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test(integration): add cumulative counter rate tests (#9976)

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This commit is contained in:
Srikanth Chekuri
2026-01-18 16:13:41 +05:30
committed by GitHub
parent c306e66bcd
commit e9501d2e0f
10 changed files with 1180 additions and 97 deletions
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331
tests/integration/fixtures/metrics.py
View File

@@ -122,6 +122,124 @@ class MetricsSample(ABC):
]
class MetricsExpHist(ABC):
"""Represents a row in the exp_hist table for exponential histograms."""
env: str
temporality: str
metric_name: str
fingerprint: np.uint64
unix_milli: np.int64
count: np.uint64
sum: np.float64
min: np.float64
max: np.float64
sketch: bytes
flags: np.uint32
def __init__(
self,
metric_name: str,
fingerprint: np.uint64,
timestamp: datetime.datetime,
count: int,
sum_value: float,
min_value: float,
max_value: float,
sketch: bytes = b"",
temporality: str = "Unspecified",
env: str = "default",
flags: int = 0,
) -> None:
self.env = env
self.temporality = temporality
self.metric_name = metric_name
self.fingerprint = fingerprint
self.unix_milli = np.int64(int(timestamp.timestamp() * 1e3))
self.count = np.uint64(count)
self.sum = np.float64(sum_value)
self.min = np.float64(min_value)
self.max = np.float64(max_value)
self.sketch = sketch
self.flags = np.uint32(flags)
def to_row(self) -> list:
return [
self.env,
self.temporality,
self.metric_name,
self.fingerprint,
self.unix_milli,
self.count,
self.sum,
self.min,
self.max,
self.sketch,
self.flags,
]
class MetricsMetadata(ABC):
"""Represents a row in the metadata table for metric metadata."""
temporality: str
metric_name: str
description: str
unit: str
type: str
is_monotonic: bool
attr_name: str
attr_type: str
attr_datatype: str
attr_string_value: str
first_reported_unix_milli: np.int64
last_reported_unix_milli: np.int64
def __init__(
self,
metric_name: str,
attr_name: str,
attr_type: str,
attr_datatype: str,
attr_string_value: str,
timestamp: datetime.datetime,
temporality: str = "Unspecified",
description: str = "",
unit: str = "",
type_: str = "Sum",
is_monotonic: bool = True,
) -> None:
self.temporality = temporality
self.metric_name = metric_name
self.description = description
self.unit = unit
self.type = type_
self.is_monotonic = is_monotonic
self.attr_name = attr_name
self.attr_type = attr_type
self.attr_datatype = attr_datatype
self.attr_string_value = attr_string_value
unix_milli = np.int64(int(timestamp.timestamp() * 1e3))
self.first_reported_unix_milli = unix_milli
self.last_reported_unix_milli = unix_milli
def to_row(self) -> list:
return [
self.temporality,
self.metric_name,
self.description,
self.unit,
self.type,
self.is_monotonic,
self.attr_name,
self.attr_type,
self.attr_datatype,
self.attr_string_value,
self.first_reported_unix_milli,
self.last_reported_unix_milli,
]
class Metrics(ABC):
"""High-level metric representation. Produces both time series and sample entries."""
@@ -189,6 +307,119 @@ class Metrics(ABC):
flags=flags,
)
def to_dict(self) -> dict:
return {
"metric_name": self.metric_name,
"labels": self.labels,
"timestamp": self.timestamp.isoformat(),
"value": self.value,
"temporality": self.temporality,
"type_": self._time_series.type,
"is_monotonic": self._time_series.is_monotonic,
"flags": self.flags,
"description": self._time_series.description,
"unit": self._time_series.unit,
"env": self._time_series.env,
"resource_attrs": self._time_series.resource_attrs,
"scope_attrs": self._time_series.scope_attrs,
}
@classmethod
def from_dict(
cls,
data: dict,
# base_time: Optional[datetime.datetime] = None,
metric_name_override: Optional[str] = None,
) -> "Metrics":
"""
Create a Metrics instance from a dict.
Args:
data: The dict containing metric data
base_time: If provided, timestamps are shifted relative to this time.
The earliest timestamp in the data becomes base_time.
metric_name_override: If provided, overrides the metric_name from data
"""
# parse timestamp from iso format
ts_str = data["timestamp"]
if ts_str.endswith("Z"):
ts_str = ts_str[:-1] + "+00:00"
timestamp = datetime.datetime.fromisoformat(ts_str)
return cls(
metric_name=metric_name_override or data["metric_name"],
labels=data.get("labels", {}),
timestamp=timestamp,
value=data["value"],
temporality=data.get("temporality", "Unspecified"),
flags=data.get("flags", 0),
description=data.get("description", ""),
unit=data.get("unit", ""),
type_=data.get("type_", "Sum"),
is_monotonic=data.get("is_monotonic", True),
env=data.get("env", "default"),
resource_attributes=data.get("resource_attrs", {}),
scope_attributes=data.get("scope_attrs", {}),
)
@classmethod
def load_from_file(
cls,
file_path: str,
base_time: Optional[datetime.datetime] = None,
metric_name_override: Optional[str] = None,
) -> List["Metrics"]:
"""
Load metrics from a JSONL file.
Each line should be a JSON object representing a metric.
Args:
file_path: Path to the JSONL file
base_time: If provided, all timestamps are shifted so the earliest
timestamp in the file maps to base_time
