesises an event from the cursor position).
|
||||
*/
|
||||
export function buildClickData(event: MouseEvent, plot: uPlot): ChartClickData {
|
||||
export function buildClickData(
|
||||
event: MouseEvent,
|
||||
plot: uPlot,
|
||||
): TooltipClickData {
|
||||
const xValue = plot.posToVal(event.offsetX, 'x');
|
||||
const yValue = plot.posToVal(event.offsetY, 'y');
|
||||
const focusedSeries = getFocusedSeriesAtPosition(event, plot);
|
||||
|
||||
@@ -10,6 +10,25 @@ import (
|
||||
)
|
||||
|
||||
func (provider *provider) addTraceDetailRoutes(router *mux.Router) error {
|
||||
if err := router.Handle("/api/v3/traces/{traceID}/waterfall", handler.New(
|
||||
provider.authzMiddleware.ViewAccess(provider.traceDetailHandler.GetWaterfall),
|
||||
handler.OpenAPIDef{
|
||||
ID: "GetWaterfall",
|
||||
Tags: []string{"tracedetail"},
|
||||
Summary: "Get waterfall view for a trace",
|
||||
Description: "Returns the waterfall view of spans for a given trace ID with tree structure, metadata, and windowed pagination",
|
||||
Request: new(spantypes.PostableWaterfall),
|
||||
RequestContentType: "application/json",
|
||||
Response: new(spantypes.GettableWaterfallTrace),
|
||||
ResponseContentType: "application/json",
|
||||
SuccessStatusCode: http.StatusOK,
|
||||
ErrorStatusCodes: []int{http.StatusBadRequest, http.StatusNotFound},
|
||||
SecuritySchemes: newSecuritySchemes(types.RoleViewer),
|
||||
},
|
||||
)).Methods(http.MethodPost).GetError(); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := router.Handle("/api/v4/traces/{traceID}/waterfall", handler.New(
|
||||
provider.authzMiddleware.ViewAccess(provider.traceDetailHandler.GetWaterfallV4),
|
||||
handler.OpenAPIDef{
|
||||
@@ -48,24 +67,5 @@ func (provider *provider) addTraceDetailRoutes(router *mux.Router) error {
|
||||
return err
|
||||
}
|
||||
|
||||
if err := router.Handle("/api/v3/traces/{traceID}/flamegraph", handler.New(
|
||||
provider.authzMiddleware.ViewAccess(provider.traceDetailHandler.GetFlamegraph),
|
||||
handler.OpenAPIDef{
|
||||
ID: "GetFlamegraph",
|
||||
Tags: []string{"tracedetail"},
|
||||
Summary: "Get flamegraph view for a trace",
|
||||
Description: "Returns the flamegraph view of spans for a given trace ID.",
|
||||
Request: new(spantypes.PostableFlamegraph),
|
||||
RequestContentType: "application/json",
|
||||
Response: new(spantypes.GettableFlamegraphTrace),
|
||||
ResponseContentType: "application/json",
|
||||
SuccessStatusCode: http.StatusOK,
|
||||
ErrorStatusCodes: []int{http.StatusBadRequest, http.StatusNotFound},
|
||||
SecuritySchemes: newSecuritySchemes(types.RoleViewer),
|
||||
},
|
||||
)).Methods(http.MethodPost).GetError(); err != nil {
|
||||
return err
|
||||
}
|
||||
|
||||
return nil
|
||||
}
|
||||
|
||||
@@ -4,7 +4,6 @@ import (
|
||||
"context"
|
||||
"log/slog"
|
||||
"net/url"
|
||||
"path"
|
||||
|
||||
"github.com/coreos/go-oidc/v3/oidc"
|
||||
"golang.org/x/oauth2"
|
||||
@@ -15,7 +14,6 @@ import (
|
||||
"github.com/SigNoz/signoz/pkg/authn"
|
||||
"github.com/SigNoz/signoz/pkg/errors"
|
||||
"github.com/SigNoz/signoz/pkg/factory"
|
||||
"github.com/SigNoz/signoz/pkg/global"
|
||||
"github.com/SigNoz/signoz/pkg/http/client"
|
||||
"github.com/SigNoz/signoz/pkg/types/authtypes"
|
||||
"github.com/SigNoz/signoz/pkg/valuer"
|
||||
@@ -31,13 +29,12 @@ var scopes []string = []string{"email", "profile"}
|
||||
var _ authn.CallbackAuthN = (*AuthN)(nil)
|
||||
|
||||
type AuthN struct {
|
||||
store authtypes.AuthNStore
|
||||
settings factory.ScopedProviderSettings
|
||||
httpClient *client.Client
|
||||
globalConfig global.Config
|
||||
store authtypes.AuthNStore
|
||||
settings factory.ScopedProviderSettings
|
||||
httpClient *client.Client
|
||||
}
|
||||
|
||||
func New(ctx context.Context, store authtypes.AuthNStore, providerSettings factory.ProviderSettings, globalConfig global.Config) (*AuthN, error) {
|
||||
func New(ctx context.Context, store authtypes.AuthNStore, providerSettings factory.ProviderSettings) (*AuthN, error) {
|
||||
settings := factory.NewScopedProviderSettings(providerSettings, "github.com/SigNoz/signoz/pkg/authn/callbackauthn/googlecallbackauthn")
|
||||
|
||||
httpClient, err := client.New(settings.Logger(), providerSettings.TracerProvider, providerSettings.MeterProvider)
|
||||
@@ -46,10 +43,9 @@ func New(ctx context.Context, store authtypes.AuthNStore, providerSettings facto
|
||||
}
|
||||
|
||||
return &AuthN{
|
||||
store: store,
|
||||
settings: settings,
|
||||
httpClient: httpClient,
|
||||
globalConfig: globalConfig,
|
||||
store: store,
|
||||
settings: settings,
|
||||
httpClient: httpClient,
|
||||
}, nil
|
||||
}
|
||||
|
||||
@@ -182,7 +178,7 @@ func (a *AuthN) oauth2Config(siteURL *url.URL, authDomain *authtypes.AuthDomain,
|
||||
RedirectURL: (&url.URL{
|
||||
Scheme: siteURL.Scheme,
|
||||
Host: siteURL.Host,
|
||||
Path: path.Join(a.globalConfig.ExternalPath(), redirectPath),
|
||||
Path: redirectPath,
|
||||
}).String(),
|
||||
}
|
||||
}
|
||||
|
||||
File diff suppressed because one or more lines are too long
@@ -1 +0,0 @@
|
||||
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|
||||
|
Before Width: | Height: | Size: 4.4 KiB |
@@ -1,293 +0,0 @@
|
||||
{
|
||||
"id": "aks",
|
||||
"title": "Azure Kubernetes Service (AKS)",
|
||||
"icon": "file://icon.svg",
|
||||
"overview": "file://overview.md",
|
||||
"supportedSignals": {
|
||||
"metrics": true,
|
||||
"logs": true
|
||||
},
|
||||
"dataCollected": {
|
||||
"metrics": [
|
||||
{
|
||||
"name": "azure_kube_pod_status_ready_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_pod_status_ready_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_pod_status_phase_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_pod_status_phase_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_node_status_condition_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_node_status_condition_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_cpu_usage_millicores_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_cpu_usage_millicores_maximum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_cpu_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_cpu_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_disk_usage_bytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_disk_usage_bytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_disk_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_disk_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_rss_bytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_rss_bytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_rss_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_rss_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_working_set_bytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_working_set_bytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_working_set_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_memory_working_set_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_network_in_bytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_network_in_bytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_network_out_bytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_node_network_out_bytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_apiserver_current_inflight_requests_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_apiserver_current_inflight_requests_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_apiserver_cpu_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_apiserver_cpu_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_apiserver_memory_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_apiserver_memory_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_etcd_cpu_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_etcd_cpu_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_etcd_database_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_etcd_database_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_etcd_memory_usage_percentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_etcd_memory_usage_percentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_node_status_allocatable_cpu_cores_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_node_status_allocatable_cpu_cores_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_node_status_allocatable_memory_bytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_kube_node_status_allocatable_memory_bytes_total",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
}
|
||||
],
|
||||
"logs": [
|
||||
{
|
||||
"name": "Resource ID",
|
||||
"path": "resources.azure.resource.id",
|
||||
"type": "string"
|
||||
}
|
||||
]
|
||||
},
|
||||
"telemetryCollectionStrategy": {
|
||||
"azure": {
|
||||
"resourceProvider": "Microsoft.ContainerService",
|
||||
"resourceType": "managedClusters",
|
||||
"metrics": {},
|
||||
"logs": {
|
||||
"categoryGroups": ["allLogs"]
|
||||
}
|
||||
}
|
||||
},
|
||||
"assets": {
|
||||
"dashboards": [
|
||||
{
|
||||
"id": "overview",
|
||||
"title": "Azure Kubernetes Service (AKS) Overview",
|
||||
"description": "Overview of Azure Kubernetes Service (AKS) metrics",
|
||||
"definition": "file://assets/dashboards/overview.json"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,5 +0,0 @@
|
||||
### Monitor Azure Kubernetes Service (AKS) with SigNoz
|
||||
|
||||
Collect key AKS metrics and view them with an out of the box dashboard.
|
||||
|
||||
Note: This integration is only for AKS with resource type `Microsoft.ContainerService/managedClusters`.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1 +0,0 @@
|
||||
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|
||||
|
Before Width: | Height: | Size: 3.1 KiB |
@@ -1,263 +0,0 @@
|
||||
{
|
||||
"id": "containerapp",
|
||||
"title": "Container App",
|
||||
"icon": "file://icon.svg",
|
||||
"overview": "file://overview.md",
|
||||
"supportedSignals": {
|
||||
"metrics": true,
|
||||
"logs": true
|
||||
},
|
||||
"dataCollected": {
|
||||
"metrics": [
|
||||
{
|
||||
"name": "azure_rxbytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_rxbytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_rxbytes_minimum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_rxbytes_total",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_txbytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_txbytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_txbytes_minimum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_txbytes_total",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_restartcount_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_restartcount_maximum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_restartcount_minimum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_restartcount_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_replicas_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_replicas_maximum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_replicas_minimum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_replicas_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_cpupercentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_cpupercentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_cpupercentage_minimum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_cpupercentage_total",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_memorypercentage_average",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_memorypercentage_maximum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_memorypercentage_minimum",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_memorypercentage_total",
|
||||
"unit": "Percent",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_usagenanocores_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_usagenanocores_maximum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_usagenanocores_minimum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_usagenanocores_total",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_workingsetbytes_average",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_workingsetbytes_maximum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_workingsetbytes_minimum",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_workingsetbytes_total",
|
||||
"unit": "Bytes",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_coresquotaused_maximum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_coresquotaused_minimum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_totalcoresquotaused_average",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_totalcoresquotaused_maximum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
},
|
||||
{
|
||||
"name": "azure_totalcoresquotaused_minimum",
|
||||
"unit": "Count",
|
||||
"type": "Gauge",
|
||||
"description": ""
|
||||
}
|
||||
],
|
||||
"logs": [
|
||||
{
|
||||
"name": "Resource ID",
|
||||
"path": "resources.azure.resource.id",
|
||||
"type": "string"
|
||||
}
|
||||
]
|
||||
},
|
||||
"telemetryCollectionStrategy": {
|
||||
"azure": {
|
||||
"resourceProvider": "Microsoft.App",
|
||||
"resourceType": "containerApps",
|
||||
"metrics": {},
|
||||
"logs": {
|
||||
"categoryGroups": ["ContainerAppConsoleLogs", "ContainerAppSystemLogs"]
|
||||
}
|
||||
}
|
||||
},
|
||||
"assets": {
|
||||
"dashboards": [
|
||||
{
|
||||
"id": "overview",
|
||||
"title": "Container App Overview",
|
||||
"description": "Overview of Container App metrics",
|
||||
"definition": "file://assets/dashboards/overview.json"
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
@@ -1,7 +0,0 @@
|
||||
### Monitor Container Apps with SigNoz
|
||||
|
||||
Collect key Container App metrics and view them with an out of the box dashboard.
|
||||
|
||||
To collect logs, you need to make sure that you have chosen "Azure Monitor" as the logging option for Container's App Environment.
|
||||
|
||||
Note: This integration ingests logs for only "ContainerAppConsoleLogs" and "ContainerAppSystemLogs" diagnostic settings categories.
|
||||
@@ -219,7 +219,19 @@ func (m *module) GetStats(ctx context.Context, orgID valuer.UUID, req *metricsex
|
||||
return nil, err
|
||||
}
|
||||
|
||||
metricStats, total, err := m.fetchMetricsStatsWithSamples(ctx, req, false)
|
||||
filterWhereClause, err := m.buildFilterClause(ctx, req.Filter, req.Start, req.End)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
// Single query to get stats with samples, timeseries counts in required sorting order
|
||||
metricStats, total, err := m.fetchMetricsStatsWithSamples(
|
||||
ctx,
|
||||
req,
|
||||
filterWhereClause,
|
||||
false,
|
||||
req.OrderBy,
|
||||
)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
@@ -256,16 +268,21 @@ func (m *module) GetTreemap(ctx context.Context, orgID valuer.UUID, req *metrics
|
||||
return nil, err
|
||||
}
|
||||
|
||||
filterWhereClause, err := m.buildFilterClause(ctx, req.Filter, req.Start, req.End)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
resp := &metricsexplorertypes.TreemapResponse{}
|
||||
switch req.Mode {
|
||||
case metricsexplorertypes.TreemapModeSamples:
|
||||
entries, err := m.computeSamplesTreemap(ctx, req)
|
||||
entries, err := m.computeSamplesTreemap(ctx, req, filterWhereClause)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
resp.Samples = entries
|
||||
default: // TreemapModeTimeSeries
|
||||
entries, err := m.computeTimeseriesTreemap(ctx, req)
|
||||
entries, err := m.computeTimeseriesTreemap(ctx, req, filterWhereClause)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
@@ -957,23 +974,15 @@ func (m *module) buildFilterClause(ctx context.Context, filter *qbtypes.Filter,
|
||||
func (m *module) fetchMetricsStatsWithSamples(
|
||||
ctx context.Context,
|
||||
req *metricsexplorertypes.StatsRequest,
|
||||
filterWhereClause *sqlbuilder.WhereClause,
|
||||
normalized bool,
|
||||
orderBy *qbtypes.OrderBy,
|
||||
) ([]metricsexplorertypes.Stat, uint64, error) {
|
||||
ctx = m.withMetricsExplorerContext(ctx, "fetchMetricsStatsWithSamples")
|
||||
|
||||
hasFilter := req.Filter != nil && strings.TrimSpace(req.Filter.Expression) != ""
|
||||
var filterWhereClause *sqlbuilder.WhereClause
|
||||
if hasFilter {
|
||||
var err error
|
||||
filterWhereClause, err = m.buildFilterClause(ctx, req.Filter, req.Start, req.End)
|
||||
if err != nil {
|
||||
return nil, 0, err
|
||||
}
|
||||
}
|
||||
|
||||
start, end, distributedTsTable, localTsTable := telemetrymetrics.WhichTSTableToUse(uint64(req.Start), uint64(req.End), nil)
|
||||
distributedSamplesTable, _ := telemetrymetrics.WhichSamplesTableToUse(uint64(req.Start), uint64(req.End), metrictypes.UnspecifiedType, metrictypes.TimeAggregationUnspecified, nil)
|
||||
countExp := telemetrymetrics.CountExpressionForSamplesTable(distributedSamplesTable)
|
||||
samplesTable, _ := telemetrymetrics.WhichSamplesTableToUse(uint64(req.Start), uint64(req.End), metrictypes.UnspecifiedType, metrictypes.TimeAggregationUnspecified, nil)
|
||||
countExp := telemetrymetrics.CountExpressionForSamplesTable(samplesTable)
|
||||
|
||||
// Timeseries counts per metric
|
||||
tsSB := sqlbuilder.NewSelectBuilder()
|
||||
@@ -996,7 +1005,7 @@ func (m *module) fetchMetricsStatsWithSamples(
|
||||
"metric_name",
|
||||
fmt.Sprintf("%s AS samples", countExp),
|
||||
)
|
||||
samplesSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, distributedSamplesTable))
|
||||
samplesSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, samplesTable))
|
||||
samplesSB.Where(samplesSB.Between("unix_milli", req.Start, req.End))
|
||||
samplesSB.Where("NOT startsWith(metric_name, 'signoz')")
|
||||
|
||||
@@ -1004,8 +1013,6 @@ func (m *module) fetchMetricsStatsWithSamples(
|
||||
sqlbuilder.CTEQuery("__time_series_counts").As(tsSB),
|
||||
}
|
||||
|
||||
// Narrow samples scan. With filter: fingerprint IN (per-fingerprint label preds can't fold to metric_name).
|
||||
// No filter (fast path): metric_name IN — aligns with samples table's leading sort key, orders of magnitude cheaper.
|
||||
if filterWhereClause != nil {
|
||||
fingerprintSB := sqlbuilder.NewSelectBuilder()
|
||||
fingerprintSB.Select("fingerprint")
|
||||
@@ -1018,15 +1025,6 @@ func (m *module) fetchMetricsStatsWithSamples(
|
||||
|
||||
ctes = append(ctes, sqlbuilder.CTEQuery("__filtered_fingerprints").As(fingerprintSB))
|
||||
samplesSB.Where("fingerprint IN (SELECT fingerprint FROM __filtered_fingerprints)")
|
||||
} else {
|
||||
metricNamesSB := sqlbuilder.NewSelectBuilder()
|
||||
metricNamesSB.Select("DISTINCT metric_name")
|
||||
metricNamesSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, localTsTable))
|
||||
metricNamesSB.Where(metricNamesSB.Between("unix_milli", start, end))
|
||||
metricNamesSB.Where("NOT startsWith(metric_name, 'signoz')")
|
||||
metricNamesSB.Where(metricNamesSB.E("__normalized", normalized))
|
||||
|
||||
samplesSB.Where(fmt.Sprintf("metric_name IN (%s)", samplesSB.Var(metricNamesSB)))
|
||||
}
|
||||
samplesSB.GroupBy("metric_name")
|
||||
|
||||
@@ -1043,7 +1041,7 @@ func (m *module) fetchMetricsStatsWithSamples(
|
||||
finalSB.JoinWithOption(sqlbuilder.FullOuterJoin, "__sample_counts s", "ts.metric_name = s.metric_name")
|
||||
finalSB.Where("(COALESCE(ts.timeseries, 0) > 0 OR COALESCE(s.samples, 0) > 0)")
|
||||
|
||||
orderByColumn, orderDirection, err := getStatsOrderByColumn(req.OrderBy)
|
||||
orderByColumn, orderDirection, err := getStatsOrderByColumn(orderBy)
|
||||
if err != nil {
|
||||
return nil, 0, err
|
||||
}
|
||||
@@ -1087,19 +1085,9 @@ func (m *module) fetchMetricsStatsWithSamples(
|
||||
return metricStats, total, nil
|
||||
}
|
||||
|
||||
func (m *module) computeTimeseriesTreemap(ctx context.Context, req *metricsexplorertypes.TreemapRequest) ([]metricsexplorertypes.TreemapEntry, error) {
|
||||
func (m *module) computeTimeseriesTreemap(ctx context.Context, req *metricsexplorertypes.TreemapRequest, filterWhereClause *sqlbuilder.WhereClause) ([]metricsexplorertypes.TreemapEntry, error) {
|
||||
ctx = m.withMetricsExplorerContext(ctx, "computeTimeseriesTreemap")
|
||||
|
||||
hasFilter := req.Filter != nil && strings.TrimSpace(req.Filter.Expression) != ""
|
||||
var filterWhereClause *sqlbuilder.WhereClause
|
||||
if hasFilter {
|
||||
var err error
|
||||
filterWhereClause, err = m.buildFilterClause(ctx, req.Filter, req.Start, req.End)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
}
|
||||
|
||||
start, end, distributedTsTable, _ := telemetrymetrics.WhichTSTableToUse(uint64(req.Start), uint64(req.End), nil)
|
||||
|
||||
totalTSBuilder := sqlbuilder.NewSelectBuilder()
|
||||
@@ -1163,22 +1151,12 @@ func (m *module) computeTimeseriesTreemap(ctx context.Context, req *metricsexplo
|
||||
return entries, nil
|
||||
}
|
||||
|
||||
func (m *module) computeSamplesTreemap(ctx context.Context, req *metricsexplorertypes.TreemapRequest) ([]metricsexplorertypes.TreemapEntry, error) {
|
||||
func (m *module) computeSamplesTreemap(ctx context.Context, req *metricsexplorertypes.TreemapRequest, filterWhereClause *sqlbuilder.WhereClause) ([]metricsexplorertypes.TreemapEntry, error) {
|
||||
ctx = m.withMetricsExplorerContext(ctx, "computeSamplesTreemap")
|
||||
|
||||
hasFilter := req.Filter != nil && strings.TrimSpace(req.Filter.Expression) != ""
|
||||
var filterWhereClause *sqlbuilder.WhereClause
|
||||
if hasFilter {
|
||||
var err error
|
||||
filterWhereClause, err = m.buildFilterClause(ctx, req.Filter, req.Start, req.End)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
}
|
||||
|
||||
start, end, distributedTsTable, localTsTable := telemetrymetrics.WhichTSTableToUse(uint64(req.Start), uint64(req.End), nil)
|
||||
distributedSamplesTable, _ := telemetrymetrics.WhichSamplesTableToUse(uint64(req.Start), uint64(req.End), metrictypes.UnspecifiedType, metrictypes.TimeAggregationUnspecified, nil)
|
||||
countExp := telemetrymetrics.CountExpressionForSamplesTable(distributedSamplesTable)
|
||||
samplesTable, _ := telemetrymetrics.WhichSamplesTableToUse(uint64(req.Start), uint64(req.End), metrictypes.UnspecifiedType, metrictypes.TimeAggregationUnspecified, nil)
|
||||
countExp := telemetrymetrics.CountExpressionForSamplesTable(samplesTable)
|
||||
|
||||
candidateLimit := req.Limit + 50
|
||||
|
||||
@@ -1201,7 +1179,7 @@ func (m *module) computeSamplesTreemap(ctx context.Context, req *metricsexplorer
|
||||
|
||||
totalSamplesSB := sqlbuilder.NewSelectBuilder()
|
||||
totalSamplesSB.Select(fmt.Sprintf("%s AS total_samples", countExp))
|
||||
totalSamplesSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, distributedSamplesTable))
|
||||
totalSamplesSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, samplesTable))
|
||||
totalSamplesSB.Where(totalSamplesSB.Between("unix_milli", req.Start, req.End))
|
||||
|
||||
sampleCountsSB := sqlbuilder.NewSelectBuilder()
|
||||
@@ -1209,7 +1187,7 @@ func (m *module) computeSamplesTreemap(ctx context.Context, req *metricsexplorer
|
||||
"metric_name",
|
||||
fmt.Sprintf("%s AS samples", countExp),
|
||||
)
|
||||
sampleCountsSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, distributedSamplesTable))
|
||||
sampleCountsSB.From(fmt.Sprintf("%s.%s", telemetrymetrics.DBName, samplesTable))
|
||||
sampleCountsSB.Where(sampleCountsSB.Between("unix_milli", req.Start, req.End))
|
||||
sampleCountsSB.Where("metric_name GLOBAL IN (SELECT metric_name FROM __metric_candidates)")
|
||||
|
||||
|
||||
@@ -1,270 +0,0 @@
|
||||
package implmetricsexplorer_test
|
||||
|
||||
import (
|
||||
"context"
|
||||
"regexp"
|
||||
"testing"
|
||||
|
||||
"github.com/DATA-DOG/go-sqlmock"
|
||||
cmock "github.com/SigNoz/clickhouse-go-mock"
|
||||
"github.com/SigNoz/signoz/pkg/cache"
|
||||
"github.com/SigNoz/signoz/pkg/cache/cachetest"
|
||||
"github.com/SigNoz/signoz/pkg/errors"
|
||||
"github.com/SigNoz/signoz/pkg/instrumentation/instrumentationtest"
|
||||
"github.com/SigNoz/signoz/pkg/modules/metricsexplorer"
|
||||
"github.com/SigNoz/signoz/pkg/modules/metricsexplorer/implmetricsexplorer"
|
||||
"github.com/SigNoz/signoz/pkg/telemetrystore"
|
||||
"github.com/SigNoz/signoz/pkg/telemetrystore/telemetrystoretest"
|
||||
"github.com/SigNoz/signoz/pkg/types/metricsexplorertypes"
|
||||
qbtypes "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes/telemetrytypestest"
|
||||
"github.com/SigNoz/signoz/pkg/valuer"
|
||||
"github.com/stretchr/testify/assert"
|
||||
)
|
||||
|
||||
const (
|
||||
// fixed, deterministic time window (range < 6h so the table selectors resolve
|
||||
// to stable table names that the expected SQL strings can hard-code).
|
||||
testStartMillis int64 = 1700000000000
|
||||
testEndMillis int64 = 1700003600000 // +1h
|
||||
statsNoFilterSQL = "WITH __time_series_counts AS (SELECT metric_name, uniq(fingerprint) AS timeseries FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? GROUP BY metric_name), __sample_counts AS (SELECT metric_name, count(*) AS samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND metric_name IN (SELECT DISTINCT metric_name FROM signoz_metrics.time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ?) GROUP BY metric_name) SELECT COALESCE(ts.metric_name, s.metric_name) AS metric_name, COALESCE(ts.timeseries, 0) AS timeseries, COALESCE(s.samples, 0) AS samples, COUNT(*) OVER() AS total FROM __time_series_counts ts FULL OUTER JOIN __sample_counts s ON ts.metric_name = s.metric_name WHERE (COALESCE(ts.timeseries, 0) > 0 OR COALESCE(s.samples, 0) > 0) ORDER BY samples DESC, metric_name ASC LIMIT ? OFFSET ?"
|
||||
statsOrderTimeseriesSQL = "WITH __time_series_counts AS (SELECT metric_name, uniq(fingerprint) AS timeseries FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? GROUP BY metric_name), __sample_counts AS (SELECT metric_name, count(*) AS samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND metric_name IN (SELECT DISTINCT metric_name FROM signoz_metrics.time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ?) GROUP BY metric_name) SELECT COALESCE(ts.metric_name, s.metric_name) AS metric_name, COALESCE(ts.timeseries, 0) AS timeseries, COALESCE(s.samples, 0) AS samples, COUNT(*) OVER() AS total FROM __time_series_counts ts FULL OUTER JOIN __sample_counts s ON ts.metric_name = s.metric_name WHERE (COALESCE(ts.timeseries, 0) > 0 OR COALESCE(s.samples, 0) > 0) ORDER BY timeseries ASC, metric_name ASC LIMIT ? OFFSET ?"
|
||||
statsWithFilterSQL = "WITH __time_series_counts AS (SELECT metric_name, uniq(fingerprint) AS timeseries FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? AND JSONExtractString(labels, 'host.name') = ? GROUP BY metric_name), __filtered_fingerprints AS (SELECT fingerprint FROM signoz_metrics.time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? AND JSONExtractString(labels, 'host.name') = ? GROUP BY fingerprint), __sample_counts AS (SELECT metric_name, count(*) AS samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND fingerprint IN (SELECT fingerprint FROM __filtered_fingerprints) GROUP BY metric_name) SELECT COALESCE(ts.metric_name, s.metric_name) AS metric_name, COALESCE(ts.timeseries, 0) AS timeseries, COALESCE(s.samples, 0) AS samples, COUNT(*) OVER() AS total FROM __time_series_counts ts FULL OUTER JOIN __sample_counts s ON ts.metric_name = s.metric_name WHERE (COALESCE(ts.timeseries, 0) > 0 OR COALESCE(s.samples, 0) > 0) ORDER BY samples DESC, metric_name ASC LIMIT ? OFFSET ?"
|
||||
treemapTimeseriesNoFilterSQL = "WITH __total_time_series AS (SELECT uniq(fingerprint) AS total_time_series FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND __normalized = ?), __metric_totals AS (SELECT metric_name, uniq(fingerprint) AS total_value FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? GROUP BY metric_name) SELECT mt.metric_name, mt.total_value, CASE WHEN tts.total_time_series = 0 THEN 0 ELSE (mt.total_value * 100.0 / tts.total_time_series) END AS percentage FROM __metric_totals mt JOIN __total_time_series tts ON 1=1 ORDER BY percentage DESC LIMIT ?"
|
||||
treemapTimeseriesWithFilterSQL = "WITH __total_time_series AS (SELECT uniq(fingerprint) AS total_time_series FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND __normalized = ?), __metric_totals AS (SELECT metric_name, uniq(fingerprint) AS total_value FROM signoz_metrics.distributed_time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? AND JSONExtractString(labels, 'host.name') = ? GROUP BY metric_name) SELECT mt.metric_name, mt.total_value, CASE WHEN tts.total_time_series = 0 THEN 0 ELSE (mt.total_value * 100.0 / tts.total_time_series) END AS percentage FROM __metric_totals mt JOIN __total_time_series tts ON 1=1 ORDER BY percentage DESC LIMIT ?"
|
||||
treemapSamplesNoFilterSQL = "WITH __metric_candidates AS (SELECT metric_name FROM signoz_metrics.distributed_time_series_v4 WHERE NOT startsWith(metric_name, 'signoz') AND __normalized = ? AND unix_milli BETWEEN ? AND ? GROUP BY metric_name ORDER BY uniq(fingerprint) DESC LIMIT ?), __sample_counts AS (SELECT metric_name, count(*) AS samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ? AND metric_name GLOBAL IN (SELECT metric_name FROM __metric_candidates) GROUP BY metric_name), __total_samples AS (SELECT count(*) AS total_samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ?) SELECT mc.metric_name, COALESCE(sc.samples, 0) AS samples, CASE WHEN ts.total_samples = 0 THEN 0 ELSE (COALESCE(sc.samples, 0) * 100.0 / ts.total_samples) END AS percentage FROM __metric_candidates mc LEFT JOIN __sample_counts sc ON mc.metric_name = sc.metric_name JOIN __total_samples ts ON 1=1 ORDER BY percentage DESC LIMIT ?"
