fix(query-builder): stop the panel-type field list growing on every change

`updateSuperSetQueryBuilderData` appended `dataSource` to the field list with
`propsRequired?.push('dataSource')`. That list is the array held in
`panelTypeDataSourceFormValuesMap`, so the module-level table grew by one entry on
every query-builder change, unbounded for the life of the page. It was harmless only
because the assignment it drives is idempotent. The field now travels on a copy.

The guard stays an `if` rather than defaulting to an empty list: the previous
optional chaining meant a panel type outside the builder set copied nothing at all,
`dataSource` included.

Two neighbours in the same area, both no-ops:

- `PANEL_TYPES_INITIAL_QUERY` had exactly one reference in the repo — its own
  definition.
- `PanelTypeKeys` was a hand-written union of the enum's key names that had fallen
  three behind (`BAR`, `PIE`, `HISTOGRAM`), so it is derived now. Nothing relied on
  the omission: `useChartMutable` builds its key array from `Object.keys` through an
  untyped `[].slice.call`, so all nine were already there at runtime.

Assisted-by: Claude Opus 5

docs/api/openapi.yml

@@ -3064,79 +3064,6 @@ components:
       - tags
       - spec
       type: object
-    DashboardtypesHeatmapAxes:
-      properties:
-        yScale:
-          $ref: '#/components/schemas/DashboardtypesHeatmapYScale'
-      type: object
-    DashboardtypesHeatmapChartAppearance:
-      properties:
-        colors:
-          $ref: '#/components/schemas/DashboardtypesHeatmapColors'
-      type: object
-    DashboardtypesHeatmapColorMode:
-      enum:
-      - palette
-      - opacity
-      type: string
-    DashboardtypesHeatmapColorScale:
-      enum:
-      - log
-      - sqrt
-      - linear
-      type: string
-    DashboardtypesHeatmapColors:
-      properties:
-        fill:
-          type: string
-        maxCount:
-          nullable: true
-          type: number
-        minCount:
-          nullable: true
-          type: number
-        mode:
-          $ref: '#/components/schemas/DashboardtypesHeatmapColorMode'
-        palette:
-          $ref: '#/components/schemas/DashboardtypesHeatmapPalette'
-        scale:
-          $ref: '#/components/schemas/DashboardtypesHeatmapColorScale'
-        steps:
-          type: integer
-      type: object
-    DashboardtypesHeatmapPalette:
-      enum:
-      - ice
-      - moss
-      - rust
-      - graphite
-      - ember
-      - lagoon
-      - orchid
-      - verdant
-      - lava
-      - beacon
-      type: string
-    DashboardtypesHeatmapPanelSpec:
-      properties:
-        axes:
-          $ref: '#/components/schemas/DashboardtypesHeatmapAxes'
-        chartAppearance:
-          $ref: '#/components/schemas/DashboardtypesHeatmapChartAppearance'
-        formatting:
-          $ref: '#/components/schemas/DashboardtypesPanelFormatting'
-        legend:
-          $ref: '#/components/schemas/DashboardtypesLegend'
-        visualization:
-          $ref: '#/components/schemas/DashboardtypesBasicVisualization'
-      type: object
-    DashboardtypesHeatmapYScale:
-      enum:
-      - auto
-      - linear
-      - log
-      - symlog
-      type: string
     DashboardtypesHistogramBuckets:
       properties:
         bucketCount:
@@ -3489,7 +3416,6 @@ components:
       discriminator:
         mapping:
           signoz/BarChartPanel: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesBarChartPanelSpec'
-          signoz/HeatmapPanel: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHeatmapPanelSpec'
           signoz/HistogramPanel: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHistogramPanelSpec'
           signoz/ListPanel: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesListPanelSpec'
           signoz/NumberPanel: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesNumberPanelSpec'
@@ -3505,7 +3431,6 @@ components:
       - $ref: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesTablePanelSpec'
       - $ref: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHistogramPanelSpec'
       - $ref: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesListPanelSpec'
-      - $ref: '#/components/schemas/DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHeatmapPanelSpec'
       type: object
     DashboardtypesPanelPluginKind:
       enum:
@@ -3516,7 +3441,6 @@ components:
       - signoz/TablePanel
       - signoz/HistogramPanel
       - signoz/ListPanel
-      - signoz/HeatmapPanel
       type: string
     DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesBarChartPanelSpec:
       properties:
@@ -3530,18 +3454,6 @@ components:
       - kind
       - spec
       type: object
-    DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHeatmapPanelSpec:
-      properties:
-        kind:
-          enum:
-          - signoz/HeatmapPanel
-          type: string
-        spec:
-          $ref: '#/components/schemas/DashboardtypesHeatmapPanelSpec'
-      required:
-      - kind
-      - spec
-      type: object
     DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHistogramPanelSpec:
       properties:
         kind:
@@ -7073,7 +6985,10 @@ components:
             $ref: '#/components/schemas/Querybuildertypesv5TimeSeries'
           type: array
         meta:
-          $ref: '#/components/schemas/Querybuildertypesv5AggregationMeta'
+          properties:
+            unit:
+              type: string
+          type: object
         predictedSeries:
           items:
             $ref: '#/components/schemas/Querybuildertypesv5TimeSeries'
@@ -7088,51 +7003,12 @@ components:
             $ref: '#/components/schemas/Querybuildertypesv5TimeSeries'
           type: array
       type: object
-    Querybuildertypesv5AggregationMeta:
+    Querybuildertypesv5Bucket:
       properties:
-        buckets:
-          items:
-            format: double
-            type: number
-          type: array
-        unit:
-          type: string
+        step:
+          format: double
+          type: number
       type: object
-    Querybuildertypesv5BucketOptions:
-      discriminator:
-        mapping:
-          linear: '#/components/schemas/Querybuildertypesv5BucketOptionsLinear'
-          log: '#/components/schemas/Querybuildertypesv5BucketOptionsLog'
-        propertyName: kind
-      oneOf:
-      - $ref: '#/components/schemas/Querybuildertypesv5BucketOptionsLinear'
-      - $ref: '#/components/schemas/Querybuildertypesv5BucketOptionsLog'
-      type: object
-    Querybuildertypesv5BucketOptionsLinear:
-      properties:
-        kind:
-          $ref: '#/components/schemas/Querybuildertypesv5BucketsKind'
-        spec:
-          $ref: '#/components/schemas/Querybuildertypesv5LinearBucketsSpec'
-      required:
-      - kind
-      - spec
-      type: object
-    Querybuildertypesv5BucketOptionsLog:
-      properties:
-        kind:
-          $ref: '#/components/schemas/Querybuildertypesv5BucketsKind'
-        spec:
-          $ref: '#/components/schemas/Querybuildertypesv5LogBucketsSpec'
-      required:
-      - kind
-      - spec
-      type: object
-    Querybuildertypesv5BucketsKind:
-      enum:
-      - linear
-      - log
-      type: string
     Querybuildertypesv5BuilderQuerySpec:
       discriminator:
         mapping:
@@ -7313,16 +7189,6 @@ components:
         value:
           type: string
       type: object
-    Querybuildertypesv5LinearBucketsSpec:
-      properties:
-        maxValue:
-          format: double
-          type: number
-        numBuckets:
-          type: integer
-      required:
-      - maxValue
-      type: object
     Querybuildertypesv5LogAggregation:
       properties:
         alias:
@@ -7330,12 +7196,6 @@ components:
         expression:
           type: string
       type: object
-    Querybuildertypesv5LogBucketsSpec:
-      properties:
-        scale:
-          nullable: true
-          type: integer
-      type: object
     Querybuildertypesv5MetricAggregation:
       properties:
         comparisonSpaceAggregationParam:
@@ -7794,8 +7654,6 @@ components:
         queries (traces, logs, metrics), formulas, joins, trace operators, PromQL,
         and ClickHouse SQL queries.
       properties:
-        bucketOptions:
-          $ref: '#/components/schemas/Querybuildertypesv5BucketOptions'
         compositeQuery:
           $ref: '#/components/schemas/Querybuildertypesv5CompositeQuery'
         end:
@@ -7895,7 +7753,6 @@ components:
       - raw
       - raw_stream
       - trace
-      - heatmap
       type: string
     Querybuildertypesv5ScalarData:
       properties:
@@ -7970,6 +7827,8 @@ components:
       type: object
     Querybuildertypesv5TimeSeriesValue:
       properties:
+        bucket:
+          $ref: '#/components/schemas/Querybuildertypesv5Bucket'
         partial:
           type: boolean
         timestamp:

frontend/src/constants/queryBuilder.ts

@@ -601,18 +601,6 @@ export const listViewInitialLogQuery: Query = {
 	},
 };
 
-export const PANEL_TYPES_INITIAL_QUERY: Record<PANEL_TYPES, Query> = {
-	[PANEL_TYPES.TIME_SERIES]: initialQueriesMap.metrics,
-	[PANEL_TYPES.VALUE]: initialQueriesMap.metrics,
-	[PANEL_TYPES.TABLE]: initialQueriesMap.metrics,
-	[PANEL_TYPES.LIST]: listViewInitialLogQuery,
-	[PANEL_TYPES.TRACE]: initialQueriesMap.traces,
-	[PANEL_TYPES.BAR]: initialQueriesMap.metrics,
-	[PANEL_TYPES.PIE]: initialQueriesMap.metrics,
-	[PANEL_TYPES.HISTOGRAM]: initialQueriesMap.metrics,
-	[PANEL_TYPES.EMPTY_WIDGET]: initialQueriesMap.metrics,
-};
-
 export const listViewInitialTraceQuery: Query = {
 	// it should be the above commented query
 	...initialQueriesMap.traces,

frontend/src/providers/QueryBuilder.tsx

@@ -766,10 +766,15 @@ export function QueryBuilderProvider({
 							queryItem.dataSource
 						].builder.queryData;
 
-					propsRequired?.push('dataSource');
-					propsRequired?.forEach((p: any) => {
-						set(queryItem, p, get(newQueryItem, p));
-					});
+					// `dataSource` travels with the panel type's fields, but is appended to a
+					// copy: `propsRequired` is the list held in
+					// `panelTypeDataSourceFormValuesMap`, and pushing onto it grew that
+					// module-level array by one entry on every call.
+					if (propsRequired) {
+						[...propsRequired, 'dataSource'].forEach((p: any) => {
+							set(queryItem, p, get(newQueryItem, p));
+						});
+					}
 					return queryItem;
 				}
 

frontend/src/types/common/queryBuilder.ts

@@ -211,13 +211,11 @@ export enum QueryFunctionsTypes {
 	FILL_ZERO = 'fillZero',
 }
 
-export type PanelTypeKeys =
-	| 'TIME_SERIES'
-	| 'VALUE'
-	| 'TABLE'
-	| 'LIST'
-	| 'TRACE'
-	| 'EMPTY_WIDGET';
+/**
+ * Key names of {@link PANEL_TYPES}. Derived rather than listed: the hand-written
+ * version had fallen behind the enum by three members (`BAR`, `PIE`, `HISTOGRAM`).
+ */
+export type PanelTypeKeys = keyof typeof PANEL_TYPES;
 
 export enum ReduceOperators {
 	LAST = 'last',

heatmap-poc/main.go

@@ -1,74 +0,0 @@
-package main
-
-import (
-	"embed"
-	"io/fs"
-	"log/slog"
-	"net/http"
-	"net/http/httputil"
-	"net/url"
-	"os"
-)
-
-//go:embed static
-var embedded embed.FS
-
-// diskAssets is preferred over the embedded copy when it exists, so editing the
-// UI and reloading the page needs no rebuild. It resolves against the launch
-// config's cwd, the repo root.
-const diskAssets = "heatmap-poc/static"
-
-func main() {
-	addr := envOr("HEATMAP_POC_ADDR", "localhost:8099")
-	upstream, err := url.Parse(envOr("SIGNOZ_URL", "http://localhost:8080"))
-	if err != nil {
-		slog.Error("SIGNOZ_URL is not a URL", "error", err)
-		os.Exit(1)
-	}
-
-	apiKey := os.Getenv("SIGNOZ_API_KEY")
-	if apiKey == "" {
-		slog.Warn("SIGNOZ_API_KEY is unset, every upstream call will be rejected as unauthenticated")
-	}
-
-	mux := http.NewServeMux()
-	mux.Handle("/api/", &httputil.ReverseProxy{
-		Rewrite: func(r *httputil.ProxyRequest) {
-			r.SetURL(upstream)
-			r.Out.Host = upstream.Host
-			r.Out.Header.Set("SIGNOZ-API-KEY", apiKey)
-		},
-		ErrorHandler: func(rw http.ResponseWriter, _ *http.Request, err error) {
-			slog.Error("upstream call failed", "error", err)
-			http.Error(rw, err.Error(), http.StatusBadGateway)
-		},
-	})
-	mux.Handle("/", http.FileServerFS(assets()))
-
-	slog.Info("listening", "url", "http://"+addr, "upstream", upstream.String())
-	if err := http.ListenAndServe(addr, mux); err != nil {
-		slog.Error("server stopped", "error", err)
-		os.Exit(1)
-	}
-}
-
-func assets() fs.FS {
-	if stat, err := os.Stat(diskAssets); err == nil && stat.IsDir() {
-		slog.Info("serving the UI from disk", "dir", diskAssets)
-		return os.DirFS(diskAssets)
-	}
-	slog.Info("serving the embedded UI")
-	sub, err := fs.Sub(embedded, "static")
-	if err != nil {
-		slog.Error("embedded assets are unreadable", "error", err)
-		os.Exit(1)
-	}
-	return sub
-}
-
-func envOr(key, fallback string) string {
-	if value := os.Getenv(key); value != "" {
-		return value
-	}
-	return fallback
-}

heatmap-poc/static/app.js

@@ -1,788 +0,0 @@
-const TIME_AGGREGATIONS = ["latest", "sum", "avg", "min", "max", "count", "count_distinct", "rate", "increase"];
-const SPACE_AGGREGATIONS = ["sum", "avg", "min", "max", "count"];
-
-// the types whose samples reach the reader as plain values, so a bucket axis has
-// to be chosen for them
-const VALUE_TYPES = new Set(["gauge", "sum", "summary"]);
-
-// single hue, dark to light: a count's magnitude is the only thing it encodes
-const RAMP = ["#1c3557", "#22406c", "#284c82", "#2e5998", "#3668ae", "#4a80c4", "#689dd6", "#8dbbe6", "#b9d8f5"];
-const ZERO_FILL = "#0e1016";
-
-const PAD = { left: 78, right: 8, top: 8, bottom: 22 };
-const MAX_CHART_HEIGHT = 560;
-const MAX_JSON_CHARS = 300_000;
-
-const state = {
-	mode: "metric",
-	rows: [],
-	groupBy: [],
-	bucketKind: "default",
-	colorScale: "linear",
-	grid: null,
-	emptyReason: "Run a query to draw the heatmap.",
-	hidden: new Set(),
-	requestText: "",
-	responseText: "",
-};
-
-const catalogue = new Map();
-const attributesByMetric = new Map();
-let nextRowId = 0;
-
-const $ = (selector) => document.querySelector(selector);
-const esc = (value) => String(value).replace(/[&<>"]/g, (c) => ({ "&": "&amp;", "<": "&lt;", ">": "&gt;", '"': "&quot;" })[c]);
-
-function timeWindow() {
-	const end = Date.now();
-	return { start: end - Number($("#range").value) * 60_000, end };
-}
-
-function rowLetter(index) {
-	return String.fromCharCode(65 + index);
-}
-
-function typeSupport(type) {
-	if (VALUE_TYPES.has(type)) {
-		return { aggregations: true, buckets: true };
-	}
-	if (type === "histogram") {
-		return { aggregations: false, buckets: false, note: "Read with increase/sum over its own le labels. Bucket options are rejected." };
-	}
-	if (type === "exponentialhistogram") {
-		return { aggregations: false, buckets: false, bad: true, note: "Exponential histograms are not supported yet — this comes back 501." };
-	}
-	return { aggregations: false, buckets: false, bad: true, note: "No type is recorded for this metric, so no bucket axis can be chosen — this comes back 400." };
-}
-
-function scaleHint(scale) {
-	const perTwice = 2 ** scale;
-	return perTwice >= 1 ? `${perTwice} bucket${perTwice === 1 ? "" : "s"} per 2x` : `1 bucket per ${2 ** -scale}x`;
-}
-
-function formatNumber(value) {
-	if (!Number.isFinite(value)) {
-		return value > 0 ? "∞" : "-∞";
-	}
-	if (value === 0) {
-		return "0";
-	}
-	const magnitude = Math.abs(value);
-	if (magnitude >= 1e6 || magnitude < 1e-3) {
-		return value.toExponential(1).replace("e+", "e");
-	}
-	if (Number.isInteger(value)) {
-		return String(value);
-	}
-	const text = value.toPrecision(magnitude >= 1 ? 4 : 3);
-	return text.includes(".") ? text.replace(/0+$/, "").replace(/\.$/, "") : text;
-}
-
-function formatTime(ms, spanMs) {
-	const at = new Date(ms);
-	const clock = at.toTimeString().slice(0, 5);
-	return spanMs > 24 * 3600_000 ? `${String(at.getMonth() + 1).padStart(2, "0")}-${String(at.getDate()).padStart(2, "0")} ${clock}` : clock;
-}
-
-/* ---------- upstream ---------- */
-
-async function getJSON(path, params) {
-	const response = await fetch(`${path}?${new URLSearchParams(params)}`);
-	if (!response.ok) {
-		throw new Error(`${path} came back ${response.status}: ${(await response.text()).slice(0, 400)}`);
-	}
-	return response.json();
-}
-
-async function searchMetrics(searchText) {
-	const { start, end } = timeWindow();
-	const body = await getJSON("/api/v2/metrics", { start, end, limit: 60, searchText });
-	const metrics = body?.data?.metrics ?? [];
-	for (const metric of metrics) {
-		catalogue.set(metric.metricName, metric);
-	}
-	return metrics;
-}
-
-async function metricAttributes(metricName) {
-	if (attributesByMetric.has(metricName)) {
-		return attributesByMetric.get(metricName);
-	}
-	const { start, end } = timeWindow();
-	const body = await getJSON("/api/v2/metrics/attributes", { metricName, start, end });
-	const keys = (body?.data?.attributes ?? []).map((attribute) => attribute.key);
-	attributesByMetric.set(metricName, keys);
-	return keys;
-}
-
-/* ---------- metric rows ---------- */
-
-function addRow() {
-	state.rows.push({ id: nextRowId++, metric: "", type: "", timeAggregation: "max", spaceAggregation: "max", filter: "" });
-	renderRows();
-}
-
-function renderRows() {
-	const host = $("#rows");
-	host.textContent = "";
-
-	state.rows.forEach((row, index) => {
-		const node = $("#row-template").content.firstElementChild.cloneNode(true);
-		const input = node.querySelector(".metric-input");
-		const list = node.querySelector("datalist");
-		const badge = node.querySelector(".badge");
-		const aggregations = node.querySelector(".agg-fields");
-		const note = node.querySelector(".row-note");
-		const listId = `metrics-${row.id}`;
-
-		node.querySelector(".row-name").textContent = rowLetter(index);
-		list.id = listId;
-		input.setAttribute("list", listId);
-		input.value = row.metric;
-		node.querySelector(".filter-input").value = row.filter;
-		node.querySelector(".remove-row").hidden = state.mode !== "formula" || state.rows.length < 2;
-
-		const support = typeSupport(row.type);
-		if (row.metric) {
-			badge.hidden = false;
-			badge.textContent = row.type || "no type";
-			badge.classList.toggle("bad", Boolean(support.bad));
-			aggregations.hidden = !support.aggregations;
-			note.hidden = !support.note;
-			note.textContent = support.note ?? "";
-			note.classList.toggle("warn", Boolean(support.bad));
-		}
-
-		for (const [select, options, chosen] of [
-			[node.querySelector(".time-agg"), TIME_AGGREGATIONS, row.timeAggregation],
-			[node.querySelector(".space-agg"), SPACE_AGGREGATIONS, row.spaceAggregation],
-		]) {
-			select.innerHTML = options.map((option) => `<option value="${option}"${option === chosen ? " selected" : ""}>${option}</option>`).join("");
-		}
-
-		const fillOptions = async () => {
-			try {
-				const metrics = await searchMetrics(input.value.trim());
-				list.innerHTML = metrics.map((metric) => `<option value="${esc(metric.metricName)}" label="${esc(metric.type || "no type")}"></option>`).join("");
-			} catch (error) {
-				showBanner(error.message);
-			}
-		};
-
-		let searchTimer = 0;
-		input.addEventListener("input", () => {
-			clearTimeout(searchTimer);
-			searchTimer = setTimeout(fillOptions, 220);
-		});
-		input.addEventListener("focus", () => {
-			if (!list.children.length) {
-				fillOptions();
-			}
-		});
-
-		input.addEventListener("change", async () => {
-			const name = input.value.trim();
-			row.metric = name;
-			row.type = catalogue.get(name)?.type ?? "";
-			if (name && !catalogue.has(name)) {
-				try {
-					await searchMetrics(name);
-					row.type = catalogue.get(name)?.type ?? "";
-				} catch (error) {
-					showBanner(error.message);
-				}
-			}
-			renderRows();
-			refreshPanes();
-			refreshGroupOptions();
-		});
-
-		node.querySelector(".time-agg").addEventListener("change", (event) => {
-			row.timeAggregation = event.target.value;
-		});
-		node.querySelector(".space-agg").addEventListener("change", (event) => {
-			row.spaceAggregation = event.target.value;
-		});
-		node.querySelector(".filter-input").addEventListener("change", (event) => {
-			row.filter = event.target.value.trim();
-		});
-		node.querySelector(".remove-row").addEventListener("click", () => {
-			state.rows = state.rows.filter((candidate) => candidate.id !== row.id);
-			renderRows();
-			refreshPanes();
-			refreshGroupOptions();
-		});
-
-		host.append(node);
-	});
-}
-
-/* ---------- group by ---------- */
-
-function renderGroupChips() {
-	$("#group-chips").innerHTML = state.groupBy
-		.map((key) => `<span class="chip">${esc(key)}<button type="button" data-key="${esc(key)}" title="Remove">&times;</button></span>`)
-		.join("");
-}
-
-async function refreshGroupOptions() {
-	const metrics = state.rows.map((row) => row.metric).filter(Boolean);
-	const hint = $("#group-hint");
-	if (!metrics.length) {
-		$("#group-options").innerHTML = "";
-		hint.hidden = false;
-		hint.textContent = "Pick a metric to load its attributes.";
-		return;
-	}
-
-	try {
-		const keys = new Set();
-		for (const row of state.rows.filter((candidate) => candidate.metric)) {
-			for (const key of await metricAttributes(row.metric)) {
-				// the builder strips `le` from a histogram's group by and reads the
-				// bucket axis off it instead, so offering it here would do nothing
-				if (key !== "le" || row.type !== "histogram") {
-					keys.add(key);
-				}
-			}
-		}
-		const available = [...keys].filter((key) => !state.groupBy.includes(key)).sort();
-		$("#group-options").innerHTML = available.map((key) => `<option value="${esc(key)}"></option>`).join("");
-		hint.hidden = available.length > 0;
-		hint.textContent = available.length ? "" : "No further attributes on the selected metrics in this window.";
-	} catch (error) {
-		showBanner(error.message);
-	}
-}
-
-/* ---------- panes ---------- */
-
-// A formula is bucketed from its own output, so its inputs may be any type. In
-// metric mode the one metric decides, and an unpicked one keeps the pane up.
-function bucketsAllowed() {
-	if (state.mode === "promql") {
-		return false;
-	}
-	if (state.mode === "formula") {
-		return true;
-	}
-	const row = state.rows[0];
-	return !row?.metric || typeSupport(row.type).buckets;
-}
-
-function refreshPanes() {
-	const isPromql = state.mode === "promql";
-	const isFormula = state.mode === "formula";
-
-	$("#builder-pane").hidden = isPromql;
-	$("#promql-pane").hidden = !isPromql;
-	$("#add-row").hidden = !isFormula;
-	$("#formula-field").hidden = !isFormula;
-
-	const allowed = bucketsAllowed();
-	$("#bucket-pane").hidden = !allowed;
-	if (!allowed) {
-		setBucketKind("default");
-	}
-}
-
-function setBucketKind(kind) {
-	state.bucketKind = kind;
-	for (const button of $("#bucket-kinds").children) {
-		button.classList.toggle("on", button.dataset.kind === kind);
-	}
-	$("#bucket-default-hint").hidden = kind !== "default";
-	$("#scale-field").hidden = kind !== "log";
-	$("#linear-fields").hidden = kind !== "linear";
-	$("#scale-hint").textContent = scaleHint(Number($("#scale").value));
-}
-
-function setMode(mode) {
-	state.mode = mode;
-	for (const button of $("#modes").children) {
-		button.classList.toggle("on", button.dataset.mode === mode);
-	}
-	if (mode === "metric") {
-		state.rows = state.rows.slice(0, 1);
-	}
-	if (!state.rows.length) {
-		addRow();
-	}
-	if (mode === "formula" && state.rows.length < 2) {
-		addRow();
-	}
-	renderRows();
-	refreshPanes();
-	refreshGroupOptions();
-}
-
-/* ---------- request ---------- */
-
-function buildRequest() {
-	const { start, end } = timeWindow();
-	const step = Number($("#step").value) || 60;
-	const request = {
-		schemaVersion: "v1",
-		start,
-		end,
-		requestType: "heatmap",
-		compositeQuery: { queries: [] },
-		formatOptions: { formatTableResultForUI: false, fillGaps: false },
-		noCache: $("#no-cache").checked,
-	};
-
-	if (state.mode === "promql") {
-		const query = $("#promql").value.trim();
-		if (!query) {
-			throw new Error("Enter a PromQL query.");
-		}
-		request.compositeQuery.queries.push({ type: "promql", spec: { name: "A", query, step, disabled: false } });
-		return request;
-	}
-
-	const inFormula = state.mode === "formula";
-	state.rows.forEach((row, index) => {
-		if (!row.metric) {
-			throw new Error(`Query ${rowLetter(index)} has no metric selected.`);
-		}
-		const histogram = row.type === "histogram";
-		const spec = {
-			name: rowLetter(index),
-			signal: "metrics",
-			aggregations: [
-				{
-					metricName: row.metric,
-					timeAggregation: histogram ? "increase" : row.timeAggregation,
-					spaceAggregation: histogram ? "sum" : row.spaceAggregation,
-				},
-			],
-			stepInterval: step,
-			// only the enabled query renders the heatmap, so a formula's inputs ride
-			// along disabled
-			disabled: inFormula,
-		};
-		if (state.groupBy.length) {
-			spec.groupBy = state.groupBy.map((name) => ({ name }));
-		}
-		if (row.filter) {
-			spec.filter = { expression: row.filter };
-		}
-		request.compositeQuery.queries.push({ type: "builder_query", spec });
-	});
-
-	if (inFormula) {
-		const expression = $("#formula").value.trim();
-		if (!expression) {
-			throw new Error("A formula heatmap needs an expression.");
-		}
-		request.compositeQuery.queries.push({ type: "builder_formula", spec: { name: "F1", expression, disabled: false } });
-	}
-
-	if (state.bucketKind === "log") {
-		request.bucketOptions = { kind: "log", spec: { scale: Number($("#scale").value) } };
-	} else if (state.bucketKind === "linear") {
-		request.bucketOptions = { kind: "linear", spec: { maxValue: Number($("#max-value").value), numBuckets: Number($("#num-buckets").value) } };
-	}
-
-	return request;
-}
-
-async function run() {
-	let request;
-	try {
-		request = buildRequest();
-	} catch (error) {
-		showBanner(error.message);
-		return;
-	}
-
-	state.window = { start: request.start, end: request.end };
-	state.requestText = JSON.stringify(request, null, "\t");
-	renderJSON("#request", state.requestText);
-	$("#run").disabled = true;
-	$("#run").textContent = "Running…";
-	showBanner("");
-
-	try {
-		const response = await fetch("/api/v5/query_range", {
-			method: "POST",
-			headers: { "content-type": "application/json" },
-			body: JSON.stringify(request),
-		});
-		const text = await response.text();
-		let parsed = null;
-		try {
-			parsed = JSON.parse(text);
-			state.responseText = JSON.stringify(parsed, null, "\t");
-		} catch {
-			state.responseText = text;
-		}
-		renderJSON("#response", state.responseText);
-
-		if (!response.ok) {
-			const problem = parsed?.error;
-			const detail = (problem?.errors ?? []).map((entry) => entry.message ?? JSON.stringify(entry)).join("\n");
-			showBanner(`${response.status} ${problem?.code ?? ""}\n${problem?.message ?? text.slice(0, 600)}${detail ? `\n${detail}` : ""}`.trim());
-			setGrid(null, "The request was rejected — see the message above.");
-			return;
-		}
-
-		const warning = parsed?.data?.warning;
-		if (warning?.message) {
-			showBanner([warning.message, ...(warning.warnings ?? []).map((entry) => entry.message)].join("\n"));
-		}
-		const grid = buildGrid(parsed);
-		setGrid(grid, "The response carried no series, so there is nothing to draw.");
-	} catch (error) {
-		showBanner(error.message);
-		setGrid(null, "The request could not be sent — see the message above.");
-	} finally {
-		$("#run").disabled = false;
-		$("#run").textContent = "Run query";
-	}
-}
-
-/* ---------- response ---------- */
-
-function buildGrid(body) {
-	const results = body?.data?.data?.results ?? [];
-	const result = results.find((entry) => Array.isArray(entry?.aggregations) && entry.aggregations.length);
-	if (!result) {
-		return null;
-	}
-
-	const aggregation = result.aggregations[0];
-	const buckets = aggregation.meta?.buckets ?? [];
-	const rows = buckets.length + 1;
-	const partial = new Set();
-
-	const series = (aggregation.series ?? []).map((entry) => {
-		const labels = (entry.labels ?? []).map((label) => `${label.key?.name ?? "?"}=${label.value}`);
-		const byTs = new Map();
-		let total = 0;
-		for (const point of entry.values ?? []) {
-			const counts = point.values ?? [];
-			byTs.set(point.timestamp, counts);
-			total += counts.reduce((sum, count) => sum + count, 0);
-			if (point.partial) {
-				partial.add(point.timestamp);
-			}
-		}
-		return { key: labels.join(", ") || "(no labels)", byTs, total };
-	});
-
-	const timestamps = [...new Set(series.flatMap((entry) => [...entry.byTs.keys()]))].sort((a, b) => a - b);
-	series.sort((a, b) => b.total - a.total || a.key.localeCompare(b.key));
-
-	return { queryName: result.queryName, buckets, rows, timestamps, partial, series };
-}
-
-function setGrid(grid, emptyReason) {
-	state.grid = grid;
-	state.emptyReason = emptyReason;
-	state.hidden = new Set();
-	renderGroups();
-	renderChart();
-}
-
-function visibleSeries() {
-	return state.grid.series.filter((entry) => !state.hidden.has(entry.key));
-}
-
-function renderGroups() {
-	const grid = state.grid;
-	const card = $("#groups-card");
-	if (!grid || grid.series.length < 2) {
-		card.hidden = true;
-		return;
-	}
-
-	card.hidden = false;
-	$("#group-count").textContent = `${grid.series.length - state.hidden.size} of ${grid.series.length} shown`;
-	$("#groups").innerHTML = grid.series
-		.map(
-			(entry) => `<label><input type="checkbox" data-key="${esc(entry.key)}"${state.hidden.has(entry.key) ? "" : " checked"}>
-				<span class="name" title="${esc(entry.key)}">${esc(entry.key)}</span>
-				<span class="total">${formatNumber(entry.total)}</span></label>`,
-		)
-		.join("");
-}
-
-/* ---------- chart ---------- */
-
-function bucketRange(grid, row) {
-	if (row === grid.rows - 1) {
-		return grid.buckets.length ? `> ${formatNumber(grid.buckets[grid.buckets.length - 1])}` : "overflow, the response carried no bucket bounds";
-	}
-	const upper = formatNumber(grid.buckets[row]);
-	return row === 0 ? `<= ${upper}` : `(${formatNumber(grid.buckets[row - 1])}, ${upper}]`;
-}
-
-function colorFor(count, max) {
-	if (count <= 0) {
-		return ZERO_FILL;
-	}
-	const fraction = max <= 0 ? 1 : state.colorScale === "log" ? Math.log1p(count) / Math.log1p(max) : count / max;
-	return RAMP[Math.min(RAMP.length - 1, Math.max(0, Math.round(fraction * (RAMP.length - 1))))];
-}
-
-function renderChart() {
-	const host = $("#chart");
-	const legend = $("#legend");
-	const grid = state.grid;
-	legend.textContent = "";
-
-	if (!grid) {
-		host.innerHTML = `<p class="empty">${esc(state.emptyReason)}</p>`;
-		return;
-	}
-	if (!grid.timestamps.length) {
-		host.innerHTML = '<p class="empty">The query returned no columns.</p>';
-		return;
-	}
-
-	const columns = grid.timestamps.length;
-	const shown = visibleSeries();
-	const matrix = grid.timestamps.map((ts) => {
-		const column = new Array(grid.rows).fill(0);
-		for (const entry of shown) {
-			const counts = entry.byTs.get(ts);
-			if (!counts) {
-				continue;
-			}
-			for (let row = 0; row < grid.rows; row++) {
-				column[row] += counts[row] ?? 0;
-			}
-		}
-		return column;
-	});
-
-	const max = Math.max(0, ...matrix.flat());
-	// clientWidth carries the 12px padding on either side of #chart
-	const available = host.clientWidth - 24 - PAD.left - PAD.right;
-	const cellWidth = Math.max(3, available / columns);
-	const cellHeight = Math.min(22, Math.max(4, MAX_CHART_HEIGHT / grid.rows));
-	const plotWidth = cellWidth * columns;
-	const plotHeight = cellHeight * grid.rows;
-	const gap = cellWidth >= 7 && cellHeight >= 7 ? 1 : 0;
-	const span = grid.timestamps[columns - 1] - grid.timestamps[0];
-
-	const cells = [];
-	for (let column = 0; column < columns; column++) {
-		for (let row = 0; row < grid.rows; row++) {
-			const x = PAD.left + column * cellWidth;
-			const y = PAD.top + (grid.rows - 1 - row) * cellHeight;
-			cells.push(`<rect x="${x.toFixed(2)}" y="${y.toFixed(2)}" width="${(cellWidth - gap).toFixed(2)}" height="${(cellHeight - gap).toFixed(2)}" fill="${colorFor(matrix[column][row], max)}"/>`);
-		}
-	}
-
-	const rowStride = Math.max(1, Math.ceil(13 / cellHeight));
-	const rowLabels = [];
-	for (let row = 0; row < grid.rows; row++) {
-		if (row % rowStride !== 0 && row !== grid.rows - 1) {
-			continue;
-		}
-		const y = PAD.top + (grid.rows - 1 - row) * cellHeight + cellHeight / 2;
-		const text = row === grid.rows - 1 ? "∞" : formatNumber(grid.buckets[row]);
-		rowLabels.push(`<text class="axis-label" x="${PAD.left - 6}" y="${(y + 3.2).toFixed(2)}" text-anchor="end">${esc(text)}</text>`);
-	}
-
-	const columnStride = Math.max(1, Math.ceil(58 / cellWidth));
-	const columnLabels = [];
-	for (let column = 0; column < columns; column += columnStride) {
-		const ts = grid.timestamps[column];
-		const x = PAD.left + column * cellWidth;
-		columnLabels.push(`<text class="axis-label" x="${x.toFixed(2)}" y="${(PAD.top + plotHeight + 14).toFixed(2)}">${esc(formatTime(ts, span))}${grid.partial.has(ts) ? "*" : ""}</text>`);
-	}
-
-	host.innerHTML = `<svg width="${PAD.left + plotWidth + PAD.right}" height="${PAD.top + plotHeight + PAD.bottom}">
-		${cells.join("")}
-		<line class="axis-line" x1="${PAD.left}" y1="${PAD.top + plotHeight + 0.5}" x2="${PAD.left + plotWidth}" y2="${PAD.top + plotHeight + 0.5}"/>
-		${rowLabels.join("")}${columnLabels.join("")}
-		<rect class="cursor-cell" hidden/>
-	</svg>`;
-
-	// a heatmap cannot fill gaps, so a chart much shorter than the window asked
-	// for means those columns hold no data at all rather than being hidden
-	const asked = state.window ? state.window.end - state.window.start : span;
-	const covered = `${formatTime(grid.timestamps[0], asked)}–${formatTime(grid.timestamps[columns - 1], asked)}`;
-	const coverage =
-		state.window && span < 0.9 * asked
-			? `${covered}, the only columns with data in the ${formatTime(state.window.start, asked)}–${formatTime(state.window.end, asked)} requested`
-			: covered;
-
-	legend.innerHTML = `<span>0</span>
-		<div class="swatches"><div class="swatch" style="background:${ZERO_FILL};border:1px solid var(--line)"></div>${RAMP.map((color) => `<div class="swatch" style="background:${color}"></div>`).join("")}</div>
-		<span>${formatNumber(max)} per cell</span>
-		<span>· ${grid.rows} buckets × ${columns} columns · ${shown.length} of ${grid.series.length} series · ${esc(coverage)}${grid.partial.size ? " · * partial column" : ""}</span>`;
-
-	attachHover(host.querySelector("svg"), { grid, matrix, shown, columns, cellWidth, cellHeight, plotHeight, span, max });
-}
-
-function attachHover(svg, view) {
-	const tooltip = $("#tooltip");
-	const cursor = svg.querySelector(".cursor-cell");
-
-	svg.addEventListener("mouseleave", () => {
-		tooltip.hidden = true;
-		cursor.setAttribute("hidden", "");
-	});
-
-	svg.addEventListener("mousemove", (event) => {
-		const box = svg.getBoundingClientRect();
-		const column = Math.floor((event.clientX - box.left - PAD.left) / view.cellWidth);
-		const row = view.grid.rows - 1 - Math.floor((event.clientY - box.top - PAD.top) / view.cellHeight);
-		if (column < 0 || column >= view.columns || row < 0 || row >= view.grid.rows) {
-			tooltip.hidden = true;
-			cursor.setAttribute("hidden", "");
-			return;
-		}
-
-		cursor.removeAttribute("hidden");
-		cursor.setAttribute("x", PAD.left + column * view.cellWidth);
-		cursor.setAttribute("y", PAD.top + (view.grid.rows - 1 - row) * view.cellHeight);
-		cursor.setAttribute("width", view.cellWidth);
-		cursor.setAttribute("height", view.cellHeight);
-
-		const ts = view.grid.timestamps[column];
-		const breakdown = view.shown
-			.map((entry) => ({ key: entry.key, count: entry.byTs.get(ts)?.[row] ?? 0 }))
-			.filter((entry) => entry.count > 0)
-			.sort((a, b) => b.count - a.count);
-
-		tooltip.innerHTML = [
-			`<b>${esc(formatNumber(view.matrix[column][row]))}</b> in ${esc(bucketRange(view.grid, row))}`,
-			`${esc(new Date(ts).toTimeString().slice(0, 8))}${view.grid.partial.has(ts) ? "  (partial)" : ""}`,
-			...breakdown.slice(0, 6).map((entry) => `  ${esc(entry.key)}  ${esc(formatNumber(entry.count))}`),
-			breakdown.length > 6 ? `  … ${breakdown.length - 6} more` : "",
-		]
-			.filter(Boolean)
-			.join("\n");
-
-		tooltip.hidden = false;
-		const width = tooltip.offsetWidth;
-		tooltip.style.left = `${Math.min(event.clientX + 14, window.innerWidth - width - 8)}px`;
-		tooltip.style.top = `${Math.min(event.clientY + 14, window.innerHeight - tooltip.offsetHeight - 8)}px`;
-	});
-}
-
-/* ---------- chrome ---------- */
-
-function showBanner(message) {
-	const banner = $("#banner");
-	banner.textContent = message;
-	banner.hidden = !message;
-}
-
-function renderJSON(selector, text) {
-	$(selector).textContent = text.length > MAX_JSON_CHARS ? `${text.slice(0, MAX_JSON_CHARS)}\n… truncated for display, Copy takes the whole thing` : text;
-}
-
-function wire() {
-	$("#modes").addEventListener("click", (event) => {
-		if (event.target.dataset.mode) {
-			setMode(event.target.dataset.mode);
-		}
-	});
-
-	$("#bucket-kinds").addEventListener("click", (event) => {
-		if (event.target.dataset.kind) {
-			setBucketKind(event.target.dataset.kind);
-		}
-	});
-
-	$("#scale").addEventListener("input", () => {
-		$("#scale-hint").textContent = scaleHint(Number($("#scale").value));
-	});
-
-	$("#add-row").addEventListener("click", () => {
-		addRow();
-		refreshPanes();
-	});
-
-	$("#range").addEventListener("change", () => {
-		attributesByMetric.clear();
-		refreshGroupOptions();
-	});
-
-	$("#group-input").addEventListener("change", (event) => {
-		const key = event.target.value.trim();
-		if (key && !state.groupBy.includes(key)) {
-			state.groupBy.push(key);
-			renderGroupChips();
-			refreshGroupOptions();
-		}
-		event.target.value = "";
-	});
-
-	$("#group-chips").addEventListener("click", (event) => {
-		const key = event.target.dataset.key;
-		if (key) {
-			state.groupBy = state.groupBy.filter((candidate) => candidate !== key);
-			renderGroupChips();
-			refreshGroupOptions();
-		}
-	});
-
-	$("#form").addEventListener("submit", (event) => {
-		event.preventDefault();
-		run();
-	});
-
-	$("#color-scale").addEventListener("change", (event) => {
-		state.colorScale = event.target.value;
-		renderChart();
-	});
-
-	$("#groups").addEventListener("change", (event) => {
-		const key = event.target.dataset.key;
-		if (!key) {
-			return;
-		}
-		if (event.target.checked) {
-			state.hidden.delete(key);
-		} else {
-			state.hidden.add(key);
-		}
-		$("#group-count").textContent = `${state.grid.series.length - state.hidden.size} of ${state.grid.series.length} shown`;
-		renderChart();
-	});
-
-	$("#select-all").addEventListener("click", () => {
-		state.hidden.clear();
-		renderGroups();
-		renderChart();
-	});
-
-	$("#select-none").addEventListener("click", () => {
-		state.hidden = new Set(state.grid.series.map((entry) => entry.key));
-		renderGroups();
-		renderChart();
-	});
-
-	for (const button of document.querySelectorAll("[data-copy]")) {
-		button.addEventListener("click", async () => {
-			const text = button.dataset.copy === "request" ? state.requestText : state.responseText;
-			try {
-				await navigator.clipboard.writeText(text);
-				button.textContent = "Copied";
-			} catch {
-				button.textContent = "Copy failed";
-			}
-			setTimeout(() => {
-				button.textContent = "Copy";
-			}, 1200);
-		});
-	}
-
-	let resizeTimer = 0;
-	window.addEventListener("resize", () => {
-		clearTimeout(resizeTimer);
-		resizeTimer = setTimeout(renderChart, 120);
-	});
-}
-
-wire();
-setMode("metric");
-renderGroupChips();
-setBucketKind("default");