metric_name_override: If provided, overrides metric_name for all metrics
"""
data_list = []
with open(file_path, "r", encoding="utf-8") as f:
for line in f:
line = line.strip()
if not line:
continue
data_list.append(json.loads(line))
if not data_list:
return []
# If base_time provided, calculate time offset
time_offset = datetime.timedelta(0)
if base_time is not None:
# Find earliest timestamp
earliest = None
for data in data_list:
ts_str = data["timestamp"]
if ts_str.endswith("Z"):
ts_str = ts_str[:-1] + "+00:00"
ts = datetime.datetime.fromisoformat(ts_str)
if earliest is None or ts < earliest:
earliest = ts
if earliest is not None:
time_offset = base_time - earliest
metrics = []
for data in data_list:
ts_str = data["timestamp"]
if ts_str.endswith("Z"):
ts_str = ts_str[:-1] + "+00:00"
original_ts = datetime.datetime.fromisoformat(ts_str)
adjusted_ts = original_ts + time_offset
data["timestamp"] = adjusted_ts.isoformat()
metrics.append(
cls.from_dict(data, metric_name_override=metric_name_override)
)
return metrics
@pytest.fixture(name="insert_metrics", scope="function")
def insert_metrics(
@@ -200,6 +431,7 @@ def insert_metrics(
This function handles insertion into:
- distributed_time_series_v4 (time series metadata)
- distributed_samples_v4 (actual sample values)
- distributed_metadata (metric attribute metadata)
"""
time_series_map: dict[int, MetricsTimeSeries] = {}
for metric in metrics:
@@ -247,15 +479,93 @@ def insert_metrics(
data=[sample.to_row() for sample in samples],
)
# (metric_name, attr_type, attr_name, attr_value) -> MetricsMetadata
metadata_map: dict[tuple, MetricsMetadata] = {}
for metric in metrics:
ts = metric.time_series
for attr_name, attr_value in metric.labels.items():
key = (ts.metric_name, "point", attr_name, str(attr_value))
if key not in metadata_map:
metadata_map[key] = MetricsMetadata(
metric_name=ts.metric_name,
attr_name=attr_name,
attr_type="point",
attr_datatype="String",
attr_string_value=str(attr_value),
timestamp=metric.timestamp,
temporality=ts.temporality,
description=ts.description,
unit=ts.unit,
type_=ts.type,
is_monotonic=ts.is_monotonic,
)
for attr_name, attr_value in ts.resource_attrs.items():
key = (ts.metric_name, "resource", attr_name, str(attr_value))
if key not in metadata_map:
metadata_map[key] = MetricsMetadata(
metric_name=ts.metric_name,
attr_name=attr_name,
attr_type="resource",
attr_datatype="String",
attr_string_value=str(attr_value),
timestamp=metric.timestamp,
temporality=ts.temporality,
description=ts.description,
unit=ts.unit,
type_=ts.type,
is_monotonic=ts.is_monotonic,
)
for attr_name, attr_value in ts.scope_attrs.items():
key = (ts.metric_name, "scope", attr_name, str(attr_value))
if key not in metadata_map:
metadata_map[key] = MetricsMetadata(
metric_name=ts.metric_name,
attr_name=attr_name,
attr_type="scope",
attr_datatype="String",
attr_string_value=str(attr_value),
timestamp=metric.timestamp,
temporality=ts.temporality,
description=ts.description,
unit=ts.unit,
type_=ts.type,
is_monotonic=ts.is_monotonic,
)
if len(metadata_map) > 0:
clickhouse.conn.insert(
database="signoz_metrics",
table="distributed_metadata",
column_names=[
"temporality",
"metric_name",
"description",
"unit",
"type",
"is_monotonic",
"attr_name",
"attr_type",
"attr_datatype",
"attr_string_value",
"first_reported_unix_milli",
"last_reported_unix_milli",
],
data=[m.to_row() for m in metadata_map.values()],
)
yield _insert_metrics
# Cleanup
clickhouse.conn.query(
f"TRUNCATE TABLE signoz_metrics.time_series_v4 ON CLUSTER '{clickhouse.env['SIGNOZ_TELEMETRYSTORE_CLICKHOUSE_CLUSTER']}' SYNC"
)
clickhouse.conn.query(
f"TRUNCATE TABLE signoz_metrics.samples_v4 ON CLUSTER '{clickhouse.env['SIGNOZ_TELEMETRYSTORE_CLICKHOUSE_CLUSTER']}' SYNC"
)
cluster = clickhouse.env["SIGNOZ_TELEMETRYSTORE_CLICKHOUSE_CLUSTER"]
tables_to_truncate = [
"time_series_v4",
"samples_v4",
"exp_hist",
"metadata",
]
for table in tables_to_truncate:
clickhouse.conn.query(
f"TRUNCATE TABLE signoz_metrics.{table} ON CLUSTER '{cluster}' SYNC"
)
@pytest.fixture(name="remove_metrics_ttl_and_storage_settings", scope="function")
@@ -272,15 +582,18 @@ def remove_metrics_ttl_and_storage_settings(signoz: types.SigNoz):
"time_series_v4_6hrs",
"time_series_v4_1day",
"time_series_v4_1week",
"exp_hist",
"metadata",
]
cluster = signoz.telemetrystore.env["SIGNOZ_TELEMETRYSTORE_CLICKHOUSE_CLUSTER"]
for table in tables:
try:
signoz.telemetrystore.conn.query(
f"ALTER TABLE signoz_metrics.{table} ON CLUSTER '{signoz.telemetrystore.env['SIGNOZ_TELEMETRYSTORE_CLICKHOUSE_CLUSTER']}' REMOVE TTL"
f"ALTER TABLE signoz_metrics.{table} ON CLUSTER '{cluster}' REMOVE TTL"
)
signoz.telemetrystore.conn.query(
f"ALTER TABLE signoz_metrics.{table} ON CLUSTER '{signoz.telemetrystore.env['SIGNOZ_TELEMETRYSTORE_CLICKHOUSE_CLUSTER']}' RESET SETTING storage_policy;"
f"ALTER TABLE signoz_metrics.{table} ON CLUSTER '{cluster}' RESET SETTING storage_policy;"
)
except Exception as e: # pylint: disable=broad-exception-caught
print(f"ttl and storage policy reset failed for {table}: {e}")