|
||||
treemapSamplesWithFilterSQL = "WITH __metric_candidates AS (SELECT metric_name FROM signoz_metrics.distributed_time_series_v4 WHERE NOT startsWith(metric_name, 'signoz') AND __normalized = ? AND unix_milli BETWEEN ? AND ? AND JSONExtractString(labels, 'host.name') = ? GROUP BY metric_name ORDER BY uniq(fingerprint) DESC LIMIT ?), __filtered_fingerprints AS (SELECT fingerprint FROM signoz_metrics.time_series_v4 WHERE unix_milli BETWEEN ? AND ? AND NOT startsWith(metric_name, 'signoz') AND __normalized = ? AND JSONExtractString(labels, 'host.name') = ? AND metric_name GLOBAL IN (SELECT metric_name FROM __metric_candidates) GROUP BY fingerprint), __sample_counts AS (SELECT metric_name, count(*) AS samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ? AND metric_name GLOBAL IN (SELECT metric_name FROM __metric_candidates) AND fingerprint IN (SELECT fingerprint FROM __filtered_fingerprints) GROUP BY metric_name), __total_samples AS (SELECT count(*) AS total_samples FROM signoz_metrics.distributed_samples_v4 WHERE unix_milli BETWEEN ? AND ?) SELECT mc.metric_name, COALESCE(sc.samples, 0) AS samples, CASE WHEN ts.total_samples = 0 THEN 0 ELSE (COALESCE(sc.samples, 0) * 100.0 / ts.total_samples) END AS percentage FROM __metric_candidates mc LEFT JOIN __sample_counts sc ON mc.metric_name = sc.metric_name JOIN __total_samples ts ON 1=1 ORDER BY percentage DESC LIMIT ?"
|
||||
)
|
||||
|
||||
var testOrgID = valuer.GenerateUUID()
|
||||
|
||||
type statsOpt func(*metricsexplorertypes.StatsRequest)
|
||||
|
||||
type treemapOpt func(*metricsexplorertypes.TreemapRequest)
|
||||
|
||||
// newTestModule builds the metricsexplorer module backed by a mocked clickhouse
|
||||
// connection, a mock metadata store, and an in-memory cache.
|
||||
func newTestModule(t *testing.T, matcher sqlmock.QueryMatcher) (metricsexplorer.Module, cmock.ClickConnMockCommon, *telemetrytypestest.MockMetadataStore) {
|
||||
t.Helper()
|
||||
|
||||
ts := telemetrystoretest.New(telemetrystore.Config{}, matcher)
|
||||
md := telemetrytypestest.NewMockMetadataStore()
|
||||
c, err := cachetest.New(cache.Config{Provider: "memory", Memory: cache.Memory{NumCounters: 1000, MaxCost: 1 << 20}})
|
||||
if err != nil {
|
||||
t.Fatalf("cachetest.New: %v", err)
|
||||
}
|
||||
|
||||
settings := instrumentationtest.New().ToProviderSettings()
|
||||
mod := implmetricsexplorer.NewModule(ts, md, c, nil /*ruleStore*/, nil /*dashboardModule*/, settings, metricsexplorer.Config{})
|
||||
|
||||
return mod, ts.Mock(), md
|
||||
}
|
||||
|
||||
func statsRequest(opts ...statsOpt) *metricsexplorertypes.StatsRequest {
|
||||
req := &metricsexplorertypes.StatsRequest{
|
||||
Start: testStartMillis,
|
||||
End: testEndMillis,
|
||||
Limit: 10,
|
||||
}
|
||||
for _, o := range opts {
|
||||
o(req)
|
||||
}
|
||||
return req
|
||||
}
|
||||
|
||||
func withStatsFilter(expr string) statsOpt {
|
||||
return func(req *metricsexplorertypes.StatsRequest) {
|
||||
req.Filter = &qbtypes.Filter{Expression: expr}
|
||||
}
|
||||
}
|
||||
|
||||
func withStatsLimit(limit int) statsOpt {
|
||||
return func(req *metricsexplorertypes.StatsRequest) {
|
||||
req.Limit = limit
|
||||
}
|
||||
}
|
||||
|
||||
func withStatsOrderBy(name string, dir qbtypes.OrderDirection) statsOpt {
|
||||
return func(req *metricsexplorertypes.StatsRequest) {
|
||||
req.OrderBy = &qbtypes.OrderBy{
|
||||
Key: qbtypes.OrderByKey{TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: name}},
|
||||
Direction: dir,
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
func treemapRequest(mode metricsexplorertypes.TreemapMode, opts ...treemapOpt) *metricsexplorertypes.TreemapRequest {
|
||||
req := &metricsexplorertypes.TreemapRequest{
|
||||
Start: testStartMillis,
|
||||
End: testEndMillis,
|
||||
Limit: 10,
|
||||
Mode: mode,
|
||||
}
|
||||
for _, o := range opts {
|
||||
o(req)
|
||||
}
|
||||
return req
|
||||
}
|
||||
|
||||
func withTreemapFilter(expr string) treemapOpt {
|
||||
return func(req *metricsexplorertypes.TreemapRequest) {
|
||||
req.Filter = &qbtypes.Filter{Expression: expr}
|
||||
}
|
||||
}
|
||||
|
||||
// seedFilterKey registers a string attribute field so buildFilterClause can
|
||||
// resolve it when parsing a filter expression that references it.
|
||||
func seedFilterKey(md *telemetrytypestest.MockMetadataStore, name string) {
|
||||
md.KeysMap[name] = []*telemetrytypes.TelemetryFieldKey{
|
||||
{
|
||||
Name: name,
|
||||
Signal: telemetrytypes.SignalMetrics,
|
||||
FieldContext: telemetrytypes.FieldContextAttribute,
|
||||
FieldDataType: telemetrytypes.FieldDataTypeString,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
// anyArgs returns n nil wildcards. cmock treats a nil expected arg as a match
|
||||
// for any actual value, so this asserts only the bound-arg count, not values
|
||||
// (the SQL text itself is what we verify). The count must match the query.
|
||||
func anyArgs(n int) []any {
|
||||
return make([]any, n)
|
||||
}
|
||||
|
||||
func treemapEntryRows() *cmock.Rows {
|
||||
return cmock.NewRows(
|
||||
[]cmock.ColumnType{
|
||||
{Name: "metric_name", Type: "String"},
|
||||
{Name: "total_value", Type: "UInt64"},
|
||||
{Name: "percentage", Type: "Float64"},
|
||||
},
|
||||
[][]any{
|
||||
{"metric_a", uint64(50), 50.0},
|
||||
{"metric_b", uint64(30), 30.0},
|
||||
},
|
||||
)
|
||||
}
|
||||
|
||||
func TestGetStats(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
opts []statsOpt
|
||||
seedKey string
|
||||
queryErr error
|
||||
expectSQL string
|
||||
argCount int
|
||||
noQuery bool // SQL never reaches clickhouse (validation/build error)
|
||||
wantCode errors.Code
|
||||
}{
|
||||
{name: "NoFilter_FastPathSQL", expectSQL: statsNoFilterSQL, argCount: 10},
|
||||
{name: "WhitespaceFilter_FastPathSQL", opts: []statsOpt{withStatsFilter(" ")}, expectSQL: statsNoFilterSQL, argCount: 10},
|
||||
{name: "WithFilter_FingerprintSQL", opts: []statsOpt{withStatsFilter("host.name = 'foo'")}, seedKey: "host.name", expectSQL: statsWithFilterSQL, argCount: 12},
|
||||
{name: "OrderByTimeseriesAsc", opts: []statsOpt{withStatsOrderBy("timeseries", qbtypes.OrderDirectionAsc)}, expectSQL: statsOrderTimeseriesSQL, argCount: 10},
|
||||
{name: "OrderByInvalid", opts: []statsOpt{withStatsOrderBy("nonsense", qbtypes.OrderDirectionAsc)}, noQuery: true, wantCode: errors.CodeInvalidInput},
|
||||
{name: "QueryError", queryErr: assert.AnError, expectSQL: statsNoFilterSQL, argCount: 10, wantCode: errors.CodeInternal},
|
||||
{name: "InvalidRequest_Limit", opts: []statsOpt{withStatsLimit(0)}, noQuery: true, wantCode: errors.CodeInvalidInput},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
mod, mock, md := newTestModule(t, sqlmock.QueryMatcherRegexp)
|
||||
if tc.seedKey != "" {
|
||||
seedFilterKey(md, tc.seedKey)
|
||||
}
|
||||
|
||||
if !tc.noQuery {
|
||||
eq := mock.ExpectQuery(regexp.QuoteMeta(tc.expectSQL)).WithArgs(anyArgs(tc.argCount)...)
|
||||
if tc.queryErr != nil {
|
||||
eq.WillReturnError(tc.queryErr)
|
||||
} else {
|
||||
eq.WillReturnRows(cmock.NewRows(nil, nil))
|
||||
}
|
||||
}
|
||||
|
||||
_, err := mod.GetStats(context.Background(), testOrgID, statsRequest(tc.opts...))
|
||||
if tc.wantCode.String() != "" {
|
||||
assert.Error(t, err)
|
||||
assert.Truef(t, errors.Asc(err, tc.wantCode), "want code %s, got %v", tc.wantCode, err)
|
||||
return
|
||||
}
|
||||
assert.NoError(t, err)
|
||||
assert.NoError(t, mock.ExpectationsWereMet())
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestGetTreemap(t *testing.T) {
|
||||
wantEntries := []metricsexplorertypes.TreemapEntry{
|
||||
{MetricName: "metric_a", TotalValue: 50, Percentage: 50.0},
|
||||
{MetricName: "metric_b", TotalValue: 30, Percentage: 30.0},
|
||||
}
|
||||
|
||||
tests := []struct {
|
||||
name string
|
||||
mode metricsexplorertypes.TreemapMode
|
||||
opts []treemapOpt
|
||||
seedKey string
|
||||
queryErr error
|
||||
expectSQL string
|
||||
argCount int
|
||||
rows *cmock.Rows
|
||||
wantSamples []metricsexplorertypes.TreemapEntry
|
||||
wantTS []metricsexplorertypes.TreemapEntry
|
||||
noQuery bool
|
||||
wantCode errors.Code
|
||||
wantErr bool
|
||||
}{
|
||||
{name: "TimeSeries_NoFilter_SQL", mode: metricsexplorertypes.TreemapModeTimeSeries, expectSQL: treemapTimeseriesNoFilterSQL, argCount: 7},
|
||||
{name: "TimeSeries_WithFilter_SQL", mode: metricsexplorertypes.TreemapModeTimeSeries, opts: []treemapOpt{withTreemapFilter("host.name = 'foo'")}, seedKey: "host.name", expectSQL: treemapTimeseriesWithFilterSQL, argCount: 8},
|
||||
{name: "TimeSeries_ScansEntries", mode: metricsexplorertypes.TreemapModeTimeSeries, expectSQL: treemapTimeseriesNoFilterSQL, argCount: 7, rows: treemapEntryRows(), wantTS: wantEntries},
|
||||
{name: "Samples_NoFilter_SQL", mode: metricsexplorertypes.TreemapModeSamples, expectSQL: treemapSamplesNoFilterSQL, argCount: 9},
|
||||
{name: "Samples_WithFilter_SQL", mode: metricsexplorertypes.TreemapModeSamples, opts: []treemapOpt{withTreemapFilter("host.name = 'foo'")}, seedKey: "host.name", expectSQL: treemapSamplesWithFilterSQL, argCount: 14},
|
||||
{name: "Samples_ScansEntries", mode: metricsexplorertypes.TreemapModeSamples, expectSQL: treemapSamplesNoFilterSQL, argCount: 9, rows: treemapEntryRows(), wantSamples: wantEntries},
|
||||
{name: "FilterBuildError", mode: metricsexplorertypes.TreemapModeTimeSeries, opts: []treemapOpt{withTreemapFilter("host.name =")}, noQuery: true, wantErr: true},
|
||||
{name: "QueryError", mode: metricsexplorertypes.TreemapModeTimeSeries, queryErr: assert.AnError, expectSQL: treemapTimeseriesNoFilterSQL, argCount: 7, wantCode: errors.CodeInternal},
|
||||
{name: "InvalidMode", mode: metricsexplorertypes.TreemapMode{}, noQuery: true, wantCode: errors.CodeInvalidInput},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
mod, mock, md := newTestModule(t, sqlmock.QueryMatcherRegexp)
|
||||
if tc.seedKey != "" {
|
||||
seedFilterKey(md, tc.seedKey)
|
||||
}
|
||||
|
||||
if !tc.noQuery {
|
||||
eq := mock.ExpectQuery(regexp.QuoteMeta(tc.expectSQL)).WithArgs(anyArgs(tc.argCount)...)
|
||||
switch {
|
||||
case tc.queryErr != nil:
|
||||
eq.WillReturnError(tc.queryErr)
|
||||
case tc.rows != nil:
|
||||
eq.WillReturnRows(tc.rows)
|
||||
default:
|
||||
eq.WillReturnRows(cmock.NewRows(nil, nil))
|
||||
}
|
||||
}
|
||||
|
||||
resp, err := mod.GetTreemap(context.Background(), testOrgID, treemapRequest(tc.mode, tc.opts...))
|
||||
switch {
|
||||
case tc.wantCode.String() != "":
|
||||
assert.Error(t, err)
|
||||
assert.Truef(t, errors.Asc(err, tc.wantCode), "want code %s, got %v", tc.wantCode, err)
|
||||
return
|
||||
case tc.wantErr:
|
||||
assert.Error(t, err)
|
||||
return
|
||||
}
|
||||
|
||||
assert.NoError(t, err)
|
||||
assert.NoError(t, mock.ExpectationsWereMet())
|
||||
if tc.wantTS != nil {
|
||||
assert.Equal(t, tc.wantTS, resp.TimeSeries)
|
||||
}
|
||||
if tc.wantSamples != nil {
|
||||
assert.Equal(t, tc.wantSamples, resp.Samples)
|
||||
}
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -4,11 +4,9 @@ import (
|
||||
"context"
|
||||
"net/http"
|
||||
"net/url"
|
||||
"path"
|
||||
"time"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/errors"
|
||||
"github.com/SigNoz/signoz/pkg/global"
|
||||
"github.com/SigNoz/signoz/pkg/http/binding"
|
||||
"github.com/SigNoz/signoz/pkg/http/render"
|
||||
"github.com/SigNoz/signoz/pkg/modules/session"
|
||||
@@ -17,12 +15,11 @@ import (
|
||||
)
|
||||
|
||||
type handler struct {
|
||||
module session.Module
|
||||
globalConfig global.Config
|
||||
module session.Module
|
||||
}
|
||||
|
||||
func NewHandler(module session.Module, globalConfig global.Config) session.Handler {
|
||||
return &handler{module: module, globalConfig: globalConfig}
|
||||
func NewHandler(module session.Module) session.Handler {
|
||||
return &handler{module: module}
|
||||
}
|
||||
|
||||
func (handler *handler) GetSessionContext(rw http.ResponseWriter, req *http.Request) {
|
||||
@@ -161,13 +158,13 @@ func (handler *handler) DeleteSession(rw http.ResponseWriter, req *http.Request)
|
||||
render.Success(rw, http.StatusNoContent, nil)
|
||||
}
|
||||
|
||||
func (handler *handler) getRedirectURLFromErr(err error) string {
|
||||
func (*handler) getRedirectURLFromErr(err error) string {
|
||||
values := errors.AsURLValues(err)
|
||||
values.Add("callbackauthnerr", "true")
|
||||
|
||||
return (&url.URL{
|
||||
// When UI is being served on a prefix, we need to redirect to the login page on the prefix.
|
||||
Path: path.Join(handler.globalConfig.ExternalPath(), "/login"),
|
||||
Path: "/login",
|
||||
RawQuery: values.Encode(),
|
||||
}).String()
|
||||
}
|
||||
|
||||
@@ -6,16 +6,7 @@ import (
|
||||
)
|
||||
|
||||
type Config struct {
|
||||
Waterfall WaterfallConfig `mapstructure:"waterfall"`
|
||||
Flamegraph FlamegraphConfig `mapstructure:"flamegraph"`
|
||||
}
|
||||
|
||||
type FlamegraphConfig struct {
|
||||
MaxSelectedLevels int `mapstructure:"max_selected_levels"`
|
||||
MaxSpansPerLevel int `mapstructure:"max_spans_per_level"`
|
||||
SamplingTopLatencySpansCount int `mapstructure:"sampling_top_latency_count"`
|
||||
SamplingBucketCount int `mapstructure:"sampling_bucket_count"`
|
||||
SelectAllSpansLimit uint `mapstructure:"select_all_spans_limit"`
|
||||
Waterfall WaterfallConfig `mapstructure:"waterfall"`
|
||||
}
|
||||
|
||||
type WaterfallConfig struct {
|
||||
@@ -38,13 +29,6 @@ func newConfig() factory.Config {
|
||||
MaxDepthToAutoExpand: 5,
|
||||
MaxLimitToSelectAllSpans: 10_000,
|
||||
},
|
||||
Flamegraph: FlamegraphConfig{
|
||||
MaxSelectedLevels: 50,
|
||||
MaxSpansPerLevel: 100,
|
||||
SamplingTopLatencySpansCount: 5,
|
||||
SamplingBucketCount: 50,
|
||||
SelectAllSpansLimit: 100_000,
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
@@ -58,20 +42,5 @@ func (c Config) Validate() error {
|
||||
if c.Waterfall.MaxLimitToSelectAllSpans == 0 {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "traces.waterfall.max_limit_to_select_all_spans must be positive")
|
||||
}
|
||||
if c.Flamegraph.MaxSelectedLevels <= 0 {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "traces.flamegraph.max_selected_levels must be positive, got %d", c.Flamegraph.MaxSelectedLevels)
|
||||
}
|
||||
if c.Flamegraph.MaxSpansPerLevel <= 0 {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "traces.flamegraph.max_spans_per_level must be positive, got %d", c.Flamegraph.MaxSpansPerLevel)
|
||||
}
|
||||
if c.Flamegraph.SamplingTopLatencySpansCount < 0 {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "traces.flamegraph.sampling_top_latency_count cannot be negative, got %d", c.Flamegraph.SamplingTopLatencySpansCount)
|
||||
}
|
||||
if c.Flamegraph.SamplingBucketCount <= 0 {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "traces.flamegraph.sampling_bucket_count must be positive, got %d", c.Flamegraph.SamplingBucketCount)
|
||||
}
|
||||
if c.Flamegraph.SelectAllSpansLimit == 0 {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "traces.flamegraph.select_all_spans_limit must be positive")
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
@@ -18,6 +18,27 @@ func NewHandler(module tracedetail.Module) tracedetail.Handler {
|
||||
return &handler{module: module}
|
||||
}
|
||||
|
||||
func (h *handler) GetWaterfall(rw http.ResponseWriter, r *http.Request) {
|
||||
req := new(spantypes.PostableWaterfall)
|
||||
if err := binding.JSON.BindBody(r.Body, req); err != nil {
|
||||
render.Error(rw, err)
|
||||
return
|
||||
}
|
||||
|
||||
if err := req.Validate(); err != nil {
|
||||
render.Error(rw, err)
|
||||
return
|
||||
}
|
||||
|
||||
result, err := h.module.GetWaterfall(r.Context(), mux.Vars(r)["traceID"], req)
|
||||
if err != nil {
|
||||
render.Error(rw, err)
|
||||
return
|
||||
}
|
||||
|
||||
render.Success(rw, http.StatusOK, result)
|
||||
}
|
||||
|
||||
func (h *handler) GetWaterfallV4(rw http.ResponseWriter, r *http.Request) {
|
||||
req := new(spantypes.PostableWaterfall)
|
||||
if err := binding.JSON.BindBody(r.Body, req); err != nil {
|
||||
@@ -30,7 +51,7 @@ func (h *handler) GetWaterfallV4(rw http.ResponseWriter, r *http.Request) {
|
||||
return
|
||||
}
|
||||
|
||||
result, err := h.module.GetWaterfallV4(r.Context(), mux.Vars(r)["traceID"], req.SelectedSpanID, req.UncollapsedSpans)
|
||||
result, err := h.module.GetWaterfallV4(r.Context(), mux.Vars(r)["traceID"], req.SelectedSpanID, req.UncollapsedSpans, req.Limit)
|
||||
if err != nil {
|
||||
render.Error(rw, err)
|
||||
return
|
||||
@@ -59,19 +80,3 @@ func (h *handler) GetTraceAggregations(rw http.ResponseWriter, r *http.Request)
|
||||
|
||||
render.Success(rw, http.StatusOK, result)
|
||||
}
|
||||
|
||||
func (h *handler) GetFlamegraph(rw http.ResponseWriter, r *http.Request) {
|
||||
req := new(spantypes.PostableFlamegraph)
|
||||
if err := binding.JSON.BindBody(r.Body, req); err != nil {
|
||||
render.Error(rw, err)
|
||||
return
|
||||
}
|
||||
|
||||
result, err := h.module.GetFlamegraph(r.Context(), mux.Vars(r)["traceID"], req.SelectedSpanID, req.SelectFields)
|
||||
if err != nil {
|
||||
render.Error(rw, err)
|
||||
return
|
||||
}
|
||||
|
||||
render.Success(rw, http.StatusOK, result)
|
||||
}
|
||||
|
||||
@@ -7,7 +7,6 @@ import (
|
||||
"github.com/SigNoz/signoz/pkg/factory"
|
||||
"github.com/SigNoz/signoz/pkg/modules/tracedetail"
|
||||
"github.com/SigNoz/signoz/pkg/types/spantypes"
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
"go.opentelemetry.io/otel/metric"
|
||||
)
|
||||
|
||||
@@ -34,21 +33,66 @@ func NewModule(traceStore spantypes.TraceStore, providerSettings factory.Provide
|
||||
}
|
||||
|
||||
m.metrics.waterfallSpanLimit.Record(context.Background(), int64(cfg.Waterfall.MaxLimitToSelectAllSpans), metric.WithAttributes(attrResponseType.String(attrResponseTypeWindowed)))
|
||||
m.metrics.flamegraphSpanLimit.Record(context.Background(), int64(cfg.Flamegraph.SelectAllSpansLimit), metric.WithAttributes(attrResponseType.String(attrResponseTypeSampled)))
|
||||
|
||||
return m
|
||||
}
|
||||
|
||||
func (m *module) GetWaterfall(ctx context.Context, traceID string, req *spantypes.PostableWaterfall) (*spantypes.GettableWaterfallTrace, error) {
|
||||
waterfallTrace, err := m.getTraceData(ctx, traceID)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
selectedSpans, uncollapsedSpans, selectedAllSpans := waterfallTrace.GetWaterfallSpans(
|
||||
req.UncollapsedSpans,
|
||||
req.SelectedSpanID,
|
||||
min(req.Limit, m.config.Waterfall.MaxLimitToSelectAllSpans),
|
||||
m.config.Waterfall.SpanPageSize,
|
||||
m.config.Waterfall.MaxDepthToAutoExpand,
|
||||
)
|
||||
|
||||
aggregationResults := make([]spantypes.SpanAggregationResult, 0, len(req.Aggregations))
|
||||
for _, a := range req.Aggregations {
|
||||
aggregationResults = append(aggregationResults, waterfallTrace.GetSpanAggregation(a.Aggregation, a.Field))
|
||||
}
|
||||
|
||||
return spantypes.NewGettableWaterfallTrace(waterfallTrace, selectedSpans, uncollapsedSpans, selectedAllSpans, aggregationResults), nil
|
||||
}
|
||||
|
||||
// getTraceData fetches all spans for a trace and builds the WaterfallTrace.
|
||||
func (m *module) getTraceData(ctx context.Context, traceID string) (*spantypes.WaterfallTrace, error) {
|
||||
summary, err := m.store.GetTraceSummary(ctx, traceID)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
spanItems, err := m.store.GetTraceSpans(ctx, traceID, summary)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if len(spanItems) == 0 {
|
||||
return nil, spantypes.ErrTraceNotFound
|
||||
}
|
||||
|
||||
nodes := make([]*spantypes.WaterfallSpan, len(spanItems))
|
||||
for i := range spanItems {
|
||||
nodes[i] = spanItems[i].ToWaterfallSpan(traceID)
|
||||
}
|
||||
return spantypes.NewWaterfallTraceFromSpans(nodes), nil
|
||||
}
|
||||
|
||||
// GetWaterfallV4 is the OOM-safe V4 waterfall.
|
||||
// For large traces (NumSpans > effectiveLimit) it uses a two-step fetch:
|
||||
// minimal fields for all spans to build the tree, then full fields for the
|
||||
// visible window only. Aggregations are not returned.
|
||||
func (m *module) GetWaterfallV4(ctx context.Context, traceID string, selectedSpanID string, uncollapsedSpans []string) (*spantypes.GettableWaterfallTrace, error) {
|
||||
func (m *module) GetWaterfallV4(ctx context.Context, traceID string, selectedSpanID string, uncollapsedSpans []string, selectAllLimit uint) (*spantypes.GettableWaterfallTrace, error) {
|
||||
summary, err := m.store.GetTraceSummary(ctx, traceID)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if summary.NumSpans > uint64(m.config.Waterfall.MaxLimitToSelectAllSpans) {
|
||||
effectiveLimit := min(selectAllLimit, m.config.Waterfall.MaxLimitToSelectAllSpans)
|
||||
if summary.NumSpans > uint64(effectiveLimit) {
|
||||
attrs := metric.WithAttributes(attrResponseType.String(attrResponseTypeWindowed))
|
||||
m.metrics.waterfallRequestCount.Add(ctx, 1, attrs)
|
||||
m.metrics.waterfallSpanCount.Add(ctx, int64(summary.NumSpans), attrs)
|
||||
@@ -74,7 +118,7 @@ func (m *module) getFullWaterfall(ctx context.Context, traceID string, summary *
|
||||
waterfallTrace := spantypes.NewWaterfallTraceFromSpans(nodes)
|
||||
selectedSpans := waterfallTrace.GetAllSpans()
|
||||
|
||||
return spantypes.NewGettableWaterfallTrace(waterfallTrace, selectedSpans, nil, true), nil
|
||||
return spantypes.NewGettableWaterfallTrace(waterfallTrace, selectedSpans, nil, true, nil), nil
|
||||
}
|
||||
|
||||
func (m *module) GetTraceAggregations(ctx context.Context, traceID string, req *spantypes.PostableTraceAggregations) (*spantypes.GettableTraceAggregations, error) {
|
||||
@@ -120,18 +164,6 @@ func (m *module) GetTraceAggregations(ctx context.Context, traceID string, req *
|
||||
return &spantypes.GettableTraceAggregations{Aggregations: results}, nil
|
||||
}
|
||||
|
||||
func (m *module) GetFlamegraph(ctx context.Context, traceID string, selectedSpanID string, selectFields []telemetrytypes.TelemetryFieldKey) (*spantypes.GettableFlamegraphTrace, error) {
|
||||
summary, err := m.store.GetTraceSummary(ctx, traceID)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if summary.NumSpans <= uint64(m.config.Flamegraph.SelectAllSpansLimit) {
|
||||
return m.getFullFlamegraph(ctx, traceID, summary, selectFields)
|
||||
}
|
||||
m.metrics.flamegraphRequestCount.Add(ctx, 1, metric.WithAttributes(attrResponseType.String(attrResponseTypeSampled)))
|
||||
return m.getWindowedFlamegraph(ctx, traceID, selectedSpanID, summary, selectFields)
|
||||
}
|
||||
|
||||
// getWindowedWaterfall builds the waterfall tree with minimal data and then returns only a window of full spans.
|
||||
func (m *module) getWindowedWaterfall(ctx context.Context, traceID, selectedSpanID string, uncollapsedSpans []string, start, end time.Time) (*spantypes.GettableWaterfallTrace, error) {
|
||||
// Step 1: minimal fetch → build full tree → select visible window
|
||||
@@ -169,50 +201,6 @@ func (m *module) getWindowedWaterfall(ctx context.Context, traceID, selectedSpan
|
||||
spantypes.EnrichSelectedSpans(selectedSpans, fullSpans)
|
||||
|
||||
return spantypes.NewGettableWaterfallTrace(
|
||||
waterfallTrace, selectedSpans, uncollapsedSpans, false,
|
||||
), nil
|
||||
}
|
||||
|
||||
func (m *module) getFullFlamegraph(ctx context.Context, traceID string, summary *spantypes.TraceSummary, selectFields []telemetrytypes.TelemetryFieldKey) (*spantypes.GettableFlamegraphTrace, error) {
|
||||
fullSpans, err := m.store.GetFlamegraphSpans(ctx, traceID, summary.Start, summary.End, nil)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if len(fullSpans) == 0 {
|
||||
return nil, spantypes.ErrTraceNotFound
|
||||
}
|
||||
flamegraphTrace := spantypes.NewFlamegraphTraceFromStorable(fullSpans, selectFields)
|
||||
return spantypes.NewGettableFlamegraphTrace(flamegraphTrace.GetAllLevels(), summary.Start.UnixMilli(), summary.End.UnixMilli(), false), nil
|
||||
}
|
||||
|
||||
// getWindowedFlamegraph returns a window of a max levels and max sampled spans per level around the selected span.