heatmap-poc/static/index.html

@@ -1,168 +0,0 @@
-<!doctype html>
-<html lang="en">
-<head>
-	<meta charset="utf-8">
-	<meta name="viewport" content="width=device-width, initial-scale=1">
-	<title>Heatmap POC</title>
-	<link rel="stylesheet" href="style.css">
-</head>
-<body>
-	<header>
-		<h1>Heatmap POC</h1>
-		<p class="sub"><code>POST /api/v5/query_range</code> with <code>requestType: "heatmap"</code></p>
-	</header>
-
-	<div class="layout">
-		<form id="form" class="card form">
-			<div class="segmented" id="modes">
-				<button type="button" data-mode="metric" class="on">Metric</button>
-				<button type="button" data-mode="formula">Formula</button>
-				<button type="button" data-mode="promql">PromQL</button>
-			</div>
-
-			<div class="grid-2">
-				<label>Time range
-					<select id="range">
-						<option value="15">Last 15 minutes</option>
-						<option value="60" selected>Last 1 hour</option>
-						<option value="180">Last 3 hours</option>
-						<option value="360">Last 6 hours</option>
-						<option value="1440">Last 24 hours</option>
-						<option value="10080">Last 7 days</option>
-					</select>
-				</label>
-				<label>Step (seconds)
-					<input id="step" type="number" min="1" step="1" value="60">
-				</label>
-			</div>
-
-			<section id="builder-pane">
-				<div id="rows"></div>
-				<button type="button" id="add-row" class="ghost" hidden>+ Add metric</button>
-
-				<label id="formula-field" hidden>Formula
-					<input id="formula" placeholder="A / B" autocomplete="off">
-				</label>
-
-				<div class="chips-field">
-					<span class="chips-label">Group by</span>
-					<div id="group-chips" class="chips"></div>
-					<input id="group-input" list="group-options" placeholder="Add an attribute…" autocomplete="off">
-					<datalist id="group-options"></datalist>
-					<p class="hint" id="group-hint" hidden></p>
-				</div>
-			</section>
-
-			<section id="promql-pane" hidden>
-				<label>PromQL
-					<textarea id="promql" rows="4" spellcheck="false" placeholder='sum by (le) (increase({__name__="http.server.duration.bucket"}[5m]))'></textarea>
-				</label>
-				<p class="hint">The bucket axis comes from the <code>le</code> labels the query returns, so bucket options are rejected here. A dotted metric name needs the <code>{__name__="…"}</code> form.</p>
-			</section>
-
-			<fieldset id="bucket-pane">
-				<legend>Bucket options</legend>
-				<div class="segmented small" id="bucket-kinds">
-					<button type="button" data-kind="default" class="on">Default</button>
-					<button type="button" data-kind="log">Log</button>
-					<button type="button" data-kind="linear">Linear</button>
-				</div>
-				<p class="hint" id="bucket-default-hint">Omitted from the request. The backend falls back to a log axis at scale 4.</p>
-				<label id="scale-field" hidden>Scale
-					<input id="scale" type="number" min="-4" max="4" step="1" value="4">
-					<span class="hint" id="scale-hint"></span>
-				</label>
-				<div class="grid-2" id="linear-fields" hidden>
-					<label>Max value
-						<input id="max-value" type="number" min="0" step="any" value="1000">
-					</label>
-					<label>Number of buckets
-						<input id="num-buckets" type="number" min="1" max="512" step="1" value="60">
-					</label>
-				</div>
-			</fieldset>
-
-			<div class="run-row">
-				<button type="submit" id="run">Run query</button>
-				<label class="inline"><input type="checkbox" id="no-cache" checked> Bypass cache</label>
-			</div>
-		</form>
-
-		<div class="results">
-			<p id="banner" hidden></p>
-
-			<section class="card">
-				<div class="card-head">
-					<h2>Heatmap</h2>
-					<div class="head-tools">
-						<label class="inline">Colour scale
-							<select id="color-scale">
-								<option value="linear" selected>Linear</option>
-								<option value="log">Log</option>
-							</select>
-						</label>
-					</div>
-				</div>
-				<div id="chart" class="chart"><p class="empty">Run a query to draw the heatmap.</p></div>
-				<div id="legend" class="legend"></div>
-			</section>
-
-			<section class="card" id="groups-card" hidden>
-				<div class="card-head">
-					<h2>Groups <span id="group-count" class="count"></span></h2>
-					<div class="head-tools">
-						<button type="button" class="ghost" id="select-all">All</button>
-						<button type="button" class="ghost" id="select-none">None</button>
-					</div>
-				</div>
-				<div id="groups" class="groups"></div>
-			</section>
-
-			<div class="grid-2 json-grid">
-				<section class="card">
-					<div class="card-head">
-						<h2>Request</h2>
-						<button type="button" class="ghost" data-copy="request">Copy</button>
-					</div>
-					<pre id="request" class="json"></pre>
-				</section>
-				<section class="card">
-					<div class="card-head">
-						<h2>Response</h2>
-						<button type="button" class="ghost" data-copy="response">Copy</button>
-					</div>
-					<pre id="response" class="json"></pre>
-				</section>
-			</div>
-		</div>
-	</div>
-
-	<div id="tooltip" class="tooltip" hidden></div>
-
-	<template id="row-template">
-		<div class="metric-row">
-			<span class="row-name"></span>
-			<div class="row-body">
-				<div class="metric-field">
-					<input class="metric-input" placeholder="Search metrics…" autocomplete="off" spellcheck="false">
-					<datalist></datalist>
-					<span class="badge" hidden></span>
-				</div>
-				<div class="agg-fields" hidden>
-					<label>Time
-						<select class="time-agg"></select>
-					</label>
-					<label>Space
-						<select class="space-agg"></select>
-					</label>
-				</div>
-				<input class="filter-input" placeholder='Filter, e.g. service = "api"' autocomplete="off" spellcheck="false">
-				<p class="row-note hint" hidden></p>
-			</div>
-			<button type="button" class="remove-row" title="Remove this metric">&times;</button>
-		</div>
-	</template>
-
-	<script src="app.js" type="module"></script>
-</body>
-</html>

heatmap-poc/static/style.css

@@ -1,552 +0,0 @@
-:root {
-	color-scheme: dark;
-
-	--surface: #0b0d12;
-	--card: #12151d;
-	--card-head: #171b25;
-	--line: #242a38;
-	--line-soft: #1b2030;
-
-	--ink: #e6e9f0;
-	--ink-soft: #a2abbd;
-	--ink-faint: #6b7488;
-
-	--accent: #4a80c4;
-	--accent-ink: #b9d8f5;
-	--danger: #e0736b;
-	--danger-bg: #2a1618;
-
-	--cell-zero: #0e1016;
-}
-
-* {
-	box-sizing: border-box;
-}
-
-/* the display rules below would otherwise beat the UA's [hidden] rule, and SVG
-   elements never honoured the attribute on their own */
-[hidden] {
-	display: none !important;
-}
-
-body {
-	margin: 0;
-	padding: 20px;
-	background: var(--surface);
-	color: var(--ink);
-	font: 13px/1.5 ui-sans-serif, system-ui, -apple-system, "Segoe UI", sans-serif;
-}
-
-header {
-	margin-bottom: 16px;
-}
-
-h1 {
-	margin: 0;
-	font-size: 17px;
-	font-weight: 600;
-	letter-spacing: -0.01em;
-}
-
-h2 {
-	margin: 0;
-	font-size: 12px;
-	font-weight: 600;
-	text-transform: uppercase;
-	letter-spacing: 0.06em;
-	color: var(--ink-soft);
-}
-
-.sub {
-	margin: 3px 0 0;
-	color: var(--ink-faint);
-}
-
-code {
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	font-size: 0.92em;
-	color: var(--ink-soft);
-}
-
-.layout {
-	display: grid;
-	grid-template-columns: minmax(320px, 380px) minmax(0, 1fr);
-	gap: 16px;
-	align-items: start;
-}
-
-@media (max-width: 900px) {
-	.layout {
-		grid-template-columns: minmax(0, 1fr);
-	}
-}
-
-.card {
-	background: var(--card);
-	border: 1px solid var(--line);
-	border-radius: 8px;
-	overflow: hidden;
-}
-
-.card-head {
-	display: flex;
-	align-items: center;
-	justify-content: space-between;
-	gap: 10px;
-	padding: 8px 12px;
-	background: var(--card-head);
-	border-bottom: 1px solid var(--line);
-}
-
-.head-tools {
-	display: flex;
-	align-items: center;
-	gap: 8px;
-}
-
-.count {
-	color: var(--ink-faint);
-	font-weight: 400;
-	text-transform: none;
-	letter-spacing: 0;
-}
-
-/* ---------- form ---------- */
-
-.form {
-	padding: 14px;
-	display: grid;
-	gap: 14px;
-	position: sticky;
-	top: 20px;
-}
-
-label {
-	display: grid;
-	gap: 5px;
-	font-size: 12px;
-	color: var(--ink-soft);
-}
-
-label.inline {
-	display: inline-flex;
-	align-items: center;
-	gap: 6px;
-}
-
-.grid-2 {
-	display: grid;
-	grid-template-columns: 1fr 1fr;
-	gap: 10px;
-}
-
-input,
-select,
-textarea {
-	width: 100%;
-	padding: 6px 8px;
-	background: var(--surface);
-	color: var(--ink);
-	border: 1px solid var(--line);
-	border-radius: 5px;
-	font: inherit;
-}
-
-input[type="checkbox"] {
-	width: auto;
-	accent-color: var(--accent);
-}
-
-textarea {
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	resize: vertical;
-}
-
-input:focus-visible,
-select:focus-visible,
-textarea:focus-visible,
-button:focus-visible {
-	outline: 2px solid var(--accent);
-	outline-offset: 1px;
-}
-
-fieldset {
-	margin: 0;
-	padding: 10px 12px 12px;
-	border: 1px solid var(--line);
-	border-radius: 6px;
-	display: grid;
-	gap: 10px;
-}
-
-legend {
-	padding: 0 5px;
-	font-size: 11px;
-	font-weight: 600;
-	text-transform: uppercase;
-	letter-spacing: 0.06em;
-	color: var(--ink-soft);
-}
-
-button {
-	padding: 6px 12px;
-	background: var(--accent);
-	color: #fff;
-	border: 1px solid transparent;
-	border-radius: 5px;
-	font: inherit;
-	font-weight: 500;
-	cursor: pointer;
-}
-
-button:hover {
-	filter: brightness(1.12);
-}
-
-button.ghost {
-	background: transparent;
-	color: var(--ink-soft);
-	border-color: var(--line);
-	font-weight: 400;
-}
-
-button.ghost:hover {
-	color: var(--ink);
-	border-color: var(--ink-faint);
-	filter: none;
-}
-
-button[disabled] {
-	opacity: 0.5;
-	cursor: default;
-	filter: none;
-}
-
-.segmented {
-	display: flex;
-	gap: 2px;
-	padding: 2px;
-	background: var(--surface);
-	border: 1px solid var(--line);
-	border-radius: 6px;
-}
-
-.segmented button {
-	flex: 1;
-	background: transparent;
-	color: var(--ink-soft);
-	font-weight: 400;
-}
-
-.segmented button.on {
-	background: var(--line);
-	color: var(--ink);
-}
-
-.segmented button:hover {
-	filter: none;
-	color: var(--ink);
-}
-
-.segmented.small button {
-	padding: 4px 8px;
-	font-size: 12px;
-}
-
-.hint {
-	margin: 0;
-	font-size: 11px;
-	color: var(--ink-faint);
-}
-
-.hint.warn {
-	color: var(--danger);
-}
-
-.run-row {
-	display: flex;
-	align-items: center;
-	gap: 12px;
-}
-
-.run-row button {
-	flex: 1;
-}
-
-/* ---------- metric rows ---------- */
-
-#rows {
-	display: grid;
-	gap: 8px;
-}
-
-.metric-row {
-	display: grid;
-	grid-template-columns: auto minmax(0, 1fr) auto;
-	gap: 8px;
-	align-items: start;
-	padding: 8px;
-	background: var(--surface);
-	border: 1px solid var(--line-soft);
-	border-radius: 6px;
-}
-
-.row-name {
-	width: 20px;
-	padding-top: 6px;
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	font-weight: 600;
-	color: var(--accent-ink);
-	text-align: center;
-}
-
-.row-body {
-	display: grid;
-	gap: 8px;
-	min-width: 0;
-}
-
-.metric-field {
-	display: flex;
-	align-items: center;
-	gap: 6px;
-	min-width: 0;
-}
-
-.badge {
-	flex: none;
-	padding: 2px 6px;
-	background: var(--line);
-	border-radius: 4px;
-	font-size: 10px;
-	font-weight: 600;
-	text-transform: uppercase;
-	letter-spacing: 0.04em;
-	color: var(--accent-ink);
-	white-space: nowrap;
-}
-
-.badge.bad {
-	background: var(--danger-bg);
-	color: var(--danger);
-}
-
-.agg-fields {
-	display: grid;
-	grid-template-columns: 1fr 1fr;
-	gap: 8px;
-}
-
-.remove-row {
-	padding: 2px 7px;
-	background: transparent;
-	color: var(--ink-faint);
-	border-color: transparent;
-	font-size: 15px;
-	line-height: 1.2;
-}
-
-.remove-row:hover {
-	color: var(--danger);
-	filter: none;
-}
-
-/* ---------- group by chips ---------- */
-
-.chips-field {
-	display: grid;
-	gap: 6px;
-}
-
-.chips-label {
-	font-size: 12px;
-	color: var(--ink-soft);
-}
-
-.chips {
-	display: flex;
-	flex-wrap: wrap;
-	gap: 5px;
-}
-
-.chips:empty {
-	display: none;
-}
-
-.chip {
-	display: inline-flex;
-	align-items: center;
-	gap: 5px;
-	padding: 2px 4px 2px 8px;
-	background: var(--line);
-	border-radius: 11px;
-	font-size: 12px;
-}
-
-.chip button {
-	padding: 0 3px;
-	background: transparent;
-	color: var(--ink-faint);
-	border: 0;
-	font-size: 13px;
-	line-height: 1;
-}
-
-.chip button:hover {
-	color: var(--danger);
-	filter: none;
-}
-
-/* ---------- results ---------- */
-
-.results {
-	display: grid;
-	gap: 16px;
-	min-width: 0;
-}
-
-#banner {
-	margin: 0;
-	padding: 10px 12px;
-	background: var(--danger-bg);
-	border: 1px solid #4a2427;
-	border-radius: 8px;
-	color: var(--danger);
-	white-space: pre-wrap;
-}
-
-.chart {
-	padding: 12px;
-	overflow: auto;
-	max-height: 620px;
-}
-
-.chart svg {
-	display: block;
-}
-
-.empty {
-	margin: 0;
-	padding: 28px 0;
-	color: var(--ink-faint);
-	text-align: center;
-}
-
-.axis-label {
-	fill: var(--ink-faint);
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	font-size: 10px;
-}
-
-.axis-line {
-	stroke: var(--line);
-	stroke-width: 1;
-}
-
-.cursor-cell {
-	fill: none;
-	stroke: var(--accent-ink);
-	stroke-width: 1.5;
-	pointer-events: none;
-}
-
-.legend {
-	display: flex;
-	align-items: center;
-	gap: 10px;
-	padding: 0 12px 12px;
-	color: var(--ink-faint);
-	font-size: 11px;
-}
-
-.legend:empty {
-	display: none;
-}
-
-.legend .swatches {
-	display: flex;
-	gap: 2px;
-}
-
-.legend .swatch {
-	width: 20px;
-	height: 10px;
-	border-radius: 2px;
-}
-
-.groups {
-	display: grid;
-	grid-template-columns: repeat(auto-fill, minmax(220px, 1fr));
-	gap: 2px 12px;
-	padding: 10px 12px;
-	max-height: 220px;
-	overflow: auto;
-}
-
-.groups label {
-	display: flex;
-	align-items: center;
-	gap: 7px;
-	min-width: 0;
-	padding: 2px 0;
-	color: var(--ink);
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	font-size: 11px;
-	cursor: pointer;
-}
-
-.groups .name {
-	overflow: hidden;
-	text-overflow: ellipsis;
-	white-space: nowrap;
-}
-
-.groups .total {
-	margin-left: auto;
-	flex: none;
-	color: var(--ink-faint);
-}
-
-.json-grid {
-	align-items: start;
-}
-
-@media (max-width: 1200px) {
-	.json-grid {
-		grid-template-columns: minmax(0, 1fr);
-	}
-}
-
-.json {
-	margin: 0;
-	padding: 12px;
-	max-height: 420px;
-	overflow: auto;
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	font-size: 11px;
-	line-height: 1.55;
-	color: var(--ink-soft);
-	white-space: pre;
-	tab-size: 2;
-}
-
-.tooltip {
-	position: fixed;
-	z-index: 10;
-	max-width: 320px;
-	padding: 7px 9px;
-	background: #1c212e;
-	border: 1px solid var(--line);
-	border-radius: 6px;
-	box-shadow: 0 6px 18px rgb(0 0 0 / 45%);
-	font-family: ui-monospace, SFMono-Regular, Menlo, monospace;
-	font-size: 11px;
-	line-height: 1.6;
-	pointer-events: none;
-	white-space: pre;
-}
-
-.tooltip b {
-	color: var(--accent-ink);
-	font-weight: 600;
-}