331
tests/integration/fixtures/querier.py Normal file
View File

@@ -0,0 +1,331 @@
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional
import requests
from fixtures import types
DEFAULT_STEP_INTERVAL = 60 # seconds
DEFAULT_TOLERANCE = 1e-9
QUERY_TIMEOUT = 30 # seconds
def make_query_request(
signoz: types.SigNoz,
token: str,
start_ms: int,
end_ms: int,
queries: List[Dict],
*,
request_type: str = "time_series",
format_options: Optional[Dict] = None,
variables: Optional[Dict] = None,
no_cache: bool = True,
timeout: int = QUERY_TIMEOUT,
) -> requests.Response:
if format_options is None:
format_options = {"formatTableResultForUI": False, "fillGaps": False}
payload = {
"schemaVersion": "v1",
"start": start_ms,
"end": end_ms,
"requestType": request_type,
"compositeQuery": {"queries": queries},
"formatOptions": format_options,
"noCache": no_cache,
}
if variables:
payload["variables"] = variables
return requests.post(
signoz.self.host_configs["8080"].get("/api/v5/query_range"),
timeout=timeout,
headers={"authorization": f"Bearer {token}"},
json=payload,
)
def build_builder_query(
name: str,
metric_name: str,
time_aggregation: str,
space_aggregation: str,
*,
temporality: str = "cumulative",
step_interval: int = DEFAULT_STEP_INTERVAL,
group_by: Optional[List[str]] = None,
filter_expression: Optional[str] = None,
functions: Optional[List[Dict]] = None,
disabled: bool = False,
) -> Dict:
spec: Dict[str, Any] = {
"name": name,
"signal": "metrics",
"aggregations": [
{
"metricName": metric_name,
"temporality": temporality,
"timeAggregation": time_aggregation,
"spaceAggregation": space_aggregation,
}
],
"stepInterval": step_interval,
"disabled": disabled,
}
if group_by:
spec["groupBy"] = [
{
"name": label,
}
for label in group_by
]
if filter_expression:
spec["filter"] = {"expression": filter_expression}
if functions:
spec["functions"] = functions
return {"type": "builder_query", "spec": spec}
def build_formula_query(
name: str,
expression: str,
*,
functions: Optional[List[Dict]] = None,
disabled: bool = False,
) -> Dict:
spec: Dict[str, Any] = {
"name": name,
"expression": expression,
"disabled": disabled,
}
if functions:
spec["functions"] = functions
return {"type": "builder_formula", "spec": spec}
def build_function(name: str, *args: Any) -> Dict:
func: Dict[str, Any] = {"name": name}
if args:
func["args"] = [{"value": arg} for arg in args]
return func
def get_series_values(response_json: Dict, query_name: str) -> List[Dict]:
results = response_json.get("data", {}).get("data", {}).get("results", [])
result = find_named_result(results, query_name)
if not result:
return []
aggregations = result.get("aggregations", [])
if not aggregations:
return []
# at the time of writing this, the series is always a list with one element
series = aggregations[0].get("series", [])
if not series:
return []
return series[0].get("values", [])
def get_all_series(response_json: Dict, query_name: str) -> List[Dict]:
results = response_json.get("data", {}).get("data", {}).get("results", [])
result = find_named_result(results, query_name)
if not result:
return []
aggregations = result.get("aggregations", [])
if not aggregations:
return []
# at the time of writing this, the series is always a list with one element
return aggregations[0].get("series", [])
def get_scalar_value(response_json: Dict, query_name: str) -> Optional[float]:
values = get_series_values(response_json, query_name)
if values:
return values[0].get("value")
return None
def compare_values(
v1: float,
v2: float,
tolerance: float = DEFAULT_TOLERANCE,
) -> bool:
return abs(v1 - v2) <= tolerance
def compare_series_values(
values1: List[Dict],
values2: List[Dict],
tolerance: float = DEFAULT_TOLERANCE,
) -> bool:
if len(values1) != len(values2):
return False
sorted1 = sorted(values1, key=lambda x: x["timestamp"])
sorted2 = sorted(values2, key=lambda x: x["timestamp"])
for v1, v2 in zip(sorted1, sorted2):
if v1["timestamp"] != v2["timestamp"]:
return False