|
||||
func (m *module) getWindowedFlamegraph(ctx context.Context, traceID, selectedSpanID string, summary *spantypes.TraceSummary, selectFields []telemetrytypes.TelemetryFieldKey) (*spantypes.GettableFlamegraphTrace, error) {
|
||||
minimalSpans, err := m.store.GetMinimalSpans(ctx, traceID, summary.Start, summary.End)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if len(minimalSpans) == 0 {
|
||||
return nil, spantypes.ErrTraceNotFound
|
||||
}
|
||||
|
||||
flamegraphTrace := spantypes.NewFlamegraphTraceFromMinimal(minimalSpans)
|
||||
minimalSpans = nil //nolint:ineffassign,wastedassign // release backing array before further db calls
|
||||
|
||||
cfg := m.config.Flamegraph
|
||||
selectedSpans := flamegraphTrace.GetSelectedLevels(selectedSpanID, cfg.MaxSelectedLevels, cfg.MaxSpansPerLevel, cfg.SamplingTopLatencySpansCount, cfg.SamplingBucketCount)
|
||||
if len(selectedSpans) == 0 {
|
||||
return nil, spantypes.ErrTraceNotFound
|
||||
}
|
||||
|
||||
fullSpans, err := m.store.GetFlamegraphSpans(ctx, traceID, summary.Start, summary.End, spantypes.FlamegraphWindowSpanIDs(selectedSpans))
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
return spantypes.NewGettableFlamegraphTrace(
|
||||
flamegraphTrace.EnrichSelectedSpans(selectedSpans, fullSpans, selectFields),
|
||||
summary.Start.UnixMilli(),
|
||||
summary.End.UnixMilli(),
|
||||
true,
|
||||
waterfallTrace, selectedSpans, uncollapsedSpans, false, nil,
|
||||
), nil
|
||||
}
|
||||
|
||||
@@ -154,47 +154,6 @@ func (s *traceStore) GetTraceSpansByIDs(ctx context.Context, traceID string, sta
|
||||
return spans, nil
|
||||
}
|
||||
|
||||
func (s *traceStore) GetFlamegraphSpans(ctx context.Context, traceID string, start, end time.Time, spanIDs []string) ([]spantypes.StorableSpan, error) {
|
||||
sb := sqlbuilder.NewSelectBuilder()
|
||||
sb.Select(
|
||||
"span_id",
|
||||
"any(parent_span_id) AS parent_span_id",
|
||||
"any(timestamp) AS timestamp",
|
||||
"any(duration_nano) AS duration_nano",
|
||||
"any(has_error) AS has_error",
|
||||
"any(name) AS name",
|
||||
"any(events) AS events",
|
||||
"any(attributes_string) AS attributes_string",
|
||||
"any(attributes_number) AS attributes_number",
|
||||
"any(attributes_bool) AS attributes_bool",
|
||||
"any(resources_string) AS resources_string",
|
||||
)
|
||||
sb.From(fmt.Sprintf("%s.%s", spantypes.TraceDB, spantypes.TraceTable))
|
||||
conditions := []string{
|
||||
sb.E("trace_id", traceID),
|
||||
sb.GE("ts_bucket_start", start.Unix()-1800),
|
||||
sb.LE("ts_bucket_start", end.Unix()),
|
||||
}
|
||||
if len(spanIDs) > 0 {
|
||||
ids := make([]any, len(spanIDs))
|
||||
for i, id := range spanIDs {
|
||||
ids[i] = id
|
||||
}
|
||||
conditions = append(conditions, sb.In("span_id", ids...))
|
||||
}
|
||||
sb.Where(conditions...)
|
||||
sb.GroupBy("span_id")
|
||||
sb.OrderByAsc("timestamp")
|
||||
sb.OrderByAsc("name")
|
||||
query, args := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
|
||||
|
||||
var spans []spantypes.StorableSpan
|
||||
if err := s.telemetryStore.ClickhouseDB().Select(ctx, &spans, query, args...); err != nil {
|
||||
return nil, errors.WrapInternalf(err, errors.CodeInternal, "error querying flamegraph spans")
|
||||
}
|
||||
return spans, nil
|
||||
}
|
||||
|
||||
func (s *traceStore) GetSpanCountByField(ctx context.Context, traceID string, summary *spantypes.TraceSummary, fieldKey telemetrytypes.TelemetryFieldKey) (map[string]uint64, error) {
|
||||
fieldExpr, err := buildFieldExpr(fieldKey)
|
||||
if err != nil {
|
||||
|
||||
@@ -91,30 +91,6 @@ func TestGetSpanCountByField(t *testing.T) {
|
||||
}
|
||||
}
|
||||
|
||||
func TestGetFlamegraphSpans(t *testing.T) {
|
||||
baseSQL := "SELECT span_id, any(parent_span_id) AS parent_span_id, any(timestamp) AS timestamp, any(duration_nano) AS duration_nano, any(has_error) AS has_error, any(name) AS name, any(events) AS events, any(attributes_string) AS attributes_string, any(attributes_number) AS attributes_number, any(attributes_bool) AS attributes_bool, any(resources_string) AS resources_string FROM signoz_traces.distributed_signoz_index_v3 WHERE trace_id = ? AND ts_bucket_start >= ? AND ts_bucket_start <= ? GROUP BY span_id ORDER BY timestamp ASC, name ASC"
|
||||
withSpanIDsSQL := "SELECT span_id, any(parent_span_id) AS parent_span_id, any(timestamp) AS timestamp, any(duration_nano) AS duration_nano, any(has_error) AS has_error, any(name) AS name, any(events) AS events, any(attributes_string) AS attributes_string, any(attributes_number) AS attributes_number, any(attributes_bool) AS attributes_bool, any(resources_string) AS resources_string FROM signoz_traces.distributed_signoz_index_v3 WHERE trace_id = ? AND ts_bucket_start >= ? AND ts_bucket_start <= ? AND span_id IN (?, ?) GROUP BY span_id ORDER BY timestamp ASC, name ASC"
|
||||
|
||||
tests := []struct {
|
||||
name string
|
||||
spanIDs []string
|
||||
sql string
|
||||
}{
|
||||
{name: "NoSpanIDs_GeneratesBaseSQL", spanIDs: nil, sql: baseSQL},
|
||||
{name: "WithSpanIDs_GeneratesInClauseSQL", spanIDs: []string{"span-1", "span-2"}, sql: withSpanIDsSQL},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
s := newTestStore(sqlmock.QueryMatcherRegexp)
|
||||
s.Mock().ExpectSelect(regexp.QuoteMeta(tc.sql)).
|
||||
WillReturnRows(cmock.NewRows(nil, nil))
|
||||
_, _ = s.Store().GetFlamegraphSpans(context.Background(), testTraceID, testStart, testEnd, tc.spanIDs)
|
||||
assert.NoError(t, s.Mock().ExpectationsWereMet())
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestGetSpanDurationByField(t *testing.T) {
|
||||
|
||||
expectedSQL := "WITH all_spans AS (SELECT DISTINCT ON (span_id) resource.`service.name`::String AS field_value, toUnixTimestamp64Nano(timestamp) AS start_ns, start_ns + duration_nano AS end_ns FROM signoz_traces.distributed_signoz_index_v3 WHERE trace_id = ? AND ts_bucket_start >= ? AND ts_bucket_start <= ? AND notEmpty(field_value) ORDER BY timestamp ASC, name ASC), effective_start AS (SELECT field_value, end_ns, greatest(start_ns, ifNull(max(end_ns) OVER (PARTITION BY field_value ORDER BY start_ns ROWS BETWEEN UNBOUNDED PRECEDING AND 1 PRECEDING), toUInt64(0))) AS effective_start_ns FROM all_spans) SELECT field_value, sum(toUInt64(greatest(end_ns - effective_start_ns, 0))) AS total_ns FROM effective_start GROUP BY field_value"
|
||||
|
||||
@@ -9,16 +9,12 @@ import (
|
||||
const (
|
||||
attrResponseType = attribute.Key("response_type")
|
||||
attrResponseTypeWindowed = "windowed"
|
||||
attrResponseTypeSampled = "sampled"
|
||||
)
|
||||
|
||||
type moduleMetrics struct {
|
||||
waterfallSpanLimit metric.Int64Gauge
|
||||
waterfallRequestCount metric.Int64Counter
|
||||
waterfallSpanCount metric.Int64Counter
|
||||
|
||||
flamegraphSpanLimit metric.Int64Gauge
|
||||
flamegraphRequestCount metric.Int64Counter
|
||||
}
|
||||
|
||||
func newModuleMetrics(meter metric.Meter) (*moduleMetrics, error) {
|
||||
@@ -51,30 +47,9 @@ func newModuleMetrics(meter metric.Meter) (*moduleMetrics, error) {
|
||||
errs = errors.Join(errs, err)
|
||||
}
|
||||
|
||||
flamegraphSpanLimit, err := meter.Int64Gauge(
|
||||
"signoz.traces.flamegraph.span.limit",
|
||||
metric.WithDescription("The span count limit above which sampled flamegraph is returned instead of the full flamegraph."),
|
||||
metric.WithUnit("{span}"),
|
||||
)
|
||||
if err != nil {
|
||||
errs = errors.Join(errs, err)
|
||||
}
|
||||
|
||||
flamegraphRequestCount, err := meter.Int64Counter(
|
||||
"signoz.traces.flamegraph.request.count",
|
||||
metric.WithDescription("Total number of flamegraph requests, by response_type."),
|
||||
metric.WithUnit("{request}"),
|
||||
)
|
||||
if err != nil {
|
||||
errs = errors.Join(errs, err)
|
||||
}
|
||||
|
||||
return &moduleMetrics{
|
||||
waterfallSpanLimit: spanLimit,
|
||||
waterfallRequestCount: requestCount,
|
||||
waterfallSpanCount: spanCount,
|
||||
|
||||
flamegraphSpanLimit: flamegraphSpanLimit,
|
||||
flamegraphRequestCount: flamegraphRequestCount,
|
||||
}, errs
|
||||
}
|
||||
|
||||
@@ -260,7 +260,7 @@ func TestGetSelectedSpans_MultipleRoots(t *testing.T) {
|
||||
trace := getWaterfallTrace([]*spantypes.WaterfallSpan{root1, root2}, spanMap)
|
||||
spans, _ := trace.GetSelectedSpans([]string{"root1", "root2"}, "root1", 500, 5)
|
||||
|
||||
traceRespnose := spantypes.NewGettableWaterfallTrace(trace, spans, nil, false)
|
||||
traceRespnose := spantypes.NewGettableWaterfallTrace(trace, spans, nil, false, nil)
|
||||
|
||||
assert.Equal(t, []string{"root1", "child1", "root2", "child2"}, spanIDs(spans), "root1 subtree must precede root2 subtree")
|
||||
assert.Equal(t, "svc-a", traceRespnose.RootServiceName, "metadata comes from first root")
|
||||
@@ -567,7 +567,7 @@ func TestGetAllSpans(t *testing.T) {
|
||||
)
|
||||
trace := getWaterfallTrace([]*spantypes.WaterfallSpan{root}, nil)
|
||||
spans := trace.GetAllSpans()
|
||||
traceResponse := spantypes.NewGettableWaterfallTrace(trace, spans, nil, true)
|
||||
traceResponse := spantypes.NewGettableWaterfallTrace(trace, spans, nil, true, nil)
|
||||
assert.ElementsMatch(t, spanIDs(spans), []string{"root", "childA", "grandchildA", "leafA", "childB", "grandchildB", "leafB"})
|
||||
assert.Equal(t, "svc", traceResponse.RootServiceName)
|
||||
assert.Equal(t, "root-op", traceResponse.RootServiceEntryPoint)
|
||||
|
||||
@@ -5,19 +5,18 @@ import (
|
||||
"net/http"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/spantypes"
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
)
|
||||
|
||||
// Handler exposes HTTP handlers for trace detail APIs.
|
||||
type Handler interface {
|
||||
GetWaterfall(http.ResponseWriter, *http.Request)
|
||||
GetWaterfallV4(http.ResponseWriter, *http.Request)
|
||||
GetTraceAggregations(http.ResponseWriter, *http.Request)
|
||||
GetFlamegraph(http.ResponseWriter, *http.Request)
|
||||
}
|
||||
|
||||
// Module defines the business logic for trace detail operations.
|
||||
type Module interface {
|
||||
GetWaterfallV4(ctx context.Context, traceID string, selectedSpanID string, uncollapsedSpans []string) (*spantypes.GettableWaterfallTrace, error)
|
||||
GetWaterfall(ctx context.Context, traceID string, req *spantypes.PostableWaterfall) (*spantypes.GettableWaterfallTrace, error)
|
||||
GetWaterfallV4(ctx context.Context, traceID string, selectedSpanID string, uncollapsedSpans []string, selectAllLimit uint) (*spantypes.GettableWaterfallTrace, error)
|
||||
GetTraceAggregations(ctx context.Context, traceID string, req *spantypes.PostableTraceAggregations) (*spantypes.GettableTraceAggregations, error)
|
||||
GetFlamegraph(ctx context.Context, traceID string, selectedSpanID string, selectFields []telemetrytypes.TelemetryFieldKey) (*spantypes.GettableFlamegraphTrace, error)
|
||||
}
|
||||
|
||||
@@ -18,6 +18,7 @@ import (
|
||||
"github.com/uptrace/bun"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/prometheus"
|
||||
"github.com/SigNoz/signoz/pkg/query-service/utils/timestamp"
|
||||
"github.com/SigNoz/signoz/pkg/sqlstore"
|
||||
"github.com/SigNoz/signoz/pkg/telemetrystore"
|
||||
"github.com/SigNoz/signoz/pkg/types"
|
||||
@@ -46,6 +47,7 @@ import (
|
||||
"github.com/SigNoz/signoz/pkg/query-service/app/resource"
|
||||
"github.com/SigNoz/signoz/pkg/query-service/app/services"
|
||||
"github.com/SigNoz/signoz/pkg/query-service/app/traces/smart"
|
||||
"github.com/SigNoz/signoz/pkg/query-service/app/traces/tracedetail"
|
||||
"github.com/SigNoz/signoz/pkg/query-service/common"
|
||||
"github.com/SigNoz/signoz/pkg/query-service/constants"
|
||||
|
||||
@@ -896,6 +898,390 @@ func (r *ClickHouseReader) GetSpansForTrace(ctx context.Context, traceID string,
|
||||
return searchScanResponses, nil
|
||||
}
|
||||
|
||||
func (r *ClickHouseReader) GetWaterfallSpansForTraceWithMetadataCache(ctx context.Context, orgID valuer.UUID, traceID string) (*model.GetWaterfallSpansForTraceWithMetadataCache, error) {
|
||||
cachedTraceData := new(model.GetWaterfallSpansForTraceWithMetadataCache)
|
||||
err := r.cacheForTraceDetail.Get(ctx, orgID, strings.Join([]string{"getWaterfallSpansForTraceWithMetadata", traceID}, "-"), cachedTraceData)
|
||||
if err != nil {
|
||||
r.logger.Debug("error in retrieving getWaterfallSpansForTraceWithMetadata cache", errorsV2.Attr(err), "traceID", traceID)
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if time.Since(time.UnixMilli(int64(cachedTraceData.EndTime))) < r.fluxIntervalForTraceDetail {
|
||||
r.logger.Info("the trace end time falls under the flux interval, skipping getWaterfallSpansForTraceWithMetadata cache", "traceID", traceID)
|
||||
return nil, errors.Errorf("the trace end time falls under the flux interval, skipping getWaterfallSpansForTraceWithMetadata cache, traceID: %s", traceID)
|
||||
}
|
||||
|
||||
r.logger.Info("cache is successfully hit, applying cache for getWaterfallSpansForTraceWithMetadata", "traceID", traceID)
|
||||
return cachedTraceData, nil
|
||||
}
|
||||
|
||||
func (r *ClickHouseReader) GetWaterfallSpansForTraceWithMetadata(ctx context.Context, orgID valuer.UUID, traceID string, req *model.GetWaterfallSpansForTraceWithMetadataParams) (*model.GetWaterfallSpansForTraceWithMetadataResponse, error) {
|
||||
response := new(model.GetWaterfallSpansForTraceWithMetadataResponse)
|
||||
var startTime, endTime, durationNano, totalErrorSpans, totalSpans uint64
|
||||
var spanIdToSpanNodeMap = map[string]*model.Span{}
|
||||
var traceRoots []*model.Span
|
||||
var serviceNameToTotalDurationMap = map[string]uint64{}
|
||||
var serviceNameIntervalMap = map[string][]tracedetail.Interval{}
|
||||
var hasMissingSpans bool
|
||||
|
||||
cachedTraceData, err := r.GetWaterfallSpansForTraceWithMetadataCache(ctx, orgID, traceID)
|
||||
if err == nil {
|
||||
startTime = cachedTraceData.StartTime
|
||||
endTime = cachedTraceData.EndTime
|
||||
durationNano = cachedTraceData.DurationNano
|
||||
spanIdToSpanNodeMap = cachedTraceData.SpanIdToSpanNodeMap
|
||||
serviceNameToTotalDurationMap = cachedTraceData.ServiceNameToTotalDurationMap
|
||||
traceRoots = cachedTraceData.TraceRoots
|
||||
totalSpans = cachedTraceData.TotalSpans
|
||||
totalErrorSpans = cachedTraceData.TotalErrorSpans
|
||||
hasMissingSpans = cachedTraceData.HasMissingSpans
|
||||
}
|
||||
|
||||
if err != nil {
|
||||
r.logger.Info("cache miss for getWaterfallSpansForTraceWithMetadata", "traceID", traceID)
|
||||
|
||||
searchScanResponses, err := r.GetSpansForTrace(ctx, traceID, fmt.Sprintf("SELECT DISTINCT ON (span_id) timestamp, duration_nano, span_id, trace_id, has_error, kind, resource_string_service$$name, name, links as references, attributes_string, attributes_number, attributes_bool, resources_string, events, status_message, status_code_string, kind_string FROM %s.%s WHERE trace_id=$1 and ts_bucket_start>=$2 and ts_bucket_start<=$3 ORDER BY timestamp ASC, name ASC", r.TraceDB, r.traceTableName))
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if len(searchScanResponses) == 0 {
|
||||
return response, nil
|
||||
}
|
||||
totalSpans = uint64(len(searchScanResponses))
|
||||
for _, item := range searchScanResponses {
|
||||
ref := []model.OtelSpanRef{}
|
||||
err := json.Unmarshal([]byte(item.References), &ref)
|
||||
if err != nil {
|
||||
r.logger.Error("getWaterfallSpansForTraceWithMetadata: error unmarshalling references", errorsV2.Attr(err), "traceID", traceID)
|
||||
return nil, errorsV2.Newf(errorsV2.TypeInvalidInput, errorsV2.CodeInvalidInput, "getWaterfallSpansForTraceWithMetadata: error unmarshalling references %s", err.Error())
|
||||
}
|
||||
|
||||
// merge attributes_number and attributes_bool to attributes_string
|
||||
for k, v := range item.Attributes_bool {
|
||||
item.Attributes_string[k] = fmt.Sprintf("%v", v)
|
||||
}
|
||||
for k, v := range item.Attributes_number {
|
||||
item.Attributes_string[k] = strconv.FormatFloat(v, 'f', -1, 64)
|
||||
}
|
||||
for k, v := range item.Resources_string {
|
||||
item.Attributes_string[k] = v
|
||||
}
|
||||
|
||||
events := make([]model.Event, 0)
|
||||
for _, event := range item.Events {
|
||||
var eventMap model.Event
|
||||
err = json.Unmarshal([]byte(event), &eventMap)
|
||||
if err != nil {
|
||||
r.logger.Error("Error unmarshalling events", errorsV2.Attr(err))
|
||||
return nil, errorsV2.Newf(errorsV2.TypeInternal, errorsV2.CodeInternal, "getWaterfallSpansForTraceWithMetadata: error in unmarshalling events %s", err.Error())
|
||||
}
|
||||
events = append(events, eventMap)
|
||||
}
|
||||
|
||||
startTimeUnixNano := uint64(item.TimeUnixNano.UnixNano())
|
||||
|
||||
jsonItem := model.Span{
|
||||
SpanID: item.SpanID,
|
||||
TraceID: item.TraceID,
|
||||
ServiceName: item.ServiceName,
|
||||
Name: item.Name,
|
||||
Kind: int32(item.Kind),
|
||||
DurationNano: item.DurationNano,
|
||||
HasError: item.HasError,
|
||||
StatusMessage: item.StatusMessage,
|
||||
StatusCodeString: item.StatusCodeString,
|
||||
SpanKind: item.SpanKind,
|
||||
References: ref,
|
||||
Events: events,
|
||||
TagMap: item.Attributes_string,
|
||||
Children: make([]*model.Span, 0),
|
||||
TimeUnixNano: startTimeUnixNano, // Store nanoseconds temporarily
|
||||
}
|
||||
|
||||
// metadata calculation
|
||||
if startTime == 0 || startTimeUnixNano < startTime {
|
||||
startTime = startTimeUnixNano
|
||||
}
|
||||
if endTime == 0 || (startTimeUnixNano+jsonItem.DurationNano) > endTime {
|
||||
endTime = (startTimeUnixNano + jsonItem.DurationNano)
|
||||
}
|
||||
if durationNano == 0 || jsonItem.DurationNano > durationNano {
|
||||
durationNano = jsonItem.DurationNano
|
||||
}
|
||||
|
||||
if jsonItem.HasError {
|
||||
totalErrorSpans = totalErrorSpans + 1
|
||||
}
|
||||
|
||||
// collect the intervals for service for execution time calculation
|
||||
serviceNameIntervalMap[jsonItem.ServiceName] =
|
||||
append(serviceNameIntervalMap[jsonItem.ServiceName], tracedetail.Interval{StartTime: jsonItem.TimeUnixNano, Duration: jsonItem.DurationNano, Service: jsonItem.ServiceName})
|
||||
|
||||
// append to the span node map
|
||||
spanIdToSpanNodeMap[jsonItem.SpanID] = &jsonItem
|
||||
}
|
||||
|
||||
// traverse through the map and append each node to the children array of the parent node
|
||||
// and add the missing spans
|
||||
for _, spanNode := range spanIdToSpanNodeMap {
|
||||
hasParentSpanNode := false
|
||||
for _, reference := range spanNode.References {
|
||||
if reference.RefType == "CHILD_OF" && reference.SpanId != "" {
|
||||
hasParentSpanNode = true
|
||||
|
||||
if parentNode, exists := spanIdToSpanNodeMap[reference.SpanId]; exists {
|
||||
parentNode.Children = append(parentNode.Children, spanNode)
|
||||
} else {
|
||||
// insert the missing span
|
||||
missingSpan := model.Span{
|
||||
SpanID: reference.SpanId,
|
||||
TraceID: spanNode.TraceID,
|
||||
ServiceName: "",
|
||||
Name: "Missing Span",
|
||||
TimeUnixNano: spanNode.TimeUnixNano,
|
||||
Kind: 0,
|
||||
DurationNano: spanNode.DurationNano,
|
||||
HasError: false,
|
||||
StatusMessage: "",
|
||||
StatusCodeString: "",
|
||||
SpanKind: "",
|
||||
Events: make([]model.Event, 0),
|
||||
Children: make([]*model.Span, 0),
|
||||
}
|
||||
missingSpan.Children = append(missingSpan.Children, spanNode)
|
||||
spanIdToSpanNodeMap[missingSpan.SpanID] = &missingSpan
|
||||
traceRoots = append(traceRoots, &missingSpan)
|
||||
hasMissingSpans = true
|
||||
}
|
||||
}
|
||||
}
|
||||
if !hasParentSpanNode && !tracedetail.ContainsWaterfallSpan(traceRoots, spanNode) {
|
||||
traceRoots = append(traceRoots, spanNode)
|
||||
}
|
||||
}
|
||||
|
||||
// sort the trace roots to add missing spans at the right order
|
||||
sort.Slice(traceRoots, func(i, j int) bool {
|
||||
if traceRoots[i].TimeUnixNano == traceRoots[j].TimeUnixNano {
|
||||
return traceRoots[i].Name < traceRoots[j].Name
|
||||
}
|
||||
return traceRoots[i].TimeUnixNano < traceRoots[j].TimeUnixNano
|
||||
})
|
||||
|
||||
serviceNameToTotalDurationMap = tracedetail.CalculateServiceTime(serviceNameIntervalMap)
|
||||
|
||||
// TODO: set the span data (model.GetWaterfallSpansForTraceWithMetadataCache) in cache here
|
||||
// removed existing cache usage since it was not getting used due to this bug https://github.com/SigNoz/engineering-pod/issues/4648
|
||||
// and was causing out of memory issues https://github.com/SigNoz/engineering-pod/issues/4638
|
||||
}
|
||||
|
||||
processingPostCache := time.Now()
|
||||
// When req.Limit is 0 (not set by the client), selectAllSpans is set to false
|
||||
// preserving the old paged behaviour for backward compatibility
|
||||
limit := min(req.Limit, tracedetail.MaxLimitToSelectAllSpans)
|
||||
selectAllSpans := totalSpans <= uint64(limit)
|
||||
|
||||
var (
|
||||
selectedSpans []*model.Span
|
||||
uncollapsedSpans []string
|
||||
rootServiceName, rootServiceEntryPoint string
|
||||
)
|
||||
if selectAllSpans {
|
||||
selectedSpans, rootServiceName, rootServiceEntryPoint = tracedetail.GetAllSpans(traceRoots)
|
||||
} else {
|
||||
selectedSpans, uncollapsedSpans, rootServiceName, rootServiceEntryPoint = tracedetail.GetSelectedSpans(req.UncollapsedSpans, req.SelectedSpanID, traceRoots, spanIdToSpanNodeMap, req.IsSelectedSpanIDUnCollapsed)
|
||||
}
|
||||
r.logger.Info("getWaterfallSpansForTraceWithMetadata: processing post cache", "duration", time.Since(processingPostCache), "traceID", traceID)
|
||||
|
||||
// convert start timestamp to millis because right now frontend is expecting it in millis
|
||||
for _, span := range selectedSpans {
|
||||
span.TimeUnixNano = span.TimeUnixNano / 1000000
|
||||
}
|
||||
|
||||
for serviceName, totalDuration := range serviceNameToTotalDurationMap {
|
||||
serviceNameToTotalDurationMap[serviceName] = totalDuration / 1000000
|
||||
}
|
||||
|
||||
response.Spans = selectedSpans
|
||||
response.UncollapsedSpans = uncollapsedSpans // ignoring if all spans are returning
|
||||
response.StartTimestampMillis = startTime / 1000000
|
||||
response.EndTimestampMillis = endTime / 1000000
|
||||
response.TotalSpansCount = totalSpans
|
||||
response.TotalErrorSpansCount = totalErrorSpans
|
||||
response.RootServiceName = rootServiceName
|
||||
response.RootServiceEntryPoint = rootServiceEntryPoint
|
||||
response.ServiceNameToTotalDurationMap = serviceNameToTotalDurationMap
|
||||
response.HasMissingSpans = hasMissingSpans
|
||||
response.HasMore = !selectAllSpans
|
||||
return response, nil
|
||||
}
|
||||
|
||||
func (r *ClickHouseReader) GetFlamegraphSpansForTraceCache(ctx context.Context, orgID valuer.UUID, traceID string) (*model.GetFlamegraphSpansForTraceCache, error) {
|
||||
cachedTraceData := new(model.GetFlamegraphSpansForTraceCache)
|
||||
err := r.cacheForTraceDetail.Get(ctx, orgID, strings.Join([]string{"getFlamegraphSpansForTrace", traceID}, "-"), cachedTraceData)
|
||||
if err != nil {
|
||||
r.logger.Debug("error in retrieving getFlamegraphSpansForTrace cache", errorsV2.Attr(err), "traceID", traceID)
|
||||
return nil, err
|
||||
}
|
||||
|
||||
if time.Since(time.UnixMilli(int64(cachedTraceData.EndTime))) < r.fluxIntervalForTraceDetail {
|
||||
r.logger.Info("the trace end time falls under the flux interval, skipping getFlamegraphSpansForTrace cache", "traceID", traceID)
|
||||
return nil, errors.Errorf("the trace end time falls under the flux interval, skipping getFlamegraphSpansForTrace cache, traceID: %s", traceID)
|
||||
}
|
||||
|
||||
r.logger.Info("cache is successfully hit, applying cache for getFlamegraphSpansForTrace", "traceID", traceID)
|
||||
return cachedTraceData, nil
|
||||
}
|
||||
|
||||
func (r *ClickHouseReader) GetFlamegraphSpansForTrace(ctx context.Context, orgID valuer.UUID, traceID string, req *model.GetFlamegraphSpansForTraceParams) (*model.GetFlamegraphSpansForTraceResponse, error) {
|
||||
trace := new(model.GetFlamegraphSpansForTraceResponse)
|
||||
var startTime, endTime, durationNano uint64
|
||||
var spanIdToSpanNodeMap = map[string]*model.FlamegraphSpan{}
|
||||
// map[traceID][level]span
|
||||
var selectedSpans = [][]*model.FlamegraphSpan{}
|
||||
var traceRoots []*model.FlamegraphSpan
|
||||
|
||||
// get the trace tree from cache!