pkg/querier/bucket_cache.go

@@ -451,7 +451,7 @@ func (bc *bucketCache) mergeBuckets(ctx context.Context, buckets []*qbtypes.Cach
 	// Merge values based on type
 	var mergedValue any
 	switch resultType {
-	case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+	case qbtypes.RequestTypeTimeSeries:
 		mergedValue = bc.mergeTimeSeriesValues(ctx, buckets)
 		// Raw and Scalar types are not cached, so no merge needed
 	}
@@ -476,36 +476,14 @@ func (bc *bucketCache) mergeTimeSeriesValues(ctx context.Context, buckets []*qbt
 	}
 	seriesMap := make(map[seriesKey]*qbtypes.TimeSeries, estimatedSeries)
 
-	decodedTimeSeriesData := make([]*qbtypes.TimeSeriesData, 0, len(buckets))
-
-	// Alias and Meta are taken from whichever cached bucket covers the latest
-	// range, and the buckets do not arrive in StartMs order, so keep the winner
-	// per AggregationBucket.Index alongside the StartMs that won it.
-	aggregationIndexToLatest := map[int]*qbtypes.AggregationBucket{}
-	aggregationIndexToLatestStartMs := map[int]uint64{}
-
 	for _, bucket := range buckets {
 		var tsData *qbtypes.TimeSeriesData
 		if err := json.Unmarshal(bucket.Value, &tsData); err != nil {
 			bc.logger.ErrorContext(ctx, "failed to unmarshal time series data", errors.Attr(err))
 			continue
 		}
-		decodedTimeSeriesData = append(decodedTimeSeriesData, tsData)
 
 		for _, aggBucket := range tsData.Aggregations {
-			if _, seen := aggregationIndexToLatest[aggBucket.Index]; !seen || bucket.StartMs >= aggregationIndexToLatestStartMs[aggBucket.Index] {
-				aggregationIndexToLatest[aggBucket.Index] = aggBucket
-				aggregationIndexToLatestStartMs[aggBucket.Index] = bucket.StartMs
-			}
-		}
-	}
-
-	mergedUpperBounds := qbtypes.MergeBucketUpperBounds(decodedTimeSeriesData...)
-
-	for _, tsData := range decodedTimeSeriesData {
-		for _, aggBucket := range tsData.Aggregations {
-			aggBucket.ReindexValuesToNewUpperBounds(mergedUpperBounds[aggBucket.Index])
-
 			for _, series := range aggBucket.Series {
 				// Create series key from labels
 				key := seriesKey{
@@ -578,15 +556,10 @@ func (bc *bucketCache) mergeTimeSeriesValues(ctx context.Context, buckets []*qbt
 			}
 		}
 
-		aggBucket := &qbtypes.AggregationBucket{
+		result.Aggregations = append(result.Aggregations, &qbtypes.AggregationBucket{
 			Index:  index,
 			Series: seriesList,
-		}
-		if latest, ok := aggregationIndexToLatest[index]; ok {
-			aggBucket.Alias = latest.Alias
-			aggBucket.Meta = latest.Meta
-		}
-		result.Aggregations = append(result.Aggregations, aggBucket)
+		})
 	}
 
 	return result
@@ -599,7 +572,7 @@ func (bc *bucketCache) isEmptyResult(result *qbtypes.Result) (isEmpty bool, isFi
 	}
 
 	switch result.Type {
-	case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+	case qbtypes.RequestTypeTimeSeries:
 		if tsData, ok := result.Value.(*qbtypes.TimeSeriesData); ok {
 			// No aggregations at all means truly empty
 			if len(tsData.Aggregations) == 0 {
@@ -726,19 +699,14 @@ func (bc *bucketCache) trimResultToFluxBoundary(result *qbtypes.Result, fluxBoun
 	}
 
 	switch result.Type {
-	case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+	case qbtypes.RequestTypeTimeSeries:
 		// Trim time series data
 		if tsData, ok := result.Value.(*qbtypes.TimeSeriesData); ok && tsData != nil {
 			trimmedData := &qbtypes.TimeSeriesData{}
 
 			for _, aggBucket := range tsData.Aggregations {
-				// Meta has to survive the trim: a heatmap's counts are
-				// positional against Meta.Buckets, so a cached bucket that
-				// lost its axis cannot be read back against anything.
 				trimmedBucket := &qbtypes.AggregationBucket{
 					Index: aggBucket.Index,
-					Alias: aggBucket.Alias,
-					Meta:  aggBucket.Meta,
 				}
 
 				for _, series := range aggBucket.Series {
@@ -798,7 +766,7 @@ func (bc *bucketCache) filterResultToTimeRange(result *qbtypes.Result, startMs,
 	}
 
 	switch result.Type {
-	case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+	case qbtypes.RequestTypeTimeSeries:
 		if tsData, ok := result.Value.(*qbtypes.TimeSeriesData); ok {
 			filteredData := &qbtypes.TimeSeriesData{
 				Aggregations: make([]*qbtypes.AggregationBucket, 0, len(tsData.Aggregations)),

pkg/querier/builder_query.go

@@ -92,10 +92,6 @@ func (q *builderQuery[T]) Fingerprint() string {
 	// This needs to include all fields that affect the query results
 	parts := []string{q.queryType.StringValue()}
 
-	// A heatmap and a time series query can share every spec field and still
-	// return different rows, so the request type has to separate their entries
-	parts = append(parts, fmt.Sprintf("requestType=%s", q.kind.StringValue()))
-
 	// Add signal type
 	parts = append(parts, fmt.Sprintf("signal=%s", q.spec.Signal.StringValue()))
 
@@ -134,9 +130,6 @@ func (q *builderQuery[T]) Fingerprint() string {
 					}
 					part += ":" + route
 				}
-				if a.HeatmapBucketing != nil {
-					part += ":" + fingerprintHeatmapBucketing(*a.HeatmapBucketing)
-				}
 				aggParts = append(aggParts, part)
 			}
 		}
@@ -192,16 +185,6 @@ func (q *builderQuery[T]) Fingerprint() string {
 	return strings.Join(parts, "&")
 }
 
-// fingerprintHeatmapBucketing captures only what changes the rows ClickHouse
-// returns, which is why LogBucketsSpec.Scale is absent: coarsening it happens in
-// postprocessing, so every scale reads one cache entry.
-func fingerprintHeatmapBucketing(b qbtypes.HeatmapBucketing) string {
-	if b.Kind == qbtypes.BucketsKindLinear {
-		return fmt.Sprintf("%s:%v:%d", b.Kind.StringValue(), b.MaxValue, b.NumBuckets)
-	}
-	return b.Kind.StringValue()
-}
-
 func fingerprintGroupByKey(gb qbtypes.GroupByKey) string {
 	return fingerprintFieldKey(gb.TelemetryFieldKey)
 }
@@ -429,7 +412,7 @@ func (q *builderQuery[T]) narrowWindowByTraceID(ctx context.Context, fromMS, toM
 func emptyResultFor(kind qbtypes.RequestType, queryName string) *qbtypes.Result {
 	var value any
 	switch kind {
-	case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+	case qbtypes.RequestTypeTimeSeries:
 		value = &qbtypes.TimeSeriesData{QueryName: queryName}
 	case qbtypes.RequestTypeScalar:
 		value = &qbtypes.ScalarData{QueryName: queryName}
@@ -482,9 +465,8 @@ func (q *builderQuery[T]) executeWithContext(ctx context.Context, query string,
 	queryWindow := &qbtypes.TimeRange{From: q.fromMS, To: q.toMS}
 
 	kind := q.kind
-	// all metric queries are time series then reduced if required, except
-	// heatmaps, whose statement returns a row per bucket rather than per point
-	if q.spec.Signal == telemetrytypes.SignalMetrics && kind != qbtypes.RequestTypeHeatmap {
+	// all metric queries are time series then reduced if required
+	if q.spec.Signal == telemetrytypes.SignalMetrics {
 		kind = qbtypes.RequestTypeTimeSeries
 	}
 

pkg/querier/builder_query_test.go

@@ -6,7 +6,6 @@ import (
 	"time"
 
 	"github.com/SigNoz/signoz/pkg/querybuilder"
-	"github.com/SigNoz/signoz/pkg/types/metrictypes"
 	qbtypes "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
 	"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
 	"github.com/stretchr/testify/assert"
@@ -121,170 +120,6 @@ func TestBuilderQueryFingerprintQueryType(t *testing.T) {
 	assert.Empty(t, ai.Fingerprint())
 }
 
-func TestBuilderQueryFingerprintHeatmapBucketing(t *testing.T) {
-	coarseLogScale := 1
-
-	testCases := []struct {
-		description   string
-		left          *builderQuery[qbtypes.MetricAggregation]
-		right         *builderQuery[qbtypes.MetricAggregation]
-		expectedEqual bool
-	}{
-		{
-			// fingerprintHeatmapBucketing leaves LogScale out, so the two are
-			// indistinguishable here by design
-			description: "a coarser logScale reads the same cache entry",
-			left: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLog, LogScale: qbtypes.MaxLogScale, NumBuckets: qbtypes.DefaultNumBuckets},
-					}},
-				},
-			},
-			right: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLog, LogScale: qbtypes.MaxLogScale, NumBuckets: qbtypes.DefaultNumBuckets},
-					}},
-				},
-			},
-			expectedEqual: true,
-		},
-		{
-			description: "linear separates on maxValue",
-			left: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLinear, MaxValue: 500, NumBuckets: 25},
-					}},
-				},
-			},
-			right: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLinear, MaxValue: 800, NumBuckets: 25},
-					}},
-				},
-			},
-			expectedEqual: false,
-		},
-		{
-			description: "linear separates on numBuckets",
-			left: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLinear, MaxValue: 500, NumBuckets: 25},
-					}},
-				},
-			},
-			right: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLinear, MaxValue: 500, NumBuckets: 40},
-					}},
-				},
-			},
-			expectedEqual: false,
-		},
-		{
-			description: "linear and log are separate entries",
-			left: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLinear, MaxValue: 500, NumBuckets: 25},
-					}},
-				},
-			},
-			right: &builderQuery[qbtypes.MetricAggregation]{
-				queryType: qbtypes.QueryTypeBuilder,
-				kind:      qbtypes.RequestTypeHeatmap,
-				spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-					Signal: telemetrytypes.SignalMetrics,
-					Aggregations: []qbtypes.MetricAggregation{{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{Kind: qbtypes.BucketsKindLog, LogScale: qbtypes.MaxLogScale, NumBuckets: qbtypes.DefaultNumBuckets},
-					}},
-				},
-			},
-			expectedEqual: false,
-		},
-	}
-
-	for _, testCase := range testCases {
-		t.Run(testCase.description, func(t *testing.T) {
-			if testCase.expectedEqual {
-				assert.Equal(t, testCase.left.Fingerprint(), testCase.right.Fingerprint())
-				return
-			}
-			assert.NotEqual(t, testCase.left.Fingerprint(), testCase.right.Fingerprint())
-		})
-	}
-
-	t.Run("a coarser scale is carried but reads the same cache entry", func(t *testing.T) {
-		finest := (&qbtypes.BucketOptions{Kind: qbtypes.BucketsKindLog, Spec: qbtypes.LogBucketsSpec{}}).ToHeatmapBucketing()
-		coarse := (&qbtypes.BucketOptions{Kind: qbtypes.BucketsKindLog, Spec: qbtypes.LogBucketsSpec{Scale: &coarseLogScale}}).ToHeatmapBucketing()
-
-		assert.NotEqual(t, finest.LogScale, coarse.LogScale)
-		assert.Equal(t, fingerprintHeatmapBucketing(finest), fingerprintHeatmapBucketing(coarse))
-	})
-
-	t.Run("a histogram folds in no bucket options at all", func(t *testing.T) {
-		// resolveHeatmapBucketing leaves histograms nil, so bucketOptions sent
-		// alongside one must not fragment its cache
-		histogram := &builderQuery[qbtypes.MetricAggregation]{
-			queryType: qbtypes.QueryTypeBuilder,
-			kind:      qbtypes.RequestTypeHeatmap,
-			spec: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal: telemetrytypes.SignalMetrics,
-				Aggregations: []qbtypes.MetricAggregation{{
-					MetricName: "signoz_latency",
-					Type:       metrictypes.HistogramType,
-				}},
-			},
-		}
-
-		fingerprint := histogram.Fingerprint()
-		assert.NotContains(t, fingerprint, qbtypes.BucketsKindLog.StringValue())
-		assert.NotContains(t, fingerprint, qbtypes.BucketsKindLinear.StringValue())
-	})
-}
-
 func TestMakeBucketsOrder(t *testing.T) {
 	// Test that makeBuckets returns buckets in reverse chronological order by default
 	// Using milliseconds as input - need > 1 hour range to get multiple buckets

pkg/querier/consume.go

@@ -31,11 +31,6 @@ var (
 	// written clickhouse query. The column alias indcate which value is
 	// to be considered as final result (or target).
 	legacyReservedColumnTargetAliases = []string{"__result", "__value", "result", "res", "value"}
-
-	// userHeatmapBucketColumn is the alias a user written clickhouse query can
-	// give its bucket upper bound column, alongside the HeatmapBucketColumn the
-	// statement builder emits.
-	userHeatmapBucketColumn = "bucket"
 )
 
 // stripKeyAlias removes the __SELECT_KEY_<n>_ / __GROUP_BY_KEY_<n>_ prefix from a result
@@ -88,8 +83,6 @@ func consume(rows driver.Rows, kind qbtypes.RequestType, queryWindow *qbtypes.Ti
 		payload, err = readAsTimeSeries(rows, queryWindow, step, queryName)
 	case qbtypes.RequestTypeScalar:
 		payload, err = readAsScalar(rows, queryName)
-	case qbtypes.RequestTypeHeatmap:
-		payload, err = readAsHeatmap(rows, queryWindow, step, queryName)
 	case qbtypes.RequestTypeRaw, qbtypes.RequestTypeTrace, qbtypes.RequestTypeRawStream:
 		payload, err = readAsRaw(rows, queryName)
 		// TODO: add support for other request types
@@ -119,6 +112,35 @@ func readAsTimeSeries(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbt
 
 	stepMs := uint64(step.Milliseconds())
 
+	// Helper function to check if a timestamp represents a partial value
+	isPartialValue := func(timestamp int64) bool {
+		if stepMs == 0 || queryWindow == nil {
+			return false
+		}
+
+		timestampMs := uint64(timestamp)
+
+		// For the first interval, check if query start is misaligned
+		// The first complete interval starts at the first timestamp >= queryWindow.From that is aligned to step
+		firstCompleteInterval := queryWindow.From
+		if queryWindow.From%stepMs != 0 {
+			// Round up to next step boundary
+			firstCompleteInterval = ((queryWindow.From / stepMs) + 1) * stepMs
+		}
+
+		// If timestamp is before the first complete interval, it's partial
+		if timestampMs < firstCompleteInterval {
+			return true
+		}
+
+		// For the last interval, check if it would extend beyond query end
+		if timestampMs+stepMs > queryWindow.To {
+			return queryWindow.To%stepMs != 0
+		}
+
+		return false
+	}
+
 	// Pre-allocate for labels based on column count
 	lblValsCapacity := len(colNames) - 1 // -1 for timestamp
 	if lblValsCapacity < 0 {
@@ -249,7 +271,7 @@ func readAsTimeSeries(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbt
 			series.Values = append(series.Values, &qbtypes.TimeSeriesValue{
 				Timestamp: ts,
 				Value:     val,
-				Partial:   isPartialValue(ts, queryWindow, stepMs),
+				Partial:   isPartialValue(ts),
 			})
 		}
 	}
@@ -293,120 +315,6 @@ func readAsTimeSeries(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbt
 	}, nil
 }
 
-func isHeatmapBucketColumn(colName string) bool {
-	name := stripKeyAlias(colName)
-	return name == qbtypes.HeatmapBucketColumn || name == userHeatmapBucketColumn
-}
-
-// readAsHeatmap folds one row per cell — (timestamp, group labels, bucket upper
-// bound, count) — into one series per group.
-func readAsHeatmap(rows driver.Rows, queryWindow *qbtypes.TimeRange, step qbtypes.Step, queryName string) (*qbtypes.TimeSeriesData, error) {
-	colTypes := rows.ColumnTypes()
-	colNames := rows.Columns()
-
-	if !slices.ContainsFunc(colNames, isHeatmapBucketColumn) {
-		// there is no heatmap bucket column so empty response is returned.
-		return &qbtypes.TimeSeriesData{QueryName: queryName}, nil
-	}
-
-	slots := make([]any, len(colTypes))
-	for i, ct := range colTypes {
-		slots[i] = reflect.New(ct.ScanType()).Interface()
-	}
-
-	stepMs := uint64(step.Milliseconds())
-
-	accumulator := newHeatmapAccumulator()
-
-	for rows.Next() {
-		if err := rows.Scan(slots...); err != nil {
-			return nil, err
-		}
-
-		var (
-			ts         int64
-			upperBound float64
-			count      float64
-			lblVals    []string
-			lblObjs    []*qbtypes.Label
-		)
-
-		for idx, ptr := range slots {
-			name := stripKeyAlias(colNames[idx])
-			value := derefValue(ptr)
-
-			if t, ok := value.(time.Time); ok {
-				ts = t.UnixMilli()
-				continue
-			}
-
-			switch name {
-			case qbtypes.HeatmapBucketColumn, userHeatmapBucketColumn:
-				upperBound = numericAsFloat(value)
-			default:
-				if aggRe.MatchString(name) || slices.Contains(legacyReservedColumnTargetAliases, name) {
-					count = numericAsFloat(value)
-					continue
-				}
-				// a nullable label column comes back as a nil any, which would
-				// otherwise key the series on the literal "<nil>"
-				if value == nil {
-					value = ""
-				}
-				lblVals = append(lblVals, fmt.Sprint(value))
-				lblObjs = append(lblObjs, &qbtypes.Label{
-					Key:   telemetrytypes.TelemetryFieldKey{Name: name},
-					Value: value,
-				})
-			}
-		}
-
-		if ts == 0 || !isValidBucketUpperBound(upperBound) || math.IsNaN(count) || math.IsInf(count, 0) {
-			continue
-		}
-		sort.Strings(lblVals)
-		labelsKey := strings.Join(lblVals, ",")
-
-		accumulator.addCell(labelsKey, lblObjs, ts, upperBound, count)
-	}
-	if err := rows.Err(); err != nil {
-		return nil, err
-	}
-
-	return accumulator.foldSeries(queryWindow, stepMs, queryName), nil
-}
-
-// isPartialValue reports whether the step interval starting at timestamp is only
-// partly covered by the query window, which happens when the window boundaries
-// are not step-aligned.
-func isPartialValue(timestamp int64, queryWindow *qbtypes.TimeRange, stepMs uint64) bool {
-	if stepMs == 0 || queryWindow == nil {
-		return false
-	}
-
-	timestampMs := uint64(timestamp)
-
-	// For the first interval, check if query start is misaligned
-	// The first complete interval starts at the first timestamp >= queryWindow.From that is aligned to step
-	firstCompleteInterval := queryWindow.From
-	if queryWindow.From%stepMs != 0 {
-		// Round up to next step boundary
-		firstCompleteInterval = ((queryWindow.From / stepMs) + 1) * stepMs
-	}
-
-	// If timestamp is before the first complete interval, it's partial
-	if timestampMs < firstCompleteInterval {
-		return true
-	}
-
-	// For the last interval, check if it would extend beyond query end
-	if timestampMs+stepMs > queryWindow.To {
-		return queryWindow.To%stepMs != 0
-	}
-
-	return false
-}
-
 func isNumericKind(t reflect.Type) bool {
 	if t == nil {
 		return false

pkg/querier/heatmap_accumulator.go

@@ -1,116 +0,0 @@
-package querier
-
-import (
-	"math"
-	"slices"
-
-	qbtypes "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
-)
-
-// heatmapColumn maps a bucket's upper bound to the count in it, holding one
-// timestamp's cells. Keyed rather than indexed by band because the axis is only
-// known once every cell has been seen.
-type heatmapColumn map[float64]float64
-
-func isValidBucketUpperBound(upperBound float64) bool {
-	return !math.IsNaN(upperBound) && !math.IsInf(upperBound, -1)
-}
-
-// heatmapSeries accumulates one group's columns while the rows are read.
-type heatmapSeries struct {
-	labels             []*qbtypes.Label
-	columnsByTimestamp map[int64]heatmapColumn
-}
-
-// heatmapAccumulator collects cells from either reader and folds them into one
-// series per group.
-type heatmapAccumulator struct {
-	seriesByKey map[string]*heatmapSeries
-	seriesOrder []string
-	upperBounds map[float64]struct{}
-}
-
-func newHeatmapAccumulator() *heatmapAccumulator {
-	return &heatmapAccumulator{
-		seriesByKey: map[string]*heatmapSeries{},
-		upperBounds: map[float64]struct{}{},
-	}
-}
-
-// addCell files one cell under the group labelsKey identifies, keeping the
-// labels from the first cell seen for it.
-func (a *heatmapAccumulator) addCell(labelsKey string, lbls []*qbtypes.Label, ts int64, upperBound, count float64) {
-	series, ok := a.seriesByKey[labelsKey]
-	if !ok {
-		series = &heatmapSeries{labels: lbls, columnsByTimestamp: map[int64]heatmapColumn{}}
-		a.seriesByKey[labelsKey] = series
-		a.seriesOrder = append(a.seriesOrder, labelsKey)
-	}
-	if series.columnsByTimestamp[ts] == nil {
-		series.columnsByTimestamp[ts] = heatmapColumn{}
-	}
-	series.columnsByTimestamp[ts][upperBound] += count
-	if !math.IsInf(upperBound, 1) {
-		a.upperBounds[upperBound] = struct{}{}
-	}
-}
-
-// foldSeries turns the collected cells into one series per group, in the order
-// the groups first appeared.
-func (a *heatmapAccumulator) foldSeries(queryWindow *qbtypes.TimeRange, stepMs uint64, queryName string) *qbtypes.TimeSeriesData {
-	if len(a.seriesOrder) == 0 {
-		return &qbtypes.TimeSeriesData{QueryName: queryName}
-	}
-
-	upperBounds := make([]float64, 0, len(a.upperBounds))
-	for upperBound := range a.upperBounds {
-		upperBounds = append(upperBounds, upperBound)
-	}
-	slices.Sort(upperBounds)
-
-	// the index past the last upper bound is where the +Inf overflow lands
-	upperBoundToIndex := make(map[float64]int, len(upperBounds)+1)
-	for index, upperBound := range upperBounds {
-		upperBoundToIndex[upperBound] = index
-	}
-	upperBoundToIndex[math.Inf(1)] = len(upperBounds)
-
-	bucket := &qbtypes.AggregationBucket{
-		Index:  0,
-		Alias:  "__result_0",
-		Meta:   qbtypes.AggregationMeta{Buckets: upperBounds},
-		Series: make([]*qbtypes.TimeSeries, 0, len(a.seriesOrder)),
-	}
-
-	for _, labelsKey := range a.seriesOrder {
-		accumulated := a.seriesByKey[labelsKey]
-
-		timestamps := make([]int64, 0, len(accumulated.columnsByTimestamp))
-		for ts := range accumulated.columnsByTimestamp {
-			timestamps = append(timestamps, ts)
-		}
-		slices.Sort(timestamps)
-
-		series := &qbtypes.TimeSeries{
-			Labels: accumulated.labels,
-			Values: make([]*qbtypes.TimeSeriesValue, 0, len(timestamps)),
-		}
-		for _, ts := range timestamps {
-			values := make([]float64, len(upperBounds)+1)
-			for upperBound, count := range accumulated.columnsByTimestamp[ts] {
-				values[upperBoundToIndex[upperBound]] = count
-			}
-			series.Values = append(series.Values, &qbtypes.TimeSeriesValue{
-				Timestamp: ts,
-				Values:    values,
-				Partial:   isPartialValue(ts, queryWindow, stepMs),
-			})
-		}
-		bucket.Series = append(bucket.Series, series)
-	}
-
-	return &qbtypes.TimeSeriesData{
-		QueryName:    queryName,
-		Aggregations: []*qbtypes.AggregationBucket{bucket},
-	}
-}