if not compare_values(v1["value"], v2["value"], tolerance):
return False
return True
def compare_all_series(
series1: List[Dict],
series2: List[Dict],
tolerance: float = DEFAULT_TOLERANCE,
) -> bool:
if len(series1) != len(series2):
return False
# oh my lovely python
def series_key(s: Dict) -> str:
labels = s.get("labels", [])
return str(
sorted(
[
(lbl.get("key", {}).get("name", ""), lbl.get("value", ""))
for lbl in labels
]
)
)
sorted1 = sorted(series1, key=series_key)
sorted2 = sorted(series2, key=series_key)
for s1, s2 in zip(sorted1, sorted2):
if series_key(s1) != series_key(s2):
return False
if not compare_series_values(
s1.get("values", []),
s2.get("values", []),
tolerance,
):
return False
return True
def assert_results_equal(
result_cached: Dict,
result_no_cache: Dict,
query_name: str,
context: str,
tolerance: float = DEFAULT_TOLERANCE,
) -> None:
values_cached = get_series_values(result_cached, query_name)
values_no_cache = get_series_values(result_no_cache, query_name)
sorted_cached = sorted(values_cached, key=lambda x: x["timestamp"])
sorted_no_cache = sorted(values_no_cache, key=lambda x: x["timestamp"])
assert len(sorted_cached) == len(sorted_no_cache), (
f"{context}: Different number of values. "
f"Cached: {len(sorted_cached)}, No-cache: {len(sorted_no_cache)}\n"
f"Cached timestamps: {[v['timestamp'] for v in sorted_cached]}\n"
f"No-cache timestamps: {[v['timestamp'] for v in sorted_no_cache]}"
)
for v_cached, v_no_cache in zip(sorted_cached, sorted_no_cache):
assert v_cached["timestamp"] == v_no_cache["timestamp"], (
f"{context}: Timestamp mismatch. "
f"Cached: {v_cached['timestamp']}, No-cache: {v_no_cache['timestamp']}"
)
assert compare_values(v_cached["value"], v_no_cache["value"], tolerance), (
f"{context}: Value mismatch at timestamp {v_cached['timestamp']}. "
f"Cached: {v_cached['value']}, No-cache: {v_no_cache['value']}"
)
def assert_all_series_equal(
result_cached: Dict,
result_no_cache: Dict,
query_name: str,
context: str,
tolerance: float = DEFAULT_TOLERANCE,
) -> None:
series_cached = get_all_series(result_cached, query_name)
series_no_cache = get_all_series(result_no_cache, query_name)
assert compare_all_series(
series_cached, series_no_cache, tolerance
), f"{context}: Cached series differ from non-cached series"
def expected_minutely_bucket_timestamps_ms(now: datetime) -> List[List[int]]:
previous_five = [
int((now - timedelta(minutes=m)).timestamp() * 1000) for m in range(5, 0, -1)
]
with_current = previous_five + [int(now.timestamp() * 1000)]
return [previous_five, with_current]
def assert_minutely_bucket_timestamps(
points: List[Dict[str, Any]],
now: datetime,
*,
context: str,
) -> List[int]:
expected = expected_minutely_bucket_timestamps_ms(now)
actual = [p["timestamp"] for p in points]
assert actual in expected, f"Unexpected timestamps for {context}: {actual}"
return actual
def assert_minutely_bucket_values(
points: List[Dict[str, Any]],
now: datetime,
*,
expected_by_ts: Dict[int, float],
context: str,
) -> None:
timestamps = assert_minutely_bucket_timestamps(points, now, context=context)
expected = {ts: 0 for ts in timestamps}
expected.update(expected_by_ts)
for point in points:
ts = point["timestamp"]
assert point["value"] == expected[ts], (
f"Unexpected value for {context} at timestamp={ts}: "
f"got {point['value']}, expected {expected[ts]}"
)
def index_series_by_label(
series: List[Dict[str, Any]],
label_name: str,
) -> Dict[str, Dict[str, Any]]:
series_by_label: Dict[str, Dict[str, Any]] = {}
for s in series:
label = next(
(
l
for l in s.get("labels", [])
if l.get("key", {}).get("name") == label_name
),
None,
)
assert label is not None, f"Expected {label_name} label in series"
series_by_label[label["value"]] = s
return series_by_label
def find_named_result(
results: List[Dict[str, Any]],
name: str,
) -> Optional[Dict[str, Any]]:
return next(
(
r
for r in results
if r.get("name") == name
or r.get("queryName") == name
or (r.get("spec") or {}).get("name") == name
),
None,
)