|
||||
cachedTraceData, err := r.GetFlamegraphSpansForTraceCache(ctx, orgID, traceID)
|
||||
|
||||
if err == nil {
|
||||
startTime = cachedTraceData.StartTime
|
||||
endTime = cachedTraceData.EndTime
|
||||
durationNano = cachedTraceData.DurationNano
|
||||
selectedSpans = cachedTraceData.SelectedSpans
|
||||
traceRoots = cachedTraceData.TraceRoots
|
||||
}
|
||||
|
||||
if err != nil {
|
||||
r.logger.Info("cache miss for getFlamegraphSpansForTrace", "traceID", traceID)
|
||||
|
||||
selectCols := "timestamp, duration_nano, span_id, trace_id, has_error, links as references, resource_string_service$$name, name, events"
|
||||
if len(req.SelectFields) > 0 {
|
||||
selectCols += ", attributes_string, attributes_number, attributes_bool, resources_string"
|
||||
}
|
||||
flamegraphQuery := fmt.Sprintf("SELECT %s FROM %s.%s WHERE trace_id=$1 and ts_bucket_start>=$2 and ts_bucket_start<=$3 ORDER BY timestamp ASC, name ASC", selectCols, r.TraceDB, r.traceTableName)
|
||||
|
||||
searchScanResponses, err := r.GetSpansForTrace(ctx, traceID, flamegraphQuery)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
if len(searchScanResponses) == 0 {
|
||||
return trace, nil
|
||||
}
|
||||
|
||||
for _, item := range searchScanResponses {
|
||||
ref := []model.OtelSpanRef{}
|
||||
err := json.Unmarshal([]byte(item.References), &ref)
|
||||
if err != nil {
|
||||
r.logger.Error("Error unmarshalling references", errorsV2.Attr(err))
|
||||
return nil, errorsV2.Newf(errorsV2.TypeInternal, errorsV2.CodeInternal, "getFlamegraphSpansForTrace: error in unmarshalling references %s", err.Error())
|
||||
}
|
||||
|
||||
events := make([]model.Event, 0)
|
||||
for _, event := range item.Events {
|
||||
var eventMap model.Event
|
||||
err = json.Unmarshal([]byte(event), &eventMap)
|
||||
if err != nil {
|
||||
r.logger.Error("Error unmarshalling events", errorsV2.Attr(err))
|
||||
return nil, errorsV2.Newf(errorsV2.TypeInternal, errorsV2.CodeInternal, "getFlamegraphSpansForTrace: error in unmarshalling events %s", err.Error())
|
||||
}
|
||||
events = append(events, eventMap)
|
||||
}
|
||||
|
||||
jsonItem := model.FlamegraphSpan{
|
||||
SpanID: item.SpanID,
|
||||
TraceID: item.TraceID,
|
||||
ServiceName: item.ServiceName,
|
||||
Name: item.Name,
|
||||
DurationNano: item.DurationNano,
|
||||
HasError: item.HasError,
|
||||
References: ref,
|
||||
Events: events,
|
||||
Children: make([]*model.FlamegraphSpan, 0),
|
||||
}
|
||||
|
||||
if len(req.SelectFields) > 0 {
|
||||
jsonItem.SetRequestedFields(item, req.SelectFields)
|
||||
}
|
||||
|
||||
// metadata calculation
|
||||
startTimeUnixNano := uint64(item.TimeUnixNano.UnixNano())
|
||||
if startTime == 0 || startTimeUnixNano < startTime {
|
||||
startTime = startTimeUnixNano
|
||||
}
|
||||
if endTime == 0 || (startTimeUnixNano+jsonItem.DurationNano) > endTime {
|
||||
endTime = (startTimeUnixNano + jsonItem.DurationNano)
|
||||
}
|
||||
if durationNano == 0 || jsonItem.DurationNano > durationNano {
|
||||
durationNano = jsonItem.DurationNano
|
||||
}
|
||||
|
||||
jsonItem.TimeUnixNano = uint64(item.TimeUnixNano.UnixNano() / 1000000)
|
||||
spanIdToSpanNodeMap[jsonItem.SpanID] = &jsonItem
|
||||
}
|
||||
|
||||
// traverse through the map and append each node to the children array of the parent node
|
||||
// and add missing spans
|
||||
for _, spanNode := range spanIdToSpanNodeMap {
|
||||
hasParentSpanNode := false
|
||||
for _, reference := range spanNode.References {
|
||||
if reference.RefType == "CHILD_OF" && reference.SpanId != "" {
|
||||
hasParentSpanNode = true
|
||||
if parentNode, exists := spanIdToSpanNodeMap[reference.SpanId]; exists {
|
||||
parentNode.Children = append(parentNode.Children, spanNode)
|
||||
} else {
|
||||
// insert the missing spans
|
||||
missingSpan := model.FlamegraphSpan{
|
||||
SpanID: reference.SpanId,
|
||||
TraceID: spanNode.TraceID,
|
||||
ServiceName: "",
|
||||
Name: "Missing Span",
|
||||
TimeUnixNano: spanNode.TimeUnixNano,
|
||||
DurationNano: spanNode.DurationNano,
|
||||
HasError: false,
|
||||
Events: make([]model.Event, 0),
|
||||
Children: make([]*model.FlamegraphSpan, 0),
|
||||
}
|
||||
missingSpan.Children = append(missingSpan.Children, spanNode)
|
||||
spanIdToSpanNodeMap[missingSpan.SpanID] = &missingSpan
|
||||
traceRoots = append(traceRoots, &missingSpan)
|
||||
}
|
||||
}
|
||||
}
|
||||
if !hasParentSpanNode && !tracedetail.ContainsFlamegraphSpan(traceRoots, spanNode) {
|
||||
traceRoots = append(traceRoots, spanNode)
|
||||
}
|
||||
}
|
||||
|
||||
selectedSpans = tracedetail.GetAllSpansForFlamegraph(traceRoots, spanIdToSpanNodeMap)
|
||||
|
||||
// TODO: set the trace data (model.GetFlamegraphSpansForTraceCache) in cache here
|
||||
// removed existing cache usage since it was not getting used due to this bug https://github.com/SigNoz/engineering-pod/issues/4648
|
||||
// and was causing out of memory issues https://github.com/SigNoz/engineering-pod/issues/4638
|
||||
}
|
||||
|
||||
processingPostCache := time.Now()
|
||||
selectedSpansForRequest := selectedSpans
|
||||
clientLimit := min(req.Limit, tracedetail.MaxLimitWithoutSampling)
|
||||
totalSpanCount := tracedetail.GetTotalSpanCount(selectedSpans)
|
||||
if totalSpanCount > uint64(clientLimit) {
|
||||
// using trace start and end time if boundary ts are set to zero (or not set)
|
||||
boundaryStart := max(timestamp.MilliToNano(req.BoundaryStartTS), startTime)
|
||||
boundaryEnd := timestamp.MilliToNano(req.BoundaryEndTS)
|
||||
if boundaryEnd == 0 {
|
||||
boundaryEnd = endTime
|
||||
}
|
||||
|
||||
selectedSpansForRequest = tracedetail.GetSelectedSpansForFlamegraphForRequest(req.SelectedSpanID, selectedSpans, boundaryStart, boundaryEnd)
|
||||
}
|
||||
r.logger.Debug("getFlamegraphSpansForTrace: processing post cache", "duration", time.Since(processingPostCache), "traceID", traceID, "totalSpans", totalSpanCount, "limit", clientLimit)
|
||||
|
||||
trace.Spans = selectedSpansForRequest
|
||||
trace.StartTimestampMillis = startTime / 1000000
|
||||
trace.EndTimestampMillis = endTime / 1000000
|
||||
trace.HasMore = totalSpanCount > uint64(clientLimit)
|
||||
return trace, nil
|
||||
}
|
||||
|
||||
func (r *ClickHouseReader) GetDependencyGraph(ctx context.Context, queryParams *model.GetServicesParams) (*[]model.ServiceMapDependencyResponseItem, error) {
|
||||
|
||||
|
||||
@@ -534,8 +534,8 @@ func (aH *APIHandler) RegisterRoutes(router *mux.Router, am *middleware.AuthZ) {
|
||||
|
||||
router.HandleFunc("/api/v2/traces/fields", am.ViewAccess(aH.traceFields)).Methods(http.MethodGet)
|
||||
router.HandleFunc("/api/v2/traces/fields", am.EditAccess(aH.updateTraceField)).Methods(http.MethodPost)
|
||||
|
||||
|
||||
router.HandleFunc("/api/v2/traces/flamegraph/{traceId}", am.ViewAccess(aH.GetFlamegraphSpansForTrace)).Methods(http.MethodPost)
|
||||
router.HandleFunc("/api/v2/traces/waterfall/{traceId}", am.ViewAccess(aH.GetWaterfallSpansForTraceWithMetadata)).Methods(http.MethodPost)
|
||||
|
||||
router.HandleFunc("/api/v1/version", am.OpenAccess(aH.getVersion)).Methods(http.MethodGet)
|
||||
router.HandleFunc("/api/v1/features", am.ViewAccess(aH.getFeatureFlags)).Methods(http.MethodGet)
|
||||
@@ -1446,6 +1446,73 @@ func (aH *APIHandler) SearchTraces(w http.ResponseWriter, r *http.Request) {
|
||||
|
||||
}
|
||||
|
||||
func (aH *APIHandler) GetWaterfallSpansForTraceWithMetadata(w http.ResponseWriter, r *http.Request) {
|
||||
claims, err := authtypes.ClaimsFromContext(r.Context())
|
||||
if err != nil {
|
||||
render.Error(w, err)
|
||||
return
|
||||
}
|
||||
orgID, err := valuer.NewUUID(claims.OrgID)
|
||||
if err != nil {
|
||||
render.Error(w, err)
|
||||
return
|
||||
}
|
||||
traceID := mux.Vars(r)["traceId"]
|
||||
if traceID == "" {
|
||||
render.Error(w, errors.NewInvalidInputf(errors.CodeInvalidInput, "traceID is required"))
|
||||
return
|
||||
}
|
||||
|
||||
req := new(model.GetWaterfallSpansForTraceWithMetadataParams)
|
||||
err = json.NewDecoder(r.Body).Decode(&req)
|
||||
if err != nil {
|
||||
RespondError(w, model.BadRequest(err), nil)
|
||||
return
|
||||
}
|
||||
|
||||
result, apiErr := aH.reader.GetWaterfallSpansForTraceWithMetadata(r.Context(), orgID, traceID, req)
|
||||
if apiErr != nil {
|
||||
render.Error(w, apiErr)
|
||||
return
|
||||
}
|
||||
|
||||
aH.WriteJSON(w, r, result)
|
||||
}
|
||||
|
||||
func (aH *APIHandler) GetFlamegraphSpansForTrace(w http.ResponseWriter, r *http.Request) {
|
||||
claims, err := authtypes.ClaimsFromContext(r.Context())
|
||||
if err != nil {
|
||||
render.Error(w, err)
|
||||
return
|
||||
}
|
||||
orgID, err := valuer.NewUUID(claims.OrgID)
|
||||
if err != nil {
|
||||
render.Error(w, err)
|
||||
return
|
||||
}
|
||||
|
||||
traceID := mux.Vars(r)["traceId"]
|
||||
if traceID == "" {
|
||||
render.Error(w, errors.NewInvalidInputf(errors.CodeInvalidInput, "traceID is required"))
|
||||
return
|
||||
}
|
||||
|
||||
req := new(model.GetFlamegraphSpansForTraceParams)
|
||||
err = json.NewDecoder(r.Body).Decode(&req)
|
||||
if err != nil {
|
||||
RespondError(w, model.BadRequest(err), nil)
|
||||
return
|
||||
}
|
||||
|
||||
result, apiErr := aH.reader.GetFlamegraphSpansForTrace(r.Context(), orgID, traceID, req)
|
||||
if apiErr != nil {
|
||||
render.Error(w, apiErr)
|
||||
return
|
||||
}
|
||||
|
||||
aH.WriteJSON(w, r, result)
|
||||
}
|
||||
|
||||
func (aH *APIHandler) listErrors(w http.ResponseWriter, r *http.Request) {
|
||||
|
||||
query, err := parseListErrorsRequest(r)
|
||||
|
||||
199
pkg/query-service/app/traces/tracedetail/flamegraph.go
Normal file
199
pkg/query-service/app/traces/tracedetail/flamegraph.go
Normal file
@@ -0,0 +1,199 @@
|
||||
package tracedetail
|
||||
|
||||
import (
|
||||
"sort"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/query-service/model"
|
||||
)
|
||||
|
||||
var (
|
||||
flamegraphSpanLevelLimit float64 = 50
|
||||
flamegraphSpanLimitPerLevel int = 100
|
||||
flamegraphSamplingBucketCount int = 50
|
||||
flamegraphTopLatencySpanCount int = 5
|
||||
|
||||
MaxLimitWithoutSampling uint = 120_000
|
||||
)
|
||||
|
||||
func ContainsFlamegraphSpan(slice []*model.FlamegraphSpan, item *model.FlamegraphSpan) bool {
|
||||
for _, v := range slice {
|
||||
if v.SpanID == item.SpanID {
|
||||
return true
|
||||
}
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
func BfsTraversalForTrace(span *model.FlamegraphSpan, level int64) map[int64][]*model.FlamegraphSpan {
|
||||
bfs := map[int64][]*model.FlamegraphSpan{}
|
||||
bfs[level] = []*model.FlamegraphSpan{span}
|
||||
|
||||
for _, child := range span.Children {
|
||||
childBfsMap := BfsTraversalForTrace(child, level+1)
|
||||
for _level, nodes := range childBfsMap {
|
||||
bfs[_level] = append(bfs[_level], nodes...)
|
||||
}
|
||||
}
|
||||
span.Level = level
|
||||
span.Children = make([]*model.FlamegraphSpan, 0)
|
||||
|
||||
return bfs
|
||||
}
|
||||
|
||||
func FindIndexForSelectedSpan(spans [][]*model.FlamegraphSpan, selectedSpanId string) int {
|
||||
var selectedSpanLevel int = 0
|
||||
|
||||
for index, _spans := range spans {
|
||||
for _, span := range _spans {
|
||||
if span.SpanID == selectedSpanId {
|
||||
selectedSpanLevel = index
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return selectedSpanLevel
|
||||
}
|
||||
|
||||
// GetAllSpansForFlamegraph groups all spans as per their level
|
||||
func GetAllSpansForFlamegraph(traceRoots []*model.FlamegraphSpan, spanIdToSpanNodeMap map[string]*model.FlamegraphSpan) [][]*model.FlamegraphSpan {
|
||||
|
||||
var traceIdLevelledFlamegraph = map[string]map[int64][]*model.FlamegraphSpan{}
|
||||
selectedSpans := [][]*model.FlamegraphSpan{}
|
||||
|
||||
// sort the trace roots to add missing spans at the right order
|
||||
sort.Slice(traceRoots, func(i, j int) bool {
|
||||
if traceRoots[i].TimeUnixNano == traceRoots[j].TimeUnixNano {
|
||||
return traceRoots[i].Name < traceRoots[j].Name
|
||||
}
|
||||
return traceRoots[i].TimeUnixNano < traceRoots[j].TimeUnixNano
|
||||
})
|
||||
|
||||
for _, rootSpanID := range traceRoots {
|
||||
if rootNode, exists := spanIdToSpanNodeMap[rootSpanID.SpanID]; exists {
|
||||
bfsMapForTrace := BfsTraversalForTrace(rootNode, 0)
|
||||
traceIdLevelledFlamegraph[rootSpanID.SpanID] = bfsMapForTrace
|
||||
}
|
||||
}
|
||||
|
||||
for _, trace := range traceRoots {
|
||||
keys := make([]int64, 0, len(traceIdLevelledFlamegraph[trace.SpanID]))
|
||||
for key := range traceIdLevelledFlamegraph[trace.SpanID] {
|
||||
keys = append(keys, key)
|
||||
}
|
||||
|
||||
sort.Slice(keys, func(i, j int) bool {
|
||||
return keys[i] < keys[j]
|
||||
})
|
||||
|
||||
for _, level := range keys {
|
||||
if ok, exists := traceIdLevelledFlamegraph[trace.SpanID][level]; exists {
|
||||
selectedSpans = append(selectedSpans, ok)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return selectedSpans
|
||||
}
|
||||
|
||||
func getLatencyAndTimestampBucketedSpans(spans []*model.FlamegraphSpan, selectedSpanID string, isSelectedSpanIDPresent bool, startTime uint64, endTime uint64) []*model.FlamegraphSpan {
|
||||
var sampledSpans []*model.FlamegraphSpan
|
||||
// sort the spans by latency for latency filtering
|
||||
sort.Slice(spans, func(i, j int) bool {
|
||||
return spans[i].DurationNano > spans[j].DurationNano
|
||||
})
|
||||
|
||||
// pick the top 5 latency spans
|
||||
for idx := range flamegraphTopLatencySpanCount {
|
||||
sampledSpans = append(sampledSpans, spans[idx])
|
||||
}
|
||||
|
||||
// always add the selectedSpan
|
||||
if isSelectedSpanIDPresent {
|
||||
idx := -1
|
||||
for _idx, span := range spans {
|
||||
if span.SpanID == selectedSpanID {
|
||||
idx = _idx
|
||||
}
|
||||
}
|
||||
if idx != -1 {
|
||||
sampledSpans = append(sampledSpans, spans[idx])
|
||||
}
|
||||
}
|
||||
|
||||
bucketSize := (endTime - startTime) / uint64(flamegraphSamplingBucketCount)
|
||||
if bucketSize == 0 {
|
||||
bucketSize = 1
|
||||
}
|
||||
|
||||
bucketedSpans := make([][]*model.FlamegraphSpan, flamegraphSamplingBucketCount)
|
||||
|
||||
for _, span := range spans {
|
||||
if span.TimeUnixNano >= startTime && span.TimeUnixNano <= endTime {
|
||||
bucketIndex := int((span.TimeUnixNano - startTime) / bucketSize)
|
||||
if bucketIndex >= 0 && bucketIndex < flamegraphSamplingBucketCount {
|
||||
bucketedSpans[bucketIndex] = append(bucketedSpans[bucketIndex], span)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for i := range bucketedSpans {
|
||||
if len(bucketedSpans[i]) > 2 {
|
||||
// Keep only the first 2 spans
|
||||
bucketedSpans[i] = bucketedSpans[i][:2]
|
||||
}
|
||||
}
|
||||
|
||||
// Flatten the bucketed spans into a single slice
|
||||
for _, bucket := range bucketedSpans {
|
||||
sampledSpans = append(sampledSpans, bucket...)
|
||||
}
|
||||
|
||||
return sampledSpans
|
||||
}
|
||||
|
||||
func GetSelectedSpansForFlamegraphForRequest(selectedSpanID string, selectedSpans [][]*model.FlamegraphSpan, startTime uint64, endTime uint64) [][]*model.FlamegraphSpan {
|
||||
var selectedSpansForRequest = make([][]*model.FlamegraphSpan, 0)
|
||||
var selectedIndex = 0
|
||||
|
||||
if selectedSpanID != "" {
|
||||
selectedIndex = FindIndexForSelectedSpan(selectedSpans, selectedSpanID)
|
||||
}
|
||||
|
||||
lowerLimit := selectedIndex - int(flamegraphSpanLevelLimit*0.4)
|
||||
upperLimit := selectedIndex + int(flamegraphSpanLevelLimit*0.6)
|
||||
|
||||
if lowerLimit < 0 {
|
||||
upperLimit = upperLimit - lowerLimit
|
||||
lowerLimit = 0
|
||||
}
|
||||
|
||||
if upperLimit > len(selectedSpans) {
|
||||
lowerLimit = lowerLimit - (upperLimit - len(selectedSpans))
|
||||
upperLimit = len(selectedSpans)
|
||||
}
|
||||
|
||||
if lowerLimit < 0 {
|
||||
lowerLimit = 0
|
||||
}
|
||||
|
||||
for i := lowerLimit; i < upperLimit; i++ {
|
||||
if len(selectedSpans[i]) > flamegraphSpanLimitPerLevel {
|
||||
_spans := getLatencyAndTimestampBucketedSpans(selectedSpans[i], selectedSpanID, i == selectedIndex, startTime, endTime)
|
||||
selectedSpansForRequest = append(selectedSpansForRequest, _spans)
|
||||
} else {
|
||||
selectedSpansForRequest = append(selectedSpansForRequest, selectedSpans[i])
|
||||
}
|
||||
}
|
||||
|
||||
return selectedSpansForRequest
|
||||
}
|
||||
|
||||
func GetTotalSpanCount(spans [][]*model.FlamegraphSpan) uint64 {
|
||||
levelCount := len(spans)
|
||||
spanCount := uint64(0)
|
||||
for i := range levelCount {
|
||||
spanCount += uint64(len(spans[i]))
|
||||
}
|
||||
return spanCount
|
||||
}
|
||||
287
pkg/query-service/app/traces/tracedetail/waterfall.go
Normal file
287
pkg/query-service/app/traces/tracedetail/waterfall.go
Normal file
@@ -0,0 +1,287 @@
|
||||
package tracedetail
|
||||
|
||||
import (
|
||||
"maps"
|
||||
"slices"
|
||||
"sort"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/query-service/model"
|
||||
)
|
||||
|
||||
var (
|
||||
SPAN_LIMIT_PER_REQUEST_FOR_WATERFALL float64 = 500
|
||||
|
||||
maxDepthForSelectedSpanChildren int = 5
|
||||
MaxLimitToSelectAllSpans uint = 10_000
|
||||
)
|
||||
|
||||
type Interval struct {
|
||||
StartTime uint64
|
||||
Duration uint64
|
||||
Service string
|
||||
}
|
||||
|
||||
func mergeIntervals(intervals []Interval) []Interval {
|
||||
if len(intervals) == 0 {
|
||||
return nil
|
||||
}
|
||||
|
||||
var merged []Interval
|
||||
current := intervals[0]
|
||||
|
||||
for i := 1; i < len(intervals); i++ {
|
||||
next := intervals[i]
|
||||
if current.StartTime+current.Duration >= next.StartTime {
|
||||
endTime := max(current.StartTime+current.Duration, next.StartTime+next.Duration)
|
||||
current.Duration = endTime - current.StartTime
|
||||
} else {
|
||||
merged = append(merged, current)
|
||||
current = next
|
||||
}
|
||||
}
|
||||
// Add the last interval
|
||||
merged = append(merged, current)
|
||||
|
||||
return merged
|
||||
}
|
||||
|
||||
func ContainsWaterfallSpan(slice []*model.Span, item *model.Span) bool {
|
||||
for _, v := range slice {
|
||||
if v.SpanID == item.SpanID {
|
||||
return true
|
||||
}
|
||||
}
|
||||
return false
|
||||
}
|
||||
|
||||
func findIndexForSelectedSpanFromPreOrder(spans []*model.Span, selectedSpanId string) int {
|
||||
var selectedSpanIndex = -1
|
||||
|
||||
for index, span := range spans {
|
||||
if span.SpanID == selectedSpanId {
|
||||
selectedSpanIndex = index
|
||||
break
|
||||
}
|
||||
}
|
||||
|
||||
return selectedSpanIndex
|
||||
}
|
||||
|
||||
func getPathFromRootToSelectedSpanId(node *model.Span, selectedSpanId string) (bool, []string) {
|
||||
spansFromRootToNode := []string{}
|
||||
|
||||
spansFromRootToNode = append(spansFromRootToNode, node.SpanID)
|
||||
if node.SpanID == selectedSpanId {
|
||||
return true, spansFromRootToNode
|
||||
}
|
||||
|
||||
isPresentInSubtreeForTheNode := false
|
||||
for _, child := range node.Children {
|
||||
isPresentInThisSubtree, _spansFromRootToNode := getPathFromRootToSelectedSpanId(child, selectedSpanId)
|
||||
// if the interested node is present in the given subtree then add the span node to uncollapsed node list
|
||||
if isPresentInThisSubtree {
|
||||
isPresentInSubtreeForTheNode = true
|
||||
spansFromRootToNode = append(spansFromRootToNode, _spansFromRootToNode...)
|
||||
break
|
||||
}
|
||||
}
|
||||
return isPresentInSubtreeForTheNode, spansFromRootToNode
|
||||
}
|
||||
|
||||
// traverseOpts holds the traversal configuration that remains constant
|
||||
// throughout the recursion. Per-call state (level, isPartOfPreOrder, etc.)
|
||||
// is passed as direct arguments.
|
||||
type traverseOpts struct {
|
||||
uncollapsedSpans map[string]struct{}
|
||||
selectedSpanID string
|
||||
isSelectedSpanUncollapsed bool
|
||||
selectAll bool
|
||||
}
|
||||
|
||||
func traverseTrace(
|
||||
span *model.Span,
|
||||
opts traverseOpts,
|
||||
level uint64,
|
||||
isPartOfPreOrder bool,
|
||||
hasSibling bool,
|
||||
autoExpandDepth int,
|
||||
) ([]*model.Span, []string) {
|
||||
|
||||
preOrderTraversal := []*model.Span{}
|
||||
autoExpandedSpans := []string{}
|
||||
|
||||
// sort the children to maintain the order across requests
|
||||
sort.Slice(span.Children, func(i, j int) bool {
|
||||
if span.Children[i].TimeUnixNano == span.Children[j].TimeUnixNano {
|
||||
return span.Children[i].Name < span.Children[j].Name
|
||||
}
|
||||
return span.Children[i].TimeUnixNano < span.Children[j].TimeUnixNano
|
||||
})
|
||||
|
||||
span.SubTreeNodeCount = 0
|
||||
nodeWithoutChildren := model.Span{
|
||||
SpanID: span.SpanID,
|
||||
TraceID: span.TraceID,
|
||||
ServiceName: span.ServiceName,
|
||||
TimeUnixNano: span.TimeUnixNano,
|
||||
Name: span.Name,
|
||||
Kind: int32(span.Kind),
|
||||
DurationNano: span.DurationNano,
|
||||
HasError: span.HasError,
|
||||
StatusMessage: span.StatusMessage,
|
||||
StatusCodeString: span.StatusCodeString,
|
||||
SpanKind: span.SpanKind,
|
||||
References: span.References,
|
||||
Events: span.Events,
|
||||
TagMap: span.TagMap,
|
||||
Children: make([]*model.Span, 0),
|
||||
HasChildren: len(span.Children) > 0,
|
||||
Level: level,
|
||||
HasSiblings: hasSibling,
|
||||
SubTreeNodeCount: 0,
|
||||
}
|
||||
|
||||
if isPartOfPreOrder {
|
||||
preOrderTraversal = append(preOrderTraversal, &nodeWithoutChildren)
|
||||
}
|
||||
|
||||
remainingAutoExpandDepth := 0
|
||||
if span.SpanID == opts.selectedSpanID && opts.isSelectedSpanUncollapsed {
|
||||
remainingAutoExpandDepth = maxDepthForSelectedSpanChildren
|
||||
} else if autoExpandDepth > 0 {
|
||||
remainingAutoExpandDepth = autoExpandDepth - 1
|
||||
}
|
||||
|
||||
_, isAlreadyUncollapsed := opts.uncollapsedSpans[span.SpanID]
|
||||
for index, child := range span.Children {
|
||||
// A child is included in the pre-order output if its parent is uncollapsed
|
||||
// OR if the child falls within MAX_DEPTH_FOR_SELECTED_SPAN_CHILDREN levels
|
||||
// below the selected span.
|
||||
isChildWithinMaxDepth := remainingAutoExpandDepth > 0
|
||||
childIsPartOfPreOrder := opts.selectAll || (isPartOfPreOrder && (isAlreadyUncollapsed || isChildWithinMaxDepth))
|
||||
|
||||
if isPartOfPreOrder && isChildWithinMaxDepth && !isAlreadyUncollapsed {
|
||||
if !slices.Contains(autoExpandedSpans, span.SpanID) {
|
||||
autoExpandedSpans = append(autoExpandedSpans, span.SpanID)
|
||||
}
|
||||
}
|
||||
|
||||
_childTraversal, _autoExpanded := traverseTrace(child, opts, level+1, childIsPartOfPreOrder, index != (len(span.Children)-1), remainingAutoExpandDepth)
|
||||
preOrderTraversal = append(preOrderTraversal, _childTraversal...)
|
||||
autoExpandedSpans = append(autoExpandedSpans, _autoExpanded...)
|
||||
nodeWithoutChildren.SubTreeNodeCount += child.SubTreeNodeCount + 1
|
||||
span.SubTreeNodeCount += child.SubTreeNodeCount + 1
|
||||
}
|
||||
|
||||
nodeWithoutChildren.SubTreeNodeCount += 1
|
||||
return preOrderTraversal, autoExpandedSpans
|
||||
|
||||
}
|
||||
|
||||
func CalculateServiceTime(serviceIntervals map[string][]Interval) map[string]uint64 {
|
||||
totalTimes := make(map[string]uint64)
|
||||
|
||||
for service, serviceIntervals := range serviceIntervals {
|
||||
sort.Slice(serviceIntervals, func(i, j int) bool {
|
||||
return serviceIntervals[i].StartTime < serviceIntervals[j].StartTime
|
||||
})
|
||||
mergedIntervals := mergeIntervals(serviceIntervals)
|
||||
totalTime := uint64(0)
|
||||
for _, interval := range mergedIntervals {
|
||||
totalTime += interval.Duration
|
||||
}
|
||||
totalTimes[service] = totalTime
|
||||
}
|
||||
|
||||
return totalTimes
|
||||
}
|
||||
|
||||
func GetSelectedSpans(uncollapsedSpans []string, selectedSpanID string, traceRoots []*model.Span, spanIdToSpanNodeMap map[string]*model.Span, isSelectedSpanIDUnCollapsed bool) ([]*model.Span, []string, string, string) {
|
||||
|
||||
var preOrderTraversal = make([]*model.Span, 0)
|
||||
var rootServiceName, rootServiceEntryPoint string
|
||||
|
||||
// create a map of uncollapsed spans for quick lookup
|
||||
uncollapsedSpanMap := make(map[string]struct{})
|
||||
for _, spanID := range uncollapsedSpans {
|
||||
uncollapsedSpanMap[spanID] = struct{}{}
|
||||
}
|
||||
|
||||
selectedSpanIndex := -1
|
||||
for _, rootSpanID := range traceRoots {
|
||||
if rootNode, exists := spanIdToSpanNodeMap[rootSpanID.SpanID]; exists {
|
||||
present, spansFromRootToNode := getPathFromRootToSelectedSpanId(rootNode, selectedSpanID)
|
||||
if present {
|
||||
for _, spanID := range spansFromRootToNode {
|
||||
if selectedSpanID == spanID && !isSelectedSpanIDUnCollapsed {
|
||||
continue
|
||||
}
|
||||
uncollapsedSpanMap[spanID] = struct{}{}
|
||||
}
|
||||
}
|
||||
|
||||
opts := traverseOpts{
|
||||
uncollapsedSpans: uncollapsedSpanMap,
|
||||
selectedSpanID: selectedSpanID,
|
||||
isSelectedSpanUncollapsed: isSelectedSpanIDUnCollapsed,
|
||||
}
|
||||
_preOrderTraversal, _autoExpanded := traverseTrace(rootNode, opts, 0, true, false, 0)
|
||||
// Merge auto-expanded spans into updatedUncollapsedSpans for returning in response
|
||||
for _, spanID := range _autoExpanded {
|
||||
uncollapsedSpanMap[spanID] = struct{}{}
|
||||
}
|
||||
_selectedSpanIndex := findIndexForSelectedSpanFromPreOrder(_preOrderTraversal, selectedSpanID)
|
||||
|
||||
if _selectedSpanIndex != -1 {
|
||||
selectedSpanIndex = _selectedSpanIndex + len(preOrderTraversal)
|
||||
}
|
||||
|
||||
preOrderTraversal = append(preOrderTraversal, _preOrderTraversal...)
|
||||
|
||||
if rootServiceName == "" {
|
||||
rootServiceName = rootNode.ServiceName
|
||||
}
|
||||
|
||||
if rootServiceEntryPoint == "" {
|
||||
rootServiceEntryPoint = rootNode.Name
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// if we couldn't find the selectedSpan in the trace then defaulting the selected index to 0
|
||||
if selectedSpanIndex == -1 && selectedSpanID != "" {
|
||||
selectedSpanIndex = 0
|
||||
}
|
||||
|
||||
// get the 0.4*[span limit] before the interested span index
|
||||
startIndex := selectedSpanIndex - int(SPAN_LIMIT_PER_REQUEST_FOR_WATERFALL*0.4)
|
||||
// get the 0.6*[span limit] after the intrested span index
|
||||
endIndex := selectedSpanIndex + int(SPAN_LIMIT_PER_REQUEST_FOR_WATERFALL*0.6)
|
||||
|
||||
// adjust the sliding window according to the available left and right spaces.