pkg/querier/heatmap_test.go

@@ -1,101 +0,0 @@
-package querier
-
-import (
-	"testing"
-
-	"github.com/SigNoz/signoz/pkg/instrumentation/instrumentationtest"
-	qbtypes "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
-	"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
-	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
-)
-
-func TestMergeTimeSeriesResultsUnionsHeatmapAxes(t *testing.T) {
-	// a log axis holds whichever bands the data reached, so a wide cached range
-	// and a narrow fresh one routinely disagree on which bands exist
-	cached := &qbtypes.TimeSeriesData{
-		QueryName: "A",
-		Aggregations: []*qbtypes.AggregationBucket{{
-			Index: 0,
-			Meta:  qbtypes.AggregationMeta{Buckets: []float64{1, 4, 16}},
-			Series: []*qbtypes.TimeSeries{{
-				Labels: []*qbtypes.Label{{Key: telemetrytypes.TelemetryFieldKey{Name: "host.name"}, Value: "node-1"}},
-				Values: []*qbtypes.TimeSeriesValue{{Timestamp: 1710000000000, Values: []float64{1, 2, 3, 4}}},
-			}},
-		}},
-	}
-	fresh := []*qbtypes.Result{{
-		Value: &qbtypes.TimeSeriesData{
-			QueryName: "A",
-			Aggregations: []*qbtypes.AggregationBucket{{
-				Index: 0,
-				Meta:  qbtypes.AggregationMeta{Buckets: []float64{2, 4}},
-				Series: []*qbtypes.TimeSeries{{
-					Labels: []*qbtypes.Label{{Key: telemetrytypes.TelemetryFieldKey{Name: "host.name"}, Value: "node-1"}},
-					Values: []*qbtypes.TimeSeriesValue{{Timestamp: 1710000060000, Values: []float64{5, 6, 7}}},
-				}},
-			}},
-		},
-	}}
-
-	merged := (&querier{}).mergeTimeSeriesResults(cached, fresh)
-
-	require.Len(t, merged.Aggregations, 1)
-	aggBucket := merged.Aggregations[0]
-	assert.Equal(t, []float64{1, 2, 4, 16}, aggBucket.Meta.Buckets)
-
-	require.Len(t, aggBucket.Series, 1)
-	require.Len(t, aggBucket.Series[0].Values, 2)
-	// the cached 16 band survives even though the fresh range never reached it
-	assert.Equal(t, []float64{1, 0, 2, 3, 4}, aggBucket.Series[0].Values[0].Values)
-	// and the fresh 2 band survives even though the cached range never had it
-	assert.Equal(t, []float64{0, 5, 6, 0, 7}, aggBucket.Series[0].Values[1].Values)
-}
-
-func TestTrimResultToFluxBoundaryKeepsTheHeatmapAxis(t *testing.T) {
-	cache := &bucketCache{logger: instrumentationtest.New().Logger()}
-
-	result := &qbtypes.Result{
-		Type: qbtypes.RequestTypeHeatmap,
-		Value: &qbtypes.TimeSeriesData{
-			Aggregations: []*qbtypes.AggregationBucket{{
-				Index: 0,
-				Alias: "__result_0",
-				Meta:  qbtypes.AggregationMeta{Unit: "By", Buckets: []float64{1, 2, 4}},
-				Series: []*qbtypes.TimeSeries{{
-					Values: []*qbtypes.TimeSeriesValue{
-						{Timestamp: 1710000000000, Values: []float64{1, 2, 3, 4}},
-					},
-				}},
-			}},
-		},
-	}
-
-	trimmed := cache.trimResultToFluxBoundary(result, 1710000060000)
-
-	tsData, ok := trimmed.Value.(*qbtypes.TimeSeriesData)
-	require.True(t, ok)
-	require.Len(t, tsData.Aggregations, 1)
-
-	// the counts are positional against the axis, so a cached bucket that lost
-	// Meta.Buckets would be realigned from an empty axis and collapse into the
-	// overflow slot on the way back out
-	aggBucket := tsData.Aggregations[0]
-	assert.Equal(t, []float64{1, 2, 4}, aggBucket.Meta.Buckets)
-	assert.Equal(t, "By", aggBucket.Meta.Unit)
-	assert.Equal(t, "__result_0", aggBucket.Alias)
-}
-
-func TestRealignFromAnEmptyAxisCollapsesIntoTheOverflow(t *testing.T) {
-	// pins the behaviour the trim bug exposed: with no axis to read the counts
-	// against, everything lands in the overflow slot
-	aggBucket := &qbtypes.AggregationBucket{
-		Series: []*qbtypes.TimeSeries{{
-			Values: []*qbtypes.TimeSeriesValue{{Timestamp: 1710000000000, Values: []float64{7, 8, 9, 10}}},
-		}},
-	}
-
-	aggBucket.ReindexValuesToNewUpperBounds([]float64{1, 2, 4})
-
-	assert.Equal(t, []float64{0, 0, 0, 7}, aggBucket.Series[0].Values[0].Values)
-}

pkg/querier/postprocess.go

@@ -195,16 +195,6 @@ func postProcessBuilderQuery[T any](
 	return result
 }
 
-// resolveHeatmapBucketAxis brings the bucket axis to the resolution the caller
-// asked for. Downscaling runs first so AddHeatmapBucketsWithNoCounts adds them
-// at that resolution rather than the finer one ClickHouse bucketed at.
-func resolveHeatmapBucketAxis(tsData *qbtypes.TimeSeriesData, bucketing qbtypes.HeatmapBucketing) {
-	if bucketing.Kind == qbtypes.BucketsKindLog {
-		qbtypes.DownscaleHeatmapResolution(tsData, bucketing.LogScale)
-	}
-	qbtypes.AddHeatmapBucketsWithNoCounts(tsData, bucketing)
-}
-
 // postProcessMetricQuery applies postprocessing to a metric query result.
 func postProcessMetricQuery(
 	q *querier,
@@ -226,12 +216,6 @@ func postProcessMetricQuery(
 		}
 	}
 
-	if req.RequestType == qbtypes.RequestTypeHeatmap && config.HeatmapBucketing != nil {
-		if tsData, ok := result.Value.(*qbtypes.TimeSeriesData); ok {
-			resolveHeatmapBucketAxis(tsData, *config.HeatmapBucketing)
-		}
-	}
-
 	result = q.applySeriesLimit(result, query.Limit, query.Order)
 
 	if len(query.Functions) > 0 {
@@ -358,19 +342,6 @@ func (q *querier) applyFormulas(ctx context.Context, results map[string]*qbtypes
 				result = q.applySeriesLimit(result, formula.Limit, formula.Order)
 				results[name] = result
 			}
-		case qbtypes.RequestTypeHeatmap:
-			// The queries a formula reads were run as time series, so what
-			// arrives here is one value per group per timestamp.
-			result := q.processTimeSeriesFormula(ctx, results, formula, req)
-			if result != nil {
-				if tsData, ok := result.Value.(*qbtypes.TimeSeriesData); ok {
-					bucketing := req.BucketOptions.ToHeatmapBucketing()
-					bucketFormulaOutputAsHeatmap(tsData, bucketing)
-					resolveHeatmapBucketAxis(tsData, bucketing)
-				}
-				result = q.applySeriesLimit(result, formula.Limit, formula.Order)
-				results[name] = result
-			}
 		case qbtypes.RequestTypeScalar:
 			result := q.processScalarFormula(ctx, results, formula, req)
 			// For scalar results, apply limit by processScalarFormula itself since it needs to be applied before converting back to scalar format
@@ -439,89 +410,6 @@ func (q *querier) processTimeSeriesFormula(
 	return result
 }
 
-func bucketFormulaOutputAsHeatmap(tsData *qbtypes.TimeSeriesData, bucketing qbtypes.HeatmapBucketing) {
-	// A formula is one expression, so processTimeSeriesFormula gives it one
-	// aggregation.
-	if tsData == nil || len(tsData.Aggregations) == 0 || tsData.Aggregations[0] == nil {
-		return
-	}
-	aggBucket := tsData.Aggregations[0]
-
-	calculateUpperBound := calculateLogValueUpperBound
-	if bucketing.Kind == qbtypes.BucketsKindLinear {
-		calculateUpperBound = func(value float64) float64 {
-			return calculateLinearValueUpperBound(bucketing, value)
-		}
-	}
-
-	// +Inf is the open-above overflow rather than an upper bound of its own, and
-	// a NaN value has no bucket at all, so neither goes on the axis.
-	upperBounds := []float64{}
-	for _, series := range aggBucket.Series {
-		for _, point := range series.Values {
-			upperBound := calculateUpperBound(point.Value)
-			if !math.IsNaN(upperBound) && !math.IsInf(upperBound, 0) {
-				upperBounds = append(upperBounds, upperBound)
-			}
-		}
-	}
-	slices.Sort(upperBounds)
-	upperBounds = slices.Compact(upperBounds)
-
-	upperBoundToIndex := make(map[float64]int, len(upperBounds))
-	for index, upperBound := range upperBounds {
-		upperBoundToIndex[upperBound] = index
-	}
-
-	overflowIndex := len(upperBounds)
-	for _, series := range aggBucket.Series {
-		for _, point := range series.Values {
-			upperBound := calculateUpperBound(point.Value)
-			point.Values = make([]float64, overflowIndex+1)
-			point.Value = 0
-			switch {
-			case math.IsNaN(upperBound):
-			case math.IsInf(upperBound, 1):
-				point.Values[overflowIndex] = 1
-			default:
-				point.Values[upperBoundToIndex[upperBound]] = 1
-			}
-		}
-	}
-
-	aggBucket.Meta.Buckets = upperBounds
-}
-
-// calculateLinearValueUpperBound and calculateLogValueUpperBound are the Go side
-// of what renderLinearUpperBoundExpr and renderLogUpperBoundExpr emit, and have
-// to stay identical to them: a formula heatmap and a metric heatmap that
-// disagreed here would put their counts in different buckets.
-func calculateLinearValueUpperBound(bucketing qbtypes.HeatmapBucketing, value float64) float64 {
-	if value > bucketing.MaxValue {
-		return math.Inf(1)
-	}
-	numBuckets := float64(bucketing.NumBuckets)
-	index := math.Min(math.Max(math.Ceil(value*numBuckets/bucketing.MaxValue), 1), numBuckets)
-	return index * bucketing.MaxValue / numBuckets
-}
-
-// Like renderLogUpperBoundExpr, this reads MaxLogScale rather than the requested
-// scale: ClickHouse buckets at the finest resolution and resolveHeatmapBucketAxis
-// folds the axis down afterwards.
-func calculateLogValueUpperBound(value float64) float64 {
-	if value <= 0 {
-		return 0
-	}
-	if value <= qbtypes.MinLogUpperBound {
-		return qbtypes.MinLogUpperBound
-	}
-	if value > qbtypes.MaxLogUpperBound {
-		return math.Inf(1)
-	}
-	bucketsPerDoubling := math.Exp2(qbtypes.MaxLogScale)
-	return math.Exp2(math.Ceil(math.Log2(value)*bucketsPerDoubling) / bucketsPerDoubling)
-}
-
 func (q *querier) processScalarFormula(
 	ctx context.Context,
 	results map[string]*qbtypes.Result,
@@ -606,7 +494,7 @@ func (q *querier) processScalarFormula(
 				bucket := &qbtypes.AggregationBucket{
 					Index:  aggIdx,
 					Alias:  scalarData.Columns[colIdx].Name,
-					Meta:   qbtypes.AggregationMeta{Unit: scalarData.Columns[colIdx].Meta.Unit},
+					Meta:   scalarData.Columns[colIdx].Meta,
 					Series: make([]*qbtypes.TimeSeries, 0),
 				}
 
@@ -779,14 +667,13 @@ func convertTimeSeriesDataToScalar(tsData *qbtypes.TimeSeriesData, queryName str
 		if name == "" {
 			name = fmt.Sprintf("__result_%d", agg.Index)
 		}
-		column := &qbtypes.ColumnDescriptor{
+		columns = append(columns, &qbtypes.ColumnDescriptor{
 			TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: name},
 			QueryName:         queryName,
 			AggregationIndex:  int64(agg.Index),
+			Meta:              agg.Meta,
 			Type:              qbtypes.ColumnTypeAggregation,
-		}
-		column.Meta.Unit = agg.Meta.Unit
-		columns = append(columns, column)
+		})
 	}
 
 	// Build rows.

pkg/querier/preview.go

@@ -50,7 +50,7 @@ func (q *querier) QueryRangePreview(
 		env := []qbtypes.QueryEnvelope{req.CompositeQuery.Queries[idx]}
 		ps.Warnings = append(ps.Warnings, q.adjustStepInterval(env, req.Start, req.End)...)
 
-		missingMetricQueries, metricWarnings, mErr := q.resolveMetricMetadata(ctx, orgID, env, req.Start, req.End, req.RequestType, req.BucketOptions)
+		missingMetricQueries, metricWarnings, mErr := q.resolveMetricMetadata(ctx, orgID, env, req.Start, req.End)
 		if mErr != nil {
 			// Report this query's error but keep previewing the rest.
 			ps.Error = mErr

pkg/querier/promql_heatmap.go

@@ -1,131 +0,0 @@
-package querier
-
-import (
-	"fmt"
-	"math"
-	"slices"
-	"sort"
-	"strconv"
-	"strings"
-
-	"github.com/prometheus/prometheus/model/labels"
-
-	"github.com/prometheus/prometheus/promql"
-
-	qbv5 "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
-	"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
-)
-
-// promHistogramBucketLabel is the label a classic histogram carries its
-// cumulative upper bound on.
-const promHistogramBucketLabel = "le"
-
-// cumulativeColumn maps a bucket's upper bound to the cumulative count at it.
-// Differencing turns it into the per-band counts a heatmapColumn holds.
-type cumulativeColumn map[float64]float64
-
-// promHeatmapGroup assembles one group across the several matrix series its `le`
-// values arrive as, since differencing needs all of them.
-type promHeatmapGroup struct {
-	labels     []*qbv5.Label
-	labelsKey  string
-	cumulative map[int64]cumulativeColumn
-}
-
-// foldMatrixAsHeatmap folds a matrix of one cumulative series per (group, `le`)
-// into one series per group whose points hold a count per band.
-func foldMatrixAsHeatmap(matrix promql.Matrix, queryWindow *qbv5.TimeRange, stepMs uint64, queryName string) *qbv5.TimeSeriesData {
-	groups, groupOrder := collectCumulativeGroups(matrix)
-
-	accumulator := newHeatmapAccumulator()
-	for _, labelsKey := range groupOrder {
-		groups[labelsKey].addDifferencedCells(accumulator)
-	}
-
-	return accumulator.foldSeries(queryWindow, stepMs, queryName)
-}
-
-// collectCumulativeGroups reads the matrix into one group per label set. A series
-// without `le` has no band to sit in, so an expression that dropped the label
-// draws nothing.
-func collectCumulativeGroups(matrix promql.Matrix) (groups map[string]*promHeatmapGroup, groupOrder []string) {
-	groups = map[string]*promHeatmapGroup{}
-
-	for _, promSeries := range matrix {
-		upperBound, ok := extractBucketUpperBound(promSeries.Metric)
-		if !ok {
-			continue
-		}
-
-		lbls, labelsKey := extractHeatmapGroup(promSeries.Metric)
-		group, ok := groups[labelsKey]
-		if !ok {
-			group = &promHeatmapGroup{labels: lbls, labelsKey: labelsKey, cumulative: map[int64]cumulativeColumn{}}
-			groups[labelsKey] = group
-			groupOrder = append(groupOrder, labelsKey)
-		}
-
-		for _, point := range promSeries.Floats {
-			// skipping widens the band above onto the next upper bound that has
-			// a count, which is what lagInFrame does with an absent row
-			if math.IsNaN(point.F) || math.IsInf(point.F, 0) {
-				continue
-			}
-			if group.cumulative[point.T] == nil {
-				group.cumulative[point.T] = cumulativeColumn{}
-			}
-			group.cumulative[point.T][upperBound] = point.F
-		}
-	}
-
-	return groups, groupOrder
-}
-
-func extractBucketUpperBound(metric labels.Labels) (float64, bool) {
-	raw := metric.Get(promHistogramBucketLabel)
-	if raw == "" {
-		return 0, false
-	}
-	upperBound, err := strconv.ParseFloat(raw, 64)
-	if err != nil || !isValidBucketUpperBound(upperBound) {
-		return 0, false
-	}
-	return upperBound, true
-}
-
-// extractHeatmapGroup returns a series' group labels — everything but `le`.
-func extractHeatmapGroup(metric labels.Labels) ([]*qbv5.Label, string) {
-	lbls := make([]*qbv5.Label, 0, metric.Len())
-	pairs := make([]string, 0, metric.Len())
-
-	metric.Range(func(l labels.Label) {
-		if l.Name == promHistogramBucketLabel || excludePromLabel(l.Name) {
-			return
-		}
-		lbls = append(lbls, &qbv5.Label{
-			Key:   telemetrytypes.TelemetryFieldKey{Name: l.Name},
-			Value: l.Value,
-		})
-		pairs = append(pairs, fmt.Sprintf("%s=%s", l.Name, l.Value))
-	})
-
-	sort.Strings(pairs)
-	return lbls, strings.Join(pairs, ",")
-}
-
-// each cell is its upper bound's cumulative count minus the one below it.
-func (g *promHeatmapGroup) addDifferencedCells(accumulator *heatmapAccumulator) {
-	for ts, cumulative := range g.cumulative {
-		upperBounds := make([]float64, 0, len(cumulative))
-		for upperBound := range cumulative {
-			upperBounds = append(upperBounds, upperBound)
-		}
-		slices.Sort(upperBounds)
-
-		previous := float64(0)
-		for _, upperBound := range upperBounds {
-			accumulator.addCell(g.labelsKey, g.labels, ts, upperBound, math.Max(cumulative[upperBound]-previous, 0))
-			previous = cumulative[upperBound]
-		}
-	}
-}

pkg/querier/promql_heatmap_test.go

@@ -1,86 +0,0 @@
-package querier
-
-import (
-	"log/slog"
-	"math"
-	"testing"
-	"time"
-
-	"github.com/prometheus/prometheus/model/labels"
-	"github.com/prometheus/prometheus/promql"
-
-	qbv5 "github.com/SigNoz/signoz/pkg/types/querybuildertypes/querybuildertypesv5"
-	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
-)
-
-// The cache key is the fingerprint alone, so two request types over one
-// expression must not produce the same one — a time series payload served to a
-// heatmap request has no axis and reads back as a single collapsed band.
-func TestFingerprintSeparatesHeatmapFromTimeSeries(t *testing.T) {
-	fingerprintFor := func(requestType qbv5.RequestType) string {
-		q := &promqlQuery{
-			logger:      slog.New(slog.DiscardHandler),
-			query:       qbv5.PromQuery{Name: "A", Query: "sum by (le) (increase(signoz_latency_bucket[5m]))", Step: qbv5.Step{Duration: time.Minute}},
-			tr:          qbv5.TimeRange{From: 1710000000000, To: 1710003600000},
-			requestType: requestType,
-		}
-		return q.Fingerprint()
-	}
-
-	heatmap := fingerprintFor(qbv5.RequestTypeHeatmap)
-	timeSeries := fingerprintFor(qbv5.RequestTypeTimeSeries)
-
-	assert.NotEmpty(t, heatmap, "a heatmap decomposes into time buckets like a time series")
-	assert.NotEqual(t, timeSeries, heatmap)
-	assert.Empty(t, fingerprintFor(qbv5.RequestTypeScalar), "a scalar result is its window's last point")
-}
-
-func TestFoldMatrixAsHeatmapClampsADecreasingCumulativeCount(t *testing.T) {
-	at := int64(1710000000000)
-
-	matrix := promql.Matrix{
-		{
-			Metric: labels.FromStrings("le", "5"),
-			Floats: []promql.FPoint{{T: at, F: 10}},
-		},
-		{
-			Metric: labels.FromStrings("le", "10"),
-			Floats: []promql.FPoint{{T: at, F: 4}},
-		},
-	}
-
-	data := foldMatrixAsHeatmap(matrix, &qbv5.TimeRange{From: 1710000000000, To: 1710000060000}, uint64(time.Minute.Milliseconds()), "A")
-	require.Len(t, data.Aggregations, 1)
-
-	// a cumulative count that went backwards would difference to -6
-	assert.Equal(t, []float64{10, 0, 0}, data.Aggregations[0].Series[0].Values[0].Values)
-}
-
-func TestFoldMatrixAsHeatmapWidensTheBandOverAMissingUpperBound(t *testing.T) {
-	at := int64(1710000000000)
-
-	matrix := promql.Matrix{
-		{
-			Metric: labels.FromStrings("le", "5"),
-			Floats: []promql.FPoint{{T: at, F: 3}},
-		},
-		{
-			Metric: labels.FromStrings("le", "10"),
-			Floats: []promql.FPoint{{T: at, F: math.NaN()}},
-		},
-		{
-			Metric: labels.FromStrings("le", "20"),
-			Floats: []promql.FPoint{{T: at, F: 30}},
-		},
-	}
-
-	data := foldMatrixAsHeatmap(matrix, &qbv5.TimeRange{From: 1710000000000, To: 1710000060000}, uint64(time.Minute.Milliseconds()), "A")
-	require.Len(t, data.Aggregations, 1)
-
-	aggregation := data.Aggregations[0]
-	// 10 carried nothing to difference against, so it is not on the axis at all
-	// and 20 differences against 5, holding what (5,10] and (10,20] would split
-	assert.Equal(t, []float64{5, 20}, aggregation.Meta.Buckets)
-	assert.Equal(t, []float64{3, 27, 0}, aggregation.Series[0].Values[0].Values)
-}

pkg/querier/promql_query.go

@@ -155,10 +155,7 @@ func (q *promqlQuery) Fingerprint() string {
 	if q.opts.serve != nil {
 		return ""
 	}
-
-	switch q.requestType {
-	case qbv5.RequestTypeTimeSeries, qbv5.RequestTypeHeatmap:
-	default:
+	if q.requestType != qbv5.RequestTypeTimeSeries {
 		return ""
 	}
 
@@ -169,8 +166,6 @@ func (q *promqlQuery) Fingerprint() string {
 	}
 	parts := []string{
 		"promql",
-		// one expression returns a different shape per request type
-		fmt.Sprintf("requestType=%s", q.requestType.StringValue()),
 		query,
 		q.query.Step.String(),
 	}
@@ -454,52 +449,25 @@ func (q *promqlQuery) Execute(ctx context.Context) (*qbv5.Result, error) {
 	return q.toResult(matrix, warnings, began, &statsMu, &rowsScanned, &bytesScanned), nil
 }
 
-// excludePromLabel hides only known SigNoz storage keys: label names are user
-// data and may legitimately start with "__" (e.g. __address__), so a blanket
-// dunder strip mangles user labelsets. The __scope./__resource. prefixes cover
-// every exporter version's keys.
-func excludePromLabel(labelName string) bool {
-	return labelName == "__temporality__" ||
-		strings.HasPrefix(labelName, "__scope.") ||
-		strings.HasPrefix(labelName, "__resource.")
-}
-
-// collectExecStats snapshots the scan counters a query accumulated. Callers take
-// it at the point they are done with the matrix, so the duration covers the
-// shaping they did.
-func collectExecStats(began time.Time, statsMu *sync.Mutex, rowsScanned, bytesScanned *uint64) qbv5.ExecStats {
-	statsMu.Lock()
-	defer statsMu.Unlock()
-	return qbv5.ExecStats{
-		RowsScanned:  *rowsScanned,
-		BytesScanned: *bytesScanned,
-		DurationMS:   uint64(time.Since(began).Milliseconds()),
-	}
-}
-
+// toResult converts an evaluated matrix into the v5 result shape, attaching
+// the ClickHouse scan stats accumulated during evaluation.
 func (q *promqlQuery) toResult(matrix promql.Matrix, warnings []string, began time.Time, statsMu *sync.Mutex, rowsScanned, bytesScanned *uint64) *qbv5.Result {
-	if q.requestType == qbv5.RequestTypeHeatmap {
-		return q.toResultForHeatmap(matrix, warnings, began, statsMu, rowsScanned, bytesScanned)
+	// Hide only known SigNoz storage keys: label names are user data and may
+	// legitimately start with "__" (e.g. __address__), so a blanket dunder
+	// strip mangles user labelsets. The __scope./__resource. prefixes cover
+	// every exporter version's keys.
+	excludeLabel := func(labelName string) bool {
+		return labelName == "__temporality__" ||
+			strings.HasPrefix(labelName, "__scope.") ||
+			strings.HasPrefix(labelName, "__resource.")
 	}
-	return q.toResultForTimeSeriesAndScalar(matrix, warnings, began, statsMu, rowsScanned, bytesScanned)
-}
 
-func (q *promqlQuery) toResultForHeatmap(matrix promql.Matrix, warnings []string, began time.Time, statsMu *sync.Mutex, rowsScanned, bytesScanned *uint64) *qbv5.Result {
-	return &qbv5.Result{
-		Type:     q.requestType,
-		Value:    foldMatrixAsHeatmap(matrix, &q.tr, uint64(q.query.Step.Milliseconds()), q.query.Name),
-		Warnings: warnings,
-		Stats:    collectExecStats(began, statsMu, rowsScanned, bytesScanned),
-	}
-}
-
-func (q *promqlQuery) toResultForTimeSeriesAndScalar(matrix promql.Matrix, warnings []string, began time.Time, statsMu *sync.Mutex, rowsScanned, bytesScanned *uint64) *qbv5.Result {
 	var series []*qbv5.TimeSeries
 	for _, v := range matrix {
 		var s qbv5.TimeSeries
 		lbls := make([]*qbv5.Label, 0, v.Metric.Len())
 		v.Metric.Range(func(l labels.Label) {
-			if excludePromLabel(l.Name) {
+			if excludeLabel(l.Name) {
 				return
 			}
 			lbls = append(lbls, &qbv5.Label{
@@ -527,7 +495,13 @@ func (q *promqlQuery) toResultForTimeSeriesAndScalar(matrix promql.Matrix, warni
 		series = append(series, &s)
 	}
 
-	stats := collectExecStats(began, statsMu, rowsScanned, bytesScanned)
+	statsMu.Lock()
+	stats := qbv5.ExecStats{
+		RowsScanned:  *rowsScanned,
+		BytesScanned: *bytesScanned,
+		DurationMS:   uint64(time.Since(began).Milliseconds()),
+	}
+	statsMu.Unlock()
 
 	tsData := &qbv5.TimeSeriesData{QueryName: q.query.Name}
 	// No bucket at all when nothing survived: a bucket holding no series reads

pkg/querier/querier.go

@@ -156,7 +156,7 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
 	// We need to set if it is unspecified or adjust it if value is not within recommended range
 	intervalWarnings := q.adjustStepInterval(req.CompositeQuery.Queries, req.Start, req.End)
 