12
tests/integration/src/dashboard/01_dashboard.py
View File

@@ -28,15 +28,14 @@ def test_create_and_delete_dashboard_without_license(
dashboard_id = data["id"]
response = requests.delete(
signoz.self.host_configs["8080"].get(
f"/api/v1/dashboards/{dashboard_id}"
),
signoz.self.host_configs["8080"].get(f"/api/v1/dashboards/{dashboard_id}"),
headers={"Authorization": f"Bearer {admin_token}"},
timeout=2,
)
assert response.status_code == HTTPStatus.NO_CONTENT
def test_apply_license(
signoz: SigNoz,
create_user_admin: Operation, # pylint: disable=unused-argument
@@ -49,7 +48,6 @@ def test_apply_license(
add_license(signoz, make_http_mocks, get_token)
def test_create_and_delete_dashboard_with_license(
signoz: SigNoz,
create_user_admin: Operation, # pylint: disable=unused-argument
@@ -70,11 +68,9 @@ def test_create_and_delete_dashboard_with_license(
dashboard_id = data["id"]
response = requests.delete(
signoz.self.host_configs["8080"].get(
f"/api/v1/dashboards/{dashboard_id}"
),
signoz.self.host_configs["8080"].get(f"/api/v1/dashboards/{dashboard_id}"),
headers={"Authorization": f"Bearer {admin_token}"},
timeout=2,
)
assert response.status_code == HTTPStatus.NO_CONTENT
assert response.status_code == HTTPStatus.NO_CONTENT

2
tests/integration/src/querier/01_logs.py
View File

@@ -7,7 +7,7 @@ import requests
from fixtures import types
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
from fixtures.logs import Logs
from src.querier.timeseries_utils import (
from fixtures.querier import (
assert_minutely_bucket_values,
find_named_result,
index_series_by_label,

2
tests/integration/src/querier/03_metrics.py
View File

@@ -7,7 +7,7 @@ import requests
from fixtures import types
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
from fixtures.metrics import Metrics
from src.querier.timeseries_utils import (
from fixtures.querier import (
assert_minutely_bucket_values,
find_named_result,
index_series_by_label,

