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https://github.com/hyperdxio/hyperdx
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fix: fix histogram metric query (#737)
Fix for the histogram query based on the late night session at KubeCon. Ref: HDX-1572
This commit is contained in:
Dan Hable
GitHub
parent
08009ac3b3
commit
4865ce7a62
5 changed files with 216 additions and 98 deletions
5
.changeset/old-rules-check.md
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5
.changeset/old-rules-check.md
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@ -0,0 +1,5 @@
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---
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"@hyperdx/common-utils": patch
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---
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Fixes the histogram query to perform quantile calculation across all data points
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@ -60,6 +60,15 @@ Array [
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]
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`;
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exports[`renderChartConfig Query Metrics should include multiple data points in percentile computation (p50) 1`] = `
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Array [
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Object {
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"__hdx_time_bucket": "2022-01-05T00:00:00Z",
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"any(toFloat64OrNull(toString(Rate)))": 2.9,
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},
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]
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`;
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exports[`renderChartConfig Query Metrics single avg gauge with group-by 1`] = `
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Array [
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Object {
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@ -192,11 +201,11 @@ exports[`renderChartConfig Query Metrics two_timestamps_bounded histogram (p25)
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Array [
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Object {
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"__hdx_time_bucket": "2022-01-05T00:00:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 0,
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"any(toFloat64OrNull(toString(Rate)))": 0,
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},
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Object {
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"__hdx_time_bucket": "2022-01-05T00:01:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 10,
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"any(toFloat64OrNull(toString(Rate)))": 10,
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},
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]
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`;
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@ -205,11 +214,11 @@ exports[`renderChartConfig Query Metrics two_timestamps_bounded histogram (p50)
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Array [
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Object {
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"__hdx_time_bucket": "2022-01-05T00:00:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 0,
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"any(toFloat64OrNull(toString(Rate)))": 0,
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},
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Object {
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"__hdx_time_bucket": "2022-01-05T00:01:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 20,
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"any(toFloat64OrNull(toString(Rate)))": 13.333333333333332,
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},
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]
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`;
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@ -218,11 +227,11 @@ exports[`renderChartConfig Query Metrics two_timestamps_bounded histogram (p90)
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Array [
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Object {
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"__hdx_time_bucket": "2022-01-05T00:00:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 0,
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"any(toFloat64OrNull(toString(Rate)))": 0,
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},
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Object {
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"__hdx_time_bucket": "2022-01-05T00:01:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 30,
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"any(toFloat64OrNull(toString(Rate)))": 30,
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},
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]
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`;
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@ -231,11 +240,11 @@ exports[`renderChartConfig Query Metrics two_timestamps_lower_bound_inf histogra
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Array [
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Object {
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"__hdx_time_bucket": "2022-01-05T00:00:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 0,
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"any(toFloat64OrNull(toString(Rate)))": 0,
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},
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Object {
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"__hdx_time_bucket": "2022-01-05T00:01:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 1,
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"any(toFloat64OrNull(toString(Rate)))": 1,
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},
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]
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`;
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@ -244,11 +253,11 @@ exports[`renderChartConfig Query Metrics two_timestamps_upper_bound_inf histogra
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Array [
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Object {
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"__hdx_time_bucket": "2022-01-05T00:00:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 0,
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"any(toFloat64OrNull(toString(Rate)))": 0,
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},
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Object {
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"__hdx_time_bucket": "2022-01-05T00:01:00Z",
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"sum(toFloat64OrNull(toString(Rate)))": 30,
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"any(toFloat64OrNull(toString(Rate)))": 30,
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},
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]
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`;
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@ -446,6 +446,36 @@ describe('renderChartConfig', () => {
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AggregationTemporality: 2, // Cumulative
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...point,
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}));
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const histPointsE = [
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{
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BucketCounts: [1, 1, 1, 1, 1, 1],
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ExplicitBounds: [1, 2, 5, 8, 13],
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TimeUnix: new Date(now),
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ResourceAttributes: { host: 'test-a' },
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},
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{
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BucketCounts: [2, 2, 2, 2, 2, 2],
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ExplicitBounds: [1, 2, 5, 8, 13],
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TimeUnix: new Date(now + ms('5s')),
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ResourceAttributes: { host: 'test-b' },
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},
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{
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BucketCounts: [2, 1, 2, 1, 2, 1],
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ExplicitBounds: [1, 2, 5, 8, 13],
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TimeUnix: new Date(now + ms('1m')),
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ResourceAttributes: { host: 'test-a' },
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},
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{
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BucketCounts: [3, 3, 2, 2, 3, 3],
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ExplicitBounds: [1, 2, 5, 8, 13],
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TimeUnix: new Date(now + ms('65s')),
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ResourceAttributes: { host: 'test-b' },
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},
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].map(point => ({
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MetricName: 'test.multiple_series',
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AggregationTemporality: 2, // Cumulative
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...point,
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}));
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await Promise.all([
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bulkInsertMetricsGauge([...gaugePointsA, ...gaugePointsB]),
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@ -462,6 +492,7 @@ describe('renderChartConfig', () => {
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...histPointsB,
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...histPointsC,
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...histPointsD,
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...histPointsE,
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]),
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]);
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});
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@ -655,6 +686,10 @@ describe('renderChartConfig', () => {
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});
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it('calculates min_rate/max_rate correctly for sum metrics', async () => {
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// Raw Data is
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// MIN_VARIANT_0: [0, 1, 8, 0, 7, 7, 15, 17, 0, 42]
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// MIN_VARIANT_1: [0, 2, 9, 0, 15, 25 35, 57, 0, 92]
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//
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// Based on the data inserted in the fixture, the expected stream of values
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// for each series after adjusting for the zero reset should be:
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// MIN_VARIANT_0: [0, 1, 8, 8, 15, 15, 23, 25, 25, 67]
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@ -725,20 +760,10 @@ describe('renderChartConfig', () => {
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Since the AggregationTemporality is 2(cumulative), we need to calculate the delta between the two points:
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delta: [10, 10, 10] - [0, 0, 0] = [10, 10, 10]
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Total observations: 10 + 10 + 10 = 30
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Cumulative counts: [10, 20, 30]
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p50 point:
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Rank = 0.5 * 30 = 15
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This falls in the second bucket (since 10 < 15 ≤ 20)
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We need to interpolate between the lower and upper bounds of the second bucket:
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Lower bound: 10
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Upper bound: 30
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Position in bucket: (15 - 10) / (20 - 10) = 0.5
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Interpolated value: 10 + (30 - 10) * 0.5 = 10 + 10 = 20
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Thus the first point value would be 0 since it's at the start of the bounds.