-	missingMetricQueries, metricWarnings, err := q.resolveMetricMetadata(ctx, orgID, req.CompositeQuery.Queries, req.Start, req.End, req.RequestType, req.BucketOptions)
+	missingMetricQueries, metricWarnings, err := q.resolveMetricMetadata(ctx, orgID, req.CompositeQuery.Queries, req.Start, req.End)
 	if err != nil {
 		return nil, err
 	}
@@ -177,7 +177,7 @@ func (q *querier) QueryRange(ctx context.Context, orgID valuer.UUID, req *qbtype
 	preseededResults := make(map[string]any)
 	for _, name := range missingMetricQueries {
 		switch req.RequestType {
-		case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+		case qbtypes.RequestTypeTimeSeries:
 			preseededResults[name] = &qbtypes.TimeSeriesData{QueryName: name}
 		case qbtypes.RequestTypeScalar:
 			preseededResults[name] = &qbtypes.ScalarData{QueryName: name}
@@ -334,22 +334,15 @@ func (q *querier) buildQueries(
 				if missingMetricQuerySet[spec.Name] {
 					continue
 				}
-				requestType := req.RequestType
-				if requestType == qbtypes.RequestTypeHeatmap && spec.Disabled {
-					// A disabled query in a heatmap request feeds a formula, and the
-					// formula converts time series into heatmap data, so its inputs
-					// run as time series queries.
-					requestType = qbtypes.RequestTypeTimeSeries
-				}
 				spec.ShiftBy = extractShiftFromBuilderQuery(spec)
-				timeRange := adjustTimeRangeForShift(spec, qbtypes.TimeRange{From: req.Start, To: req.End}, requestType)
+				timeRange := adjustTimeRangeForShift(spec, qbtypes.TimeRange{From: req.Start, To: req.End}, req.RequestType)
 				var bq *builderQuery[qbtypes.MetricAggregation]
 
 				if spec.Source == telemetrytypes.SourceMeter {
 					event.Source = telemetrytypes.SourceMeter.StringValue()
-					bq = newBuilderQuery(q.logger, q.telemetryStore, orgID, q.meterStmtBuilder, query.Type, spec, timeRange, requestType, tmplVars, builderConfig{})
+					bq = newBuilderQuery(q.logger, q.telemetryStore, orgID, q.meterStmtBuilder, query.Type, spec, timeRange, req.RequestType, tmplVars, builderConfig{})
 				} else {
-					bq = newBuilderQuery(q.logger, q.telemetryStore, orgID, q.metricStmtBuilder, query.Type, spec, timeRange, requestType, tmplVars, builderConfig{})
+					bq = newBuilderQuery(q.logger, q.telemetryStore, orgID, q.metricStmtBuilder, query.Type, spec, timeRange, req.RequestType, tmplVars, builderConfig{})
 				}
 
 				queries[spec.Name] = bq
@@ -422,7 +415,7 @@ func (q *querier) populateQBEvent(event *qbtypes.QBEvent, queries []qbtypes.Quer
 //     resolved: never-seen metrics and dormant metrics (seen but no data in
 //     the query window).
 //   - err: Internal when a metadata fetch fails.
-func (q *querier) resolveMetricMetadata(ctx context.Context, orgID valuer.UUID, queries []qbtypes.QueryEnvelope, start, end uint64, requestType qbtypes.RequestType, bucketOptions *qbtypes.BucketOptions) (missingMetricQueries []string, metricWarnings []string, err error) {
+func (q *querier) resolveMetricMetadata(ctx context.Context, orgID valuer.UUID, queries []qbtypes.QueryEnvelope, start, end uint64) (missingMetricQueries []string, metricWarnings []string, err error) {
 	metricNames := make([]string, 0)
 	for idx := range queries {
 		if queries[idx].Type != qbtypes.QueryTypeBuilder {
@@ -472,13 +465,6 @@ func (q *querier) resolveMetricMetadata(ctx context.Context, orgID valuer.UUID,
 					spec.Aggregations[i].Type = foundMetricType
 				}
 			}
-			// Only the enabled query is used to render the heatmap, so bucket
-			// options are only applied to the enabled query.
-			if requestType == qbtypes.RequestTypeHeatmap && !spec.Disabled {
-				if err := spec.Aggregations[i].VerifyAndApplyBucketOptions(bucketOptions); err != nil {
-					return nil, nil, err
-				}
-			}
 			if spec.Aggregations[i].Type == metrictypes.UnspecifiedType {
 				missingMetrics = append(missingMetrics, spec.Aggregations[i].MetricName)
 				continue
@@ -693,7 +679,7 @@ func (q *querier) run(
 			if val, ok := result.Value.(*qbtypes.RawData); ok && val != nil {
 				return len(val.Rows) != 0
 			}
-		case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+		case qbtypes.RequestTypeTimeSeries:
 			if val, ok := result.Value.(*qbtypes.TimeSeriesData); ok && val != nil {
 				if len(val.Aggregations) != 0 {
 					anyNonEmpty := false
@@ -1014,7 +1000,7 @@ func (q *querier) mergeResults(cached *qbtypes.Result, fresh []*qbtypes.Result)
 
 		// Merge all fresh results including the first one
 		switch merged.Type {
-		case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+		case qbtypes.RequestTypeTimeSeries:
 			// Pass nil as cached value to ensure proper merging of all fresh results
 			merged.Value = q.mergeTimeSeriesResults(nil, fresh)
 		}
@@ -1037,7 +1023,7 @@ func (q *querier) mergeResults(cached *qbtypes.Result, fresh []*qbtypes.Result)
 	}
 
 	switch merged.Type {
-	case qbtypes.RequestTypeTimeSeries, qbtypes.RequestTypeHeatmap:
+	case qbtypes.RequestTypeTimeSeries:
 		merged.Value = q.mergeTimeSeriesResults(cached.Value.(*qbtypes.TimeSeriesData), fresh)
 	}
 
@@ -1058,16 +1044,6 @@ func (q *querier) mergeResults(cached *qbtypes.Result, fresh []*qbtypes.Result)
 	return merged
 }
 
-func mergeBucketUpperBounds(cachedValue *qbtypes.TimeSeriesData, freshResults []*qbtypes.Result) map[int][]float64 {
-	upperBoundSources := make([]*qbtypes.TimeSeriesData, 0, len(freshResults)+1)
-	upperBoundSources = append(upperBoundSources, cachedValue)
-	for _, result := range freshResults {
-		freshTS, _ := result.Value.(*qbtypes.TimeSeriesData)
-		upperBoundSources = append(upperBoundSources, freshTS)
-	}
-	return qbtypes.MergeBucketUpperBounds(upperBoundSources...)
-}
-
 // mergeTimeSeriesResults merges time series data.
 func (q *querier) mergeTimeSeriesResults(cachedValue *qbtypes.TimeSeriesData, freshResults []*qbtypes.Result) *qbtypes.TimeSeriesData {
 
@@ -1076,15 +1052,12 @@ func (q *querier) mergeTimeSeriesResults(cachedValue *qbtypes.TimeSeriesData, fr
 	// Map to store aggregation bucket metadata
 	bucketMetadata := make(map[int]*qbtypes.AggregationBucket)
 
-	mergedUpperBounds := mergeBucketUpperBounds(cachedValue, freshResults)
-
 	// Process cached data if available
 	if cachedValue != nil && cachedValue.Aggregations != nil {
 		for _, aggBucket := range cachedValue.Aggregations {
 			if seriesMap[aggBucket.Index] == nil {
 				seriesMap[aggBucket.Index] = make(map[string]*qbtypes.TimeSeries)
 			}
-			aggBucket.ReindexValuesToNewUpperBounds(mergedUpperBounds[aggBucket.Index])
 			if bucketMetadata[aggBucket.Index] == nil {
 				bucketMetadata[aggBucket.Index] = aggBucket
 			}
@@ -1136,7 +1109,6 @@ func (q *querier) mergeTimeSeriesResults(cachedValue *qbtypes.TimeSeriesData, fr
 		}
 
 		for _, aggBucket := range freshTS.Aggregations {
-			aggBucket.ReindexValuesToNewUpperBounds(mergedUpperBounds[aggBucket.Index])
 			for _, series := range aggBucket.Series {
 				key := qbtypes.GetUniqueSeriesKey(series.Labels)
 

pkg/statementbuilder/meterstatementbuilder/statement_builder.go

@@ -129,7 +129,7 @@ func (b *meterQueryStatementBuilder) buildPipelineStatement(
 	}
 
 	// final SELECT
-	return b.metricsStatementBuilder.BuildFinalSelect(cteFragments, cteArgs, qbtypes.RequestTypeTimeSeries, query)
+	return b.metricsStatementBuilder.BuildFinalSelect(cteFragments, cteArgs, query)
 }
 
 func (b *meterQueryStatementBuilder) buildTemporalAggDeltaFastPath(

pkg/statementbuilder/metricsstatementbuilder/statement_builder.go

@@ -4,13 +4,9 @@ import (
 	"context"
 	"fmt"
 	"log/slog"
-	"math"
 	"slices"
-	"strconv"
-	"strings"
 	"time"
 
-	"github.com/SigNoz/signoz/pkg/errors"
 	"github.com/SigNoz/signoz/pkg/factory"
 	"github.com/SigNoz/signoz/pkg/flagger"
 	"github.com/SigNoz/signoz/pkg/querybuilder"
@@ -117,7 +113,7 @@ func (b *StatementBuilder) Build(
 	orgID valuer.UUID,
 	start uint64,
 	end uint64,
-	requestType qbtypes.RequestType,
+	_ qbtypes.RequestType,
 	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
 	variables map[string]qbtypes.VariableItem,
 ) (*qbtypes.Statement, error) {
@@ -129,14 +125,13 @@ func (b *StatementBuilder) Build(
 
 	start, end = querybuilder.AdjustedMetricTimeRange(start, end, uint64(query.StepInterval.Seconds()), query)
 
-	return b.buildPipelineStatement(ctx, orgID, start, end, requestType, query, keys, variables)
+	return b.buildPipelineStatement(ctx, orgID, start, end, query, keys, variables)
 }
 
 func (b *StatementBuilder) buildPipelineStatement(
 	ctx context.Context,
 	orgID valuer.UUID,
 	start, end uint64,
-	requestType qbtypes.RequestType,
 	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
 	keys map[string][]*telemetrytypes.TelemetryFieldKey,
 	variables map[string]qbtypes.VariableItem,
@@ -149,7 +144,7 @@ func (b *StatementBuilder) buildPipelineStatement(
 	cteQuery := query
 	if query.Aggregations[0].Type == metrictypes.HistogramType {
 		query.GroupBy = slices.DeleteFunc(slices.Clone(query.GroupBy), isHistogramBucket)
-		cteQuery = rewriteQueryForHistogramCTE(requestType, query)
+		cteQuery = histogramCTEQuery(query)
 	}
 
 	agg := cteQuery.Aggregations[0]
@@ -221,7 +216,7 @@ func (b *StatementBuilder) buildPipelineStatement(
 		}
 	}
 
-	mainStmt, err := b.BuildFinalSelect(cteFragments, cteArgs, requestType, query)
+	mainStmt, err := b.BuildFinalSelect(cteFragments, cteArgs, query)
 	if err != nil {
 		return nil, err
 	}
@@ -229,29 +224,13 @@ func (b *StatementBuilder) buildPipelineStatement(
 	if reducedFragments == nil {
 		return mainStmt, nil
 	}
-	reducedStmt, err := b.BuildFinalSelect(reducedFragments, reducedArgs, requestType, query)
+	reducedStmt, err := b.BuildFinalSelect(reducedFragments, reducedArgs, query)
 	if err != nil {
 		return nil, err
 	}
 	return unionStatements(mainStmt, reducedStmt, query)
 }
 
-func rewriteQueryForHistogramCTE(requestType qbtypes.RequestType, query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]) qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation] {
-	query.GroupBy = append(slices.Clone(query.GroupBy), qbtypes.GroupByKey{
-		TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: histogramBucketKey},
-	})
-
-	query.Aggregations = slices.Clone(query.Aggregations)
-	if query.Aggregations[0].SpaceAggregation.IsPercentile() && requestType != qbtypes.RequestTypeHeatmap {
-		query.Aggregations[0].TimeAggregation = metrictypes.TimeAggregationRate
-	} else {
-		query.Aggregations[0].TimeAggregation = metrictypes.TimeAggregationIncrease
-	}
-	query.Aggregations[0].SpaceAggregation = metrictypes.SpaceAggregationSum
-
-	return query
-}
-
 func unionStatements(main, reduced *qbtypes.Statement, query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]) (*qbtypes.Statement, error) {
 	orderBy := "ts"
 	for i, g := range query.GroupBy {
@@ -779,9 +758,11 @@ func (b *StatementBuilder) buildSpatialAggregationCTE(
 func (b *StatementBuilder) BuildFinalSelect(
 	cteFragments []string,
 	cteArgs [][]any,
-	requestType qbtypes.RequestType,
 	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
 ) (*qbtypes.Statement, error) {
+	metricType := query.Aggregations[0].Type
+	spaceAgg := query.Aggregations[0].SpaceAggregation
+
 	combined := querybuilder.CombineCTEs(cteFragments)
 
 	var args []any
@@ -789,22 +770,6 @@ func (b *StatementBuilder) BuildFinalSelect(
 		args = append(args, a...)
 	}
 
-	if requestType == qbtypes.RequestTypeHeatmap {
-		return buildHeatmapFinalSelect(combined, args, query)
-	}
-	return buildAggregationFinalSelect(combined, args, query)
-}
-
-// buildAggregationFinalSelect reads __spatial_aggregation_cte as one value per
-// (group, timestamp), which is what every request type but heatmap wants.
-func buildAggregationFinalSelect(
-	combined string,
-	args []any,
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-) (*qbtypes.Statement, error) {
-	metricType := query.Aggregations[0].Type
-	spaceAgg := query.Aggregations[0].SpaceAggregation
-
 	sb := sqlbuilder.NewSelectBuilder()
 
 	if metricType == metrictypes.HistogramType && spaceAgg.IsPercentile() {
@@ -877,136 +842,24 @@ func buildAggregationFinalSelect(
 	return &qbtypes.Statement{Query: combined + q, Args: append(args, a...)}, nil
 }
 
-const (
-	histogramBucketKey = "le"
-
-	heatmapValueAlias = "__result_0"
-	heatmapWindow     = "__heatmap_window"
-)
+const histogramBucketKey = "le"
 
 func isHistogramBucket(k qbtypes.GroupByKey) bool { return k.Name == histogramBucketKey }
 
-// buildHeatmapFinalSelect turns __spatial_aggregation_cte into one row per
-// heatmap cell: (ts, group labels..., bucket upper bound, count).
-func buildHeatmapFinalSelect(
-	combined string,
-	args []any,
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-) (*qbtypes.Statement, error) {
-	if query.Aggregations[0].Type == metrictypes.HistogramType {
-		return buildHistogramHeatmapFinalSelect(combined, args, query)
+func histogramCTEQuery(query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]) qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation] {
+	query.GroupBy = append(slices.Clone(query.GroupBy), qbtypes.GroupByKey{
+		TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{Name: histogramBucketKey},
+	})
+
+	query.Aggregations = slices.Clone(query.Aggregations)
+	if query.Aggregations[0].SpaceAggregation.IsPercentile() {
+		query.Aggregations[0].TimeAggregation = metrictypes.TimeAggregationRate
+	} else {
+		query.Aggregations[0].TimeAggregation = metrictypes.TimeAggregationIncrease
 	}
-	return buildValueHeatmapFinalSelect(combined, args, query)
-}
+	query.Aggregations[0].SpaceAggregation = metrictypes.SpaceAggregationSum
 
-// buildHistogramHeatmapFinalSelect differences the cumulative per-`le` counts in
-// __spatial_aggregation_cte into a count per band. The upper bound reported is the
-// `le` itself, so the `le=+Inf` row reaches the reader as an infinite upper bound
-// for it to fold into the overflow band.
-func buildHistogramHeatmapFinalSelect(
-	combined string,
-	args []any,
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-) (*qbtypes.Statement, error) {
-	groupAliases := GroupByAliases(query.GroupBy)
-	partitionBy := append(append([]string{}, groupAliases...), "ts")
-
-	sb := sqlbuilder.NewSelectBuilder()
-	sb.Select("ts")
-	sb.SelectMore(groupAliases...)
-	sb.SelectMore(fmt.Sprintf("toFloat64(%s) AS %s", histogramBucketKey, qbtypes.HeatmapBucketColumn))
-	// a partial scrape can break monotonicity across `le`, and a negative cell
-	// count has no meaning
-	sb.SelectMore(fmt.Sprintf(
-		"greatest(value - lagInFrame(value, 1, 0) OVER %s, 0) AS %s",
-		heatmapWindow, heatmapValueAlias,
-	))
-	// sqlbuilder has no WINDOW clause; appending it to FROM lands it between FROM
-	// and ORDER BY, since these statements carry no WHERE or GROUP BY
-	sb.From(fmt.Sprintf(
-		"__spatial_aggregation_cte WINDOW %s AS (PARTITION BY %s ORDER BY toFloat64(%s))",
-		heatmapWindow, strings.Join(partitionBy, ", "), histogramBucketKey,
-	))
-	sb.OrderBy(groupAliases...)
-	sb.OrderBy("ts", fmt.Sprintf("toFloat64(%s)", histogramBucketKey))
-
-	q, a := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
-	return &qbtypes.Statement{Query: combined + q, Args: append(args, a...)}, nil
-}
-
-// buildValueHeatmapFinalSelect places each spatially aggregated value in a band
-// of the requested axis. __spatial_aggregation_cte holds one row per (group,
-// timestamp), so every cell counts exactly one.
-func buildValueHeatmapFinalSelect(
-	combined string,
-	args []any,
-	query qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation],
-) (*qbtypes.Statement, error) {
-	bucketing := query.Aggregations[0].HeatmapBucketing
-	if bucketing == nil {
-		return nil, errors.NewInternalf(errors.CodeInternal,
-			"heatmap over a %s metric reached the statement builder without a resolved bucket axis",
-			query.Aggregations[0].Type.StringValue())
-	}
-
-	upperBound, err := renderHeatmapUpperBoundExpr(*bucketing)
-	if err != nil {
-		return nil, err
-	}
-
-	groupAliases := GroupByAliases(query.GroupBy)
-
-	sb := sqlbuilder.NewSelectBuilder()
-	sb.Select("ts")
-	sb.SelectMore(groupAliases...)
-	sb.SelectMore(fmt.Sprintf("%s AS %s", upperBound, qbtypes.HeatmapBucketColumn))
-	sb.SelectMore(fmt.Sprintf("toFloat64(1) AS %s", heatmapValueAlias))
-	sb.From("__spatial_aggregation_cte")
-	sb.OrderBy(groupAliases...)
-	sb.OrderBy("ts", qbtypes.HeatmapBucketColumn)
-
-	q, a := sb.BuildWithFlavor(sqlbuilder.ClickHouse)
-	return &qbtypes.Statement{Query: combined + q, Args: append(args, a...)}, nil
-}
-
-// renderHeatmapUpperBoundExpr renders the upper bound of the band `value` falls in.
-func renderHeatmapUpperBoundExpr(bucketing qbtypes.HeatmapBucketing) (string, error) {
-	switch bucketing.Kind {
-	case qbtypes.BucketsKindLinear:
-		return renderLinearUpperBoundExpr(bucketing), nil
-	case qbtypes.BucketsKindLog:
-		return renderLogUpperBoundExpr(), nil
-	default:
-		return "", errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"unsupported bucketsScaling %q for heatmap requests", bucketing.Kind.StringValue())
-	}
-}
-
-func renderLinearUpperBoundExpr(bucketing qbtypes.HeatmapBucketing) string {
-	maxValue := formatFloat(bucketing.MaxValue)
-	numBuckets := strconv.Itoa(bucketing.NumBuckets)
-	return fmt.Sprintf(
-		"multiIf(value > %s, toFloat64('+Inf'), least(greatest(ceil(value * %s / %s), 1), %s) * %s / %s)",
-		maxValue, numBuckets, maxValue, numBuckets, maxValue, numBuckets,
-	)
-}
-
-// ClickHouse buckets at MaxLogScale whatever HeatmapBucketing.LogScale asks for;
-// postprocessing folds the axis down afterwards.
-func renderLogUpperBoundExpr() string {
-	bandsPerDoubling := formatFloat(math.Exp2(qbtypes.MaxLogScale))
-	lowest := formatFloat(qbtypes.MinLogUpperBound)
-	highest := formatFloat(qbtypes.MaxLogUpperBound)
-	return fmt.Sprintf(
-		"multiIf(value <= 0, toFloat64(0), value <= %s, %s, value > %s, toFloat64('+Inf'), pow(2, ceil(log2(value) * %s) / %s))",
-		lowest, lowest, highest, bandsPerDoubling, bandsPerDoubling,
-	)
-}
-
-// formatFloat renders a float64 as the shortest literal that reads back as the
-// same value, so an upper bound computed from it is identical on every row.
-func formatFloat(v float64) string {
-	return strconv.FormatFloat(v, 'g', -1, 64)
+	return query
 }
 
 func GroupByColumnAlias(i int, name string) string {

pkg/statementbuilder/metricsstatementbuilder/stmt_builder_test.go

@@ -284,199 +284,6 @@ func TestStatementBuilder(t *testing.T) {
 			},
 			expectedErr: nil,
 		},
-		{
-			name:        "test_histogram_heatmap_sum",
-			requestType: qbtypes.RequestTypeHeatmap,
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 60 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
-					{
-						MetricName:       "signoz_latency",
-						Type:             metrictypes.HistogramType,
-						Temporality:      metrictypes.Delta,
-						TimeAggregation:  metrictypes.TimeAggregationIncrease,
-						SpaceAggregation: metrictypes.SpaceAggregationSum,
-					},
-				},
-				GroupBy: []qbtypes.GroupByKey{
-					{
-						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
-							Name: "service.name",
-						},
-					},
-				},
-			},
-			expected: qbtypes.Statement{
-				Query: "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(60)) AS ts, `__GROUP_BY_KEY_0_service.name`, `le`, sum(value) AS value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `__GROUP_BY_KEY_0_service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, `__GROUP_BY_KEY_0_service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY ts, `__GROUP_BY_KEY_0_service.name`, `le`) SELECT ts, `__GROUP_BY_KEY_0_service.name`, toFloat64(le) AS __bucket, greatest(value - lagInFrame(value, 1, 0) OVER __heatmap_window, 0) AS __result_0 FROM __spatial_aggregation_cte WINDOW __heatmap_window AS (PARTITION BY `__GROUP_BY_KEY_0_service.name`, ts ORDER BY toFloat64(le)) ORDER BY `__GROUP_BY_KEY_0_service.name`, ts, toFloat64(le)",
-				Args:  []any{"signoz_latency", uint64(1747936800000), uint64(1747983420000), "delta", "signoz_latency", uint64(1747947360000), uint64(1747983420000)},
-			},
-			expectedErr: nil,
-		},
-		{
-			name:        "test_histogram_heatmap_percentile",
-			requestType: qbtypes.RequestTypeHeatmap,
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 60 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
-					{
-						MetricName:       "signoz_latency",
-						Type:             metrictypes.HistogramType,
-						Temporality:      metrictypes.Delta,
-						TimeAggregation:  metrictypes.TimeAggregationRate,
-						SpaceAggregation: metrictypes.SpaceAggregationPercentile95,
-					},
-				},
-				GroupBy: []qbtypes.GroupByKey{
-					{
-						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
-							Name: "service.name",
-						},
-					},
-				},
-			},
-			expected: qbtypes.Statement{
-				Query: "WITH __spatial_aggregation_cte AS (SELECT toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(60)) AS ts, `__GROUP_BY_KEY_0_service.name`, `le`, sum(value) AS value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `__GROUP_BY_KEY_0_service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, `__GROUP_BY_KEY_0_service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY ts, `__GROUP_BY_KEY_0_service.name`, `le`) SELECT ts, `__GROUP_BY_KEY_0_service.name`, toFloat64(le) AS __bucket, greatest(value - lagInFrame(value, 1, 0) OVER __heatmap_window, 0) AS __result_0 FROM __spatial_aggregation_cte WINDOW __heatmap_window AS (PARTITION BY `__GROUP_BY_KEY_0_service.name`, ts ORDER BY toFloat64(le)) ORDER BY `__GROUP_BY_KEY_0_service.name`, ts, toFloat64(le)",
-				Args:  []any{"signoz_latency", uint64(1747936800000), uint64(1747983420000), "delta", "signoz_latency", uint64(1747947360000), uint64(1747983420000)},
-			},
-			expectedErr: nil,
-		},
-		{
-			name:        "test_gauge_heatmap_log",
-			requestType: qbtypes.RequestTypeHeatmap,
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 60 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
-					{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						Temporality:      metrictypes.Unspecified,
-						TimeAggregation:  metrictypes.TimeAggregationAvg,
-						SpaceAggregation: metrictypes.SpaceAggregationSum,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{
-							Kind:       qbtypes.BucketsKindLog,
-							LogScale:   qbtypes.MaxLogScale,
-							NumBuckets: qbtypes.DefaultNumBuckets,
-						},
-					},
-				},
-				GroupBy: []qbtypes.GroupByKey{
-					{
-						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
-							Name: "host.name",
-						},
-					},
-				},
-			},
-			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(60)) AS ts, `__GROUP_BY_KEY_0_host.name`, avg(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'host.name') AS `__GROUP_BY_KEY_0_host.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, `__GROUP_BY_KEY_0_host.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `__GROUP_BY_KEY_0_host.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `__GROUP_BY_KEY_0_host.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `__GROUP_BY_KEY_0_host.name`) SELECT ts, `__GROUP_BY_KEY_0_host.name`, multiIf(value <= 0, toFloat64(0), value <= 2.3283064365386963e-10, 2.3283064365386963e-10, value > 1.8446744073709552e+19, toFloat64('+Inf'), pow(2, ceil(log2(value) * 16) / 16)) AS __bucket, toFloat64(1) AS __result_0 FROM __spatial_aggregation_cte ORDER BY `__GROUP_BY_KEY_0_host.name`, ts, __bucket",
-				Args:  []any{"system.memory.usage", uint64(1747936800000), uint64(1747983420000), "unspecified", "system.memory.usage", uint64(1747947360000), uint64(1747983420000), 0},
-			},
-			expectedErr: nil,
-		},
-		{
-			name:        "test_gauge_heatmap_linear",
-			requestType: qbtypes.RequestTypeHeatmap,
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 60 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
-					{
-						MetricName:       "system.memory.usage",
-						Type:             metrictypes.GaugeType,
-						Temporality:      metrictypes.Unspecified,
-						TimeAggregation:  metrictypes.TimeAggregationAvg,
-						SpaceAggregation: metrictypes.SpaceAggregationSum,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{
-							Kind:       qbtypes.BucketsKindLinear,
-							LogScale:   qbtypes.MaxLogScale,
-							MaxValue:   500,
-							NumBuckets: 25,
-						},
-					},
-				},
-				GroupBy: []qbtypes.GroupByKey{
-					{
-						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
-							Name: "host.name",
-						},
-					},
-				},
-			},
-			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(60)) AS ts, `__GROUP_BY_KEY_0_host.name`, avg(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'host.name') AS `__GROUP_BY_KEY_0_host.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, `__GROUP_BY_KEY_0_host.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `__GROUP_BY_KEY_0_host.name` ORDER BY fingerprint, ts), __spatial_aggregation_cte AS (SELECT ts, `__GROUP_BY_KEY_0_host.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `__GROUP_BY_KEY_0_host.name`) SELECT ts, `__GROUP_BY_KEY_0_host.name`, multiIf(value > 500, toFloat64('+Inf'), least(greatest(ceil(value * 25 / 500), 1), 25) * 500 / 25) AS __bucket, toFloat64(1) AS __result_0 FROM __spatial_aggregation_cte ORDER BY `__GROUP_BY_KEY_0_host.name`, ts, __bucket",
-				Args:  []any{"system.memory.usage", uint64(1747936800000), uint64(1747983420000), "unspecified", "system.memory.usage", uint64(1747947360000), uint64(1747983420000), 0},
-			},
-			expectedErr: nil,
-		},
-		{
-			// cumulative keeps CanShortCircuitDelta false, so the counts reach the
-			// bucket differencing through the temporal CTE rather than the delta
-			// fast path
-			name:        "test_histogram_heatmap_cumulative",
-			requestType: qbtypes.RequestTypeHeatmap,
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 60 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
-					{
-						MetricName:       "http_server_duration_bucket",
-						Type:             metrictypes.HistogramType,
-						Temporality:      metrictypes.Cumulative,
-						TimeAggregation:  metrictypes.TimeAggregationRate,
-						SpaceAggregation: metrictypes.SpaceAggregationPercentile95,
-					},
-				},
-				GroupBy: []qbtypes.GroupByKey{
-					{
-						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
-							Name: "service.name",
-						},
-					},
-				},
-			},
-			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `__GROUP_BY_KEY_0_service.name`, `le`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value, per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(60)) AS ts, `__GROUP_BY_KEY_0_service.name`, `le`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `__GROUP_BY_KEY_0_service.name`, JSONExtractString(labels, 'le') AS `le` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, `__GROUP_BY_KEY_0_service.name`, `le`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `__GROUP_BY_KEY_0_service.name`, `le` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `__GROUP_BY_KEY_0_service.name`, `le`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `__GROUP_BY_KEY_0_service.name`, `le`) SELECT ts, `__GROUP_BY_KEY_0_service.name`, toFloat64(le) AS __bucket, greatest(value - lagInFrame(value, 1, 0) OVER __heatmap_window, 0) AS __result_0 FROM __spatial_aggregation_cte WINDOW __heatmap_window AS (PARTITION BY `__GROUP_BY_KEY_0_service.name`, ts ORDER BY toFloat64(le)) ORDER BY `__GROUP_BY_KEY_0_service.name`, ts, toFloat64(le)",
-				Args:  []any{"http_server_duration_bucket", uint64(1747936800000), uint64(1747983420000), "cumulative", "http_server_duration_bucket", uint64(1747947300000), uint64(1747983420000), 0},
-			},
-			expectedErr: nil,
-		},
-		{
-			name:        "test_sum_heatmap_cumulative",
-			requestType: qbtypes.RequestTypeHeatmap,
-			query: qbtypes.QueryBuilderQuery[qbtypes.MetricAggregation]{
-				Signal:       telemetrytypes.SignalMetrics,
-				StepInterval: qbtypes.Step{Duration: 60 * time.Second},
-				Aggregations: []qbtypes.MetricAggregation{
-					{
-						MetricName:       "signoz_calls_total",
-						Type:             metrictypes.SumType,
-						Temporality:      metrictypes.Cumulative,
-						TimeAggregation:  metrictypes.TimeAggregationIncrease,
-						SpaceAggregation: metrictypes.SpaceAggregationSum,
-						HeatmapBucketing: &qbtypes.HeatmapBucketing{
-							Kind:       qbtypes.BucketsKindLog,
-							LogScale:   qbtypes.MaxLogScale,
-							NumBuckets: qbtypes.DefaultNumBuckets,
-						},
-					},
-				},
-				GroupBy: []qbtypes.GroupByKey{
-					{
-						TelemetryFieldKey: telemetrytypes.TelemetryFieldKey{
-							Name: "service.name",
-						},
-					},
-				},
-			},
-			expected: qbtypes.Statement{
-				Query: "WITH __temporal_aggregation_cte AS (SELECT ts, `__GROUP_BY_KEY_0_service.name`, multiIf(row_number() OVER rate_window = 1, nan, (per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) < 0, per_series_value, per_series_value - lagInFrame(per_series_value, 1) OVER rate_window) AS per_series_value FROM (SELECT fingerprint, toStartOfInterval(toDateTime(intDiv(unix_milli, 1000)), toIntervalSecond(60)) AS ts, `__GROUP_BY_KEY_0_service.name`, max(value) AS per_series_value FROM signoz_metrics.distributed_samples_v4 AS points INNER JOIN (SELECT fingerprint, JSONExtractString(labels, 'service.name') AS `__GROUP_BY_KEY_0_service.name` FROM signoz_metrics.time_series_v4_6hrs WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli <= ? AND LOWER(temporality) LIKE LOWER(?) GROUP BY fingerprint, `__GROUP_BY_KEY_0_service.name`) AS filtered_time_series ON points.fingerprint = filtered_time_series.fingerprint WHERE metric_name IN (?) AND unix_milli >= ? AND unix_milli < ? GROUP BY fingerprint, ts, `__GROUP_BY_KEY_0_service.name` ORDER BY fingerprint, ts) WINDOW rate_window AS (PARTITION BY fingerprint ORDER BY fingerprint, ts)), __spatial_aggregation_cte AS (SELECT ts, `__GROUP_BY_KEY_0_service.name`, sum(per_series_value) AS value FROM __temporal_aggregation_cte WHERE isNaN(per_series_value) = ? GROUP BY ts, `__GROUP_BY_KEY_0_service.name`) SELECT ts, `__GROUP_BY_KEY_0_service.name`, multiIf(value <= 0, toFloat64(0), value <= 2.3283064365386963e-10, 2.3283064365386963e-10, value > 1.8446744073709552e+19, toFloat64('+Inf'), pow(2, ceil(log2(value) * 16) / 16)) AS __bucket, toFloat64(1) AS __result_0 FROM __spatial_aggregation_cte ORDER BY `__GROUP_BY_KEY_0_service.name`, ts, __bucket",
-				Args:  []any{"signoz_calls_total", uint64(1747936800000), uint64(1747983420000), "cumulative", "signoz_calls_total", uint64(1747947300000), uint64(1747983420000), 0},
-			},
-			expectedErr: nil,
-		},
 		{
 			name:        "test_gauge_avg_sum",
 			requestType: qbtypes.RequestTypeTimeSeries,