|
||||
if startIndex < 0 {
|
||||
endIndex = endIndex - startIndex
|
||||
startIndex = 0
|
||||
}
|
||||
if endIndex > len(preOrderTraversal) {
|
||||
startIndex = startIndex - (endIndex - len(preOrderTraversal))
|
||||
endIndex = len(preOrderTraversal)
|
||||
}
|
||||
if startIndex < 0 {
|
||||
startIndex = 0
|
||||
}
|
||||
|
||||
return preOrderTraversal[startIndex:endIndex], slices.Collect(maps.Keys(uncollapsedSpanMap)), rootServiceName, rootServiceEntryPoint
|
||||
}
|
||||
|
||||
func GetAllSpans(traceRoots []*model.Span) (spans []*model.Span, rootServiceName, rootEntryPoint string) {
|
||||
if len(traceRoots) > 0 {
|
||||
rootServiceName = traceRoots[0].ServiceName
|
||||
rootEntryPoint = traceRoots[0].Name
|
||||
}
|
||||
for _, root := range traceRoots {
|
||||
childSpans, _ := traverseTrace(root, traverseOpts{selectAll: true}, 0, true, false, 0)
|
||||
spans = append(spans, childSpans...)
|
||||
}
|
||||
return
|
||||
}
|
||||
446
pkg/query-service/app/traces/tracedetail/waterfall_test.go
Normal file
446
pkg/query-service/app/traces/tracedetail/waterfall_test.go
Normal file
@@ -0,0 +1,446 @@
|
||||
// Package tracedetail tests — waterfall
|
||||
//
|
||||
// # Background
|
||||
//
|
||||
// The waterfall view renders a trace as a scrollable list of spans in
|
||||
// pre-order (parent before children, siblings left-to-right). Because a trace
|
||||
// can have thousands of spans, only a window of ~500 is returned per request.
|
||||
// The window is centred on the selected span.
|
||||
//
|
||||
// # Key concepts
|
||||
//
|
||||
// uncollapsedSpans
|
||||
//
|
||||
// The set of span IDs the user has manually expanded in the UI.
|
||||
// Only the direct children of an uncollapsed span are included in the
|
||||
// output; grandchildren stay hidden until their parent is also uncollapsed.
|
||||
// When multiple spans are uncollapsed their children are all visible at once.
|
||||
//
|
||||
// selectedSpanID
|
||||
//
|
||||
// The span currently focused — set when the user clicks a span in the
|
||||
// waterfall or selects one from the flamegraph. The output window is always
|
||||
// centred on this span. The path from the trace root down to the selected
|
||||
// span is automatically uncollapsed so ancestors are visible even if they are
|
||||
// not in uncollapsedSpans.
|
||||
//
|
||||
// isSelectedSpanIDUnCollapsed
|
||||
//
|
||||
// Controls whether the selected span's own children are shown:
|
||||
// true — user expanded the span (click-to-open in waterfall or flamegraph);
|
||||
// direct children of the selected span are included.
|
||||
// false — user selected without expanding;
|
||||
// the span is visible but its children remain hidden.
|
||||
//
|
||||
// traceRoots
|
||||
//
|
||||
// Root spans of the trace — spans with no parent in the current dataset.
|
||||
// Normally one, but multiple roots are common when upstream services are
|
||||
// not instrumented or their spans were not sampled/exported.
|
||||
|
||||
package tracedetail
|
||||
|
||||
import (
|
||||
"fmt"
|
||||
"testing"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/query-service/model"
|
||||
"github.com/stretchr/testify/assert"
|
||||
)
|
||||
|
||||
// Pre-order traversal is preserved: parent before children, siblings left-to-right.
|
||||
func TestGetSelectedSpans_PreOrderTraversal(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("child1", "svc", mkSpan("grandchild", "svc")),
|
||||
mkSpan("child2", "svc"),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, _, _, _ := GetSelectedSpans([]string{"root", "child1"}, "root", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.Equal(t, []string{"root", "child1", "grandchild", "child2"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// Multiple roots: both trees are flattened into a single pre-order list with
|
||||
// root1's subtree before root2's. Service/entry-point come from the first root.
|
||||
//
|
||||
// root1 svc-a ← selected
|
||||
// └─ child1
|
||||
// root2 svc-b
|
||||
// └─ child2
|
||||
//
|
||||
// Expected output order: root1 → child1 → root2 → child2
|
||||
func TestGetSelectedSpans_MultipleRoots(t *testing.T) {
|
||||
root1 := mkSpan("root1", "svc-a", mkSpan("child1", "svc-a"))
|
||||
root2 := mkSpan("root2", "svc-b", mkSpan("child2", "svc-b"))
|
||||
spanMap := buildSpanMap(root1, root2)
|
||||
|
||||
spans, _, svcName, entryPoint := GetSelectedSpans([]string{"root1", "root2"}, "root1", []*model.Span{root1, root2}, spanMap, false)
|
||||
|
||||
assert.Equal(t, []string{"root1", "child1", "root2", "child2"}, spanIDs(spans), "root1 subtree must precede root2 subtree")
|
||||
assert.Equal(t, "svc-a", svcName, "metadata comes from first root")
|
||||
assert.Equal(t, "root1-op", entryPoint, "metadata comes from first root")
|
||||
}
|
||||
|
||||
// Multiple spans uncollapsed simultaneously: children of all uncollapsed spans
|
||||
// are visible at once.
|
||||
//
|
||||
// root
|
||||
// ├─ childA (uncollapsed) → grandchildA ✓
|
||||
// └─ childB (uncollapsed) → grandchildB ✓
|
||||
func TestGetSelectedSpans_MultipleUncollapsed(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("childA", "svc", mkSpan("grandchildA", "svc")),
|
||||
mkSpan("childB", "svc", mkSpan("grandchildB", "svc")),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, _, _, _ := GetSelectedSpans([]string{"root", "childA", "childB"}, "root", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.Equal(t, []string{"root", "childA", "grandchildA", "childB", "grandchildB"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// Collapsing a span with other uncollapsed spans
|
||||
//
|
||||
// root
|
||||
// ├─ childA (previously expanded — in uncollapsedSpans)
|
||||
// │ ├─ grandchild1 ✓
|
||||
// │ │ └─ greatGrandchild ✗ (grandchild1 not in uncollapsedSpans)
|
||||
// │ └─ grandchild2 ✓
|
||||
// └─ childB ← selected (not expanded)
|
||||
func TestGetSelectedSpans_ManualUncollapse(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("childA", "svc",
|
||||
mkSpan("grandchild1", "svc", mkSpan("greatGrandchild", "svc")),
|
||||
mkSpan("grandchild2", "svc"),
|
||||
),
|
||||
mkSpan("childB", "svc"),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
// childA was expanded in a previous interaction; childB is now selected without expanding
|
||||
spans, _, _, _ := GetSelectedSpans([]string{"childA"}, "childB", []*model.Span{root}, spanMap, false)
|
||||
|
||||
// path to childB auto-uncollpases root → childA and childB appear; childA is in
|
||||
// uncollapsedSpans so its children appear; greatGrandchild stays hidden.
|
||||
assert.Equal(t, []string{"root", "childA", "grandchild1", "grandchild2", "childB"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// A collapsed span hides all children.
|
||||
func TestGetSelectedSpans_CollapsedSpan(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("child1", "svc"),
|
||||
mkSpan("child2", "svc"),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, _, _, _ := GetSelectedSpans([]string{}, "root", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.Equal(t, []string{"root"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// Selecting a span auto-uncollpases the path from root to that span so it is visible.
|
||||
//
|
||||
// root → parent → selected
|
||||
func TestGetSelectedSpans_PathToSelectedIsUncollapsed(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("parent", "svc",
|
||||
mkSpan("selected", "svc"),
|
||||
),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
// no manually uncollapsed spans — path should still be opened
|
||||
spans, _, _, _ := GetSelectedSpans([]string{}, "selected", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.Equal(t, []string{"root", "parent", "selected"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// The path-to-selected spans are returned in updatedUncollapsedSpans.
|
||||
func TestGetSelectedSpans_PathReturnedInUncollapsed(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("parent", "svc",
|
||||
mkSpan("selected", "svc"),
|
||||
),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, uncollapsed, _, _ := GetSelectedSpans([]string{}, "selected", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.ElementsMatch(t, []string{"root", "parent"}, uncollapsed)
|
||||
assert.Equal(t, []string{"root", "parent", "selected"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// Siblings of ancestors are rendered as collapsed nodes but their subtrees
|
||||
// must NOT be expanded.
|
||||
//
|
||||
// root
|
||||
// ├─ unrelated → unrelated-child (✗)
|
||||
// └─ parent → selected
|
||||
func TestGetSelectedSpans_SiblingsNotExpanded(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("unrelated", "svc", mkSpan("unrelated-child", "svc")),
|
||||
mkSpan("parent", "svc",
|
||||
mkSpan("selected", "svc"),
|
||||
),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, uncollapsed, _, _ := GetSelectedSpans([]string{}, "selected", []*model.Span{root}, spanMap, false)
|
||||
|
||||
// children of root sort alphabetically: parent < unrelated; unrelated-child stays hidden
|
||||
assert.Equal(t, []string{"root", "parent", "selected", "unrelated"}, spanIDs(spans))
|
||||
// only the path nodes are tracked as uncollapsed — unrelated is not
|
||||
assert.ElementsMatch(t, []string{"root", "parent"}, uncollapsed)
|
||||
}
|
||||
|
||||
// An unknown selectedSpanID must not panic; returns a window from index 0.
|
||||
func TestGetSelectedSpans_UnknownSelectedSpan(t *testing.T) {
|
||||
root := mkSpan("root", "svc", mkSpan("child", "svc"))
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, _, _, _ := GetSelectedSpans([]string{}, "nonexistent", []*model.Span{root}, spanMap, false)
|
||||
assert.Equal(t, []string{"root"}, spanIDs(spans))
|
||||
}
|
||||
|
||||
// Test to check if Level, HasChildren, HasSiblings, and SubTreeNodeCount are populated correctly.
|
||||
//
|
||||
// root level=0, hasChildren=true, hasSiblings=false, subTree=4
|
||||
// child1 level=1, hasChildren=true, hasSiblings=true, subTree=2
|
||||
// grandchild level=2, hasChildren=false, hasSiblings=false, subTree=1
|
||||
// child2 level=1, hasChildren=false, hasSiblings=false, subTree=1
|
||||
func TestGetSelectedSpans_SpanMetadata(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("child1", "svc", mkSpan("grandchild", "svc")),
|
||||
mkSpan("child2", "svc"),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, _, _, _ := GetSelectedSpans([]string{"root", "child1"}, "root", []*model.Span{root}, spanMap, false)
|
||||
|
||||
byID := map[string]*model.Span{}
|
||||
for _, s := range spans {
|
||||
byID[s.SpanID] = s
|
||||
}
|
||||
|
||||
assert.Equal(t, uint64(0), byID["root"].Level)
|
||||
assert.Equal(t, uint64(1), byID["child1"].Level)
|
||||
assert.Equal(t, uint64(1), byID["child2"].Level)
|
||||
assert.Equal(t, uint64(2), byID["grandchild"].Level)
|
||||
|
||||
assert.True(t, byID["root"].HasChildren)
|
||||
assert.True(t, byID["child1"].HasChildren)
|
||||
assert.False(t, byID["child2"].HasChildren)
|
||||
assert.False(t, byID["grandchild"].HasChildren)
|
||||
|
||||
assert.False(t, byID["root"].HasSiblings, "root has no siblings")
|
||||
assert.True(t, byID["child1"].HasSiblings, "child1 has sibling child2")
|
||||
assert.False(t, byID["child2"].HasSiblings, "child2 is the last child")
|
||||
assert.False(t, byID["grandchild"].HasSiblings, "grandchild has no siblings")
|
||||
|
||||
assert.Equal(t, uint64(4), byID["root"].SubTreeNodeCount)
|
||||
assert.Equal(t, uint64(2), byID["child1"].SubTreeNodeCount)
|
||||
assert.Equal(t, uint64(1), byID["grandchild"].SubTreeNodeCount)
|
||||
assert.Equal(t, uint64(1), byID["child2"].SubTreeNodeCount)
|
||||
}
|
||||
|
||||
// If the selected span is already in uncollapsedSpans AND isSelectedSpanIDUnCollapsed=true,
|
||||
func TestGetSelectedSpans_DuplicateInUncollapsed(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("selected", "svc", mkSpan("child", "svc")),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
_, uncollapsed, _, _ := GetSelectedSpans(
|
||||
[]string{"selected"}, // already present
|
||||
"selected",
|
||||
[]*model.Span{root}, spanMap,
|
||||
true,
|
||||
)
|
||||
|
||||
count := 0
|
||||
for _, id := range uncollapsed {
|
||||
if id == "selected" {
|
||||
count++
|
||||
}
|
||||
}
|
||||
assert.Equal(t, 1, count, "should appear once")
|
||||
}
|
||||
|
||||
// makeChain builds a linear trace: span0 → span1 → … → span(n-1).
|
||||
// All span IDs are "span0", "span1", … so the caller can reference them by index.
|
||||
func makeChain(n int) (*model.Span, map[string]*model.Span, []string) {
|
||||
spans := make([]*model.Span, n)
|
||||
for i := n - 1; i >= 0; i-- {
|
||||
if i == n-1 {
|
||||
spans[i] = mkSpan(fmt.Sprintf("span%d", i), "svc")
|
||||
} else {
|
||||
spans[i] = mkSpan(fmt.Sprintf("span%d", i), "svc", spans[i+1])
|
||||
}
|
||||
}
|
||||
uncollapsed := make([]string, n)
|
||||
for i := range spans {
|
||||
uncollapsed[i] = fmt.Sprintf("span%d", i)
|
||||
}
|
||||
return spans[0], buildSpanMap(spans[0]), uncollapsed
|
||||
}
|
||||
|
||||
// The selected span is centred: 200 spans before it, 300 after (0.4 / 0.6 split).
|
||||
func TestGetSelectedSpans_WindowCentredOnSelected(t *testing.T) {
|
||||
root, spanMap, uncollapsed := makeChain(600)
|
||||
spans, _, _, _ := GetSelectedSpans(uncollapsed, "span300", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.Equal(t, 500, len(spans), "window should be 500 spans")
|
||||
// window is [100, 600): span300 lands at position 200 (300 - 100)
|
||||
assert.Equal(t, "span100", spans[0].SpanID, "window starts 200 before selected")
|
||||
assert.Equal(t, "span300", spans[200].SpanID, "selected span at position 200 in window")
|
||||
assert.Equal(t, "span599", spans[499].SpanID, "window ends 300 after selected")
|
||||
}
|
||||
|
||||
// When the selected span is near the start, the window shifts right so no
|
||||
// negative index is used — the result is still 500 spans.
|
||||
func TestGetSelectedSpans_WindowShiftsAtStart(t *testing.T) {
|
||||
root, spanMap, uncollapsed := makeChain(600)
|
||||
spans, _, _, _ := GetSelectedSpans(uncollapsed, "span10", []*model.Span{root}, spanMap, false)
|
||||
|
||||
assert.Equal(t, 500, len(spans))
|
||||
assert.Equal(t, "span0", spans[0].SpanID, "window clamped to start of trace")
|
||||
assert.Equal(t, "span10", spans[10].SpanID, "selected span still in window")
|
||||
}
|
||||
|
||||
// Auto-expanded span IDs from ALL branches are returned in
|
||||
// updatedUncollapsedSpans. Only internal nodes (spans with children) are
|
||||
// tracked — leaf spans are never added.
|
||||
//
|
||||
// root (selected)
|
||||
// ├─ childA (internal ✓)
|
||||
// │ └─ grandchildA (internal ✓)
|
||||
// │ └─ leafA (leaf ✗)
|
||||
// └─ childB (internal ✓)
|
||||
// └─ grandchildB (internal ✓)
|
||||
// └─ leafB (leaf ✗)
|
||||
func TestGetSelectedSpans_AutoExpandedSpansReturnedInUncollapsed(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("childA", "svc",
|
||||
mkSpan("grandchildA", "svc",
|
||||
mkSpan("leafA", "svc"),
|
||||
),
|
||||
),
|
||||
mkSpan("childB", "svc",
|
||||
mkSpan("grandchildB", "svc",
|
||||
mkSpan("leafB", "svc"),
|
||||
),
|
||||
),
|
||||
)
|
||||
spanMap := buildSpanMap(root)
|
||||
_, uncollapsed, _, _ := GetSelectedSpans([]string{}, "root", []*model.Span{root}, spanMap, true)
|
||||
|
||||
// all internal nodes across both branches must be tracked
|
||||
assert.Contains(t, uncollapsed, "root")
|
||||
assert.Contains(t, uncollapsed, "childA", "internal node depth 1, branch A")
|
||||
assert.Contains(t, uncollapsed, "childB", "internal node depth 1, branch B")
|
||||
assert.Contains(t, uncollapsed, "grandchildA", "internal node depth 2, branch A")
|
||||
assert.Contains(t, uncollapsed, "grandchildB", "internal node depth 2, branch B")
|
||||
// leaves have no children to show — never added to uncollapsedSpans
|
||||
assert.NotContains(t, uncollapsed, "leafA", "leaf spans are never added to uncollapsedSpans")
|
||||
assert.NotContains(t, uncollapsed, "leafB", "leaf spans are never added to uncollapsedSpans")
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
// maxDepthForSelectedSpanChildren boundary tests
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
// Depth is measured from the selected span, not the trace root.
|
||||
// Ancestors appear via the path-to-root logic, not the depth limit.
|
||||
// Each depth level has two children to confirm the limit is enforced on all
|
||||
// branches, not just the first.
|
||||
//
|
||||
// root
|
||||
// └─ A ancestor ✓ (path-to-root)
|
||||
// └─ selected
|
||||
// ├─ d1a depth 1 ✓
|
||||
// │ ├─ d2a depth 2 ✓
|
||||
// │ │ ├─ d3a depth 3 ✓
|
||||
// │ │ │ ├─ d4a depth 4 ✓
|
||||
// │ │ │ │ ├─ d5a depth 5 ✓
|
||||
// │ │ │ │ │ └─ d6a depth 6 ✗
|
||||
// │ │ │ │ └─ d5b depth 5 ✓
|
||||
// │ │ │ └─ d4b depth 4 ✓
|
||||
// │ │ └─ d3b depth 3 ✓
|
||||
// │ └─ d2b depth 2 ✓
|
||||
// └─ d1b depth 1 ✓
|
||||
func TestGetSelectedSpans_DepthCountedFromSelectedSpan(t *testing.T) {
|
||||
selected := mkSpan("selected", "svc",
|
||||
mkSpan("d1a", "svc",
|
||||
mkSpan("d2a", "svc",
|
||||
mkSpan("d3a", "svc",
|
||||
mkSpan("d4a", "svc",
|
||||
mkSpan("d5a", "svc",
|
||||
mkSpan("d6a", "svc"), // depth 6 — excluded
|
||||
),
|
||||
mkSpan("d5b", "svc"), // depth 5 — included
|
||||
),
|
||||
mkSpan("d4b", "svc"), // depth 4 — included
|
||||
),
|
||||
mkSpan("d3b", "svc"), // depth 3 — included
|
||||
),
|
||||
mkSpan("d2b", "svc"), // depth 2 — included
|
||||
),
|
||||
mkSpan("d1b", "svc"), // depth 1 — included
|
||||
)
|
||||
root := mkSpan("root", "svc", mkSpan("A", "svc", selected))
|
||||
|
||||
spanMap := buildSpanMap(root)
|
||||
spans, _, _, _ := GetSelectedSpans([]string{}, "selected", []*model.Span{root}, spanMap, true)
|
||||
ids := spanIDs(spans)
|
||||
|
||||
assert.Contains(t, ids, "root", "ancestor shown via path-to-root")
|
||||
assert.Contains(t, ids, "A", "ancestor shown via path-to-root")
|
||||
for _, id := range []string{"d1a", "d1b", "d2a", "d2b", "d3a", "d3b", "d4a", "d4b", "d5a", "d5b"} {
|
||||
assert.Contains(t, ids, id, "depth ≤ 5 — must be included")
|
||||
}
|
||||
assert.NotContains(t, ids, "d6a", "depth 6 > limit — excluded")
|
||||
}
|
||||
|
||||
func TestGetAllSpans(t *testing.T) {
|
||||
root := mkSpan("root", "svc",
|
||||
mkSpan("childA", "svc",
|
||||
mkSpan("grandchildA", "svc",
|
||||
mkSpan("leafA", "svc2"),
|
||||
),
|
||||
),
|
||||
mkSpan("childB", "svc3",
|
||||
mkSpan("grandchildB", "svc",
|
||||
mkSpan("leafB", "svc2"),
|
||||
),
|
||||
),
|
||||
)
|
||||
spans, rootServiceName, rootEntryPoint := GetAllSpans([]*model.Span{root})
|
||||
assert.ElementsMatch(t, spanIDs(spans), []string{"root", "childA", "grandchildA", "leafA", "childB", "grandchildB", "leafB"})
|
||||
assert.Equal(t, rootServiceName, "svc")
|
||||
assert.Equal(t, rootEntryPoint, "root-op")
|
||||
}
|
||||
|
||||
func mkSpan(id, service string, children ...*model.Span) *model.Span {
|
||||
return &model.Span{
|
||||
SpanID: id,
|
||||
ServiceName: service,
|
||||
Name: id + "-op",
|
||||
Children: children,
|
||||
}
|
||||
}
|
||||
|
||||
// spanIDs returns SpanIDs in order.
|
||||
func spanIDs(spans []*model.Span) []string {
|
||||
ids := make([]string, len(spans))
|
||||
for i, s := range spans {
|
||||
ids[i] = s.SpanID
|
||||
}
|
||||
return ids
|
||||
}
|
||||
|
||||
// buildSpanMap indexes every span in a set of trees by SpanID.
|
||||
func buildSpanMap(roots ...*model.Span) map[string]*model.Span {
|
||||
m := map[string]*model.Span{}
|
||||
var walk func(*model.Span)
|
||||
walk = func(s *model.Span) {
|
||||
m[s.SpanID] = s
|
||||
for _, c := range s.Children {
|
||||
walk(c)
|
||||
}
|
||||
}
|
||||
for _, r := range roots {
|
||||
walk(r)
|
||||
}
|
||||
return m
|
||||
}
|
||||
@@ -43,6 +43,9 @@ type Reader interface {
|
||||
|
||||
// Search Interfaces
|
||||
SearchTraces(ctx context.Context, params *model.SearchTracesParams) (*[]model.SearchSpansResult, error)
|
||||
GetWaterfallSpansForTraceWithMetadata(ctx context.Context, orgID valuer.UUID, traceID string, req *model.GetWaterfallSpansForTraceWithMetadataParams) (*model.GetWaterfallSpansForTraceWithMetadataResponse, error)
|
||||
GetFlamegraphSpansForTrace(ctx context.Context, orgID valuer.UUID, traceID string, req *model.GetFlamegraphSpansForTraceParams) (*model.GetFlamegraphSpansForTraceResponse, error)
|
||||
|
||||
// Setter Interfaces
|
||||
SetTTL(ctx context.Context, orgID string, ttlParams *retentiontypes.TTLParams) (*retentiontypes.SetTTLResponseItem, *model.ApiError)
|
||||
SetTTLV2(ctx context.Context, orgID string, params *retentiontypes.CustomRetentionTTLParams) (*retentiontypes.CustomRetentionTTLResponse, error)
|
||||
|
||||
89
pkg/query-service/model/cacheable.go
Normal file
89
pkg/query-service/model/cacheable.go
Normal file
@@ -0,0 +1,89 @@
|
||||
package model
|
||||
|
||||
import (
|
||||
"encoding/json"
|
||||
"maps"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/cachetypes"
|
||||
)
|
||||
|
||||
type GetWaterfallSpansForTraceWithMetadataCache struct {
|
||||
StartTime uint64 `json:"startTime"`
|
||||
EndTime uint64 `json:"endTime"`
|
||||
DurationNano uint64 `json:"durationNano"`
|
||||
TotalSpans uint64 `json:"totalSpans"`
|
||||
TotalErrorSpans uint64 `json:"totalErrorSpans"`
|
||||
ServiceNameToTotalDurationMap map[string]uint64 `json:"serviceNameToTotalDurationMap"`
|
||||
SpanIdToSpanNodeMap map[string]*Span `json:"spanIdToSpanNodeMap"`
|
||||
TraceRoots []*Span `json:"traceRoots"`
|
||||
HasMissingSpans bool `json:"hasMissingSpans"`
|
||||
}
|
||||
|
||||
func (c *GetWaterfallSpansForTraceWithMetadataCache) Clone() cachetypes.Cacheable {
|
||||
copyOfServiceNameToTotalDurationMap := make(map[string]uint64)
|
||||
maps.Copy(copyOfServiceNameToTotalDurationMap, c.ServiceNameToTotalDurationMap)
|
||||
|
||||
copyOfSpanIdToSpanNodeMap := make(map[string]*Span)
|
||||
maps.Copy(copyOfSpanIdToSpanNodeMap, c.SpanIdToSpanNodeMap)
|
||||
|
||||
copyOfTraceRoots := make([]*Span, len(c.TraceRoots))
|
||||
copy(copyOfTraceRoots, c.TraceRoots)
|
||||
return &GetWaterfallSpansForTraceWithMetadataCache{
|
||||
StartTime: c.StartTime,
|
||||
EndTime: c.EndTime,
|
||||
DurationNano: c.DurationNano,
|
||||
TotalSpans: c.TotalSpans,
|
||||
TotalErrorSpans: c.TotalErrorSpans,
|
||||
ServiceNameToTotalDurationMap: copyOfServiceNameToTotalDurationMap,
|
||||
SpanIdToSpanNodeMap: copyOfSpanIdToSpanNodeMap,
|
||||
TraceRoots: copyOfTraceRoots,
|
||||
HasMissingSpans: c.HasMissingSpans,
|
||||
}
|
||||
}
|
||||
|
||||
func (c *GetWaterfallSpansForTraceWithMetadataCache) Cost() int64 {
|
||||
const perSpanBytes = 256
|
||||
return int64(c.TotalSpans) * perSpanBytes
|
||||
}
|
||||
|
||||
func (c *GetWaterfallSpansForTraceWithMetadataCache) MarshalBinary() (data []byte, err error) {
|
||||
return json.Marshal(c)
|
||||
}
|
||||
func (c *GetWaterfallSpansForTraceWithMetadataCache) UnmarshalBinary(data []byte) error {
|
||||
return json.Unmarshal(data, c)
|
||||
}
|
||||
|
||||
type GetFlamegraphSpansForTraceCache struct {
|
||||
StartTime uint64 `json:"startTime"`
|
||||
EndTime uint64 `json:"endTime"`
|
||||
DurationNano uint64 `json:"durationNano"`
|
||||
SelectedSpans [][]*FlamegraphSpan `json:"selectedSpans"`
|
||||
TraceRoots []*FlamegraphSpan `json:"traceRoots"`
|
||||
}
|
||||
|
||||
func (c *GetFlamegraphSpansForTraceCache) Clone() cachetypes.Cacheable {
|
||||
return &GetFlamegraphSpansForTraceCache{
|
||||
StartTime: c.StartTime,
|
||||
EndTime: c.EndTime,
|
||||
DurationNano: c.DurationNano,
|
||||
SelectedSpans: c.SelectedSpans,
|
||||
TraceRoots: c.TraceRoots,
|
||||
}
|
||||
}
|
||||
|
||||
func (c *GetFlamegraphSpansForTraceCache) Cost() int64 {
|
||||
const perSpanBytes = 128
|
||||
var spans int64
|
||||
for _, row := range c.SelectedSpans {
|
||||
spans += int64(len(row))
|
||||
}
|
||||
spans += int64(len(c.TraceRoots))
|
||||
return spans * perSpanBytes
|
||||
}
|
||||
|
||||
func (c *GetFlamegraphSpansForTraceCache) MarshalBinary() (data []byte, err error) {
|
||||
return json.Marshal(c)
|
||||
}
|
||||
func (c *GetFlamegraphSpansForTraceCache) UnmarshalBinary(data []byte) error {
|
||||
return json.Unmarshal(data, c)
|
||||
}
|
||||
@@ -2,6 +2,8 @@ package model
|
||||
|
||||
import (
|
||||
"time"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
)
|
||||
|
||||
type InstantQueryMetricsParams struct {
|
||||
@@ -329,6 +331,21 @@ type SearchTracesParams struct {
|
||||
MaxSpansInTrace int `json:"maxSpansInTrace"`
|
||||
}
|
||||
|
||||
type GetWaterfallSpansForTraceWithMetadataParams struct {
|
||||
SelectedSpanID string `json:"selectedSpanId"`
|
||||
IsSelectedSpanIDUnCollapsed bool `json:"isSelectedSpanIDUnCollapsed"`
|
||||
UncollapsedSpans []string `json:"uncollapsedSpans"`
|
||||
Limit uint `json:"limit"`
|
||||
}
|
||||
|
||||
type GetFlamegraphSpansForTraceParams struct {
|
||||
SelectedSpanID string `json:"selectedSpanId"`
|
||||
Limit uint `json:"limit"`
|
||||
BoundaryStartTS uint64 `json:"boundaryStartTsMilli"`
|
||||
BoundaryEndTS uint64 `json:"boundarEndTsMilli"`
|
||||
SelectFields []telemetrytypes.TelemetryFieldKey `json:"selectFields"`
|
||||
}
|
||||
|
||||
type SpanFilterParams struct {
|
||||
TraceID []string `json:"traceID"`
|
||||
Status []string `json:"status"`
|
||||
|
||||
@@ -7,6 +7,7 @@ import (
|
||||
"strconv"
|
||||
"time"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
"github.com/pkg/errors"
|
||||
"github.com/prometheus/prometheus/promql/parser"
|
||||
"github.com/prometheus/prometheus/util/stats"
|
||||
@@ -292,6 +293,69 @@ type Span struct {
|
||||
Level uint64 `json:"level"`
|
||||
}
|
||||
|
||||
type FlamegraphSpan struct {
|
||||
TimeUnixNano uint64 `json:"timestamp"`
|
||||
DurationNano uint64 `json:"durationNano"`
|
||||
SpanID string `json:"spanId"`
|
||||
TraceID string `json:"traceId"`
|
||||
HasError bool `json:"hasError"`
|
||||
ServiceName string `json:"serviceName"`
|
||||
Name string `json:"name"`
|
||||
Level int64 `json:"level"`
|
||||
Events []Event `json:"event"`
|
||||
References []OtelSpanRef `json:"references,omitempty"`
|
||||
Children []*FlamegraphSpan `json:"children"`
|
||||
Attributes map[string]any `json:"attributes,omitempty"`
|
||||
Resource map[string]string `json:"resource,omitempty"`
|
||||
}
|
||||
|
||||
// SetRequestedFields extracts the requested attribute/resource fields from item into s.