4
tests/integration/src/querier/04_traces.py
View File

@@ -6,12 +6,12 @@ import requests
from fixtures import types
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
from fixtures.traces import TraceIdGenerator, Traces, TracesKind, TracesStatusCode
from src.querier.timeseries_utils import (
from fixtures.querier import (
assert_minutely_bucket_values,
find_named_result,
index_series_by_label,
)
from fixtures.traces import TraceIdGenerator, Traces, TracesKind, TracesStatusCode
def test_traces_list(

231
tests/integration/src/querier/05_metrics_rate_cumulative_counter.py Normal file
View File

@@ -0,0 +1,231 @@
"""
Look at the cumulative_counters_1h.jsonl file for the relevant data
"""
import os
from datetime import datetime, timedelta, timezone
from http import HTTPStatus
from typing import Any, Callable, List
from fixtures import types
from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
from fixtures.metrics import Metrics
from fixtures.querier import (
build_builder_query,
get_all_series,
get_series_values,
make_query_request,
)
TESTDATA_DIR = os.path.join(os.path.dirname(__file__), "..", "..", "testdata")
CUMULATIVE_COUNTERS_FILE = os.path.join(TESTDATA_DIR, "cumulative_counters_1h.jsonl")
def test_rate_with_steady_values_and_reset(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[List[Metrics]], None],
) -> None:
now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
end_ms = int(now.timestamp() * 1000)
metric_name = "test_rate_stale"
metrics = Metrics.load_from_file(
CUMULATIVE_COUNTERS_FILE,
base_time=now - timedelta(minutes=60),
metric_name_override=metric_name,
)
insert_metrics(metrics)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
query = build_builder_query(
"A",
metric_name,
"rate",
"sum",
temporality="cumulative",
filter_expression='endpoint = "/orders"',
)
response = make_query_request(signoz, token, start_ms, end_ms, [query])
assert response.status_code == HTTPStatus.OK
data = response.json()
result_values = sorted(get_series_values(data, "A"), key=lambda x: x["timestamp"])
assert len(result_values) >= 59
# the counter reset happened at 31st minute
assert (
result_values[30]["value"] == 0.0167
) # i.e 2/120 i.e 29th to 31st minute changes
assert (
result_values[31]["value"] == 0.133
) # i.e 10/60 i.e 31st to 32nd minute changes
count_of_steady_rate = sum(1 for v in result_values if v["value"] == 0.0833)
assert (
count_of_steady_rate >= 56
) # 59 - (1 reset + 1 high rate + 1 at the beginning)
# All rates should be non-negative (stale periods = 0 rate)
for v in result_values:
assert v["value"] >= 0, f"Rate should not be negative: {v['value']}"
def test_rate_group_by_endpoint(
signoz: types.SigNoz,
create_user_admin: None, # pylint: disable=unused-argument
get_token: Callable[[str, str], str],
insert_metrics: Callable[[List[Metrics]], None],
) -> None:
now = datetime.now(tz=timezone.utc).replace(second=0, microsecond=0)
start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
end_ms = int(now.timestamp() * 1000)
metric_name = "test_rate_groupby"
metrics = Metrics.load_from_file(
CUMULATIVE_COUNTERS_FILE,
base_time=now - timedelta(minutes=60),
metric_name_override=metric_name,
)
insert_metrics(metrics)
token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
query = build_builder_query(
"A",
metric_name,
"rate",
"sum",
temporality="cumulative",
group_by=["endpoint"],
)
response = make_query_request(signoz, token, start_ms, end_ms, [query])
assert response.status_code == HTTPStatus.OK
data = response.json()
all_series = get_all_series(data, "A")
# Should have 5 different endpoints
assert (
len(all_series) == 5
), f"Expected 5 series for 5 endpoints, got {len(all_series)}"
# endpoint -> values
endpoint_values = {}
for series in all_series:
endpoint = series.get("labels", [{}])[0].get("value", "unknown")
values = sorted(series.get("values", []), key=lambda x: x["timestamp"])
endpoint_values[endpoint] = values
expected_endpoints = {"/products", "/health", "/checkout", "/orders", "/users"}
assert (
set(endpoint_values.keys()) == expected_endpoints
), f"Expected endpoints {expected_endpoints}, got {set(endpoint_values.keys())}"
# at no point rate should be negative
for endpoint, values in endpoint_values.items():
for v in values:
assert (
v["value"] >= 0
), f"Rate for {endpoint} should not be negative: {v['value']}"
# /health: 60 data points (t01-t60), steady +10/min
# rate = 10/60 = 0.167
health_values = endpoint_values["/health"]