pkg/types/dashboardtypes/perses_dashboard_test.go

@@ -524,96 +524,6 @@ func TestInvalidateUnknownPluginKind(t *testing.T) {
 	}
 }
 
-// TestHeatmapPanelQueryKinds pins the panel allowlist to what validateHeatmap
-// accepts in querybuildertypesv5: everything but a trace operator.
-func TestHeatmapPanelQueryKinds(t *testing.T) {
-	testCases := []struct {
-		description     string
-		queryPluginKind string
-		queryPluginSpec string
-		expectedAllowed bool
-	}{
-		{
-			description:     "a metrics builder query is allowed",
-			queryPluginKind: "signoz/BuilderQuery",
-			queryPluginSpec: `{"name": "A", "signal": "metrics", "aggregations": [
-				{"metricName": "http.server.request.duration", "timeAggregation": "increase", "spaceAggregation": "sum"}
-			]}`,
-			expectedAllowed: true,
-		},
-		{
-			description:     "a promql query is allowed",
-			queryPluginKind: "signoz/PromQLQuery",
-			queryPluginSpec: `{"name": "A", "query": "sum by (le) (increase(signoz_latency_bucket[5m]))"}`,
-			expectedAllowed: true,
-		},
-		{
-			description:     "a clickhouse query is allowed",
-			queryPluginKind: "signoz/ClickHouseSQL",
-			queryPluginSpec: `{"name": "A", "query": "SELECT ts, bucket, value FROM cells"}`,
-			expectedAllowed: true,
-		},
-		{
-			description:     "a formula is allowed",
-			queryPluginKind: "signoz/Formula",
-			queryPluginSpec: `{"name": "F1", "expression": "A / B"}`,
-			expectedAllowed: true,
-		},
-		{
-			description:     "a composite query is allowed, since a formula needs its disabled inputs alongside it",
-			queryPluginKind: "signoz/CompositeQuery",
-			queryPluginSpec: `{"queries": [
-				{"type": "builder_query", "spec": {"name": "A", "signal": "metrics", "disabled": true, "aggregations": [
-					{"metricName": "http.server.request.duration", "timeAggregation": "increase", "spaceAggregation": "sum"}
-				]}},
-				{"type": "builder_formula", "spec": {"name": "F1", "expression": "A * 2"}}
-			]}`,
-			expectedAllowed: true,
-		},
-		{
-			description:     "a trace operator is refused",
-			queryPluginKind: "signoz/TraceOperator",
-			queryPluginSpec: `{"name": "T1", "expression": "A => B"}`,
-			expectedAllowed: false,
-		},
-	}
-
-	for _, testCase := range testCases {
-		t.Run(testCase.description, func(t *testing.T) {
-			data := fmt.Sprintf(`{
-				"variables": [],
-				"panels": {
-					"p1": {
-						"kind": "Panel",
-						"spec": {
-							"links": [],
-							"plugin": {"kind": "signoz/HeatmapPanel", "spec": {}},
-							"queries": [{
-								"kind": "heatmap",
-								"spec": {
-									"plugin": {"kind": %q, "spec": %s}
-								}
-							}]
-						}
-					}
-				},
-				"links": [],
-				"layouts": []
-			}`, testCase.queryPluginKind, testCase.queryPluginSpec)
-
-			_, err := unmarshalDashboard([]byte(data))
-
-			if testCase.expectedAllowed {
-				require.NoError(t, err)
-				return
-			}
-
-			require.Error(t, err)
-			assert.Contains(t, err.Error(), "is not supported by panel kind")
-		})
-	}
-}
-
 func TestInvalidateOneInvalidPanel(t *testing.T) {
 	data := []byte(`{
 		"variables": [],

pkg/types/dashboardtypes/perses_plugin_wrappers.go

@@ -35,7 +35,6 @@ func (PanelPlugin) PrepareJSONSchema(s *jsonschema.Schema) error {
 		string(PanelKindTable):      schemaRef("DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesTablePanelSpec"),
 		string(PanelKindHistogram):  schemaRef("DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHistogramPanelSpec"),
 		string(PanelKindList):       schemaRef("DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesListPanelSpec"),
-		string(PanelKindHeatmap):    schemaRef("DashboardtypesPanelPluginVariantGithubComSigNozSignozPkgTypesDashboardtypesHeatmapPanelSpec"),
 	})
 }
 
@@ -66,7 +65,6 @@ func (PanelPlugin) JSONSchemaOneOf() []any {
 		PanelPluginVariant[TablePanelSpec]{Kind: string(PanelKindTable)},
 		PanelPluginVariant[HistogramPanelSpec]{Kind: string(PanelKindHistogram)},
 		PanelPluginVariant[ListPanelSpec]{Kind: string(PanelKindList)},
-		PanelPluginVariant[HeatmapPanelSpec]{Kind: string(PanelKindHeatmap)},
 	}
 }
 
@@ -230,7 +228,6 @@ var (
 		PanelKindTable:      func() any { return new(TablePanelSpec) },
 		PanelKindHistogram:  func() any { return new(HistogramPanelSpec) },
 		PanelKindList:       func() any { return new(ListPanelSpec) },
-		PanelKindHeatmap:    func() any { return new(HeatmapPanelSpec) },
 	}
 	queryPluginSpecs = map[QueryPluginKind]func() any{
 		QueryKindBuilder:       func() any { return new(BuilderQuerySpec) },
@@ -253,7 +250,6 @@ var (
 		PanelKindPieChart:   {QueryKindBuilder, QueryKindComposite, QueryKindFormula, QueryKindTraceOperator, QueryKindClickHouseSQL},
 		PanelKindTable:      {QueryKindBuilder, QueryKindComposite, QueryKindFormula, QueryKindTraceOperator, QueryKindClickHouseSQL},
 		PanelKindList:       {QueryKindBuilder},
-		PanelKindHeatmap:    {QueryKindBuilder, QueryKindComposite, QueryKindFormula, QueryKindPromQL, QueryKindClickHouseSQL},
 	}
 )
 

pkg/types/dashboardtypes/perses_signoz_plugins.go

@@ -173,11 +173,10 @@ const (
 	PanelKindTable      PanelPluginKind = "signoz/TablePanel"
 	PanelKindHistogram  PanelPluginKind = "signoz/HistogramPanel"
 	PanelKindList       PanelPluginKind = "signoz/ListPanel"
-	PanelKindHeatmap    PanelPluginKind = "signoz/HeatmapPanel"
 )
 
 func (PanelPluginKind) Enum() []any {
-	return []any{PanelKindTimeSeries, PanelKindBarChart, PanelKindNumber, PanelKindPieChart, PanelKindTable, PanelKindHistogram, PanelKindList, PanelKindHeatmap}
+	return []any{PanelKindTimeSeries, PanelKindBarChart, PanelKindNumber, PanelKindPieChart, PanelKindTable, PanelKindHistogram, PanelKindList}
 }
 
 type TimeSeriesPanelSpec struct {
@@ -238,56 +237,6 @@ type ListPanelSpec struct {
 	SelectFields []telemetrytypes.TelemetryFieldKey `json:"selectFields,omitzero" validate:"dive"`
 }
 
-type HeatmapPanelSpec struct {
-	Visualization   BasicVisualization     `json:"visualization"`
-	Formatting      PanelFormatting        `json:"formatting"`
-	Axes            HeatmapAxes            `json:"axes"`
-	Legend          Legend                 `json:"legend"`
-	ChartAppearance HeatmapChartAppearance `json:"chartAppearance"`
-}
-
-// HeatmapAxes carries only the Y scale. The shared Axes type models a value
-// axis with soft bounds, where a heatmap's Y axis is the bucket boundaries the
-// response already fixed.
-type HeatmapAxes struct {
-	YScale HeatmapYScale `json:"yScale"`
-}
-
-type HeatmapChartAppearance struct {
-	Colors HeatmapColors `json:"colors"`
-}
-
-type HeatmapColors struct {
-	Mode    HeatmapColorMode  `json:"mode"`
-	Palette HeatmapPalette    `json:"palette"`
-	Scale   HeatmapColorScale `json:"scale"`
-	Steps   int               `json:"steps" validate:"omitempty,min=2,max=128"`
-	// MinCount and MaxCount clamp the colour scale; nil derives them from the
-	// grid, 0 and the highest count in it.
-	MinCount *float64 `json:"minCount"`
-	MaxCount *float64 `json:"maxCount"`
-	// Fill applies in opacity mode; empty means the selected group's legend colour.
-	Fill string `json:"fill"`
-}
-
-func (c *HeatmapColors) UnmarshalJSON(data []byte) error {
-	type alias HeatmapColors
-	var tmp alias
-	if err := json.Unmarshal(data, &tmp); err != nil {
-		return errors.WrapInvalidInputf(err, ErrCodeDashboardInvalidInput, "invalid heatmap colors")
-	}
-	*c = HeatmapColors(tmp)
-	return c.validate()
-}
-
-func (c HeatmapColors) validate() error {
-	if c.MinCount != nil && c.MaxCount != nil && *c.MinCount > *c.MaxCount {
-		return errors.NewInvalidInputf(ErrCodeDashboardInvalidInput,
-			"heatmap colors.minCount %v is greater than colors.maxCount %v", *c.MinCount, *c.MaxCount)
-	}
-	return nil
-}
-
 // ══════════════════════════════════════════════
 // Panel common types
 // ══════════════════════════════════════════════
@@ -760,168 +709,3 @@ func (p *PrecisionOption) UnmarshalJSON(data []byte) error {
 		return errors.NewInvalidInputf(ErrCodeDashboardInvalidInput, "invalid precision option %q: must be `0`, `1`, `2`, `3`, `4`, or `full`", v)
 	}
 }
-
-type HeatmapColorMode struct{ valuer.String }
-
-var (
-	HeatmapColorModePalette = HeatmapColorMode{valuer.NewString("palette")} // default
-	HeatmapColorModeOpacity = HeatmapColorMode{valuer.NewString("opacity")}
-)
-
-func (HeatmapColorMode) Enum() []any {
-	return []any{HeatmapColorModePalette, HeatmapColorModeOpacity}
-}
-
-func (m HeatmapColorMode) ValueOrDefault() string {
-	if m.IsZero() {
-		return HeatmapColorModePalette.StringValue()
-	}
-	return m.StringValue()
-}
-
-func (m HeatmapColorMode) MarshalJSON() ([]byte, error) {
-	return json.Marshal(m.ValueOrDefault())
-}
-
-func (m *HeatmapColorMode) UnmarshalJSON(data []byte) error {
-	var v string
-	if err := json.Unmarshal(data, &v); err != nil {
-		return errors.WrapInvalidInputf(err, ErrCodeDashboardInvalidInput, "invalid heatmap color mode: must be a string, one of `palette` or `opacity`")
-	}
-	mode := HeatmapColorMode{valuer.NewString(v)}
-	switch mode {
-	case HeatmapColorModePalette, HeatmapColorModeOpacity:
-		*m = mode
-		return nil
-	default:
-		return errors.NewInvalidInputf(ErrCodeDashboardInvalidInput, "invalid heatmap color mode %q: must be `palette` or `opacity`", v)
-	}
-}
-
-type HeatmapPalette struct{ valuer.String }
-
-var (
-	HeatmapPaletteIce      = HeatmapPalette{valuer.NewString("ice")}
-	HeatmapPaletteMoss     = HeatmapPalette{valuer.NewString("moss")}
-	HeatmapPaletteRust     = HeatmapPalette{valuer.NewString("rust")}
-	HeatmapPaletteGraphite = HeatmapPalette{valuer.NewString("graphite")}
-	HeatmapPaletteEmber    = HeatmapPalette{valuer.NewString("ember")}
-	HeatmapPaletteLagoon   = HeatmapPalette{valuer.NewString("lagoon")}
-	HeatmapPaletteOrchid   = HeatmapPalette{valuer.NewString("orchid")}
-	HeatmapPaletteVerdant  = HeatmapPalette{valuer.NewString("verdant")}
-	HeatmapPaletteLava     = HeatmapPalette{valuer.NewString("lava")} // default
-	HeatmapPaletteBeacon   = HeatmapPalette{valuer.NewString("beacon")}
-)
-
-func (HeatmapPalette) Enum() []any {
-	return []any{
-		HeatmapPaletteIce, HeatmapPaletteMoss, HeatmapPaletteRust, HeatmapPaletteGraphite,
-		HeatmapPaletteEmber, HeatmapPaletteLagoon, HeatmapPaletteOrchid, HeatmapPaletteVerdant,
-		HeatmapPaletteLava, HeatmapPaletteBeacon,
-	}
-}
-
-func (p HeatmapPalette) ValueOrDefault() string {
-	if p.IsZero() {
-		return HeatmapPaletteLava.StringValue()
-	}
-	return p.StringValue()
-}
-
-func (p HeatmapPalette) MarshalJSON() ([]byte, error) {
-	return json.Marshal(p.ValueOrDefault())
-}
-
-func (p *HeatmapPalette) UnmarshalJSON(data []byte) error {
-	var v string
-	if err := json.Unmarshal(data, &v); err != nil {
-		return errors.WrapInvalidInputf(err, ErrCodeDashboardInvalidInput, "invalid heatmap palette: must be a string, one of `ice`, `moss`, `rust`, `graphite`, `ember`, `lagoon`, `orchid`, `verdant`, `lava`, or `beacon`")
-	}
-	palette := HeatmapPalette{valuer.NewString(v)}
-	switch palette {
-	case HeatmapPaletteIce, HeatmapPaletteMoss, HeatmapPaletteRust, HeatmapPaletteGraphite,
-		HeatmapPaletteEmber, HeatmapPaletteLagoon, HeatmapPaletteOrchid, HeatmapPaletteVerdant,
-		HeatmapPaletteLava, HeatmapPaletteBeacon:
-		*p = palette
-		return nil
-	default:
-		return errors.NewInvalidInputf(ErrCodeDashboardInvalidInput, "invalid heatmap palette %q: must be `ice`, `moss`, `rust`, `graphite`, `ember`, `lagoon`, `orchid`, `verdant`, `lava`, or `beacon`", v)
-	}
-}
-
-type HeatmapYScale struct{ valuer.String }
-
-var (
-	HeatmapYScaleAuto   = HeatmapYScale{valuer.NewString("auto")} // default
-	HeatmapYScaleLinear = HeatmapYScale{valuer.NewString("linear")}
-	HeatmapYScaleLog    = HeatmapYScale{valuer.NewString("log")}
-	HeatmapYScaleSymlog = HeatmapYScale{valuer.NewString("symlog")}
-)
-
-func (HeatmapYScale) Enum() []any {
-	return []any{HeatmapYScaleAuto, HeatmapYScaleLinear, HeatmapYScaleLog, HeatmapYScaleSymlog}
-}
-
-func (s HeatmapYScale) ValueOrDefault() string {
-	if s.IsZero() {
-		return HeatmapYScaleAuto.StringValue()
-	}
-	return s.StringValue()
-}
-
-func (s HeatmapYScale) MarshalJSON() ([]byte, error) {
-	return json.Marshal(s.ValueOrDefault())
-}
-
-func (s *HeatmapYScale) UnmarshalJSON(data []byte) error {
-	var v string
-	if err := json.Unmarshal(data, &v); err != nil {
-		return errors.WrapInvalidInputf(err, ErrCodeDashboardInvalidInput, "invalid heatmap y scale: must be a string, one of `auto`, `linear`, `log`, or `symlog`")
-	}
-	scale := HeatmapYScale{valuer.NewString(v)}
-	switch scale {
-	case HeatmapYScaleAuto, HeatmapYScaleLinear, HeatmapYScaleLog, HeatmapYScaleSymlog:
-		*s = scale
-		return nil
-	default:
-		return errors.NewInvalidInputf(ErrCodeDashboardInvalidInput, "invalid heatmap y scale %q: must be `auto`, `linear`, `log`, or `symlog`", v)
-	}
-}
-
-type HeatmapColorScale struct{ valuer.String }
-
-var (
-	HeatmapColorScaleLog    = HeatmapColorScale{valuer.NewString("log")} // default
-	HeatmapColorScaleSqrt   = HeatmapColorScale{valuer.NewString("sqrt")}
-	HeatmapColorScaleLinear = HeatmapColorScale{valuer.NewString("linear")}
-)
-
-func (HeatmapColorScale) Enum() []any {
-	return []any{HeatmapColorScaleLog, HeatmapColorScaleSqrt, HeatmapColorScaleLinear}
-}
-
-func (s HeatmapColorScale) ValueOrDefault() string {
-	if s.IsZero() {
-		return HeatmapColorScaleLog.StringValue()
-	}
-	return s.StringValue()
-}
-
-func (s HeatmapColorScale) MarshalJSON() ([]byte, error) {
-	return json.Marshal(s.ValueOrDefault())
-}
-
-func (s *HeatmapColorScale) UnmarshalJSON(data []byte) error {
-	var v string
-	if err := json.Unmarshal(data, &v); err != nil {
-		return errors.WrapInvalidInputf(err, ErrCodeDashboardInvalidInput, "invalid heatmap color scale: must be a string, one of `log`, `sqrt`, or `linear`")
-	}
-	scale := HeatmapColorScale{valuer.NewString(v)}
-	switch scale {
-	case HeatmapColorScaleLog, HeatmapColorScaleSqrt, HeatmapColorScaleLinear:
-		*s = scale
-		return nil
-	default:
-		return errors.NewInvalidInputf(ErrCodeDashboardInvalidInput, "invalid heatmap color scale %q: must be `log`, `sqrt`, or `linear`", v)
-	}
-}

pkg/types/querybuildertypes/querybuildertypesv5/builder_elements.go

@@ -540,8 +540,6 @@ type MetricAggregation struct {
 	// reduce to operator for metric scalar requests
 	ReduceTo ReduceTo `json:"reduceTo,omitzero"`
 
-	HeatmapBucketing *HeatmapBucketing `json:"-"`
-
 	Reduced bool `json:"-"`
 }
 
@@ -556,10 +554,6 @@ func (m MetricAggregation) Copy() MetricAggregation {
 		valueFilterCopy := *m.ValueFilter
 		c.ValueFilter = &valueFilterCopy
 	}
-	if m.HeatmapBucketing != nil {
-		bucketingCopy := *m.HeatmapBucketing
-		c.HeatmapBucketing = &bucketingCopy
-	}
 	return c
 }
 

pkg/types/querybuildertypes/querybuildertypesv5/heatmap.go

@@ -1,296 +0,0 @@
-package querybuildertypesv5
-
-import (
-	"math"
-	"slices"
-
-	"github.com/SigNoz/signoz/pkg/errors"
-	"github.com/SigNoz/signoz/pkg/types/metrictypes"
-)
-
-const (
-	// HeatmapBucketColumn is the alias a heatmap statement gives the column holding
-	// a row's bucket upper bound. Every other aggregation returns a single numeric
-	// column the reader treats as the value; this name tells the two apart.
-	HeatmapBucketColumn = "__bucket"
-
-	DefaultNumBuckets = 60
-
-	// MaxLogScale is the resolution ClickHouse buckets every log heatmap at:
-	// 2^MaxLogScale bands per doubling. It is both the default and the finest
-	// available, since a coarser LogBucketsSpec.Scale folds down from it.
-	MaxLogScale = 4
-	// MinLogScale is one band per 16x, the coarsest axis worth rendering.
-	MinLogScale = -4
-
-	// A positive value approaching zero runs its band index off to -inf, so
-	// without a clamp one near-zero sample would stretch the axis by thousands
-	// of bands once AddHeatmapBucketsWithNoCounts spans it.
-	MinLogBandIndex = -512 // 2^-32, about 2.3e-10
-	MaxLogBandIndex = 1024 // 2^64, about 1.8e19
-)
-
-// MinLogUpperBound and MaxLogUpperBound are the ends the log axis is clamped
-// to. They do not vary with the requested scale.
-var (
-	MinLogUpperBound = math.Exp2(float64(MinLogBandIndex) / math.Exp2(MaxLogScale))
-	MaxLogUpperBound = math.Exp2(float64(MaxLogBandIndex) / math.Exp2(MaxLogScale))
-)
-
-// HeatmapBucketing is the bucket axis a heatmap statement builds in ClickHouse,
-// resolved from BucketOptions once the metric type is known. It stays nil for
-// histograms, whose upper bounds come from their own `le` labels.
-type HeatmapBucketing struct {
-	Kind BucketsKind
-	// LogScale is the resolution the caller asked for. ClickHouse always buckets
-	// at MaxLogScale, and postprocessing folds the axis down to this.
-	LogScale int
-	// MaxValue and NumBuckets are linear only.
-	MaxValue   float64
-	NumBuckets int
-}
-
-// ToHeatmapBucketing fills in what the caller left unset. An absent
-// BucketOptions resolves to the finest log axis, the one kind that needs nothing
-// from the caller.
-func (b *BucketOptions) ToHeatmapBucketing() HeatmapBucketing {
-	resolved := HeatmapBucketing{
-		Kind:       BucketsKindLog,
-		LogScale:   MaxLogScale,
-		NumBuckets: DefaultNumBuckets,
-	}
-	if b == nil {
-		return resolved
-	}
-
-	switch spec := b.Spec.(type) {
-	case LinearBucketsSpec:
-		resolved.Kind = BucketsKindLinear
-		resolved.MaxValue = spec.MaxValue
-		if spec.NumBuckets > 0 {
-			resolved.NumBuckets = spec.NumBuckets
-		}
-	case LogBucketsSpec:
-		if spec.Scale != nil {
-			resolved.LogScale = *spec.Scale
-		}
-	}
-
-	return resolved
-}
-
-// This cannot be called in validateHeatmap cuz type is resolved in querier.go.
-func (a *MetricAggregation) VerifyAndApplyBucketOptions(bucketOptions *BucketOptions) error {
-	switch a.Type {
-	case metrictypes.HistogramType:
-		if bucketOptions != nil {
-			return errors.NewInvalidInputf(errors.CodeInvalidInput,
-				"bucketOptions are not supported for histogram metrics: %q takes its bucket axis from its own `le` labels, so nothing in the spec would be applied", a.MetricName)
-		}
-		a.HeatmapBucketing = nil
-		return nil
-	// A summary carries no upper bounds of its own either, and its samples reach
-	// the final select the same way a gauge's do, so it buckets identically.
-	case metrictypes.GaugeType, metrictypes.SumType, metrictypes.SummaryType:
-		bucketing := bucketOptions.ToHeatmapBucketing()
-		a.HeatmapBucketing = &bucketing
-		return nil
-	case metrictypes.UnspecifiedType:
-		return errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"heatmaps need a metric whose type is known: no type is recorded for %q, so its bucket axis cannot be chosen", a.MetricName)
-	case metrictypes.ExpHistogramType:
-		return errors.Newf(errors.TypeUnsupported, errors.CodeUnsupported,
-			"heatmaps are not supported for exponential histograms yet: %q keeps its bucket counts in a sketch column, which needs its own reader", a.MetricName)
-	default:
-		return errors.Newf(errors.TypeUnsupported, errors.CodeUnsupported,
-			"heatmaps are not supported for %s metrics", a.Type.StringValue())
-	}
-}
-
-func MergeBucketUpperBounds(tsData ...*TimeSeriesData) map[int][]float64 {
-	upperBoundsByAggregation := map[int][]float64{}
-
-	for _, data := range tsData {
-		if data == nil {
-			continue
-		}
-		for _, aggBucket := range data.Aggregations {
-			if len(aggBucket.Meta.Buckets) == 0 {
-				continue
-			}
-			upperBoundsByAggregation[aggBucket.Index] = append(upperBoundsByAggregation[aggBucket.Index], aggBucket.Meta.Buckets...)
-		}
-	}
-
-	for index, upperBounds := range upperBoundsByAggregation {
-		slices.Sort(upperBounds)
-		upperBoundsByAggregation[index] = slices.Compact(upperBounds)
-	}
-
-	return upperBoundsByAggregation
-}
-
-// DownscaleHeatmapResolution folds the MaxLogScale axis ClickHouse buckets at
-// down to toScale, merging every 2^(MaxLogScale-toScale) adjacent bands into
-// one. The coarser upper bounds are a subset of the finer ones, so the fold is
-// exact.
-func DownscaleHeatmapResolution(tsData *TimeSeriesData, toScale int) {
-	if tsData == nil || toScale >= MaxLogScale {
-		return
-	}
-	for _, aggBucket := range tsData.Aggregations {
-		downscaleHeatmapResolutionForAggregation(aggBucket, toScale)
-	}
-}
-
-func downscaleHeatmapResolutionForAggregation(aggBucket *AggregationBucket, toScale int) {
-	if aggBucket == nil || len(aggBucket.Meta.Buckets) == 0 {
-		return
-	}
-
-	factor := int(math.Exp2(float64(MaxLogScale - toScale)))
-
-	// Merging is by index in the exponential mapping, not by position in
-	// Meta.Buckets, which lists only the upper bounds some series reached.
-	coarseUpperBounds := make([]float64, 0, len(aggBucket.Meta.Buckets))
-	upperBoundToCoarseIndex := make(map[float64]int, len(aggBucket.Meta.Buckets))
-	mergedInto := make([]int, len(aggBucket.Meta.Buckets))
-	for index, upperBound := range aggBucket.Meta.Buckets {
-		coarsened := coarsenUpperBound(upperBound, toScale, factor)
-		coarseIndex, ok := upperBoundToCoarseIndex[coarsened]
-		if !ok {
-			coarseIndex = len(coarseUpperBounds)
-			coarseUpperBounds = append(coarseUpperBounds, coarsened)
-			upperBoundToCoarseIndex[coarsened] = coarseIndex
-		}
-		mergedInto[index] = coarseIndex
-	}
-
-	overflowIndex := len(coarseUpperBounds)
-	for _, series := range aggBucket.Series {
-		for _, point := range series.Values {
-			if len(point.Values) == 0 {
-				continue
-			}
-			coarseCounts := make([]float64, overflowIndex+1)
-			for index, count := range point.Values {
-				if index >= len(mergedInto) {
-					coarseCounts[overflowIndex] += count
-					continue
-				}
-				coarseCounts[mergedInto[index]] += count
-			}
-			point.Values = coarseCounts
-		}
-	}
-	aggBucket.Meta.Buckets = coarseUpperBounds
-}
-
-// coarsenUpperBound moves an upper bound from the MaxLogScale exponential axis
-// onto the toScale one. The zero band has no exponent to rescale and stays put.
-func coarsenUpperBound(upperBound float64, toScale, factor int) float64 {
-	if upperBound <= 0 || math.IsInf(upperBound, 0) || math.IsNaN(upperBound) {
-		return upperBound
-	}
-	index := int(math.Round(math.Log2(upperBound) * math.Exp2(MaxLogScale)))
-	merged := int(math.Ceil(float64(index) / float64(factor)))
-	return math.Exp2(float64(merged) / math.Exp2(float64(toScale)))
-}
-
-// AddHeatmapBucketsWithNoCounts spans the range from the lowest upper bound some
-// series reached to the highest. Meta.Buckets leaves the ones in between out
-// entirely, so without this a gap renders with its two sides touching.
-//
-// Only a value-derived axis can be spanned: its upper bounds come from an index
-// that is a pure function of the value, so the ones in between are known without
-// having seen them. Nothing says what sits between two `le` labels.
-func AddHeatmapBucketsWithNoCounts(tsData *TimeSeriesData, bucketing HeatmapBucketing) {
-	if tsData == nil {
-		return
-	}
-	for _, aggBucket := range tsData.Aggregations {
-		addHeatmapBucketsWithNoCountsForAggregation(aggBucket, bucketing)
-	}
-}
-
-func addHeatmapBucketsWithNoCountsForAggregation(aggBucket *AggregationBucket, bucketing HeatmapBucketing) {
-	if aggBucket == nil || len(aggBucket.Meta.Buckets) == 0 {
-		return
-	}
-
-	// The zero bucket holds everything at or below zero. It has no index on either
-	// axis and sits below every other upper bound, so it keeps index 0 and the
-	// fill runs over the rest.
-	offset := 0
-	if aggBucket.Meta.Buckets[0] <= 0 {
-		offset = 1
-	}
-	positive := aggBucket.Meta.Buckets[offset:]
-	if len(positive) == 0 {
-		return
-	}
-
-	// Only finite upper bounds have an index, and the fill sizes a slice from
-	// one. Nothing should put +Inf or NaN on the axis, but bail if it happens.
-	indexes := make([]int, len(positive))
-	for i, upperBound := range positive {
-		if math.IsInf(upperBound, 0) || math.IsNaN(upperBound) {
-			return
-		}
-		indexes[i] = bucketing.calculateIndexOfUpperBound(upperBound)
-	}
-	lowest, highest := slices.Min(indexes), slices.Max(indexes)
-
-	denseUpperBounds := append([]float64{}, aggBucket.Meta.Buckets[:offset]...)
-	for index := lowest; index <= highest; index++ {
-		denseUpperBounds = append(denseUpperBounds, bucketing.calculateUpperBoundAtIndex(index))
-	}
-	if len(denseUpperBounds) == len(aggBucket.Meta.Buckets) {
-		return
-	}
-
-	// Counts map through their index rather than by matching upper bounds, so a
-	// regenerated upper bound differing from ClickHouse's in its last bit still
-	// lands where it came from.
-	shiftedTo := make([]int, len(aggBucket.Meta.Buckets))
-	for i, index := range indexes {
-		shiftedTo[i+offset] = index - lowest + offset
-	}
-
-	overflowIndex := len(denseUpperBounds)
-	for _, series := range aggBucket.Series {
-		for _, point := range series.Values {
-			if len(point.Values) == 0 {
-				continue
-			}
-			denseCounts := make([]float64, overflowIndex+1)
-			for index, count := range point.Values {
-				if index >= len(shiftedTo) {
-					denseCounts[overflowIndex] += count
-					continue
-				}
-				denseCounts[shiftedTo[index]] += count
-			}
-			point.Values = denseCounts
-		}
-	}
-	aggBucket.Meta.Buckets = denseUpperBounds
-}
-
-// calculateIndexOfUpperBound and calculateUpperBoundAtIndex are inverses over
-// the axis being returned, so they read h.LogScale rather than the MaxLogScale
-// ClickHouse bucketed at: k * maxValue / numBuckets on a linear axis,
-// 2^(k / 2^scale) on a log one.
-func (h HeatmapBucketing) calculateIndexOfUpperBound(upperBound float64) int {
-	if h.Kind == BucketsKindLinear {
-		return int(math.Round(upperBound * float64(h.NumBuckets) / h.MaxValue))
-	}
-	return int(math.Round(math.Log2(upperBound) * math.Exp2(float64(h.LogScale))))
-}
-
-func (h HeatmapBucketing) calculateUpperBoundAtIndex(index int) float64 {
-	if h.Kind == BucketsKindLinear {
-		return float64(index) * h.MaxValue / float64(h.NumBuckets)
-	}
-	return math.Exp2(float64(index) / math.Exp2(float64(h.LogScale)))
-}