|
||||
// This can eventually support missing fieldContext by checking both
|
||||
func (s *FlamegraphSpan) SetRequestedFields(item SpanItemV2, fields []telemetrytypes.TelemetryFieldKey) {
|
||||
for _, field := range fields {
|
||||
switch field.FieldContext {
|
||||
case telemetrytypes.FieldContextResource:
|
||||
if v, ok := item.Resources_string[field.Name]; ok && v != "" {
|
||||
if s.Resource == nil {
|
||||
s.Resource = make(map[string]string)
|
||||
}
|
||||
s.Resource[field.Name] = v
|
||||
}
|
||||
case telemetrytypes.FieldContextAttribute:
|
||||
if v := item.AttributeValue(field.Name); v != nil {
|
||||
if s.Attributes == nil {
|
||||
s.Attributes = make(map[string]any)
|
||||
}
|
||||
s.Attributes[field.Name] = v
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
type GetWaterfallSpansForTraceWithMetadataResponse struct {
|
||||
StartTimestampMillis uint64 `json:"startTimestampMillis"`
|
||||
EndTimestampMillis uint64 `json:"endTimestampMillis"`
|
||||
DurationNano uint64 `json:"durationNano"`
|
||||
RootServiceName string `json:"rootServiceName"`
|
||||
RootServiceEntryPoint string `json:"rootServiceEntryPoint"`
|
||||
TotalSpansCount uint64 `json:"totalSpansCount"`
|
||||
TotalErrorSpansCount uint64 `json:"totalErrorSpansCount"`
|
||||
ServiceNameToTotalDurationMap map[string]uint64 `json:"serviceNameToTotalDurationMap"`
|
||||
Spans []*Span `json:"spans"`
|
||||
HasMissingSpans bool `json:"hasMissingSpans"`
|
||||
// this is needed for frontend and query service sync
|
||||
UncollapsedSpans []string `json:"uncollapsedSpans"`
|
||||
HasMore bool `json:"hasMore"`
|
||||
}
|
||||
|
||||
type GetFlamegraphSpansForTraceResponse struct {
|
||||
StartTimestampMillis uint64 `json:"startTimestampMillis"`
|
||||
EndTimestampMillis uint64 `json:"endTimestampMillis"`
|
||||
DurationNano uint64 `json:"durationNano"`
|
||||
Spans [][]*FlamegraphSpan `json:"spans"`
|
||||
HasMore bool `json:"hasMore"`
|
||||
}
|
||||
|
||||
type OtelSpanRef struct {
|
||||
TraceId string `json:"traceId,omitempty"`
|
||||
SpanId string `json:"spanId,omitempty"`
|
||||
|
||||
@@ -7,15 +7,14 @@ import (
|
||||
"github.com/SigNoz/signoz/pkg/authn/callbackauthn/googlecallbackauthn"
|
||||
"github.com/SigNoz/signoz/pkg/authn/passwordauthn/emailpasswordauthn"
|
||||
"github.com/SigNoz/signoz/pkg/factory"
|
||||
"github.com/SigNoz/signoz/pkg/global"
|
||||
"github.com/SigNoz/signoz/pkg/licensing"
|
||||
"github.com/SigNoz/signoz/pkg/types/authtypes"
|
||||
)
|
||||
|
||||
func NewAuthNs(ctx context.Context, providerSettings factory.ProviderSettings, store authtypes.AuthNStore, licensing licensing.Licensing, globalConfig global.Config) (map[authtypes.AuthNProvider]authn.AuthN, error) {
|
||||
func NewAuthNs(ctx context.Context, providerSettings factory.ProviderSettings, store authtypes.AuthNStore, licensing licensing.Licensing) (map[authtypes.AuthNProvider]authn.AuthN, error) {
|
||||
emailPasswordAuthN := emailpasswordauthn.New(store)
|
||||
|
||||
googleCallbackAuthN, err := googlecallbackauthn.New(ctx, store, providerSettings, globalConfig)
|
||||
googleCallbackAuthN, err := googlecallbackauthn.New(ctx, store, providerSettings)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
|
||||
@@ -275,14 +275,14 @@ func NewQuerierProviderFactories(telemetryStore telemetrystore.TelemetryStore, p
|
||||
)
|
||||
}
|
||||
|
||||
func NewAPIServerProviderFactories(orgGetter organization.Getter, authz authz.AuthZ, modules Modules, handlers Handlers, globalConfig global.Config) factory.NamedMap[factory.ProviderFactory[apiserver.APIServer, apiserver.Config]] {
|
||||
func NewAPIServerProviderFactories(orgGetter organization.Getter, authz authz.AuthZ, modules Modules, handlers Handlers) factory.NamedMap[factory.ProviderFactory[apiserver.APIServer, apiserver.Config]] {
|
||||
return factory.MustNewNamedMap(
|
||||
signozapiserver.NewFactory(
|
||||
orgGetter,
|
||||
authz,
|
||||
implorganization.NewHandler(modules.OrgGetter, modules.OrgSetter),
|
||||
impluser.NewHandler(modules.UserSetter, modules.UserGetter),
|
||||
implsession.NewHandler(modules.Session, globalConfig),
|
||||
implsession.NewHandler(modules.Session),
|
||||
implauthdomain.NewHandler(modules.AuthDomain),
|
||||
implpreference.NewHandler(modules.Preference),
|
||||
handlers.Global,
|
||||
|
||||
@@ -95,7 +95,6 @@ func TestNewProviderFactories(t *testing.T) {
|
||||
nil,
|
||||
Modules{},
|
||||
Handlers{},
|
||||
global.Config{},
|
||||
)
|
||||
})
|
||||
}
|
||||
|
||||
@@ -542,7 +542,7 @@ func New(
|
||||
ctx,
|
||||
providerSettings,
|
||||
config.APIServer,
|
||||
NewAPIServerProviderFactories(orgGetter, authz, modules, handlers, config.Global),
|
||||
NewAPIServerProviderFactories(orgGetter, authz, modules, handlers),
|
||||
"signoz",
|
||||
)
|
||||
if err != nil {
|
||||
|
||||
@@ -27,8 +27,6 @@ var (
|
||||
// Azure services.
|
||||
AzureServiceStorageAccountsBlob = ServiceID{valuer.NewString("storageaccountsblob")}
|
||||
AzureServiceCDNProfile = ServiceID{valuer.NewString("cdnprofile")}
|
||||
AzureServiceContainerApp = ServiceID{valuer.NewString("containerapp")}
|
||||
AzureServiceAKS = ServiceID{valuer.NewString("aks")}
|
||||
)
|
||||
|
||||
func (ServiceID) Enum() []any {
|
||||
@@ -48,8 +46,6 @@ func (ServiceID) Enum() []any {
|
||||
AWSServiceSQS,
|
||||
AzureServiceStorageAccountsBlob,
|
||||
AzureServiceCDNProfile,
|
||||
AzureServiceContainerApp,
|
||||
AzureServiceAKS,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -73,8 +69,6 @@ var SupportedServices = map[CloudProviderType][]ServiceID{
|
||||
CloudProviderTypeAzure: {
|
||||
AzureServiceStorageAccountsBlob,
|
||||
AzureServiceCDNProfile,
|
||||
AzureServiceContainerApp,
|
||||
AzureServiceAKS,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
@@ -135,6 +135,9 @@ type seriesLookup struct {
|
||||
data map[string]map[int64]float64
|
||||
// seriesKey -> original series for metadata preservation
|
||||
seriesMetadata map[string]*TimeSeries
|
||||
// maps a variable to its series keys, letting evaluation iterate a single
|
||||
// variable's series directly.
|
||||
variableToSeriesKeys map[string][]string
|
||||
}
|
||||
|
||||
// FormulaEvaluator handles formula evaluation b/w time series from different aggregations
|
||||
@@ -291,34 +294,35 @@ func (fe *FormulaEvaluator) EvaluateFormula(timeSeriesData map[string]*TimeSerie
|
||||
// Find all unique label combinations across referenced series
|
||||
uniqueLabelSets := fe.findUniqueLabelSets(lookup)
|
||||
|
||||
// Process each unique label set
|
||||
var resultSeries []*TimeSeries
|
||||
var wg sync.WaitGroup
|
||||
// Work per label-set is cheap enough that spawning a goroutine per item
|
||||
// costs more in scheduler signaling than it saves in parallelism.
|
||||
const numWorkers = 4
|
||||
workCh := make(chan []*Label, len(uniqueLabelSets))
|
||||
resultChan := make(chan *TimeSeries, len(uniqueLabelSets))
|
||||
maxSeries := make(chan struct{}, 4)
|
||||
|
||||
// For each candidate label set, evaluate the formula expression
|
||||
// and store the result in the resultChan
|
||||
for _, labelSet := range uniqueLabelSets {
|
||||
wg.Add(1)
|
||||
go func(labels []*Label) {
|
||||
var wg sync.WaitGroup
|
||||
wg.Add(numWorkers)
|
||||
for range numWorkers {
|
||||
go func() {
|
||||
defer wg.Done()
|
||||
maxSeries <- struct{}{}
|
||||
defer func() { <-maxSeries }()
|
||||
|
||||
// main workhorse of the formula evaluation
|
||||
series := fe.evaluateForLabelSet(labels, lookup)
|
||||
if series != nil && len(series.Values) > 0 {
|
||||
resultChan <- series
|
||||
for labels := range workCh {
|
||||
series := fe.evaluateForLabelSet(labels, lookup)
|
||||
if series != nil && len(series.Values) > 0 {
|
||||
resultChan <- series
|
||||
}
|
||||
}
|
||||
}(labelSet)
|
||||
}()
|
||||
}
|
||||
|
||||
go func() {
|
||||
wg.Wait()
|
||||
close(resultChan)
|
||||
}()
|
||||
for _, labelSet := range uniqueLabelSets {
|
||||
workCh <- labelSet
|
||||
}
|
||||
close(workCh)
|
||||
|
||||
wg.Wait()
|
||||
close(resultChan)
|
||||
|
||||
resultSeries := make([]*TimeSeries, 0, len(uniqueLabelSets))
|
||||
for series := range resultChan {
|
||||
resultSeries = append(resultSeries, series)
|
||||
}
|
||||
@@ -340,6 +344,8 @@ func (fe *FormulaEvaluator) buildSeriesLookup(timeSeriesData map[string]*TimeSer
|
||||
// when the series is returned to the caller
|
||||
// It's also used for finding matching series for a variable
|
||||
seriesMetadata: make(map[string]*TimeSeries),
|
||||
|
||||
variableToSeriesKeys: make(map[string][]string),
|
||||
}
|
||||
|
||||
for variable, aggRef := range fe.aggRefs {
|
||||
@@ -391,6 +397,7 @@ func (fe *FormulaEvaluator) buildSeriesLookup(timeSeriesData map[string]*TimeSer
|
||||
if _, exists := lookup.data[seriesKey]; !exists {
|
||||
lookup.data[seriesKey] = make(map[int64]float64, len(series.Values))
|
||||
lookup.seriesMetadata[seriesKey] = series
|
||||
lookup.variableToSeriesKeys[variable] = append(lookup.variableToSeriesKeys[variable], seriesKey)
|
||||
}
|
||||
|
||||
// Store all timestamp-value pairs
|
||||
@@ -473,35 +480,37 @@ func (fe *FormulaEvaluator) findUniqueLabelSets(lookup *seriesLookup) [][]*Label
|
||||
|
||||
// Find unique label sets using proper label comparison
|
||||
var uniqueSets [][]*Label
|
||||
var uniqueMaps []map[string]any
|
||||
for _, labelSet := range allLabelSets {
|
||||
isUnique := true
|
||||
for _, uniqueSet := range uniqueSets {
|
||||
if fe.isSubset(uniqueSet, labelSet) {
|
||||
for _, uniqueMap := range uniqueMaps {
|
||||
if isSubset(uniqueMap, labelSet) {
|
||||
isUnique = false
|
||||
break
|
||||
}
|
||||
}
|
||||
if isUnique {
|
||||
uniqueSets = append(uniqueSets, labelSet)
|
||||
uniqueMaps = append(uniqueMaps, labelsToMap(labelSet))
|
||||
}
|
||||
}
|
||||
|
||||
return uniqueSets
|
||||
}
|
||||
|
||||
func (fe *FormulaEvaluator) isSubset(labels1, labels2 []*Label) bool {
|
||||
labelMap1 := make(map[string]any)
|
||||
labelMap2 := make(map[string]any)
|
||||
|
||||
for _, label := range labels1 {
|
||||
labelMap1[label.Key.Name] = label.Value
|
||||
}
|
||||
for _, label := range labels2 {
|
||||
labelMap2[label.Key.Name] = label.Value
|
||||
func labelsToMap(labels []*Label) map[string]any {
|
||||
m := make(map[string]any, len(labels))
|
||||
for _, label := range labels {
|
||||
m[label.Key.Name] = label.Value
|
||||
}
|
||||
return m
|
||||
}
|
||||
|
||||
for k, v := range labelMap2 {
|
||||
if val, ok := labelMap1[k]; !ok || val != v {
|
||||
// isSubset reports whether every label in subset is present with the same value in
|
||||
// supersetMap (i.e. subset ⊆ superset).
|
||||
func isSubset(supersetMap map[string]any, subset []*Label) bool {
|
||||
for _, label := range subset {
|
||||
if val, ok := supersetMap[label.Key.Name]; !ok || val != label.Value {
|
||||
return false
|
||||
}
|
||||
}
|
||||
@@ -517,10 +526,14 @@ func (fe *FormulaEvaluator) evaluateForLabelSet(targetLabels []*Label, lookup *s
|
||||
// for the variable
|
||||
var allTimestamps = make(map[int64]struct{})
|
||||
|
||||
// targetLabels is fixed for this call, so build its lookup once and reuse it
|
||||
// across every series comparison below.
|
||||
targetMap := labelsToMap(targetLabels)
|
||||
|
||||
for variable := range fe.aggRefs {
|
||||
// Find series with matching labels for this variable
|
||||
for seriesKey, series := range lookup.seriesMetadata {
|
||||
if strings.HasPrefix(seriesKey, variable+"|") && fe.isSubset(targetLabels, series.Labels) {
|
||||
// only this variable's series.
|
||||
for _, seriesKey := range lookup.variableToSeriesKeys[variable] {
|
||||
if isSubset(targetMap, lookup.seriesMetadata[seriesKey].Labels) {
|
||||
if timestampData, exists := lookup.data[seriesKey]; exists {
|
||||
variableData[variable] = timestampData
|
||||
// Collect all timestamps
|
||||
@@ -546,8 +559,11 @@ func (fe *FormulaEvaluator) evaluateForLabelSet(targetLabels []*Label, lookup *s
|
||||
}
|
||||
slices.Sort(timestamps)
|
||||
|
||||
// Evaluate formula at each timestamp
|
||||
var resultValues []*TimeSeriesValue
|
||||
// backing slab-allocates all values in one block; resultValues holds interior
|
||||
// pointers into it. Fixed length and never appended to, so it never moves.
|
||||
backing := make([]TimeSeriesValue, len(timestamps))
|
||||
resultValues := make([]*TimeSeriesValue, 0, len(timestamps))
|
||||
n := 0
|
||||
values := fe.valuesPool.Get().(map[string]any)
|
||||
defer fe.valuesPool.Put(values)
|
||||
|
||||
@@ -592,10 +608,12 @@ func (fe *FormulaEvaluator) evaluateForLabelSet(targetLabels []*Label, lookup *s
|
||||
continue
|
||||
}
|
||||
|
||||
resultValues = append(resultValues, &TimeSeriesValue{
|
||||
backing[n] = TimeSeriesValue{
|
||||
Timestamp: timestamp,
|
||||
Value: value,
|
||||
})
|
||||
}
|
||||
resultValues = append(resultValues, &backing[n])
|
||||
n++
|
||||
}
|
||||
|
||||
if len(resultValues) == 0 {
|
||||
|
||||
251
pkg/types/spantypes/aggregation_test.go
Normal file
251
pkg/types/spantypes/aggregation_test.go
Normal file
@@ -0,0 +1,251 @@
|
||||
package spantypes
|
||||
|
||||
import (
|
||||
"testing"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
"github.com/stretchr/testify/assert"
|
||||
)
|
||||
|
||||
// mkASpan builds a WaterfallSpan with timing and field data for analytics tests.
|
||||
func mkASpan(id string, resource map[string]string, attributes map[string]any, startNs, durationNs uint64) *WaterfallSpan {
|
||||
return &WaterfallSpan{
|
||||
SpanID: id,
|
||||
Resource: resource,
|
||||
Attributes: attributes,
|
||||
TimeUnix: startNs,
|
||||
DurationNano: durationNs,
|
||||
Children: make([]*WaterfallSpan, 0),
|
||||
}
|
||||
}
|
||||
|
||||
func buildTraceFromSpans(spans ...*WaterfallSpan) *WaterfallTrace {
|
||||
spanMap := make(map[string]*WaterfallSpan, len(spans))
|
||||
var startTime, endTime uint64
|
||||
initialized := false
|
||||
for _, s := range spans {
|
||||
spanMap[s.SpanID] = s
|
||||
if !initialized || s.TimeUnix < startTime {
|
||||
startTime = s.TimeUnix
|
||||
initialized = true
|
||||
}
|
||||
if end := s.TimeUnix + s.DurationNano; end > endTime {
|
||||
endTime = end
|
||||
}
|
||||
}
|
||||
return NewWaterfallTrace(startTime, endTime, uint64(len(spanMap)), 0, spanMap, nil, false)
|
||||
}
|
||||
|
||||
var (
|
||||
fieldServiceName = telemetrytypes.TelemetryFieldKey{
|
||||
Name: "service.name",
|
||||
FieldContext: telemetrytypes.FieldContextResource,
|
||||
}
|
||||
fieldHTTPMethod = telemetrytypes.TelemetryFieldKey{
|
||||
Name: "http.method",
|
||||
FieldContext: telemetrytypes.FieldContextAttribute,
|
||||
}
|
||||
fieldCached = telemetrytypes.TelemetryFieldKey{
|
||||
Name: "db.cached",
|
||||
FieldContext: telemetrytypes.FieldContextAttribute,
|
||||
}
|
||||
)
|
||||
|
||||
func TestGetSpanAggregation_SpanCount(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
trace *WaterfallTrace
|
||||
field telemetrytypes.TelemetryFieldKey
|
||||
want map[string]uint64
|
||||
}{
|
||||
{
|
||||
name: "counts by resource field",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "frontend"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{"service.name": "frontend"}, nil, 10, 5),
|
||||
mkASpan("s3", map[string]string{"service.name": "backend"}, nil, 20, 8),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"frontend": 2, "backend": 1},
|
||||
},
|
||||
{
|
||||
name: "counts by string attribute field",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", nil, map[string]any{"http.method": "GET"}, 0, 10),
|
||||
mkASpan("s2", nil, map[string]any{"http.method": "POST"}, 10, 5),
|
||||
mkASpan("s3", nil, map[string]any{"http.method": "GET"}, 20, 8),
|
||||
),
|
||||
field: fieldHTTPMethod,
|
||||
want: map[string]uint64{"GET": 2, "POST": 1},
|
||||
},
|
||||
{
|
||||
name: "counts by boolean attribute field",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", nil, map[string]any{"db.cached": true}, 0, 10),
|
||||
mkASpan("s2", nil, map[string]any{"db.cached": false}, 10, 5),
|
||||
mkASpan("s3", nil, map[string]any{"db.cached": true}, 20, 8),
|
||||
),
|
||||
field: fieldCached,
|
||||
want: map[string]uint64{"true": 2, "false": 1},
|
||||
},
|
||||
{
|
||||
name: "spans missing the field are excluded",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "frontend"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{}, nil, 10, 5), // no service.name
|
||||
mkASpan("s3", map[string]string{"service.name": "backend"}, nil, 20, 8),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"frontend": 1, "backend": 1},
|
||||
},
|
||||
{
|
||||
// empty string is a valid field value — counted under the "" key, unlike a missing field
|
||||
name: "span with empty service.name is counted under empty string key",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "frontend"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{"service.name": ""}, nil, 10, 5),
|
||||
mkASpan("s3", map[string]string{"service.name": "backend"}, nil, 20, 8),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"frontend": 1, "backend": 1, "": 1},
|
||||
},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
result := tc.trace.GetSpanAggregation(SpanAggregationSpanCount, tc.field)
|
||||
assert.Equal(t, tc.field, result.Field)
|
||||
assert.Equal(t, SpanAggregationSpanCount, result.Aggregation)
|
||||
assert.Equal(t, tc.want, result.Value)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestGetSpanAggregation_Duration(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
trace *WaterfallTrace
|
||||
field telemetrytypes.TelemetryFieldKey
|
||||
want map[string]uint64
|
||||
}{
|
||||
{
|
||||
name: "non-overlapping spans — merged equals sum",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "frontend"}, nil, 0, 100),
|
||||
mkASpan("s2", map[string]string{"service.name": "frontend"}, nil, 100, 50),
|
||||
mkASpan("s3", map[string]string{"service.name": "backend"}, nil, 0, 80),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"frontend": 150, "backend": 80},
|
||||
},
|
||||
{
|
||||
name: "non-overlapping attribute groups — merged equals sum",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", nil, map[string]any{"http.method": "GET"}, 0, 30),
|
||||
mkASpan("s2", nil, map[string]any{"http.method": "GET"}, 50, 20),
|
||||
mkASpan("s3", nil, map[string]any{"http.method": "POST"}, 0, 70),
|
||||
),
|
||||
field: fieldHTTPMethod,
|
||||
want: map[string]uint64{"GET": 50, "POST": 70},
|
||||
},
|
||||
{
|
||||
name: "overlapping spans — non-overlapping interval merge",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "svc"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{"service.name": "svc"}, nil, 5, 10),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"svc": 15}, // [0,10] ∪ [5,15] = [0,15]
|
||||
},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
result := tc.trace.GetSpanAggregation(SpanAggregationDuration, tc.field)
|
||||
assert.Equal(t, tc.field, result.Field)
|
||||
assert.Equal(t, SpanAggregationDuration, result.Aggregation)
|
||||
assert.Equal(t, tc.want, result.Value)
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
func TestGetSpanAggregation_ExecutionTimePercentage(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
trace *WaterfallTrace
|
||||
field telemetrytypes.TelemetryFieldKey
|
||||
want map[string]uint64
|
||||
}{
|
||||
{
|
||||
// trace [0,30]: svc occupies [0,10]+[20,30]=20 → 20*100/30 = 66%
|
||||
name: "non-overlapping spans",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "svc"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{"service.name": "svc"}, nil, 20, 10),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"svc": 66},
|
||||
},
|
||||
{
|
||||
// trace [0,15]: svc [0,15]=15 → 100%
|
||||
name: "partially overlapping spans",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "svc"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{"service.name": "svc"}, nil, 5, 10),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"svc": 100},
|
||||
},
|
||||
{
|
||||
// trace [0,20]: outer absorbs inner → 100%
|
||||
name: "fully contained span",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("outer", map[string]string{"service.name": "svc"}, nil, 0, 20),
|
||||
mkASpan("inner", map[string]string{"service.name": "svc"}, nil, 5, 5),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"svc": 100},
|
||||
},
|
||||
{
|
||||
// trace [0,30]: svc [0,15]+[20,30]=25 → 25*100/30 = 83%
|
||||
name: "three spans with two merges",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "svc"}, nil, 0, 10),
|
||||
mkASpan("s2", map[string]string{"service.name": "svc"}, nil, 5, 10),
|
||||
mkASpan("s3", map[string]string{"service.name": "svc"}, nil, 20, 10),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"svc": 83},
|
||||
},
|
||||
{
|
||||
// trace [0,28]: frontend [0,15]=15 → 53%, backend [0,5]+[20,28]=13 → 46%
|
||||
name: "independent groups are computed separately",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("a1", map[string]string{"service.name": "frontend"}, nil, 0, 10),
|
||||
mkASpan("a2", map[string]string{"service.name": "frontend"}, nil, 5, 10),
|
||||
mkASpan("b1", map[string]string{"service.name": "backend"}, nil, 0, 5),
|
||||
mkASpan("b2", map[string]string{"service.name": "backend"}, nil, 20, 8),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"frontend": 53, "backend": 46},
|
||||
},
|
||||
{
|
||||
// trace [100,150]: svc [100,150]=50 → 100%
|
||||
name: "single span",
|
||||
trace: buildTraceFromSpans(
|
||||
mkASpan("s1", map[string]string{"service.name": "svc"}, nil, 100, 50),
|
||||
),
|
||||
field: fieldServiceName,
|
||||
want: map[string]uint64{"svc": 100},
|
||||
},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
result := tc.trace.GetSpanAggregation(SpanAggregationExecutionTimePercentage, tc.field)
|
||||
assert.Equal(t, tc.field, result.Field)
|
||||
assert.Equal(t, SpanAggregationExecutionTimePercentage, result.Aggregation)
|
||||
assert.Equal(t, tc.want, result.Value)
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -1,102 +0,0 @@
|
||||
package spantypes
|
||||
|
||||
import (
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
)
|
||||
|
||||
type FlamegraphSpan struct {
|
||||
SpanID string `json:"spanId" required:"true"`
|
||||
ParentSpanID string `json:"parentSpanId" required:"true"`
|
||||
Timestamp uint64 `json:"timestamp" required:"true"`
|
||||
DurationNano uint64 `json:"durationNano" required:"true"`
|
||||
HasError bool `json:"hasError" required:"true"`
|
||||
Name string `json:"name" required:"true"`
|
||||
Level int64 `json:"level" required:"true"`
|
||||
Events []Event `json:"event" required:"true" nullable:"false"`
|
||||
Attributes map[string]any `json:"attributes" required:"true" nullable:"false"`
|
||||
Resource map[string]string `json:"resource" required:"true" nullable:"false"`
|
||||
Children []*FlamegraphSpan `json:"-"` // internal tree use only
|
||||
}
|
||||
|
||||
// FlamegraphLevel groups span IDs at a single level within the selected window.
|
||||
type FlamegraphLevel struct {
|
||||
Level int64
|
||||
SpanIDs []string
|
||||
}
|
||||
|
||||
type PostableFlamegraph struct {
|
||||
SelectedSpanID string `json:"selectedSpanId"`
|
||||
SelectFields []telemetrytypes.TelemetryFieldKey `json:"selectFields,omitempty"`
|
||||
}
|
||||
|
||||
// GettableFlamegraphTrace is the response for the v3 flamegraph API.
|
||||
type GettableFlamegraphTrace struct {
|
||||
Spans [][]*FlamegraphSpan `json:"spans" required:"true" nullable:"false"`
|
||||
StartTimestampMillis int64 `json:"startTimestampMillis" required:"true"`
|
||||
EndTimestampMillis int64 `json:"endTimestampMillis" required:"true"`
|
||||
HasMore bool `json:"hasMore" required:"true"`
|
||||
}
|
||||
|
||||
func NewGettableFlamegraphTrace(spans [][]*FlamegraphSpan, startMs, endMs int64, hasMore bool) *GettableFlamegraphTrace {
|
||||
return &GettableFlamegraphTrace{
|
||||
Spans: spans,
|
||||
StartTimestampMillis: startMs,
|
||||
EndTimestampMillis: endMs,
|
||||
HasMore: hasMore,
|
||||
}
|
||||
}
|
||||
|
||||
func NewFlamegraphSpanFromStorable(s *StorableSpan, level int64, selectFields []telemetrytypes.TelemetryFieldKey) *FlamegraphSpan {
|
||||
span := &FlamegraphSpan{
|
||||
SpanID: s.SpanID,
|
||||
ParentSpanID: s.ParentSpanID,
|
||||
Timestamp: uint64(s.StartTime.UnixNano()),
|
||||
DurationNano: s.DurationNano,
|
||||
HasError: s.HasError,
|
||||
Name: s.Name,
|
||||
Level: level,
|
||||
Events: s.UnmarshalledEvents(),
|
||||
Attributes: make(map[string]any),
|
||||
Resource: make(map[string]string),
|
||||
}
|
||||
if len(selectFields) == 0 {
|
||||
return span
|
||||
}
|
||||
for _, field := range selectFields {
|
||||
switch field.FieldContext {
|
||||
case telemetrytypes.FieldContextResource:
|
||||
if v, ok := s.ResourcesString[field.Name]; ok && v != "" {
|
||||
span.Resource[field.Name] = v
|
||||
}
|
||||
case telemetrytypes.FieldContextAttribute:
|
||||
if v := s.AttributeValue(field.Name); v != nil {
|
||||
span.Attributes[field.Name] = v
|
||||
}
|
||||
}
|
||||
}
|
||||
return span
|
||||
}
|
||||
|
||||
func NewMissingParentFlamegraphSpan(node *FlamegraphSpan) *FlamegraphSpan {
|
||||
return &FlamegraphSpan{
|
||||
SpanID: node.ParentSpanID,
|
||||
Name: "Missing Span",
|
||||
Timestamp: node.Timestamp,
|
||||
DurationNano: node.DurationNano,
|
||||
Events: []Event{},
|
||||
Children: []*FlamegraphSpan{node},
|
||||
}
|
||||
}
|
||||
|
||||
// FlamegraphWindowSpanIDs collects all span IDs from a level window into a flat slice.
|
||||
func FlamegraphWindowSpanIDs(window []FlamegraphLevel) []string {
|
||||
total := 0
|
||||
for _, lvl := range window {
|
||||
total += len(lvl.SpanIDs)
|
||||
}
|
||||
ids := make([]string, 0, total)
|
||||
for _, lvl := range window {
|
||||
ids = append(ids, lvl.SpanIDs...)
|
||||
}
|
||||
return ids
|
||||
}
|
||||
@@ -1,225 +0,0 @@
|
||||
package spantypes
|
||||
|
||||
import (
|
||||
"sort"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
)
|
||||
|
||||
// FlamegraphTrace holds the level wise tree built from minimal spans.