assert (
len(health_values) >= 58
), f"Expected >= 58 values for /health, got {len(health_values)}"
count_steady_health = sum(1 for v in health_values if v["value"] == 0.167)
assert (
count_steady_health >= 57
), f"Expected >= 57 steady rate values (0.167) for /health, got {count_steady_health}"
# all /health rates should be 0.167 except possibly first/last due to boundaries
for v in health_values[1:-1]:
assert v["value"] == 0.167, f"Expected /health rate 0.167, got {v['value']}"
# /products: 51 data points with 10-minute gap (t20-t29 missing), steady +20/min
# rate = 20/60 = 0.333, gap causes lower averaged rate at boundary
products_values = endpoint_values["/products"]
assert (
len(products_values) >= 49
), f"Expected >= 49 values for /products, got {len(products_values)}"
count_steady_products = sum(1 for v in products_values if v["value"] == 0.333)
# most values should be 0.333, some boundary values differ due to 10-min gap
assert (
count_steady_products >= 46
), f"Expected >= 46 steady rate values (0.333) for /products, got {count_steady_products}"
# check that non-0.333 values are due to gap averaging (should be lower)
gap_boundary_values = [v["value"] for v in products_values if v["value"] != 0.333]
for val in gap_boundary_values:
assert (
0 < val < 0.333
), f"Gap boundary values should be between 0 and 0.333, got {val}"
# /checkout: 61 data points (t00-t60), +1/min normal, +50/min spike at t40-t44
# normal rate = 1/60 = 0.0167, spike rate = 50/60 = 0.833
checkout_values = endpoint_values["/checkout"]
assert (
len(checkout_values) >= 59
), f"Expected >= 59 values for /checkout, got {len(checkout_values)}"
count_steady_checkout = sum(1 for v in checkout_values if v["value"] == 0.0167)
assert (
count_steady_checkout >= 53
), f"Expected >= 53 steady rate values (0.0167) for /checkout, got {count_steady_checkout}"
# check that spike values exist (traffic spike +50/min at t40-t44)
count_spike_checkout = sum(1 for v in checkout_values if v["value"] == 0.833)
assert (
count_spike_checkout >= 4
), f"Expected >= 4 spike rate values (0.833) for /checkout, got {count_spike_checkout}"
# spike values should be consecutive
spike_indices = [
i for i, v in enumerate[Any](checkout_values) if v["value"] == 0.833
]
assert len(spike_indices) >= 4, f"Expected >= 4 spike indices, got {spike_indices}"
# consecutiveness
for i in range(1, len(spike_indices)):
assert (
spike_indices[i] == spike_indices[i - 1] + 1
), f"Spike indices should be consecutive, got {spike_indices}"
# /orders: 60 data points (t00-t60) with gap at t30, counter reset at t31 (150->2)
# rate = 5/60 = 0.0833
# reset at t31 causes: rate at t30 includes gap (lower), t31 has high rate after reset
orders_values = endpoint_values["/orders"]
assert (
len(orders_values) >= 58
), f"Expected >= 58 values for /orders, got {len(orders_values)}"
count_steady_orders = sum(1 for v in orders_values if v["value"] == 0.0833)
assert (
count_steady_orders >= 55
), f"Expected >= 55 steady rate values (0.0833) for /orders, got {count_steady_orders}"
# check for counter reset effects - there should be some non-standard values
non_standard_orders = [v["value"] for v in orders_values if v["value"] != 0.0833]
assert (
len(non_standard_orders) >= 2
), f"Expected >= 2 non-standard values due to counter reset, got {non_standard_orders}"
# post-reset value should be higher (new counter value / interval)
high_rate_orders = [v for v in non_standard_orders if v > 0.0833]
assert (
len(high_rate_orders) >= 1
), f"Expected at least one high rate value after counter reset, got {non_standard_orders}"
# /users: 56 data points (t05-t60), sparse +1 every 5 minutes
# Rate = 1/60 = 0.0167 during increment, 0 during flat periods
users_values = endpoint_values["/users"]
assert (
len(users_values) >= 54
), f"Expected >= 54 values for /users, got {len(users_values)}"
count_zero_users = sum(1 for v in users_values if v["value"] == 0)
# most values should be 0 (flat periods between increments)
assert (
count_zero_users >= 40
), f"Expected >= 40 zero rate values for /users (sparse data), got {count_zero_users}"
# non-zero values should be 0.0167 (1/60 increment rate)
non_zero_users = [v["value"] for v in users_values if v["value"] != 0]
count_increment_rate = sum(1 for v in non_zero_users if v == 0.0167)
assert (
count_increment_rate >= 8
), f"Expected >= 8 increment rate values (0.0167) for /users, got {count_increment_rate}"