pkg/types/querybuildertypes/querybuildertypesv5/req.go

@@ -397,8 +397,6 @@ type QueryRangeRequest struct {
 	PromQLProvider string `json:"-"`
 
 	FormatOptions *FormatOptions `json:"formatOptions,omitempty"`
-
-	BucketOptions *BucketOptions `json:"bucketOptions,omitempty"`
 }
 
 // PrepareJSONSchema adds description to the QueryRangeRequest schema.
@@ -736,130 +734,3 @@ func (r *QueryRangeRequest) GetQueriesSupportingZeroDefault() map[string]bool {
 
 	return canDefaultZero
 }
-
-type BucketOptions struct {
-	Kind BucketsKind `json:"kind"`
-	Spec any         `json:"spec"`
-}
-
-type BucketsKind struct {
-	valuer.String
-}
-
-var (
-	BucketsKindLinear = BucketsKind{valuer.NewString("linear")}
-	BucketsKindLog    = BucketsKind{valuer.NewString("log")}
-)
-
-// Enum implements jsonschema.Enum.
-func (BucketsKind) Enum() []any {
-	return []any{
-		BucketsKindLinear,
-		BucketsKindLog,
-	}
-}
-
-// LinearBucketsSpec divides (0, MaxValue] into NumBuckets equal bands.
-type LinearBucketsSpec struct {
-	// Everything above MaxValue is counted in the trailing overflow band. Evenly
-	// spaced upper bounds have no top to divide without it, so it is required.
-	MaxValue   float64 `json:"maxValue" required:"true"`
-	NumBuckets int     `json:"numBuckets,omitempty"`
-}
-
-// LogBucketsSpec spaces upper bounds at 2^Scale bands per doubling, the mapping
-// an exponential histogram uses.
-type LogBucketsSpec struct {
-	// ClickHouse always buckets at MaxLogScale and the surplus is folded away
-	// afterwards, so every Scale reads the same cache entry. MaxLogScale applies
-	// when unset.
-	Scale *int `json:"scale,omitempty"`
-}
-
-func (b *BucketOptions) UnmarshalJSON(data []byte) error {
-	var shadow struct {
-		Kind BucketsKind     `json:"kind"`
-		Spec json.RawMessage `json:"spec"`
-	}
-	if err := binding.JSON.BindBody(bytes.NewReader(data), &shadow, binding.WithDisallowUnknownFields(true)); err != nil {
-		return err
-	}
-
-	b.Kind = shadow.Kind
-
-	// An absent spec is a malformed pair rather than a request for defaults;
-	// `"spec": {}` asks for those.
-	if len(shadow.Spec) == 0 {
-		return errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"bucketOptions spec is required, use an empty object for the kind's defaults")
-	}
-
-	switch shadow.Kind {
-	case BucketsKindLinear:
-		var spec LinearBucketsSpec
-		if err := binding.JSON.BindBody(bytes.NewReader(shadow.Spec), &spec, binding.WithDisallowUnknownFields(true), binding.WithUnknownFieldContext("linear buckets spec")); err != nil {
-			return err
-		}
-		b.Spec = spec
-
-	case BucketsKindLog:
-		var spec LogBucketsSpec
-		if err := binding.JSON.BindBody(bytes.NewReader(shadow.Spec), &spec, binding.WithDisallowUnknownFields(true), binding.WithUnknownFieldContext("log buckets spec")); err != nil {
-			return err
-		}
-		b.Spec = spec
-
-	default:
-		return errors.NewInvalidInputf(
-			errors.CodeInvalidInput,
-			"invalid bucketOptions kind: %s",
-			shadow.Kind.StringValue(),
-		).WithAdditional(
-			"Valid bucket kinds are: linear, log",
-		)
-	}
-
-	return nil
-}
-
-// bucketOptionsLinear and bucketOptionsLog are the OpenAPI schemas for the two
-// BucketOptions variants. They have to be named types: the reflector turns an
-// anonymous one into an inline subschema, leaving the discriminator mapping in
-// PrepareJSONSchema pointing at components that were never emitted. `kind` is
-// required:"true" on both so oapi-codegen renders the discriminator non-pointer.
-type bucketOptionsLinear struct {
-	Kind BucketsKind       `json:"kind" required:"true" description:"How the upper bounds are spaced."`
-	Spec LinearBucketsSpec `json:"spec" required:"true" description:"The evenly spaced bucket specification."`
-}
-
-type bucketOptionsLog struct {
-	Kind BucketsKind    `json:"kind" required:"true" description:"How the upper bounds are spaced."`
-	Spec LogBucketsSpec `json:"spec" required:"true" description:"The logarithmic bucket specification."`
-}
-
-var _ jsonschema.OneOfExposer = BucketOptions{}
-
-func (BucketOptions) JSONSchemaOneOf() []any {
-	return []any{
-		bucketOptionsLinear{},
-		bucketOptionsLog{},
-	}
-}
-
-var _ jsonschema.Preparer = BucketOptions{}
-
-// PrepareJSONSchema marks the options as a `kind`-discriminated union;
-// signoz.attachDiscriminators promotes it and strips the base properties.
-func (BucketOptions) PrepareJSONSchema(s *jsonschema.Schema) error {
-	if s.ExtraProperties == nil {
-		s.ExtraProperties = map[string]any{}
-	}
-	s.ExtraProperties["x-signoz-discriminator"] = map[string]any{
-		"propertyName": "kind",
-		"mapping": map[string]string{
-			BucketsKindLinear.StringValue(): "#/components/schemas/Querybuildertypesv5BucketOptionsLinear",
-			BucketsKindLog.StringValue():    "#/components/schemas/Querybuildertypesv5BucketOptionsLog",
-		},
-	}
-	return nil
-}

pkg/types/querybuildertypes/querybuildertypesv5/request_type.go

@@ -19,11 +19,11 @@ func (r *RequestType) UnmarshalJSON(data []byte) error {
 	}
 	v := RequestType{valuer.NewString(s)}
 	switch v {
-	case RequestTypeScalar, RequestTypeTimeSeries, RequestTypeRaw, RequestTypeRawStream, RequestTypeTrace, RequestTypeDistribution, RequestTypeHeatmap:
+	case RequestTypeScalar, RequestTypeTimeSeries, RequestTypeRaw, RequestTypeRawStream, RequestTypeTrace, RequestTypeDistribution:
 		*r = v
 		return nil
 	default:
-		return errors.NewInvalidInputf(errors.CodeInvalidInput, "unknown request type %q; allowed values: %s", s, "`scalar`, `time_series`, `raw`, `raw_stream`, `trace`, `distribution`, `heatmap`")
+		return errors.NewInvalidInputf(errors.CodeInvalidInput, "unknown request type %q; allowed values: %s", s, "`scalar`, `time_series`, `raw`, `raw_stream`, `trace`, `distribution`")
 	}
 }
 
@@ -41,9 +41,6 @@ var (
 	RequestTypeTrace = RequestType{valuer.NewString("trace")}
 	// []Bucket (struct{Lower,Upper,Count float64}), example: histogram.
 	RequestTypeDistribution = RequestType{valuer.NewString("distribution")}
-	// TimeSeriesData carrying one count per histogram bucket at each timestamp,
-	// with the shared bucket upper bounds on the aggregation's meta.
-	RequestTypeHeatmap = RequestType{valuer.NewString("heatmap")}
 )
 
 // IsAggregation returns true for request types that produce aggregated results
@@ -52,7 +49,7 @@ var (
 // For non-aggregation types (raw, raw_stream, trace), those fields are ignored
 // and don't need to be validated.
 func (r RequestType) IsAggregation() bool {
-	return r == RequestTypeTimeSeries || r == RequestTypeScalar || r == RequestTypeDistribution || r == RequestTypeHeatmap
+	return r == RequestTypeTimeSeries || r == RequestTypeScalar || r == RequestTypeDistribution
 }
 
 // Enum implements jsonschema.Enum; returns the acceptable values for RequestType.
@@ -63,7 +60,6 @@ func (RequestType) Enum() []any {
 		RequestTypeRaw,
 		RequestTypeRawStream,
 		RequestTypeTrace,
-		RequestTypeHeatmap,
 		// RequestTypeDistribution,
 	}
 }

pkg/types/querybuildertypes/querybuildertypesv5/resp.go

@@ -138,10 +138,12 @@ type TimeSeriesData struct {
 }
 
 type AggregationBucket struct {
-	Index  int             `json:"index"` // or string Alias
-	Alias  string          `json:"alias"`
-	Meta   AggregationMeta `json:"meta,omitempty"`
-	Series []*TimeSeries   `json:"series"` // no extra nesting
+	Index int    `json:"index"` // or string Alias
+	Alias string `json:"alias"`
+	Meta  struct {
+		Unit string `json:"unit,omitempty"`
+	} `json:"meta,omitempty"`
+	Series []*TimeSeries `json:"series"` // no extra nesting
 
 	PredictedSeries  []*TimeSeries `json:"predictedSeries,omitempty"`
 	UpperBoundSeries []*TimeSeries `json:"upperBoundSeries,omitempty"`
@@ -149,54 +151,6 @@ type AggregationBucket struct {
 	AnomalyScores    []*TimeSeries `json:"anomalyScores,omitempty"`
 }
 
-// ReindexValuesToNewUpperBounds moves each count to the index its upper bound
-// holds in onto, a superset of Meta.Buckets. No count changes, only its position
-// in Values.
-func (a *AggregationBucket) ReindexValuesToNewUpperBounds(onto []float64) {
-	if a == nil {
-		return
-	}
-
-	from := a.Meta.Buckets
-	if len(onto) == 0 || slices.Equal(from, onto) {
-		return
-	}
-
-	upperBoundToIndex := make(map[float64]int, len(onto))
-	for index, upperBound := range onto {
-		upperBoundToIndex[upperBound] = index
-	}
-
-	for _, series := range a.Series {
-		for _, point := range series.Values {
-			if len(point.Values) == 0 {
-				continue
-			}
-			reindexed := make([]float64, len(onto)+1)
-			for index, count := range point.Values {
-				if index >= len(from) {
-					reindexed[len(onto)] = count
-					break
-				}
-				if newIndex, ok := upperBoundToIndex[from[index]]; ok {
-					reindexed[newIndex] = count
-				}
-			}
-			point.Values = reindexed
-		}
-	}
-
-	a.Meta.Buckets = onto
-}
-
-type AggregationMeta struct {
-	Unit string `json:"unit,omitempty"`
-	// Buckets holds ascending upper bounds shared by every series in the
-	// AggregationBucket, set only for heatmap results. Each point's Values holds
-	// len(Buckets)+1 counts: one per bound, then the open-above overflow.
-	Buckets []float64 `json:"buckets,omitempty"`
-}
-
 type TimeSeries struct {
 	Labels []*Label           `json:"labels,omitempty"`
 	Values []*TimeSeriesValue `json:"values"`
@@ -300,9 +254,13 @@ type TimeSeriesValue struct {
 	// on the client side, these partial values are rendered differently.
 	Partial bool `json:"partial,omitempty"`
 
-	// Values holds one count per histogram bucket for heatmap results, in the
-	// order of the aggregation's Meta.Buckets. Value is unused in that case.
+	// for the heatmap type chart
 	Values []float64 `json:"values,omitempty"`
+	Bucket *Bucket   `json:"bucket,omitempty"`
+}
+
+type Bucket struct {
+	Step float64 `json:"step"`
 }
 
 type ColumnType struct {

pkg/types/querybuildertypes/querybuildertypesv5/series_limit.go

@@ -127,7 +127,7 @@ func calculateSeriesValue(series *TimeSeries) float64 {
 
 	// For single-point series, return that value directly
 	if len(series.Values) == 1 {
-		value := calculatePointValue(series.Values[0])
+		value := series.Values[0].Value
 		if math.IsNaN(value) || math.IsInf(value, 0) {
 			return 0.0
 		}
@@ -139,11 +139,10 @@ func calculateSeriesValue(series *TimeSeries) float64 {
 	var count float64
 
 	for _, point := range series.Values {
-		value := calculatePointValue(point)
-		if math.IsNaN(value) || math.IsInf(value, 0) {
+		if math.IsNaN(point.Value) || math.IsInf(point.Value, 0) {
 			continue
 		}
-		sum += value
+		sum += point.Value
 		count++
 	}
 
@@ -155,25 +154,6 @@ func calculateSeriesValue(series *TimeSeries) float64 {
 	return sum / count
 }
 
-// calculatePointValue returns what a point contributes to its series' rank.
-// Heatmap points carry one count per bucket in Values and leave Value at zero,
-// so they rank on the total across buckets.
-func calculatePointValue(point *TimeSeriesValue) float64 {
-	if len(point.Values) == 0 {
-		return point.Value
-	}
-
-	var total float64
-	for _, value := range point.Values {
-		if math.IsNaN(value) || math.IsInf(value, 0) {
-			continue
-		}
-		total += value
-	}
-
-	return total
-}
-
 // convertValueToString converts various types to string for comparison.
 func convertValueToString(value any) string {
 	switch v := value.(type) {

pkg/types/querybuildertypes/querybuildertypesv5/series_limit_test.go

@@ -1,12 +1,10 @@
 package querybuildertypesv5
 
 import (
-	"math"
 	"testing"
 
 	"github.com/SigNoz/signoz/pkg/types/telemetrytypes"
 	"github.com/stretchr/testify/assert"
-	"github.com/stretchr/testify/require"
 )
 
 func TestApplySeriesLimit(t *testing.T) {
@@ -234,81 +232,3 @@ func TestApplySeriesLimit(t *testing.T) {
 		assert.Equal(t, 40.0, result[2].Values[0].Value)
 	})
 }
-
-func TestApplySeriesLimitRanksHeatmapSeriesByBucketTotals(t *testing.T) {
-	// A reshaped heatmap point leaves Value at zero and holds one count per
-	// bucket in Values, so ranking has to sum the buckets to see any difference.
-	series := []*TimeSeries{
-		{
-			Labels: []*Label{{
-				Key:   telemetrytypes.TelemetryFieldKey{Name: "service.name"},
-				Value: "quiet",
-			}},
-			Values: []*TimeSeriesValue{
-				{Timestamp: 1000, Values: []float64{1, 2, 0}},
-				{Timestamp: 1060, Values: []float64{0, 1, 0}},
-			},
-		},
-		{
-			Labels: []*Label{{
-				Key:   telemetrytypes.TelemetryFieldKey{Name: "service.name"},
-				Value: "busy",
-			}},
-			Values: []*TimeSeriesValue{
-				{Timestamp: 1000, Values: []float64{40, 60, 5}},
-				{Timestamp: 1060, Values: []float64{30, 70, 5}},
-			},
-		},
-		{
-			Labels: []*Label{{
-				Key:   telemetrytypes.TelemetryFieldKey{Name: "service.name"},
-				Value: "middling",
-			}},
-			Values: []*TimeSeriesValue{
-				{Timestamp: 1000, Values: []float64{5, 5, 0}},
-				{Timestamp: 1060, Values: []float64{4, 6, 0}},
-			},
-		},
-	}
-
-	result := ApplySeriesLimit(series, nil, 2)
-
-	require.Len(t, result, 2)
-	assert.Equal(t, "busy", result[0].Labels[0].Value)
-	assert.Equal(t, "middling", result[1].Labels[0].Value)
-}
-
-func TestCalculatePointValue(t *testing.T) {
-	testCases := []struct {
-		description   string
-		point         *TimeSeriesValue
-		expectedValue float64
-	}{
-		{
-			description:   "a plain time series point ranks on its single value",
-			point:         &TimeSeriesValue{Timestamp: 1000, Value: 7},
-			expectedValue: 7,
-		},
-		{
-			description:   "a heatmap point ranks on the total across its buckets",
-			point:         &TimeSeriesValue{Timestamp: 1000, Values: []float64{1, 12, 14, 3}},
-			expectedValue: 30,
-		},
-		{
-			description:   "non-finite bucket counts are skipped",
-			point:         &TimeSeriesValue{Timestamp: 1000, Values: []float64{2, math.NaN(), math.Inf(1), 3}},
-			expectedValue: 5,
-		},
-		{
-			description:   "an empty bucket list falls back to the single value",
-			point:         &TimeSeriesValue{Timestamp: 1000, Value: 4, Values: []float64{}},
-			expectedValue: 4,
-		},
-	}
-
-	for _, testCase := range testCases {
-		t.Run(testCase.description, func(t *testing.T) {
-			assert.Equal(t, testCase.expectedValue, calculatePointValue(testCase.point))
-		})
-	}
-}

pkg/types/querybuildertypes/querybuildertypesv5/validation.go

@@ -2,7 +2,6 @@ package querybuildertypesv5
 
 import (
 	"fmt"
-	"math"
 	"slices"
 	"strings"
 
@@ -66,8 +65,6 @@ func wrapValidationError(cause error, contextIdentifier string, errorFormat stri
 const (
 	// Maximum limit for query results.
 	MaxQueryLimit = 10000
-
-	MaxNumBuckets = 512
 )
 
 // ValidationOption is a functional option for configuring validation behaviour.
@@ -584,7 +581,7 @@ func (r *QueryRangeRequest) Validate(opts ...ValidationOption) error {
 
 	// Validate request type
 	switch r.RequestType {
-	case RequestTypeRaw, RequestTypeRawStream, RequestTypeTrace, RequestTypeTimeSeries, RequestTypeScalar, RequestTypeHeatmap:
+	case RequestTypeRaw, RequestTypeRawStream, RequestTypeTrace, RequestTypeTimeSeries, RequestTypeScalar:
 		opts = append(opts, GetValidationOptions(r.RequestType)...)
 	default:
 		return errors.NewInvalidInputf(
@@ -592,14 +589,10 @@ func (r *QueryRangeRequest) Validate(opts ...ValidationOption) error {
 			"invalid request type: %s",
 			r.RequestType,
 		).WithAdditional(
-			"Valid request types are: raw, timeseries, scalar, heatmap",
+			"Valid request types are: raw, timeseries, scalar",
 		)
 	}
 
-	if err := r.validateHeatmap(); err != nil {
-		return err
-	}
-
 	// raw/trace request types don't support metric queries;
 	// metrics are always aggregated and there is no raw form.
 	if r.RequestType == RequestTypeRaw || r.RequestType == RequestTypeRawStream || r.RequestType == RequestTypeTrace {
@@ -637,15 +630,11 @@ func (r *QueryRangeRequest) ValidateRequestScope() ([]ValidationOption, error) {
 
 	var opts []ValidationOption
 	switch r.RequestType {
-	case RequestTypeRaw, RequestTypeRawStream, RequestTypeTrace, RequestTypeTimeSeries, RequestTypeScalar, RequestTypeHeatmap:
+	case RequestTypeRaw, RequestTypeRawStream, RequestTypeTrace, RequestTypeTimeSeries, RequestTypeScalar:
 		opts = GetValidationOptions(r.RequestType)
 	default:
 		return nil, errors.NewInvalidInputf(errors.CodeInvalidInput, "invalid request type: %s", r.RequestType).
-			WithAdditional("Valid request types are: raw, timeseries, scalar, heatmap")
-	}
-
-	if err := r.validateHeatmap(); err != nil {
-		return nil, err
+			WithAdditional("Valid request types are: raw, timeseries, scalar")
 	}
 
 	if r.RequestType == RequestTypeRaw || r.RequestType == RequestTypeRawStream || r.RequestType == RequestTypeTrace {
@@ -849,135 +838,9 @@ func validateQueryEnvelope(envelope QueryEnvelope, opts ...ValidationOption) err
 	}
 }
 
-func (r *QueryRangeRequest) validateHeatmap() error {
-	if r.RequestType != RequestTypeHeatmap {
-		if r.BucketOptions != nil {
-			return errors.NewInvalidInputf(
-				errors.CodeInvalidInput,
-				"bucketOptions are only supported for heatmap requests, got %s",
-				r.RequestType,
-			)
-		}
-		return nil
-	}
-
-	if r.FormatOptions != nil && r.FormatOptions.FillGaps {
-		return errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"fillGaps is not supported for heatmap requests: an absent column means collection stopped, which a zero-filled column would hide")
-	}
-
-	if err := r.BucketOptions.validateBucketOptions(); err != nil {
-		return err
-	}
-
-	enabled := 0
-	for _, envelope := range r.CompositeQuery.Queries {
-		switch spec := envelope.Spec.(type) {
-		case QueryBuilderQuery[MetricAggregation]:
-			if err := validateHeatmapQuery(spec.Functions, spec.Having); err != nil {
-				return err
-			}
-			if spec.Disabled {
-				continue
-			}
-			enabled++
-		case QueryBuilderFormula:
-			if err := validateHeatmapQuery(spec.Functions, spec.Having); err != nil {
-				return err
-			}
-			if spec.Disabled {
-				continue
-			}
-			enabled++
-		case ClickHouseQuery:
-			if spec.Disabled {
-				continue
-			}
-			enabled++
-		case PromQuery:
-			if r.BucketOptions != nil {
-				return errors.NewInvalidInputf(errors.CodeInvalidInput,
-					"bucketOptions are not supported for promql heatmap requests: the bucket axis comes from the `le` labels the query returns, so nothing in the spec would be applied")
-			}
-			if spec.Disabled {
-				continue
-			}
-			enabled++
-		// An AI query decodes to the traces spec, so it lands here too. Admitting
-		// either signal means capping Aggregations at one: each carries its own
-		// Meta.Buckets, and a heatmap renders against a single bucket axis.
-		case QueryBuilderQuery[LogAggregation], QueryBuilderQuery[TraceAggregation]:
-			return errors.New(errors.TypeUnsupported, errors.CodeUnsupported,
-				"heatmaps are not supported for the logs and traces signals yet")
-		default:
-			return errors.NewInvalidInputf(errors.CodeInvalidInput,
-				"heatmap requests support one metrics builder query, one formula over them, one clickhouse query, or one promql query, got %q", envelope.Type.StringValue())
-		}
-	}
-
-	if enabled != 1 {
-		return errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"heatmap requests need exactly one enabled query, got %d", enabled)
-	}
-
-	return nil
-}
-
-func (b *BucketOptions) validateBucketOptions() error {
-	if b == nil {
-		return nil
-	}
-
-	switch spec := b.Spec.(type) {
-	case LinearBucketsSpec:
-		if math.IsNaN(spec.MaxValue) || math.IsInf(spec.MaxValue, 0) || spec.MaxValue <= 0 {
-			return errors.NewInvalidInputf(errors.CodeInvalidInput,
-				"linear buckets need a finite maxValue greater than 0, got %v", spec.MaxValue)
-		}
-		if spec.NumBuckets < 0 || spec.NumBuckets > MaxNumBuckets {
-			return errors.NewInvalidInputf(errors.CodeInvalidInput,
-				"numBuckets must be between 1 and %d, got %d", MaxNumBuckets, spec.NumBuckets)
-		}
-
-	case LogBucketsSpec:
-		if spec.Scale != nil && (*spec.Scale < MinLogScale || *spec.Scale > MaxLogScale) {
-			return errors.NewInvalidInputf(errors.CodeInvalidInput,
-				"scale must be between %d and %d, got %d", MinLogScale, MaxLogScale, *spec.Scale)
-		}
-
-	default:
-		return errors.NewInvalidInputf(
-			errors.CodeInvalidInput,
-			"invalid bucketOptions kind: %s",
-			b.Kind.StringValue(),
-		).WithAdditional(
-			"Valid bucket kinds are: linear, log",
-		)
-	}
-
-	return nil
-}
-
-// validateHeatmapQuery refuses the per-query settings that cannot mean anything
-// on a heatmap. It runs on disabled queries too: a disabled query is a formula
-// input, so whatever it does still reaches the cells.
-func validateHeatmapQuery(functions []Function, having *Having) error {
-	if len(functions) > 0 {
-		return errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"functions are not supported for heatmap requests: a heatmap point is a count per bucket, not a single value")
-	}
-
-	if having != nil && having.Expression != "" {
-		return errors.NewInvalidInputf(errors.CodeInvalidInput,
-			"having is not supported for heatmap requests: it filters individual cells, which breaks the cumulative differencing")
-	}
-
-	return nil
-}
-
 func GetValidationOptions(requestType RequestType) []ValidationOption {
 	switch requestType {
-	case RequestTypeTimeSeries, RequestTypeHeatmap:
+	case RequestTypeTimeSeries:
 		return []ValidationOption{WithSkipSelectFieldValidation(), WithTimestampGroupByValidation()}
 	case RequestTypeScalar:
 		return []ValidationOption{WithSkipSelectFieldValidation(), WithReduceToValidation()}

tests/fixtures/querier.py

@@ -33,7 +33,6 @@ class RequestType:
     TIME_SERIES = "time_series"
     SCALAR = "scalar"
     TABLE = "table"
-    HEATMAP = "heatmap"
 