|
||||
type FlamegraphTrace struct {
|
||||
roots []*FlamegraphSpan
|
||||
nodeByID map[string]*FlamegraphSpan
|
||||
startTime uint64
|
||||
endTime uint64
|
||||
}
|
||||
|
||||
func NewFlamegraphTraceFromMinimal(spans []MinimalSpan) *FlamegraphTrace {
|
||||
t := &FlamegraphTrace{
|
||||
nodeByID: make(map[string]*FlamegraphSpan, len(spans)),
|
||||
}
|
||||
for i := range spans {
|
||||
node := spans[i].ToFlamegraphSpan()
|
||||
t.updateTimeRange(node.Timestamp, node.DurationNano)
|
||||
t.nodeByID[node.SpanID] = node
|
||||
}
|
||||
t.buildSpanTree()
|
||||
return t
|
||||
}
|
||||
|
||||
func NewFlamegraphTraceFromStorable(spans []StorableSpan, selectFields []telemetrytypes.TelemetryFieldKey) *FlamegraphTrace {
|
||||
t := &FlamegraphTrace{
|
||||
nodeByID: make(map[string]*FlamegraphSpan, len(spans)),
|
||||
}
|
||||
for i := range spans {
|
||||
node := NewFlamegraphSpanFromStorable(&spans[i], 0, selectFields) // level is set later by BFS
|
||||
t.updateTimeRange(node.Timestamp, node.DurationNano)
|
||||
t.nodeByID[node.SpanID] = node
|
||||
}
|
||||
t.buildSpanTree()
|
||||
return t
|
||||
}
|
||||
|
||||
// GetAllLevels return level wise spans using BFS on trace.
|
||||
func (t *FlamegraphTrace) GetAllLevels() [][]*FlamegraphSpan {
|
||||
var result [][]*FlamegraphSpan
|
||||
for _, root := range t.roots {
|
||||
currentLevel := []*FlamegraphSpan{root}
|
||||
for depth := int64(0); len(currentLevel) > 0; depth++ {
|
||||
var nextLevel []*FlamegraphSpan
|
||||
for _, node := range currentLevel {
|
||||
node.Level = depth
|
||||
nextLevel = append(nextLevel, node.Children...)
|
||||
node.Children = nil // release tree reference
|
||||
}
|
||||
result = append(result, currentLevel)
|
||||
currentLevel = nextLevel
|
||||
}
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
// GetSelectedLevels returns the window of levels around selectedSpanID with sampling applied to dense levels.
|
||||
func (t *FlamegraphTrace) GetSelectedLevels(selectedSpanID string, levelLimit, spansPerLevel, topLatencyCount, bucketCount int) []FlamegraphLevel {
|
||||
allLevels := t.GetAllLevels()
|
||||
|
||||
selectedIndex := getLevelIndex(allLevels, selectedSpanID)
|
||||
|
||||
// 40% window above level with selected span and 60% below that
|
||||
beforeSelectedLevel := int(float64(levelLimit) * 0.4)
|
||||
startLevel := max(0, selectedIndex-beforeSelectedLevel)
|
||||
endLevel := min(len(allLevels), startLevel+levelLimit)
|
||||
startLevel = max(0, endLevel-levelLimit) // utilize the page size if endLevel is clamped
|
||||
|
||||
result := make([]FlamegraphLevel, 0, endLevel-startLevel)
|
||||
for i := startLevel; i < endLevel; i++ {
|
||||
spans := allLevels[i]
|
||||
sampled := spans
|
||||
if len(spans) > spansPerLevel {
|
||||
sampled = t.sampleLevel(spans, selectedSpanID, i == selectedIndex, topLatencyCount, bucketCount)
|
||||
}
|
||||
if len(sampled) == 0 {
|
||||
continue
|
||||
}
|
||||
spanIDs := make([]string, len(sampled))
|
||||
for j, s := range sampled {
|
||||
spanIDs[j] = s.SpanID
|
||||
}
|
||||
result = append(result, FlamegraphLevel{Level: spans[0].Level, SpanIDs: spanIDs})
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
func (t *FlamegraphTrace) EnrichSelectedSpans(selectedSpans []FlamegraphLevel, fullSpans []StorableSpan, selectFields []telemetrytypes.TelemetryFieldKey) [][]*FlamegraphSpan {
|
||||
fullByID := make(map[string]*StorableSpan, len(fullSpans))
|
||||
for i := range fullSpans {
|
||||
fullByID[fullSpans[i].SpanID] = &fullSpans[i]
|
||||
}
|
||||
|
||||
result := make([][]*FlamegraphSpan, len(selectedSpans))
|
||||
for i, lvl := range selectedSpans {
|
||||
result[i] = make([]*FlamegraphSpan, 0, len(lvl.SpanIDs))
|
||||
for _, spanID := range lvl.SpanIDs {
|
||||
if full, ok := fullByID[spanID]; ok {
|
||||
result[i] = append(result[i], NewFlamegraphSpanFromStorable(full, lvl.Level, selectFields))
|
||||
} else if lean, ok := t.nodeByID[spanID]; ok {
|
||||
result[i] = append(result[i], lean)
|
||||
}
|
||||
}
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
func (t *FlamegraphTrace) updateTimeRange(timestamp, durationNano uint64) {
|
||||
if t.startTime == 0 || timestamp < t.startTime {
|
||||
t.startTime = timestamp
|
||||
}
|
||||
if end := timestamp + durationNano; end > t.endTime {
|
||||
t.endTime = end
|
||||
}
|
||||
}
|
||||
|
||||
func (t *FlamegraphTrace) buildSpanTree() {
|
||||
for _, node := range t.nodeByID {
|
||||
if node.ParentSpanID != "" {
|
||||
if parent, ok := t.nodeByID[node.ParentSpanID]; ok {
|
||||
parent.Children = append(parent.Children, node)
|
||||
} else {
|
||||
missing := NewMissingParentFlamegraphSpan(node)
|
||||
t.nodeByID[missing.SpanID] = missing
|
||||
t.roots = append(t.roots, missing)
|
||||
}
|
||||
} else if flamegraphSpanIndex(t.roots, node.SpanID) == -1 {
|
||||
t.roots = append(t.roots, node)
|
||||
}
|
||||
}
|
||||
|
||||
sort.Slice(t.roots, func(i, j int) bool {
|
||||
if t.roots[i].Timestamp == t.roots[j].Timestamp {
|
||||
return t.roots[i].SpanID < t.roots[j].SpanID
|
||||
}
|
||||
return t.roots[i].Timestamp < t.roots[j].Timestamp
|
||||
})
|
||||
}
|
||||
|
||||
func (t *FlamegraphTrace) sampleLevel(spans []*FlamegraphSpan, selectedSpanID string, isSelectedLevel bool, topLatencyCount, bucketCount int) []*FlamegraphSpan {
|
||||
sorted := make([]*FlamegraphSpan, len(spans))
|
||||
copy(sorted, spans)
|
||||
sort.Slice(sorted, func(i, j int) bool {
|
||||
return sorted[i].DurationNano > sorted[j].DurationNano
|
||||
})
|
||||
|
||||
sampled := make(map[string]*FlamegraphSpan, topLatencyCount+1)
|
||||
|
||||
topK := min(topLatencyCount, len(sorted))
|
||||
for _, span := range sorted[:topK] {
|
||||
sampled[span.SpanID] = span
|
||||
}
|
||||
|
||||
// include the selected span.
|
||||
if isSelectedLevel {
|
||||
for _, span := range sorted {
|
||||
if span.SpanID == selectedSpanID {
|
||||
sampled[span.SpanID] = span
|
||||
break
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
for _, span := range t.bucketSampleSpans(sorted, bucketCount, sampled) {
|
||||
sampled[span.SpanID] = span
|
||||
}
|
||||
|
||||
result := make([]*FlamegraphSpan, 0, len(sampled))
|
||||
for _, span := range sampled {
|
||||
result = append(result, span)
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
func (t *FlamegraphTrace) bucketSampleSpans(sorted []*FlamegraphSpan, bucketCount int, exclude map[string]*FlamegraphSpan) []*FlamegraphSpan {
|
||||
bucketSize := (t.endTime - t.startTime) / uint64(bucketCount)
|
||||
if bucketSize == 0 {
|
||||
bucketSize = 1
|
||||
}
|
||||
buckets := make([][]*FlamegraphSpan, bucketCount)
|
||||
for _, span := range sorted {
|
||||
if _, ok := exclude[span.SpanID]; ok {
|
||||
continue
|
||||
}
|
||||
if span.Timestamp < t.startTime || span.Timestamp > t.endTime {
|
||||
continue
|
||||
}
|
||||
idx := min(int((span.Timestamp-t.startTime)/bucketSize), bucketCount-1)
|
||||
if len(buckets[idx]) < 2 {
|
||||
buckets[idx] = append(buckets[idx], span)
|
||||
}
|
||||
}
|
||||
var result []*FlamegraphSpan
|
||||
for _, bucket := range buckets {
|
||||
result = append(result, bucket...)
|
||||
}
|
||||
return result
|
||||
}
|
||||
|
||||
func getLevelIndex(levels [][]*FlamegraphSpan, spanID string) int {
|
||||
for i, lvl := range levels {
|
||||
for _, span := range lvl {
|
||||
if span.SpanID == spanID {
|
||||
return i
|
||||
}
|
||||
}
|
||||
}
|
||||
return 0
|
||||
}
|
||||
|
||||
func flamegraphSpanIndex(spans []*FlamegraphSpan, spanID string) int {
|
||||
for i, s := range spans {
|
||||
if s.SpanID == spanID {
|
||||
return i
|
||||
}
|
||||
}
|
||||
return -1
|
||||
}
|
||||
@@ -1,270 +0,0 @@
|
||||
package spantypes
|
||||
|
||||
import (
|
||||
"fmt"
|
||||
"testing"
|
||||
"time"
|
||||
|
||||
"github.com/stretchr/testify/assert"
|
||||
)
|
||||
|
||||
// mkMinimal builds a MinimalSpan. timestamp and duration are in nanoseconds.
|
||||
func mkMinimal(id, parentID string, timestamp, duration uint64) MinimalSpan {
|
||||
return MinimalSpan{
|
||||
SpanID: id,
|
||||
ParentSpanID: parentID,
|
||||
StartTime: time.Unix(0, int64(timestamp)),
|
||||
DurationNano: duration,
|
||||
}
|
||||
}
|
||||
|
||||
// levelIDs extracts span IDs level-by-level from GetAllLevels output.
|
||||
func levelIDs(levels [][]*FlamegraphSpan) [][]string {
|
||||
out := make([][]string, len(levels))
|
||||
for i, lvl := range levels {
|
||||
ids := make([]string, len(lvl))
|
||||
for j, s := range lvl {
|
||||
ids[j] = s.SpanID
|
||||
}
|
||||
out[i] = ids
|
||||
}
|
||||
return out
|
||||
}
|
||||
|
||||
// makeChainFG builds a linear trace of n spans: span0 → span1 → … → span(n-1).
|
||||
func makeChainFG(n int) *FlamegraphTrace {
|
||||
spans := make([]MinimalSpan, n)
|
||||
for i := range n {
|
||||
parent := ""
|
||||
if i > 0 {
|
||||
parent = fmt.Sprintf("span%d", i-1)
|
||||
}
|
||||
spans[i] = mkMinimal(fmt.Sprintf("span%d", i), parent, uint64(i*100), 100)
|
||||
}
|
||||
return NewFlamegraphTraceFromMinimal(spans)
|
||||
}
|
||||
|
||||
// makeBroadTrace builds a trace: root → N children (wide, one level deep).
|
||||
func makeBroadTrace(n int) *FlamegraphTrace {
|
||||
spans := make([]MinimalSpan, n+1)
|
||||
spans[0] = mkMinimal("root", "", 0, uint64(n*100))
|
||||
for i := range n {
|
||||
spans[i+1] = mkMinimal(fmt.Sprintf("child%d", i), "root", uint64(i*100), 100)
|
||||
}
|
||||
return NewFlamegraphTraceFromMinimal(spans)
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
// buildSpanTree / GetAllLevels
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
func TestGetAllLevels(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
spans []MinimalSpan
|
||||
check func(t *testing.T, levels [][]*FlamegraphSpan)
|
||||
}{
|
||||
{
|
||||
// root → child → grandchild: three levels, one span each.
|
||||
name: "linear_chain",
|
||||
spans: []MinimalSpan{
|
||||
mkMinimal("root", "", 100, 300),
|
||||
mkMinimal("child", "root", 150, 200),
|
||||
mkMinimal("grandchild", "child", 200, 100),
|
||||
},
|
||||
check: func(t *testing.T, levels [][]*FlamegraphSpan) {
|
||||
assert.Equal(t, [][]string{{"root"}, {"child"}, {"grandchild"}}, levelIDs(levels))
|
||||
},
|
||||
},
|
||||
{
|
||||
// root → [A, B]: level 0 has root, level 1 has both children.
|
||||
name: "two_siblings",
|
||||
spans: []MinimalSpan{
|
||||
mkMinimal("root", "", 100, 300),
|
||||
mkMinimal("A", "root", 150, 100),
|
||||
mkMinimal("B", "root", 200, 100),
|
||||
},
|
||||
check: func(t *testing.T, levels [][]*FlamegraphSpan) {
|
||||
assert.Equal(t, []string{"root"}, levelIDs(levels)[0])
|
||||
assert.ElementsMatch(t, []string{"A", "B"}, levelIDs(levels)[1])
|
||||
},
|
||||
},
|
||||
{
|
||||
// child references a non-existent parent → synthetic "Missing Span" root at level 0.
|
||||
name: "missing_parent",
|
||||
spans: []MinimalSpan{
|
||||
mkMinimal("child", "ghost", 100, 100),
|
||||
},
|
||||
check: func(t *testing.T, levels [][]*FlamegraphSpan) {
|
||||
assert.Len(t, levels, 2)
|
||||
assert.Equal(t, "ghost", levels[0][0].SpanID)
|
||||
assert.Equal(t, "Missing Span", levels[0][0].Name)
|
||||
assert.Equal(t, "child", levels[1][0].SpanID)
|
||||
},
|
||||
},
|
||||
{
|
||||
// Children must be nil after BFS so the tree does not stay live in memory.
|
||||
name: "children_nilled_after_bfs",
|
||||
spans: []MinimalSpan{
|
||||
mkMinimal("root", "", 100, 200),
|
||||
mkMinimal("child", "root", 150, 100),
|
||||
},
|
||||
check: func(t *testing.T, levels [][]*FlamegraphSpan) {
|
||||
for _, lvl := range levels {
|
||||
for _, s := range lvl {
|
||||
assert.Nil(t, s.Children)
|
||||
}
|
||||
}
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
tc.check(t, NewFlamegraphTraceFromMinimal(tc.spans).GetAllLevels())
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
// GetSelectedLevels
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
|
||||
func TestGetSelectedLevels(t *testing.T) {
|
||||
tests := []struct {
|
||||
name string
|
||||
buildTrace func() *FlamegraphTrace
|
||||
selectedSpan string
|
||||
levelLimit int
|
||||
spansPerLevel int
|
||||
topK int
|
||||
bucketCount int
|
||||
check func(t *testing.T, levels []FlamegraphLevel)
|
||||
}{
|
||||
{
|
||||
// Middle: 40% above (20 levels) + 60% below (30 levels) = 50 total.
|
||||
name: "window_middle",
|
||||
buildTrace: func() *FlamegraphTrace { return makeChainFG(100) },
|
||||
selectedSpan: "span50",
|
||||
levelLimit: 50,
|
||||
spansPerLevel: 1000,
|
||||
topK: 5,
|
||||
bucketCount: 50,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
assert.Equal(t, 50, len(levels))
|
||||
assert.Equal(t, "span30", levels[0].SpanIDs[0])
|
||||
},
|
||||
},
|
||||
{
|
||||
// Near start: clamp at 0, redistribute budget downward — still 50 levels.
|
||||
name: "window_near_start",
|
||||
buildTrace: func() *FlamegraphTrace { return makeChainFG(100) },
|
||||
selectedSpan: "span5",
|
||||
levelLimit: 50,
|
||||
spansPerLevel: 1000,
|
||||
topK: 5,
|
||||
bucketCount: 50,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
assert.Equal(t, 50, len(levels))
|
||||
assert.Equal(t, "span0", levels[0].SpanIDs[0])
|
||||
},
|
||||
},
|
||||
{
|
||||
// Near end: clamp at total, redistribute budget upward — still 50 levels.
|
||||
name: "window_near_end",
|
||||
buildTrace: func() *FlamegraphTrace { return makeChainFG(100) },
|
||||
selectedSpan: "span95",
|
||||
levelLimit: 50,
|
||||
spansPerLevel: 1000,
|
||||
topK: 5,
|
||||
bucketCount: 50,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
assert.Equal(t, 50, len(levels))
|
||||
assert.Equal(t, "span50", levels[0].SpanIDs[0])
|
||||
},
|
||||
},
|
||||
{
|
||||
// Unknown span ID falls back to level 0.
|
||||
name: "unknown_span_defaults_to_level_zero",
|
||||
buildTrace: func() *FlamegraphTrace { return makeChainFG(10) },
|
||||
selectedSpan: "nonexistent",
|
||||
levelLimit: 5,
|
||||
spansPerLevel: 1000,
|
||||
topK: 5,
|
||||
bucketCount: 50,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
assert.Equal(t, "span0", levels[0].SpanIDs[0])
|
||||
},
|
||||
},
|
||||
{
|
||||
// Dense levels are sampled down to approximately spansPerLevel.
|
||||
name: "sampling_respects_cap",
|
||||
buildTrace: func() *FlamegraphTrace { return makeBroadTrace(200) },
|
||||
selectedSpan: "root",
|
||||
levelLimit: 10,
|
||||
spansPerLevel: 10,
|
||||
topK: 3,
|
||||
bucketCount: 5,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
for _, lvl := range levels {
|
||||
assert.LessOrEqual(t, len(lvl.SpanIDs), 10+3+5*2)
|
||||
}
|
||||
},
|
||||
},
|
||||
{
|
||||
// Selected span always survives sampling even when not in topK.
|
||||
name: "selected_span_always_included",
|
||||
buildTrace: func() *FlamegraphTrace { return makeBroadTrace(200) },
|
||||
selectedSpan: "child99",
|
||||
levelLimit: 10,
|
||||
spansPerLevel: 5,
|
||||
topK: 3,
|
||||
bucketCount: 5,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
found := false
|
||||
for _, lvl := range levels {
|
||||
for _, id := range lvl.SpanIDs {
|
||||
if id == "child99" {
|
||||
found = true
|
||||
}
|
||||
}
|
||||
}
|
||||
assert.True(t, found, "selected span must survive sampling")
|
||||
},
|
||||
},
|
||||
{
|
||||
// Selected span is also the highest-latency span (lands in topK) — must not appear twice.
|
||||
name: "no_duplicate_span_ids",
|
||||
buildTrace: func() *FlamegraphTrace {
|
||||
spans := make([]MinimalSpan, 201)
|
||||
spans[0] = mkMinimal("root", "", 0, 10000)
|
||||
for i := range 200 {
|
||||
spans[i+1] = mkMinimal(fmt.Sprintf("child%d", i), "root", uint64(i*50), uint64(200-i)*10)
|
||||
}
|
||||
return NewFlamegraphTraceFromMinimal(spans)
|
||||
},
|
||||
selectedSpan: "child0",
|
||||
levelLimit: 10,
|
||||
spansPerLevel: 5,
|
||||
topK: 3,
|
||||
bucketCount: 10,
|
||||
check: func(t *testing.T, levels []FlamegraphLevel) {
|
||||
for _, lvl := range levels {
|
||||
seen := map[string]bool{}
|
||||
for _, id := range lvl.SpanIDs {
|
||||
assert.False(t, seen[id], "duplicate span ID %q at level %d", id, lvl.Level)
|
||||
seen[id] = true
|
||||
}
|
||||
}
|
||||
},
|
||||
},
|
||||
}
|
||||
|
||||
for _, tc := range tests {
|
||||
t.Run(tc.name, func(t *testing.T) {
|
||||
// fresh trace per subtest: GetAllLevels is destructive (nils Children)
|
||||
levels := tc.buildTrace().GetSelectedLevels(tc.selectedSpan, tc.levelLimit, tc.spansPerLevel, tc.topK, tc.bucketCount)
|
||||
tc.check(t, levels)
|
||||
})
|
||||
}
|
||||
}
|
||||
@@ -30,7 +30,6 @@ type TraceStore interface {
|
||||
GetTraceSpans(ctx context.Context, traceID string, summary *TraceSummary) ([]StorableSpan, error)
|
||||
GetMinimalSpans(ctx context.Context, traceID string, start, end time.Time) ([]MinimalSpan, error)
|
||||
GetTraceSpansByIDs(ctx context.Context, traceID string, start, end time.Time, spanIDs []string) ([]StorableSpan, error)
|
||||
GetFlamegraphSpans(ctx context.Context, traceID string, start, end time.Time, spanIDs []string) ([]StorableSpan, error)
|
||||
|
||||
GetSpanCountByField(ctx context.Context, traceID string, summary *TraceSummary, fieldKey telemetrytypes.TelemetryFieldKey) (map[string]uint64, error)
|
||||
GetSpanDurationByField(ctx context.Context, traceID string, summary *TraceSummary, fieldKey telemetrytypes.TelemetryFieldKey) (map[string]uint64, error)
|
||||
|
||||
@@ -21,13 +21,28 @@ const (
|
||||
// ErrTraceNotFound is returned when a trace ID has no matching spans in ClickHouse.
|
||||
var ErrTraceNotFound = errors.NewNotFoundf(errors.CodeNotFound, "trace not found")
|
||||
|
||||
// PostableWaterfall is the request body for the waterfall API.
|
||||
// PostableWaterfall is the request body for the v3 waterfall API.
|
||||
type PostableWaterfall struct {
|
||||
SelectedSpanID string `json:"selectedSpanId"`
|
||||
UncollapsedSpans []string `json:"uncollapsedSpans"`
|
||||
SelectedSpanID string `json:"selectedSpanId"`
|
||||
UncollapsedSpans []string `json:"uncollapsedSpans"`
|
||||
Limit uint `json:"limit"`
|
||||
Aggregations []SpanAggregation `json:"aggregations"`
|
||||
}
|
||||
|
||||
func (p *PostableWaterfall) Validate() error {
|
||||
if len(p.Aggregations) > maxAggregationItems {
|
||||
return ErrTooManyAggregationItems
|
||||
}
|
||||
for _, a := range p.Aggregations {
|
||||
if !a.Aggregation.isValid() {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "unknown aggregation type: %q", a.Aggregation)
|
||||
}
|
||||
fc := a.Field.FieldContext
|
||||
if fc != telemetrytypes.FieldContextResource && fc != telemetrytypes.FieldContextAttribute {
|
||||
return errors.NewInvalidInputf(errors.CodeInvalidInput, "aggregation field context must be %q or %q, got %q",
|
||||
telemetrytypes.FieldContextResource, telemetrytypes.FieldContextAttribute, fc)
|
||||
}
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
@@ -149,17 +164,6 @@ func (item *MinimalSpan) ToWaterfallSpan(traceID string) *WaterfallSpan {
|
||||
}
|
||||
}
|
||||
|
||||
func (item *MinimalSpan) ToFlamegraphSpan() *FlamegraphSpan {
|
||||
return &FlamegraphSpan{
|
||||
SpanID: item.SpanID,
|
||||
ParentSpanID: item.ParentSpanID,
|
||||
Timestamp: uint64(item.StartTime.UnixNano()),
|
||||
DurationNano: item.DurationNano,
|
||||
HasError: item.HasError,
|
||||
Children: make([]*FlamegraphSpan, 0),
|
||||
}
|
||||
}
|
||||
|
||||
// NewMissingWaterfallSpan creates a synthetic placeholder span for a parent that has no recorded data.
|
||||
func NewMissingWaterfallSpan(spanID, traceID string, timeUnixNano, durationNano uint64) *WaterfallSpan {
|
||||
return &WaterfallSpan{
|
||||
@@ -263,19 +267,6 @@ func (ws *WaterfallSpan) getPathToSelectedSpanID(selectedSpanID string) ([]strin
|
||||
return nil, false
|
||||
}
|
||||
|
||||
func (item *StorableSpan) AttributeValue(name string) any {
|
||||
if v, ok := item.AttributesString[name]; ok {
|
||||
return v
|
||||
}
|
||||
if v, ok := item.AttributesNumber[name]; ok {
|
||||
return v
|
||||
}
|
||||
if v, ok := item.AttributesBool[name]; ok {
|
||||
return v
|
||||
}
|
||||
return nil
|
||||
}
|
||||
|
||||
func (item *StorableSpan) Attributes() map[string]any {
|
||||
attributes := make(map[string]any, len(item.AttributesString)+len(item.AttributesNumber)+len(item.AttributesBool))
|
||||
for k, v := range item.AttributesString {
|
||||
@@ -305,7 +296,7 @@ func (item *StorableSpan) UnmarshalledEvents() []Event {
|
||||
func (item *StorableSpan) UnmarshalledRefs() []OtelSpanRef {
|
||||
refs := []OtelSpanRef{}
|
||||
if err := json.Unmarshal([]byte(item.References), &refs); err != nil {
|
||||
return []OtelSpanRef{} // skip malformed values
|
||||
return nil // skip malformed values
|
||||
}
|
||||
return refs
|
||||
}
|
||||
|
||||
@@ -8,6 +8,7 @@ import (
|
||||
"time"
|
||||
|
||||
"github.com/SigNoz/signoz/pkg/types/cachetypes"
|
||||
"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
|
||||
)
|
||||
|
||||
type TraceSummary struct {
|
||||
@@ -28,18 +29,19 @@ type WaterfallTrace struct {
|
||||
HasMissingSpans bool `json:"hasMissingSpans"`
|
||||
}
|
||||
|
||||
// GettableWaterfallTrace is the response for the waterfall API.
|
||||
// GettableWaterfallTrace is the response for the v3 waterfall API.
|
||||
type GettableWaterfallTrace struct {
|
||||
StartTimestampMillis uint64 `json:"startTimestampMillis"`
|
||||
EndTimestampMillis uint64 `json:"endTimestampMillis"`
|
||||
RootServiceName string `json:"rootServiceName"`
|
||||
RootServiceEntryPoint string `json:"rootServiceEntryPoint"`
|
||||
TotalSpansCount uint64 `json:"totalSpansCount"`
|
||||
TotalErrorSpansCount uint64 `json:"totalErrorSpansCount"`
|
||||
Spans []*WaterfallSpan `json:"spans"`
|
||||
HasMissingSpans bool `json:"hasMissingSpans"`
|
||||
UncollapsedSpans []string `json:"uncollapsedSpans"`
|
||||
HasMore bool `json:"hasMore"`
|
||||
StartTimestampMillis uint64 `json:"startTimestampMillis"`
|
||||
EndTimestampMillis uint64 `json:"endTimestampMillis"`
|
||||
RootServiceName string `json:"rootServiceName"`
|
||||
RootServiceEntryPoint string `json:"rootServiceEntryPoint"`
|
||||
TotalSpansCount uint64 `json:"totalSpansCount"`
|
||||
TotalErrorSpansCount uint64 `json:"totalErrorSpansCount"`
|
||||
Spans []*WaterfallSpan `json:"spans"`
|
||||
HasMissingSpans bool `json:"hasMissingSpans"`
|
||||
UncollapsedSpans []string `json:"uncollapsedSpans"`
|
||||
HasMore bool `json:"hasMore"`
|
||||
Aggregations []SpanAggregationResult `json:"aggregations"`
|
||||
}
|
||||
|
||||
// NewWaterfallTrace constructs a WaterfallTrace from processed span data.
|
||||
@@ -120,6 +122,23 @@ func NewWaterfallTraceFromSpans(nodes []*WaterfallSpan) *WaterfallTrace {
|
||||
)
|
||||
}
|
||||
|
||||
func (wt *WaterfallTrace) GetWaterfallSpans(uncollapsedSpanIDs []string, selectedSpanID string, limit uint, spanPageSize float64, maxDepthToAutoExpand int) ([]*WaterfallSpan, []string, bool) {
|
||||
// Span selection decision: all spans or windowed
|
||||
selectAllSpans := wt.TotalSpans <= uint64(limit)
|
||||
|
||||
var (
|
||||
selectedSpans []*WaterfallSpan
|
||||
uncollapsedSpans []string
|
||||
)
|
||||
|
||||
if selectAllSpans {
|
||||
selectedSpans = wt.GetAllSpans()
|
||||
} else {
|
||||
selectedSpans, uncollapsedSpans = wt.GetSelectedSpans(uncollapsedSpanIDs, selectedSpanID, spanPageSize, maxDepthToAutoExpand)
|
||||
}
|
||||
return selectedSpans, uncollapsedSpans, selectAllSpans
|
||||
}
|
||||
|
||||
// GetAllSpans returns all spans with pre order traversal.
|
||||
func (wt *WaterfallTrace) GetAllSpans() []*WaterfallSpan {
|
||||
var preOrderedSpans []*WaterfallSpan
|
||||
@@ -218,6 +237,7 @@ func NewGettableWaterfallTrace(
|
||||
selectedSpans []*WaterfallSpan,
|
||||
uncollapsedSpans []string,
|
||||
selectAllSpans bool,
|
||||
aggregations []SpanAggregationResult,
|
||||
) *GettableWaterfallTrace {
|
||||
var rootServiceName, rootServiceEntryPoint string
|
||||
if len(traceData.TraceRoots) > 0 {
|
||||
@@ -230,6 +250,15 @@ func NewGettableWaterfallTrace(
|
||||
span.TimeUnix = span.TimeUnix / 1_000_000
|
||||
}
|
||||
|
||||
// duration values are in nanoseconds; convert in-place to milliseconds.