0
tests/integration/src/querier/__init__.py
View File

76
tests/integration/src/querier/timeseries_utils.py
View File

@@ -1,76 +0,0 @@
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional
def expected_minutely_bucket_timestamps_ms(now: datetime) -> List[List[int]]:
previous_five = [
int((now - timedelta(minutes=m)).timestamp() * 1000) for m in range(5, 0, -1)
]
with_current = previous_five + [int(now.timestamp() * 1000)]
return [previous_five, with_current]
def assert_minutely_bucket_timestamps(
points: List[Dict[str, Any]],
now: datetime,
*,
context: str,
) -> List[int]:
expected = expected_minutely_bucket_timestamps_ms(now)
actual = [p["timestamp"] for p in points]
assert actual in expected, f"Unexpected timestamps for {context}: {actual}"
return actual
def assert_minutely_bucket_values(
points: List[Dict[str, Any]],
now: datetime,
*,
expected_by_ts: Dict[int, float],
context: str,
) -> None:
timestamps = assert_minutely_bucket_timestamps(points, now, context=context)
expected = {ts: 0 for ts in timestamps}
expected.update(expected_by_ts)
for point in points:
ts = point["timestamp"]
assert point["value"] == expected[ts], (
f"Unexpected value for {context} at timestamp={ts}: "
f"got {point['value']}, expected {expected[ts]}"
)
def index_series_by_label(
series: List[Dict[str, Any]],
label_name: str,
) -> Dict[str, Dict[str, Any]]:
series_by_label: Dict[str, Dict[str, Any]] = {}
for s in series:
label = next(
(
l
for l in s.get("labels", [])
if l.get("key", {}).get("name") == label_name
),
None,
)
assert label is not None, f"Expected {label_name} label in series"
series_by_label[label["value"]] = s
return series_by_label
def find_named_result(
results: List[Dict[str, Any]],
name: str,
) -> Optional[Dict[str, Any]]:
return next(
(
r
for r in results
if r.get("name") == name
or r.get("queryName") == name
or (r.get("spec") or {}).get("name") == name
),
None,
)

288
tests/integration/testdata/cumulative_counters_1h.jsonl vendored Normal file
View File

@@ -0,0 +1,288 @@
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:01:00+00:00","value":10,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:02:00+00:00","value":20,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:03:00+00:00","value":30,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:04:00+00:00","value":40,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:05:00+00:00","value":50,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:06:00+00:00","value":60,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:07:00+00:00","value":70,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:08:00+00:00","value":80,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:09:00+00:00","value":90,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:10:00+00:00","value":100,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:11:00+00:00","value":110,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:12:00+00:00","value":120,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:13:00+00:00","value":130,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:14:00+00:00","value":140,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:15:00+00:00","value":150,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:16:00+00:00","value":160,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:17:00+00:00","value":170,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:18:00+00:00","value":180,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:19:00+00:00","value":190,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:20:00+00:00","value":200,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:21:00+00:00","value":210,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:22:00+00:00","value":220,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:23:00+00:00","value":230,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:24:00+00:00","value":240,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:25:00+00:00","value":250,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:26:00+00:00","value":260,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:27:00+00:00","value":270,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:28:00+00:00","value":280,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:29:00+00:00","value":290,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:30:00+00:00","value":300,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:31:00+00:00","value":310,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:32:00+00:00","value":320,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:33:00+00:00","value":330,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:34:00+00:00","value":340,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:35:00+00:00","value":350,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:36:00+00:00","value":360,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:37:00+00:00","value":370,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:38:00+00:00","value":380,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:39:00+00:00","value":390,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:40:00+00:00","value":400,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:41:00+00:00","value":410,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:42:00+00:00","value":420,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
{"metric_name":"http.request.count","labels":{"service":"api","endpoint":"/health","status_code":"200"},"timestamp":"2025-01-10T10:43:00+00:00","value":430,"temporality":"Cumulative","type_":"Sum","is_monotonic":true,"flags":0,"description":"","unit":"","env":"default","resource_attrs":{},"scope_attrs":{}}
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