 
 @dataclass
@@ -174,7 +173,6 @@ def make_query_request(
     request_type: str = RequestType.TIME_SERIES,
     format_options: dict | None = None,
     variables: dict | None = None,
-    bucket_options: dict | None = None,
     no_cache: bool = True,
     timeout: int = QUERY_TIMEOUT,
     headers: dict | None = None,
@@ -193,8 +191,6 @@ def make_query_request(
     }
     if variables:
         payload["variables"] = variables
-    if bucket_options is not None:
-        payload["bucketOptions"] = bucket_options
 
     return requests.post(
         signoz.self.host_configs["8080"].get("/api/v5/query_range"),
@@ -363,20 +359,6 @@ def build_formula_query(
     return {"type": "builder_formula", "spec": spec}
 
 
-def build_log_bucket_options(scale: int | None = None) -> dict:
-    spec: dict[str, Any] = {}
-    if scale is not None:
-        spec["scale"] = scale
-    return {"kind": "log", "spec": spec}
-
-
-def build_linear_bucket_options(max_value: float, num_buckets: int | None = None) -> dict:
-    spec: dict[str, Any] = {"maxValue": max_value}
-    if num_buckets is not None:
-        spec["numBuckets"] = num_buckets
-    return {"kind": "linear", "spec": spec}
-
-
 def build_function(name: str, *args: Any) -> dict:
     func: dict[str, Any] = {"name": name}
     if args:
@@ -411,24 +393,6 @@ def get_all_series(response_json: dict, query_name: str) -> list[dict]:
     return aggregations[0].get("series", [])
 
 
-def get_heatmap_buckets(response_json: dict, query_name: str) -> list[float]:
-    """The ascending bucket upper bounds a heatmap result's counts are positional against.
-    Each point holds one more count than there are bounds: the trailing one is the open-above overflow."""
-    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 []
-    return aggregations[0].get("meta", {}).get("buckets", [])
-
-
-def get_heatmap_columns(response_json: dict, query_name: str) -> list[dict]:
-    """A heatmap result's points for its single series, oldest first."""
-    return sorted(get_series_values(response_json, query_name), key=lambda point: point["timestamp"])
-
-
 def get_scalar_value(response_json: dict, query_name: str) -> float | None:
     values = get_series_values(response_json, query_name)
     if values:

tests/integration/testdata/heatmap_histogram_3m.jsonl

@@ -1,24 +0,0 @@
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "1"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 10, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "2"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 20, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "4"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 30, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "+Inf"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 40, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "1"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "2"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 60, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "4"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 70, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "+Inf"}, "timestamp": "2025-01-10T10:00:00+00:00", "value": 80, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "1"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 11, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "2"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 23, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "4"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 33, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "+Inf"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 44, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "1"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "2"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 61, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "4"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 72, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "+Inf"}, "timestamp": "2025-01-10T10:01:00+00:00", "value": 82, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "1"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 13, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "2"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 25, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "4"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 39, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "api", "le": "+Inf"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 50, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "1"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 51, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "2"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 62, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "4"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 73, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}
-{"metric_name": "heatmap_request_duration_bucket", "labels": {"__temporality__": "Cumulative", "service": "web", "le": "+Inf"}, "timestamp": "2025-01-10T10:02:00+00:00", "value": 85, "temporality": "Cumulative", "type_": "Histogram", "is_monotonic": true, "flags": 0, "description": "", "unit": "", "env": "default", "resource_attrs": {}, "scope_attrs": {}}

tests/integration/tests/queriermetrics/15_heatmap.py

@@ -1,883 +0,0 @@
-from collections.abc import Callable
-from datetime import UTC, datetime, timedelta
-from http import HTTPStatus
-from uuid import uuid4
-
-import pytest
-
-from fixtures import types
-from fixtures.auth import USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD
-from fixtures.fs import get_testdata_file_path
-from fixtures.metrics import Metrics
-from fixtures.querier import (
-    RequestType,
-    assert_identical_query_response,
-    build_builder_query,
-    build_formula_query,
-    build_function,
-    build_linear_bucket_options,
-    build_log_bucket_options,
-    get_all_series,
-    get_error_message,
-    get_heatmap_buckets,
-    get_heatmap_columns,
-    index_series_by_label,
-    make_query_request,
-)
-
-HISTOGRAM_FILE = get_testdata_file_path("histogram_data_1h.jsonl")
-HISTOGRAM_COUNTERS_FILE = get_testdata_file_path("heatmap_histogram_3m.jsonl")
-MINUTE_MS = 60_000
-
-# the request-level rules below are checked before the metric is resolved, so
-# they need no data behind the query
-MISSING_METRIC = "test_heatmap_metric_that_is_never_written"
-
-
-def test_gauge_heatmap(
-    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=UTC).replace(second=0, microsecond=0)
-    minutes = 3
-    start_ms = int((now - timedelta(minutes=minutes + 1)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_gauge"
-
-    value_by_host = {f"host-{host:02d}": (200, 400, 800)[host // 8] + host for host in range(24)}
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"host": host},
-                timestamp=now - timedelta(minutes=minutes - minute),
-                value=value,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for host, value in value_by_host.items()
-            for minute in range(minutes)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max", group_by=["host"])],
-        request_type=RequestType.HEATMAP,
-        bucket_options=build_log_bucket_options(0),
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    # a group by gives one series per host, and all of them are counted against
-    # this one axis
-    assert get_heatmap_buckets(data, "A") == pytest.approx([256.0, 512.0, 1024.0])
-
-    columns_by_host = {
-        host: sorted(series["values"], key=lambda column: column["timestamp"])
-        for host, series in index_series_by_label(get_all_series(data, "A"), "host").items()
-    }
-    assert len(columns_by_host) == len(value_by_host)
-
-    # a column holds one count per bucket plus a trailing one for the overflow
-    for host, columns in columns_by_host.items():
-        occupied = int(host.removeprefix("host-")) // 8
-        assert [column["values"] for column in columns] == [[1 if slot == occupied else 0 for slot in range(4)]] * minutes
-
-    # summed across the hosts, a column is the spread of the 24 of them
-    for minute in range(minutes):
-        assert [sum(columns[minute]["values"][slot] for columns in columns_by_host.values()) for slot in range(4)] == [8, 8, 8, 0]
-
-
-def test_sum_heatmap(
-    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=UTC).replace(second=0, microsecond=0)
-    minutes = 3
-    start_ms = int((now - timedelta(minutes=minutes + 1)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_sum"
-
-    value_by_endpoint = {f"/endpoint-{endpoint:02d}": (100, 800)[endpoint // 8] + endpoint for endpoint in range(16)}
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"endpoint": endpoint},
-                timestamp=now - timedelta(minutes=minutes - minute),
-                value=value,
-                temporality="Cumulative",
-                type_="Sum",
-            )
-            for endpoint, value in value_by_endpoint.items()
-            for minute in range(minutes)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max", temporality="cumulative", group_by=["endpoint"])],
-        request_type=RequestType.HEATMAP,
-        bucket_options=build_log_bucket_options(0),
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    assert get_heatmap_buckets(data, "A") == pytest.approx([128.0, 256.0, 512.0, 1024.0])
-
-    columns_by_endpoint = {
-        endpoint: sorted(series["values"], key=lambda column: column["timestamp"])
-        for endpoint, series in index_series_by_label(get_all_series(data, "A"), "endpoint").items()
-    }
-    assert len(columns_by_endpoint) == len(value_by_endpoint)
-
-    for endpoint, columns in columns_by_endpoint.items():
-        occupied = 0 if int(endpoint.removeprefix("/endpoint-")) < 8 else 3
-        assert [column["values"] for column in columns] == [[1 if slot == occupied else 0 for slot in range(5)]] * minutes
-
-    for minute in range(minutes):
-        assert [sum(columns[minute]["values"][slot] for columns in columns_by_endpoint.values()) for slot in range(5)] == [8, 0, 0, 8, 0]
-
-
-def test_histogram_heatmap(
-    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=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_histogram"
-
-    insert_metrics(
-        Metrics.load_from_file(
-            HISTOGRAM_FILE,
-            base_time=now - timedelta(minutes=60),
-            metric_name_override=metric_name,
-        )
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "increase", "p50", group_by=["le"], filter_expression='endpoint = "/health"')],
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    # a histogram's axis is its recorded `le` bounds exactly, since nothing is
-    # known about what sits between two of them; `le=+Inf` has no finite bound
-    # and is counted in the trailing overflow slot
-    assert get_heatmap_buckets(data, "A") == [1000, 1500, 2000, 4000, 5000, 6000, 8000]
-
-    columns = get_heatmap_columns(data, "A")
-    assert columns
-    for column in columns:
-        assert len(column["values"]) == 8
-        assert all(count >= 0 for count in column["values"])
-    assert any(sum(column["values"]) > 0 for column in columns)
-
-
-def test_linear_buckets(
-    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=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=30)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_linear"
-
-    # 100 wide buckets: 100 lands on the first, 250 on the third, and 1500 is
-    # past maxValue so it counts in the overflow
-    values = [100, 250, 1500]
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"service": "api"},
-                timestamp=now - timedelta(minutes=len(values) - minute),
-                value=value,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for minute, value in enumerate(values)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max")],
-        request_type=RequestType.HEATMAP,
-        bucket_options=build_linear_bucket_options(1000, 10),
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    # 200 is on the axis although nothing reached it, so the gap between 100 and
-    # 300 renders as a gap
-    assert get_heatmap_buckets(data, "A") == pytest.approx([100.0, 200.0, 300.0])
-
-    columns = get_heatmap_columns(data, "A")
-    assert [column["values"] for column in columns] == [
-        [1, 0, 0, 0],
-        [0, 0, 1, 0],
-        [0, 0, 0, 1],
-    ]
-
-def test_zero_bucket(
-    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=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=30)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_zero_bucket"
-
-    values = [0, 256, 1024]
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"service": "api"},
-                timestamp=now - timedelta(minutes=len(values) - minute),
-                value=value,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for minute, value in enumerate(values)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max")],
-        request_type=RequestType.HEATMAP,
-        bucket_options=build_log_bucket_options(0),
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    # a log axis cannot place a value at or below zero, so those share a bucket
-    # of their own beneath the rest, and 512 is spanned above it
-    assert get_heatmap_buckets(data, "A") == pytest.approx([0.0, 256.0, 512.0, 1024.0])
-
-    columns = get_heatmap_columns(data, "A")
-    assert [column["values"] for column in columns] == [
-        [1, 0, 0, 0, 0],
-        [0, 1, 0, 0, 0],
-        [0, 0, 0, 1, 0],
-    ]
-
-
-def test_single_bucket(
-    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=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=30)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_single_bucket"
-
-    values = [256, 256, 256]
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"service": "api"},
-                timestamp=now - timedelta(minutes=len(values) - minute),
-                value=value,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for minute, value in enumerate(values)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max")],
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    # a single bucket leaves no gap to span
-    data = response.json()
-    assert get_heatmap_buckets(data, "A") == [256.0]
-    assert [column["values"] for column in get_heatmap_columns(data, "A")] == [[1, 0], [1, 0], [1, 0]]
-
-
-# the values seeded below are 256 and 512, so each axis here runs from the bucket
-# holding 256 to the one holding 512 at that option's resolution: 16 log buckets
-# to the 2x, one bucket per 16x, or a linear bucket every maxValue/numBuckets
-@pytest.mark.parametrize(
-    "bucket_options, expected_buckets",
-    [
-        (None, [256 * 2 ** (step / 16) for step in range(17)]),
-        ({"kind": "log", "spec": {}}, [256 * 2 ** (step / 16) for step in range(17)]),
-        (build_log_bucket_options(4), [256 * 2 ** (step / 16) for step in range(17)]),
-        (build_log_bucket_options(-4), [2**16]),
-        (build_linear_bucket_options(1024), [step * 1024 / 60 for step in range(15, 31)]),
-        (build_linear_bucket_options(1024, 512), [step * 1024 / 512 for step in range(128, 257)]),
-    ],
-    ids=["absent", "log_defaults", "the_finest_scale", "the_coarsest_scale", "linear_without_num_buckets", "the_most_buckets"],
-)
-def test_bucket_option_limits(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[list[Metrics]], None],
-    bucket_options: dict | None,
-    expected_buckets: list[float],
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=30)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_bucket_option_limits"
-
-    values = [256, 512]
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"service": "api"},
-                timestamp=now - timedelta(minutes=len(values) - minute),
-                value=value,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for minute, value in enumerate(values)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max")],
-        request_type=RequestType.HEATMAP,
-        bucket_options=bucket_options,
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    assert get_heatmap_buckets(data, "A") == pytest.approx(expected_buckets)
-
-    columns = get_heatmap_columns(data, "A")
-    assert len(columns) == len(values)
-    for column in columns:
-        assert len(column["values"]) == len(expected_buckets) + 1
-        assert sum(column["values"]) == 1
-
-
-def test_formula_heatmap(
-    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=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=30)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_formula"
-
-    values = [100, 260, 295, 512, 1500]
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"service": "api"},
-                timestamp=now - timedelta(minutes=len(values) - minute),
-                value=value,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for minute, value in enumerate(values)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    from_metric = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "max", "max")],
-        request_type=RequestType.HEATMAP,
-    )
-    assert from_metric.status_code == HTTPStatus.OK, from_metric.text
-
-    # a formula over the same query has to land its counts on the same axis
-    from_formula = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [
-            build_builder_query("A", metric_name, "max", "max", disabled=True),
-            build_formula_query("F1", "A"),
-        ],
-        request_type=RequestType.HEATMAP,
-    )
-    assert from_formula.status_code == HTTPStatus.OK, from_formula.text
-
-    assert get_heatmap_buckets(from_formula.json(), "F1") == pytest.approx(get_heatmap_buckets(from_metric.json(), "A"))
-    assert [column["values"] for column in get_heatmap_columns(from_formula.json(), "F1")] == [column["values"] for column in get_heatmap_columns(from_metric.json(), "A")]
-
-
-def test_promql_heatmap(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[list[Metrics]], None],
-) -> None:
-    end_ms = (int((datetime.now(tz=UTC) - timedelta(minutes=5)).timestamp() * 1000) // MINUTE_MS) * MINUTE_MS
-    start_ms = end_ms - MINUTE_MS
-
-    # the file's three columns are one minute apart, and the first sits a minute
-    # before the query window so the earliest step has something to increase over.
-    # Every counter in it stays above its own rise across a window, below which
-    # increase clips its back-extrapolation at the counter's zero point.
-    insert_metrics(Metrics.load_from_file(HISTOGRAM_COUNTERS_FILE, base_time=datetime.fromtimestamp((start_ms - MINUTE_MS) / 1000, tz=UTC)))
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [{"type": "promql", "spec": {"name": "A", "query": "sum by (le) (increase(heatmap_request_duration_bucket[2m]))", "step": 60}}],
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    assert get_heatmap_buckets(data, "A") == [1, 2, 4]
-
-    series = get_all_series(data, "A")
-    assert len(series) == 1
-    # `le` is what the bucket axis is read off, so it is never a group label
-    assert series[0].get("labels") in (None, [])
-
-    # what the query returns per `le` is still cumulative across `le`, so each
-    # count here is its own minus the one below it, and `le=+Inf` has no finite
-    # bound to sit on and lands in the trailing slot. increase over a 2m window of
-    # minutely samples extrapolates one minute's rise to two, hence the scaling.
-    assert [column["values"] for column in get_heatmap_columns(data, "A")] == [[2, 6, 2, 2], [6, 0, 8, 4]]
-
-
-def test_promql_heatmap_without_le(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    insert_metrics: Callable[[list[Metrics]], None],
-) -> None:
-    metric = f"promql_heatmap_gauge_{uuid4().hex[:8]}"
-    end_ms = (int((datetime.now(tz=UTC) - timedelta(minutes=5)).timestamp() * 1000) // MINUTE_MS) * MINUTE_MS
-    start_ms = end_ms - MINUTE_MS
-
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric,
-                labels={"service": "api"},
-                timestamp=datetime.fromtimestamp(ts_ms / 1000, tz=UTC),
-                value=42.0,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for ts_ms in range(start_ms, end_ms + 1, MINUTE_MS)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [{"type": "promql", "spec": {"name": "A", "query": metric, "step": 60}}],
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    # the bucket axis of a promql heatmap comes from `le`, and a series without
-    # it has no bucket to sit in
-    assert get_heatmap_buckets(response.json(), "A") == []
-    assert get_heatmap_columns(response.json(), "A") == []
-
-
-def test_clickhouse_heatmap(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    start = now - timedelta(minutes=2)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    # a clickhouse statement names its bucket upper bound column `bucket`
-    response = make_query_request(
-        signoz,
-        token,
-        int(start.timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        [
-            {
-                "type": "clickhouse_sql",
-                "spec": {
-                    "name": "A",
-                    "query": (f"SELECT toDateTime({int(start.timestamp())}) AS ts, toFloat64(10) AS bucket, toFloat64(3) AS `__result_0` UNION ALL SELECT toDateTime({int(start.timestamp())}) AS ts, toFloat64(20) AS bucket, toFloat64(7) AS `__result_0`"),
-                    "disabled": False,
-                },
-            }
-        ],
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.OK, response.text
-
-    data = response.json()
-    assert get_heatmap_buckets(data, "A") == [10, 20]
-    # a clickhouse heatmap takes no bucketOptions, so the counts land exactly
-    # where the statement put them, plus the overflow slot
-    assert [column["values"] for column in get_heatmap_columns(data, "A")] == [[3, 7, 0]]
-
-
-def test_cached_heatmap_matches_uncached(
-    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=UTC).replace(second=0, microsecond=0)
-    metric_name = "test_heatmap_cache"
-
-    # the first half sits 16x below the second, so the cached range and the fresh
-    # one reach disjoint parts of the axis and neither may lose its counts
-    insert_metrics(
-        [
-            Metrics(
-                metric_name=metric_name,
-                labels={"service": "api"},
-                timestamp=now - timedelta(minutes=60 - minute),
-                value=256 if minute < 15 else 4096,
-                type_="Gauge",
-                is_monotonic=False,
-            )
-            for minute in range(30)
-        ]
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    query = [build_builder_query("A", metric_name, "max", "max")]
-    wide_start_ms = int((now - timedelta(minutes=60)).timestamp() * 1000)
-    wide_end_ms = int((now - timedelta(minutes=30)).timestamp() * 1000)
-
-    warmup = make_query_request(
-        signoz,
-        token,
-        wide_start_ms,
-        int((now - timedelta(minutes=45)).timestamp() * 1000),
-        query,
-        request_type=RequestType.HEATMAP,
-        no_cache=False,
-    )
-    assert warmup.status_code == HTTPStatus.OK, warmup.text
-
-    from_cache = make_query_request(signoz, token, wide_start_ms, wide_end_ms, query, request_type=RequestType.HEATMAP, no_cache=False)
-    assert from_cache.status_code == HTTPStatus.OK, from_cache.text
-
-    uncached = make_query_request(signoz, token, wide_start_ms, wide_end_ms, query, request_type=RequestType.HEATMAP, no_cache=True)
-    assert uncached.status_code == HTTPStatus.OK, uncached.text
-
-    assert_identical_query_response(from_cache, uncached)
-
-    # 256 and 4096 are 16x apart, which the axis covers at 16 buckets per 2x, and
-    # every column holds the one value its minute recorded
-    assert len(get_heatmap_buckets(uncached.json(), "A")) == 65
-    assert [sum(column["values"]) for column in get_heatmap_columns(uncached.json(), "A")] == [1] * 30
-
-
-def test_histogram_rejects_bucket_options(
-    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=UTC).replace(second=0, microsecond=0)
-    start_ms = int((now - timedelta(minutes=65)).timestamp() * 1000)
-    end_ms = int(now.timestamp() * 1000)
-    metric_name = "test_heatmap_histogram_with_bucket_options"
-
-    insert_metrics(
-        Metrics.load_from_file(
-            HISTOGRAM_FILE,
-            base_time=now - timedelta(minutes=60),
-            metric_name_override=metric_name,
-        )
-    )
-
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-    response = make_query_request(
-        signoz,
-        token,
-        start_ms,
-        end_ms,
-        [build_builder_query("A", metric_name, "increase", "p50")],
-        request_type=RequestType.HEATMAP,
-        bucket_options=build_log_bucket_options(2),
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-    assert "bucketOptions are not supported for histogram metrics" in get_error_message(response.json())
-
-
-@pytest.mark.parametrize(
-    "request_type",
-    [RequestType.TIME_SERIES, RequestType.SCALAR, RequestType.RAW],
-    ids=["time_series", "scalar", "raw"],
-)
-def test_bucket_options_outside_a_heatmap(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    request_type: str,
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    response = make_query_request(
-        signoz,
-        token,
-        int((now - timedelta(minutes=30)).timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        [build_builder_query("A", MISSING_METRIC, "max", "max")],
-        request_type=request_type,
-        bucket_options=build_log_bucket_options(2),
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-    assert "bucketOptions are only supported for heatmap requests" in get_error_message(response.json())
-
-
-def test_fill_gaps_is_rejected(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    response = make_query_request(
-        signoz,
-        token,
-        int((now - timedelta(minutes=30)).timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        [build_builder_query("A", MISSING_METRIC, "max", "max")],
-        request_type=RequestType.HEATMAP,
-        format_options={"formatTableResultForUI": False, "fillGaps": True},
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-    assert "fillGaps is not supported for heatmap requests" in get_error_message(response.json())
-
-
-@pytest.mark.parametrize(
-    "queries",
-    [
-        [
-            build_builder_query("A", MISSING_METRIC, "max", "max"),
-            build_builder_query("B", MISSING_METRIC, "min", "min"),
-        ],
-        [build_builder_query("A", MISSING_METRIC, "max", "max", disabled=True)],
-        [
-            build_builder_query("A", MISSING_METRIC, "max", "max"),
-            build_builder_query("B", MISSING_METRIC, "min", "min", disabled=True),
-            build_formula_query("F1", "B"),
-        ],
-        [],
-    ],
-    ids=["two_enabled_queries", "only_a_disabled_query", "a_formula_beside_an_enabled_query", "no_queries"],
-)
-def test_wrong_number_of_enabled_queries(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    queries: list[dict],
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    response = make_query_request(
-        signoz,
-        token,
-        int((now - timedelta(minutes=30)).timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        queries,
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-
-
-@pytest.mark.parametrize(
-    "queries, expected_message",
-    [
-        (
-            [build_builder_query("A", MISSING_METRIC, "max", "max", functions=[build_function("absolute")])],
-            "functions are not supported for heatmap requests",
-        ),
-        (
-            [
-                build_builder_query("A", MISSING_METRIC, "max", "max", disabled=True, functions=[build_function("absolute")]),
-                build_formula_query("F1", "A"),
-            ],
-            "functions are not supported for heatmap requests",
-        ),
-        (
-            [
-                build_builder_query("A", MISSING_METRIC, "max", "max", disabled=True),
-                build_formula_query("F1", "A", functions=[build_function("absolute")]),
-            ],
-            "functions are not supported for heatmap requests",
-        ),
-        (
-            [
-                {
-                    "type": "builder_query",
-                    "spec": {
-                        "name": "A",
-                        "signal": "metrics",
-                        "aggregations": [{"metricName": MISSING_METRIC, "timeAggregation": "max", "spaceAggregation": "max"}],
-                        "stepInterval": 60,
-                        "having": {"expression": "value > 1"},
-                    },
-                }
-            ],
-            "having is not supported for heatmap requests",
-        ),
-    ],
-    ids=["functions_on_the_query", "functions_on_a_disabled_formula_input", "functions_on_the_formula", "having_on_the_query"],
-)
-def test_functions_and_having_are_rejected(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    queries: list[dict],
-    expected_message: str,
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    response = make_query_request(
-        signoz,
-        token,
-        int((now - timedelta(minutes=30)).timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        queries,
-        request_type=RequestType.HEATMAP,
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-    assert expected_message in get_error_message(response.json())
-
-
-def test_promql_rejects_bucket_options(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    response = make_query_request(
-        signoz,
-        token,
-        int((now - timedelta(minutes=30)).timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        [{"type": "promql", "spec": {"name": "A", "query": MISSING_METRIC}}],
-        request_type=RequestType.HEATMAP,
-        bucket_options=build_log_bucket_options(2),
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-    assert "bucketOptions are not supported for promql heatmap requests" in get_error_message(response.json())
-
-
-@pytest.mark.parametrize(
-    "bucket_options, expected_message",
-    [
-        ({"kind": "quadratic", "spec": {}}, "invalid bucketOptions kind"),
-        ({"kind": "log"}, "bucketOptions spec is required"),
-        ({"kind": "linear"}, "bucketOptions spec is required"),
-        ({"kind": "linear", "spec": {"maxValue": 1000, "scale": 2}}, 'unknown field "scale" in linear buckets spec'),
-        ({"kind": "log", "spec": {"scale": 5}}, "scale must be between -4 and 4"),
-        ({"kind": "log", "spec": {"scale": -5}}, "scale must be between -4 and 4"),
-        ({"kind": "linear", "spec": {"maxValue": 0}}, "linear buckets need a finite maxValue greater than 0"),
-        ({"kind": "linear", "spec": {"maxValue": -10}}, "linear buckets need a finite maxValue greater than 0"),
-        ({"kind": "linear", "spec": {"maxValue": 1000, "numBuckets": 513}}, "numBuckets must be between 1 and 512"),
-    ],
-    ids=[
-        "unknown_kind",
-        "log_without_a_spec",
-        "linear_without_a_spec",
-        "scale_under_the_linear_kind",
-        "scale_above_the_maximum",
-        "scale_below_the_minimum",
-        "zero_max_value",
-        "negative_max_value",
-        "too_many_buckets",
-    ],
-)
-def test_malformed_bucket_options(
-    signoz: types.SigNoz,
-    create_user_admin: None,  # pylint: disable=unused-argument
-    get_token: Callable[[str, str], str],
-    bucket_options: dict,
-    expected_message: str,
-) -> None:
-    now = datetime.now(tz=UTC).replace(second=0, microsecond=0)
-    token = get_token(USER_ADMIN_EMAIL, USER_ADMIN_PASSWORD)
-
-    response = make_query_request(
-        signoz,
-        token,
-        int((now - timedelta(minutes=30)).timestamp() * 1000),
-        int(now.timestamp() * 1000),
-        [build_builder_query("A", MISSING_METRIC, "max", "max")],
-        request_type=RequestType.HEATMAP,
-        bucket_options=bucket_options,
-    )
-    assert response.status_code == HTTPStatus.BAD_REQUEST, response.text
-    assert expected_message in get_error_message(response.json())