|
||||
for i := range aggregations {
|
||||
if aggregations[i].Aggregation == SpanAggregationDuration {
|
||||
for k, v := range aggregations[i].Value {
|
||||
aggregations[i].Value[k] = v / 1_000_000
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return &GettableWaterfallTrace{
|
||||
Spans: selectedSpans,
|
||||
UncollapsedSpans: uncollapsedSpans,
|
||||
@@ -241,6 +270,7 @@ func NewGettableWaterfallTrace(
|
||||
RootServiceEntryPoint: rootServiceEntryPoint,
|
||||
HasMissingSpans: traceData.HasMissingSpans,
|
||||
HasMore: !selectAllSpans,
|
||||
Aggregations: aggregations,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -262,3 +292,78 @@ func windowAroundIndex(selectedIndex, total int, spanLimitPerRequest float64) (s
|
||||
start = max(start, 0)
|
||||
return
|
||||
}
|
||||
|
||||
// mergeSpanIntervals computes non-overlapping execution time for a set of spans.
|
||||
func mergeSpanIntervals(spans []*WaterfallSpan) uint64 {
|
||||
if len(spans) == 0 {
|
||||
return 0
|
||||
}
|
||||
sort.Slice(spans, func(i, j int) bool {
|
||||
return spans[i].TimeUnix < spans[j].TimeUnix
|
||||
})
|
||||
|
||||
currentStart := spans[0].TimeUnix
|
||||
currentEnd := currentStart + spans[0].DurationNano
|
||||
total := uint64(0)
|
||||
|
||||
for _, span := range spans[1:] {
|
||||
startNano := span.TimeUnix
|
||||
endNano := startNano + span.DurationNano
|
||||
if currentEnd >= startNano {
|
||||
if endNano > currentEnd {
|
||||
currentEnd = endNano
|
||||
}
|
||||
} else {
|
||||
total += currentEnd - currentStart
|
||||
currentStart = startNano
|
||||
currentEnd = endNano
|
||||
}
|
||||
}
|
||||
return total + (currentEnd - currentStart)
|
||||
}
|
||||
|
||||
// GetSpanAggregation computes one aggregation result over all spans in the trace.
|
||||
// Duration values are returned in nanoseconds; callers convert to milliseconds as needed.
|
||||
func (wt *WaterfallTrace) GetSpanAggregation(aggregation SpanAggregationType, field telemetrytypes.TelemetryFieldKey) SpanAggregationResult {
|
||||
result := SpanAggregationResult{
|
||||
Field: field,
|
||||
Aggregation: aggregation,
|
||||
Value: make(map[string]uint64),
|
||||
}
|
||||
|
||||
switch aggregation {
|
||||
case SpanAggregationSpanCount:
|
||||
for _, span := range wt.SpanIDToSpanNodeMap {
|
||||
if key, ok := span.FieldValue(field); ok {
|
||||
result.Value[key]++
|
||||
}
|
||||
}
|
||||
|
||||
case SpanAggregationDuration:
|
||||
spansByField := make(map[string][]*WaterfallSpan)
|
||||
for _, span := range wt.SpanIDToSpanNodeMap {
|
||||
if key, ok := span.FieldValue(field); ok {
|
||||
spansByField[key] = append(spansByField[key], span)
|
||||
}
|
||||
}
|
||||
for key, spans := range spansByField {
|
||||
result.Value[key] = mergeSpanIntervals(spans)
|
||||
}
|
||||
|
||||
case SpanAggregationExecutionTimePercentage:
|
||||
traceDuration := wt.EndTime - wt.StartTime
|
||||
spansByField := make(map[string][]*WaterfallSpan)
|
||||
for _, span := range wt.SpanIDToSpanNodeMap {
|
||||
if key, ok := span.FieldValue(field); ok {
|
||||
spansByField[key] = append(spansByField[key], span)
|
||||
}
|
||||
}
|
||||
if traceDuration > 0 {
|
||||
for key, spans := range spansByField {
|
||||
result.Value[key] = mergeSpanIntervals(spans) * 100 / traceDuration
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return result
|
||||
}
|
||||
|
||||
140
tests/fixtures/auth.py
vendored
140
tests/fixtures/auth.py
vendored
@@ -56,18 +56,11 @@ def _login(signoz: types.SigNoz, email: str, password: str) -> str:
|
||||
return login.json()["data"]["accessToken"]
|
||||
|
||||
|
||||
def register_admin(
|
||||
signoz: types.SigNoz,
|
||||
request: pytest.FixtureRequest,
|
||||
pytestconfig: pytest.Config,
|
||||
cache_key: str = "create_user_admin",
|
||||
base_path: str = "",
|
||||
) -> types.Operation:
|
||||
"""Register the first admin (creates the org), under base_path. Reuse-wrapped."""
|
||||
|
||||
def create() -> types.Operation:
|
||||
@pytest.fixture(name="create_user_admin", scope="package")
|
||||
def create_user_admin(signoz: types.SigNoz, request: pytest.FixtureRequest, pytestconfig: pytest.Config) -> types.Operation:
|
||||
def create() -> None:
|
||||
response = requests.post(
|
||||
signoz.self.host_configs["8080"].get(f"{base_path}/api/v1/register"),
|
||||
signoz.self.host_configs["8080"].get("/api/v1/register"),
|
||||
json={
|
||||
"name": USER_ADMIN_NAME,
|
||||
"orgName": "",
|
||||
@@ -90,7 +83,7 @@ def register_admin(
|
||||
return reuse.wrap(
|
||||
request,
|
||||
pytestconfig,
|
||||
cache_key,
|
||||
"create_user_admin",
|
||||
lambda: types.Operation(name=""),
|
||||
create,
|
||||
delete,
|
||||
@@ -98,86 +91,86 @@ def register_admin(
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture(name="create_user_admin", scope="package")
|
||||
def create_user_admin(signoz: types.SigNoz, request: pytest.FixtureRequest, pytestconfig: pytest.Config) -> types.Operation:
|
||||
return register_admin(signoz, request, pytestconfig)
|
||||
|
||||
|
||||
def session_context_getter(signoz: types.SigNoz, base_path: str = "") -> Callable[[str], dict]:
|
||||
"""Build a callable that fetches the session context for an email (under base_path)."""
|
||||
|
||||
def fetch_session_context(email: str) -> dict:
|
||||
@pytest.fixture(name="get_session_context", scope="function")
|
||||
def get_session_context(signoz: types.SigNoz) -> Callable[[str, str], str]:
|
||||
def _get_session_context(email: str) -> str:
|
||||
response = requests.get(
|
||||
signoz.self.host_configs["8080"].get(f"{base_path}/api/v2/sessions/context"),
|
||||
params={"email": email, "ref": f"{signoz.self.host_configs['8080'].base()}"},
|
||||
signoz.self.host_configs["8080"].get("/api/v2/sessions/context"),
|
||||
params={
|
||||
"email": email,
|
||||
"ref": f"{signoz.self.host_configs['8080'].base()}",
|
||||
},
|
||||
timeout=5,
|
||||
)
|
||||
|
||||
assert response.status_code == HTTPStatus.OK
|
||||
return response.json()["data"]
|
||||
|
||||
return fetch_session_context
|
||||
|
||||
|
||||
@pytest.fixture(name="get_session_context", scope="function")
|
||||
def get_session_context(signoz: types.SigNoz) -> Callable[[str], dict]:
|
||||
return session_context_getter(signoz)
|
||||
|
||||
|
||||
def token_getter(signoz: types.SigNoz, base_path: str = "") -> Callable[[str, str], str]:
|
||||
"""Build a callable that logs in (email/password) and returns the access token (under base_path)."""
|
||||
|
||||
def fetch_token(email: str, password: str) -> str:
|
||||
context = requests.get(
|
||||
signoz.self.host_configs["8080"].get(f"{base_path}/api/v2/sessions/context"),
|
||||
params={"email": email, "ref": f"{signoz.self.host_configs['8080'].base()}"},
|
||||
timeout=5,
|
||||
)
|
||||
assert context.status_code == HTTPStatus.OK
|
||||
org_id = context.json()["data"]["orgs"][0]["id"]
|
||||
|
||||
login = requests.post(
|
||||
signoz.self.host_configs["8080"].get(f"{base_path}/api/v2/sessions/email_password"),
|
||||
json={"email": email, "password": password, "orgId": org_id},
|
||||
timeout=5,
|
||||
)
|
||||
assert login.status_code == HTTPStatus.OK
|
||||
return login.json()["data"]["accessToken"]
|
||||
|
||||
return fetch_token
|
||||
return _get_session_context
|
||||
|
||||
|
||||
@pytest.fixture(name="get_token", scope="function")
|
||||
def get_token(signoz: types.SigNoz) -> Callable[[str, str], str]:
|
||||
return token_getter(signoz)
|
||||
|
||||
|
||||
def tokens_getter(signoz: types.SigNoz, base_path: str = "") -> Callable[[str, str], tuple[str, str]]:
|
||||
"""Build a callable that logs in and returns the (access, refresh) token pair (under base_path)."""
|
||||
|
||||
def fetch_tokens(email: str, password: str) -> tuple[str, str]:
|
||||
context = requests.get(
|
||||
signoz.self.host_configs["8080"].get(f"{base_path}/api/v2/sessions/context"),
|
||||
params={"email": email, "ref": f"{signoz.self.host_configs['8080'].base()}"},
|
||||
def _get_token(email: str, password: str) -> str:
|
||||
response = requests.get(
|
||||
signoz.self.host_configs["8080"].get("/api/v2/sessions/context"),
|
||||
params={
|
||||
"email": email,
|
||||
"ref": f"{signoz.self.host_configs['8080'].base()}",
|
||||
},
|
||||
timeout=5,
|
||||
)
|
||||
assert context.status_code == HTTPStatus.OK
|
||||
org_id = context.json()["data"]["orgs"][0]["id"]
|
||||
|
||||
login = requests.post(
|
||||
signoz.self.host_configs["8080"].get(f"{base_path}/api/v2/sessions/email_password"),
|
||||
json={"email": email, "password": password, "orgId": org_id},
|
||||
assert response.status_code == HTTPStatus.OK
|
||||
org_id = response.json()["data"]["orgs"][0]["id"]
|
||||
|
||||
response = requests.post(
|
||||
signoz.self.host_configs["8080"].get("/api/v2/sessions/email_password"),
|
||||
json={
|
||||
"email": email,
|
||||
"password": password,
|
||||
"orgId": org_id,
|
||||
},
|
||||
timeout=5,
|
||||
)
|
||||
assert login.status_code == HTTPStatus.OK
|
||||
data = login.json()["data"]
|
||||
return data["accessToken"], data["refreshToken"]
|
||||
|
||||
return fetch_tokens
|
||||
assert response.status_code == HTTPStatus.OK
|
||||
return response.json()["data"]["accessToken"]
|
||||
|
||||
return _get_token
|
||||
|
||||
|
||||
@pytest.fixture(name="get_tokens", scope="function")
|
||||
def get_tokens(signoz: types.SigNoz) -> Callable[[str, str], tuple[str, str]]:
|
||||
return tokens_getter(signoz)
|
||||
def _get_tokens(email: str, password: str) -> str:
|
||||
response = requests.get(
|
||||
signoz.self.host_configs["8080"].get("/api/v2/sessions/context"),
|
||||
params={
|
||||
"email": email,
|
||||
"ref": f"{signoz.self.host_configs['8080'].base()}",
|
||||
},
|
||||
timeout=5,
|
||||
)
|
||||
|
||||
assert response.status_code == HTTPStatus.OK
|
||||
org_id = response.json()["data"]["orgs"][0]["id"]
|
||||
|
||||
response = requests.post(
|
||||
signoz.self.host_configs["8080"].get("/api/v2/sessions/email_password"),
|
||||
json={
|
||||
"email": email,
|
||||
"password": password,
|
||||
"orgId": org_id,
|
||||
},
|
||||
timeout=5,
|
||||
)
|
||||
|
||||
assert response.status_code == HTTPStatus.OK
|
||||
access_token = response.json()["data"]["accessToken"]
|
||||
refresh_token = response.json()["data"]["refreshToken"]
|
||||
return access_token, refresh_token
|
||||
|
||||
return _get_tokens
|
||||
|
||||
|
||||
@pytest.fixture(name="apply_license", scope="package")
|
||||
@@ -277,7 +270,6 @@ def add_license(
|
||||
signoz: types.SigNoz,
|
||||
make_http_mocks: Callable[[types.TestContainerDocker, list[Mapping]], None],
|
||||
get_token: Callable[[str, str], str], # pylint: disable=redefined-outer-name
|
||||
base_path: str = "",
|
||||
) -> None:
|
||||
make_http_mocks(
|
||||
signoz.zeus,
|
||||
@@ -316,7 +308,7 @@ def add_license(
|
||||
access_token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
|
||||
|
||||
response = requests.post(
|
||||
url=signoz.self.host_configs["8080"].get(f"{base_path}/api/v3/licenses"),
|
||||
url=signoz.self.host_configs["8080"].get("/api/v3/licenses"),
|
||||
json={"key": "secret-key"},
|
||||
headers={"Authorization": "Bearer " + access_token},
|
||||
timeout=5,
|
||||
|
||||
@@ -1,144 +0,0 @@
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "user"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "user"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 200, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "user"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 300, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "system"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "system"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "system"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 150, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "idle"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 400, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "idle"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 600, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "idle"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 800, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "wait"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 10, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "wait"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 20, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "wait"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 30, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2100000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2200000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 6000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5900000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5800000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "buffered"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "buffered"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "buffered"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "cached"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "cached"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "cached"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "prod-linux-1", "os.type": "linux"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "prod-linux-1", "os.type": "linux"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1.55, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "prod-linux-1", "os.type": "linux"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1.6, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 51000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 52000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 49000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 48000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "reserved"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "reserved"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-linux-1", "os.type": "linux", "state": "reserved"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "user"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "user"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 200, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "user"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 300, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "system"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "system"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "system"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 150, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "idle"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 400, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "idle"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 600, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "idle"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 800, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "wait"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 10, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "wait"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 20, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "wait"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 30, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2100000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2200000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 6000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5900000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5800000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "buffered"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "buffered"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "buffered"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "cached"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "cached"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "cached"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "prod-windows-1", "os.type": "windows"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "prod-windows-1", "os.type": "windows"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1.55, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "prod-windows-1", "os.type": "windows"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1.6, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 51000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 52000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 49000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 48000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "reserved"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "reserved"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "prod-windows-1", "os.type": "windows", "state": "reserved"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "user"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "user"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 200, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "user"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 300, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "system"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "system"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "system"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 150, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "idle"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 400, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "idle"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 600, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "idle"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 800, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "wait"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 10, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "wait"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 20, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "wait"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 30, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2100000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2200000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 6000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5900000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5800000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "buffered"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "buffered"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "buffered"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "cached"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "cached"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "cached"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "dev-linux-1", "os.type": "linux"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "dev-linux-1", "os.type": "linux"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1.55, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "dev-linux-1", "os.type": "linux"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1.6, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 51000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 52000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 49000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 48000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "reserved"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "reserved"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-linux-1", "os.type": "linux", "state": "reserved"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "user"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "user"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 200, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "user"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 300, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "system"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "system"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "system"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 150, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "idle"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 400, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "idle"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 600, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "idle"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 800, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "wait"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 10, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "wait"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 20, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.cpu.time", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "wait"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 30, "temporality": "Cumulative", "type_": "Sum", "is_monotonic": true}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2100000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2200000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 6000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5900000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5800000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "buffered"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "buffered"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "buffered"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "cached"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "cached"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.memory.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "cached"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1500000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "dev-windows-1", "os.type": "windows"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 1.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "dev-windows-1", "os.type": "windows"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 1.55, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.cpu.load_average.15m", "labels": {"host.name": "dev-windows-1", "os.type": "windows"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 1.6, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 51000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "used"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 52000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 49000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "free"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 48000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "reserved"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "reserved"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
{"metric_name": "system.filesystem.usage", "labels": {"host.name": "dev-windows-1", "os.type": "windows", "state": "reserved"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 5000000000, "temporality": "Unspecified", "type_": "Sum", "is_monotonic": false}
|
||||
@@ -1,126 +0,0 @@
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"inactive-linux-1","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"inactive-linux-1","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"inactive-linux-1","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-linux-1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"used"},"timestamp":"2025-01-10T10:24:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"free"},"timestamp":"2025-01-10T10:20:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"free"},"timestamp":"2025-01-10T10:22:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"free"},"timestamp":"2025-01-10T10:24:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"active-windows-1","os.type":"windows"},"timestamp":"2025-01-10T10:20:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"active-windows-1","os.type":"windows"},"timestamp":"2025-01-10T10:22:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"active-windows-1","os.type":"windows"},"timestamp":"2025-01-10T10:24:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"used"},"timestamp":"2025-01-10T10:20:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"used"},"timestamp":"2025-01-10T10:22:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"used"},"timestamp":"2025-01-10T10:24:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"free"},"timestamp":"2025-01-10T10:20:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"free"},"timestamp":"2025-01-10T10:22:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-windows-1","os.type":"windows","state":"free"},"timestamp":"2025-01-10T10:24:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
@@ -1,12 +0,0 @@
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:00:00+00:00","value":50,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:02:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:04:00+00:00","value":150,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:00:00+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:02:00+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"miss-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:00+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
@@ -1,120 +0,0 @@
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":0,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:00:00+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:02:00+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:00+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h1","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h1","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h1","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:10+00:00","value":0,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:10+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:10+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:10+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:10+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:10+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:00:10+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:02:10+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h2","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:10+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:10+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:10+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:10+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:10+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:10+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:10+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h2","os.type":"linux"},"timestamp":"2025-01-10T10:00:10+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h2","os.type":"linux"},"timestamp":"2025-01-10T10:02:10+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h2","os.type":"linux"},"timestamp":"2025-01-10T10:04:10+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:10+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:10+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:10+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:10+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:10+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:10+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:20+00:00","value":0,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:20+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:20+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:20+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:20+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:20+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:00:20+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:02:20+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h3","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:20+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h3","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:20+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h3","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:20+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h3","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:20+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h3","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:20+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h3","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:20+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"order-h3","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:20+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h3","os.type":"linux"},"timestamp":"2025-01-10T10:00:20+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h3","os.type":"linux"},"timestamp":"2025-01-10T10:02:20+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h3","os.type":"linux"},"timestamp":"2025-01-10T10:04:20+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
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|
||||
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|
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|
||||
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||||
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|
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|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"order-h5","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:40+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"order-h5","os.type":"linux"},"timestamp":"2025-01-10T10:04:40+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
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||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h5","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:40+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h5","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:40+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h5","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:40+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h5","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:40+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h5","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:40+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"order-h5","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:40+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
@@ -1,147 +0,0 @@
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":0,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":50,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"page-h1","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"page-h1","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"page-h1","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":0,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
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|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
{"metric_name":"system.cpu.time","labels":{"host.name":"page-h7","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"page-h7","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"page-h7","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"page-h7","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"page-h7","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
@@ -1,42 +0,0 @@
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"inactive-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"inactive-h1","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"inactive-h1","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"inactive-h1","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"inactive-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"active-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:20:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"active-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:22:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"active-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:24:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"active-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:20:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"active-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:22:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"active-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:24:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:20:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:22:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:24:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:20:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:22:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:24:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"active-h1","os.type":"linux"},"timestamp":"2025-01-10T10:20:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"active-h1","os.type":"linux"},"timestamp":"2025-01-10T10:22:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"active-h1","os.type":"linux"},"timestamp":"2025-01-10T10:24:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:20:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:22:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:24:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:20:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:22:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"active-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:24:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
@@ -1,72 +0,0 @@
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:00:00+00:00","value":50,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:02:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:04:00+00:00","value":150,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":600,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":800,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:00:00+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:02:00+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h1","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:00+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:00:00+00:00","value":80,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:02:00+00:00","value":160,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"user"},"timestamp":"2025-01-10T10:04:00+00:00","value":240,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:00:00+00:00","value":40,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:02:00+00:00","value":80,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"system"},"timestamp":"2025-01-10T10:04:00+00:00","value":120,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:00:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:02:00+00:00","value":500,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"idle"},"timestamp":"2025-01-10T10:04:00+00:00","value":700,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:00:00+00:00","value":5,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:02:00+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.cpu.time","labels":{"host.name":"acc-h2","os.type":"linux","state":"wait"},"timestamp":"2025-01-10T10:04:00+00:00","value":15,"temporality":"Cumulative","type_":"Sum","is_monotonic":true}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":2000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":2100000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":2200000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":6000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":5900000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":5800000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"buffered"},"timestamp":"2025-01-10T10:00:00+00:00","value":500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"buffered"},"timestamp":"2025-01-10T10:02:00+00:00","value":500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"buffered"},"timestamp":"2025-01-10T10:04:00+00:00","value":500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"cached"},"timestamp":"2025-01-10T10:00:00+00:00","value":1500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"cached"},"timestamp":"2025-01-10T10:02:00+00:00","value":1500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"cached"},"timestamp":"2025-01-10T10:04:00+00:00","value":1500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
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|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":3250000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":3500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
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|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":4500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"buffered"},"timestamp":"2025-01-10T10:00:00+00:00","value":500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"buffered"},"timestamp":"2025-01-10T10:02:00+00:00","value":500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"buffered"},"timestamp":"2025-01-10T10:04:00+00:00","value":500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"cached"},"timestamp":"2025-01-10T10:00:00+00:00","value":1500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"cached"},"timestamp":"2025-01-10T10:02:00+00:00","value":1500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.memory.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"cached"},"timestamp":"2025-01-10T10:04:00+00:00","value":1500000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"acc-h1","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"acc-h1","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.55,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"acc-h1","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.6,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"acc-h2","os.type":"linux"},"timestamp":"2025-01-10T10:00:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"acc-h2","os.type":"linux"},"timestamp":"2025-01-10T10:02:00+00:00","value":2.05,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.cpu.load_average.15m","labels":{"host.name":"acc-h2","os.type":"linux"},"timestamp":"2025-01-10T10:04:00+00:00","value":2.1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":51000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":52000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":50000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":49000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":48000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"reserved"},"timestamp":"2025-01-10T10:00:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"reserved"},"timestamp":"2025-01-10T10:02:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h1","os.type":"linux","state":"reserved"},"timestamp":"2025-01-10T10:04:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:00:00+00:00","value":70000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:02:00+00:00","value":71000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"used"},"timestamp":"2025-01-10T10:04:00+00:00","value":72000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:00:00+00:00","value":30000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:02:00+00:00","value":29000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"free"},"timestamp":"2025-01-10T10:04:00+00:00","value":28000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"reserved"},"timestamp":"2025-01-10T10:00:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"reserved"},"timestamp":"2025-01-10T10:02:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
{"metric_name":"system.filesystem.usage","labels":{"host.name":"acc-h2","os.type":"linux","state":"reserved"},"timestamp":"2025-01-10T10:04:00+00:00","value":5000000000,"temporality":"Unspecified","type_":"Sum","is_monotonic":false}
|
||||
@@ -1,20 +0,0 @@
|
||||
{
|
||||
"records": [
|
||||
{
|
||||
"hostName": "acc-h1",
|
||||
"cpu": 0.4444444444444445,
|
||||
"memory": 0.205,
|
||||
"wait": 0.027777777777777776,
|
||||
"load15": 1.525,
|
||||
"diskUsage": 0.48095238095238096
|
||||
},
|
||||
{
|
||||
"hostName": "acc-h2",
|
||||
"cpu": 0.3846153846153845,
|
||||
"memory": 0.3125,
|
||||
"wait": 0.015384615384615384,
|
||||
"load15": 2.025,
|
||||
"diskUsage": 0.6714285714285714
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -1,144 +0,0 @@
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1a-uid", "k8s.pod.name": "web-a-prod-acc-p1a", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-prod-acc-p1b-uid", "k8s.pod.name": "web-a-prod-acc-p1b", "k8s.namespace.name": "web-a-prod", "k8s.cluster.name": "cluster-a", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-dev-acc-p1a-uid", "k8s.pod.name": "web-a-dev-acc-p1a", "k8s.namespace.name": "web-a-dev", "k8s.cluster.name": "cluster-a", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-dev-acc-p1a-uid", "k8s.pod.name": "web-a-dev-acc-p1a", "k8s.namespace.name": "web-a-dev", "k8s.cluster.name": "cluster-a", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "web-a-dev-acc-p1a-uid", "k8s.pod.name": "web-a-dev-acc-p1a", "k8s.namespace.name": "web-a-dev", "k8s.cluster.name": "cluster-a", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-dev-acc-p1a-uid", "k8s.pod.name": "web-a-dev-acc-p1a", "k8s.namespace.name": "web-a-dev", "k8s.cluster.name": "cluster-a", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "web-a-dev-acc-p1a-uid", "k8s.pod.name": "web-a-dev-acc-p1a", "k8s.namespace.name": "web-a-dev", "k8s.cluster.name": "cluster-a", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
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||||
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|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "web-a-dev-acc-p1a-uid", "k8s.pod.name": "web-a-dev-acc-p1a", "k8s.namespace.name": "web-a-dev", "k8s.cluster.name": "cluster-a", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
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|
||||
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||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-prod-acc-p1b-uid", "k8s.pod.name": "api-b-prod-acc-p1b", "k8s.namespace.name": "api-b-prod", "k8s.cluster.name": "cluster-b", "env": "prod"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1a-uid", "k8s.pod.name": "api-b-dev-acc-p1a", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.cpu.usage", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 0.5, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.memory.working_set", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 100000000.0, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
{"metric_name": "k8s.pod.phase", "labels": {"k8s.pod.uid": "api-b-dev-acc-p1b-uid", "k8s.pod.name": "api-b-dev-acc-p1b", "k8s.namespace.name": "api-b-dev", "k8s.cluster.name": "cluster-b", "env": "dev"}, "timestamp": "2025-01-10T10:04:00+00:00", "value": 2, "temporality": "Unspecified", "type_": "Gauge", "is_monotonic": false}
|
||||
@@ -1,36 +0,0 @@
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-1-uid","k8s.pod.name":"pod-gb-ns-1","k8s.namespace.name":"gb-ns-1","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-2-uid","k8s.pod.name":"pod-gb-ns-2","k8s.namespace.name":"gb-ns-2","k8s.cluster.name":"gb-cluster-a"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-3-uid","k8s.pod.name":"pod-gb-ns-3","k8s.namespace.name":"gb-ns-3","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-gb-ns-4-uid","k8s.pod.name":"pod-gb-ns-4","k8s.namespace.name":"gb-ns-4","k8s.cluster.name":"gb-cluster-b"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
@@ -1,3 +0,0 @@
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"miss-p-uid","k8s.pod.name":"miss-p","k8s.namespace.name":"miss-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"miss-p-uid","k8s.pod.name":"miss-p","k8s.namespace.name":"miss-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"miss-p-uid","k8s.pod.name":"miss-p","k8s.namespace.name":"miss-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
@@ -1,45 +0,0 @@
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":500000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":500000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":500000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-1-uid","k8s.pod.name":"pod-order-ns-1","k8s.namespace.name":"order-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":400000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":400000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":400000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-2-uid","k8s.pod.name":"pod-order-ns-2","k8s.namespace.name":"order-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":3.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":3.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":3.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":300000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":300000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":300000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-3-uid","k8s.pod.name":"pod-order-ns-3","k8s.namespace.name":"order-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":4.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":4.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":4.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-4-uid","k8s.pod.name":"pod-order-ns-4","k8s.namespace.name":"order-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":5.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":5.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":5.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-order-ns-5-uid","k8s.pod.name":"pod-order-ns-5","k8s.namespace.name":"order-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
@@ -1,63 +0,0 @@
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":7.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":7.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":7.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-1-uid","k8s.pod.name":"pod-page-ns-1","k8s.namespace.name":"page-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":6.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":6.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":6.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-2-uid","k8s.pod.name":"pod-page-ns-2","k8s.namespace.name":"page-ns-2","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":5.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":5.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":5.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-3-uid","k8s.pod.name":"pod-page-ns-3","k8s.namespace.name":"page-ns-3","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":4.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":4.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":4.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-4-uid","k8s.pod.name":"pod-page-ns-4","k8s.namespace.name":"page-ns-4","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":3.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":3.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":3.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-5-uid","k8s.pod.name":"pod-page-ns-5","k8s.namespace.name":"page-ns-5","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-6-uid","k8s.pod.name":"pod-page-ns-6","k8s.namespace.name":"page-ns-6","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pod-page-ns-7-uid","k8s.pod.name":"pod-page-ns-7","k8s.namespace.name":"page-ns-7","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
@@ -1,63 +0,0 @@
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-1-uid","k8s.pod.name":"pp-run-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-2-uid","k8s.pod.name":"pp-run-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-3-uid","k8s.pod.name":"pp-run-3","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-run-4-uid","k8s.pod.name":"pp-run-4","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-pend-1-uid","k8s.pod.name":"pp-pend-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":1,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-fail-1-uid","k8s.pod.name":"pp-fail-1","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"pp-fail-2-uid","k8s.pod.name":"pp-fail-2","k8s.namespace.name":"pp-ns","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":4,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
@@ -1,45 +0,0 @@
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p1a-uid","k8s.pod.name":"acc-p1a","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.5,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":100000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p1b-uid","k8s.pod.name":"acc-p1b","k8s.namespace.name":"acc-ns-1","k8s.cluster.name":"cluster-x"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2a-uid","k8s.pod.name":"acc-p2a","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2b-uid","k8s.pod.name":"acc-p2b","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.cpu.usage","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":0.75,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.memory.working_set","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":200000000.0,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:00:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:02:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
{"metric_name":"k8s.pod.phase","labels":{"k8s.pod.uid":"acc-p2c-uid","k8s.pod.name":"acc-p2c","k8s.namespace.name":"acc-ns-2","k8s.cluster.name":"cluster-y"},"timestamp":"2025-01-10T10:04:00+00:00","value":2,"temporality":"Unspecified","type_":"Gauge","is_monotonic":false}
|
||||
@@ -1,16 +0,0 @@
|
||||
{
|
||||
"records": [
|
||||
{
|
||||
"namespaceName": "acc-ns-1",
|
||||
"namespaceCPU": 1.0,
|
||||
"namespaceMemory": 200000000.0,
|
||||
"podCountsByPhase": {"pending": 0, "running": 2, "succeeded": 0, "failed": 0, "unknown": 0}
|
||||
},
|
||||
{
|
||||
"namespaceName": "acc-ns-2",
|
||||
"namespaceCPU": 2.25,
|
||||
"namespaceMemory": 600000000.0,
|
||||
"podCountsByPhase": {"pending": 0, "running": 3, "succeeded": 0, "failed": 0, "unknown": 0}
|
||||
}
|
||||
]
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user