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The second point value would be 20 since that is the median point value delta from the first point.
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cum sum = [10, 20, 30]
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rank = 0.5 * 30 = 15 (between bounds 10 - 30)
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interpolate: 10 + ((15 - 10) / 30) * (30 - 10) = 13.3333
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*/
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const query = await renderChartConfig(
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{
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@ -952,6 +977,57 @@ describe('renderChartConfig', () => {
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expect(res).toMatchSnapshot();
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});
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it('should include multiple data points in percentile computation (p50)', async () => {
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/*
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bounds: [1, 2, 5, 8, 13]
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host = test-a:
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p1 = [1, 1, 1, 1, 1, 1]
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p2 = [2, 1, 2, 1, 2, 1]
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host = test-b:
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p1 = [2, 2, 2, 2, 2, 2]
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p2 = [3, 3, 2, 2, 3, 3]
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Compute the diff between adjacent points for each unique host (deltaSumForEach)
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host = test-a, diff = [1, 0, 1, 0, 1, 0]
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host = test-b, diff = [1, 1, 0, 0, 1, 1]
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Sum the diffs together to obtain a combined count for the different series
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sum elements(d) = [2, 1, 1, 0, 2, 1]
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Now compute the p50 value:
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sum(d) = 7
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cum sum = [2, 3, 4, 4, 6, 7]
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rank = 0.5 * 7 = 3.5 (between bounds 2 - 5)
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interpolate: 2 + ((3.5 - 2) / 5) * (5 - 2) = 2.9
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Since all the points fall within a single granularity interval the result should be a single row
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with the value 2.9.
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*/
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const query = await renderChartConfig(
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{
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select: [
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{
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aggFn: 'quantile',
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level: 0.5,
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metricName: 'test.multiple_series',
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metricType: MetricsDataType.Histogram,
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valueExpression: 'Value',
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},
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],
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from: metricSource.from,
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where: '',
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metricTables: TEST_METRIC_TABLES,
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dateRange: [new Date(now), new Date(now + ms('2m'))],
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granularity: '5 minute',
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timestampValueExpression: metricSource.timestampValueExpression,
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connection: connection.id,
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},
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metadata,
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);
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const res = await queryData(query);
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expect(res).toMatchSnapshot();
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});
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// HDX-1515: Handle counter reset in histogram metric in the same way that the counter reset
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// is handled for sum metrics.
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it.skip('three_timestamps_bounded histogram with reset (p50)', async () => {
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@ -36,39 +36,47 @@ exports[`renderChartConfig should generate sql for a single gauge metric 1`] = `
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`;
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exports[`renderChartConfig should generate sql for a single histogram metric 1`] = `
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"WITH HistRate AS (
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"WITH Source AS (
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SELECT
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*,
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cityHash64(mapConcat(ScopeAttributes, ResourceAttributes, Attributes)) AS AttributesHash,
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length(BucketCounts) as CountLength,
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any(BucketCounts) OVER (ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS PrevBucketCounts,
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any(CountLength) OVER (ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS PrevCountLength,
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any(AttributesHash) OVER (ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS PrevAttributesHash,
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IF(AggregationTemporality = 1,
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BucketCounts,
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IF(AttributesHash = PrevAttributesHash AND CountLength = PrevCountLength,
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arrayMap((prev, curr) -> IF(curr < prev, curr, toUInt64(toInt64(curr) - toInt64(prev))), PrevBucketCounts, BucketCounts),
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BucketCounts)) as BucketRates
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sumForEach(BucketCounts) OVER (PARTITION BY AttributesHash ORDER BY AttributesHash, TimeUnix ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW),
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deltaSumForEach(BucketCounts) OVER (PARTITION BY AttributesHash ORDER BY AttributesHash, TimeUnix ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
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) AS MonoBucketCounts
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FROM default.otel_metrics_histogram
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WHERE (TimeUnix >= fromUnixTimestamp64Milli(1739318400000) AND TimeUnix <= fromUnixTimestamp64Milli(1765670400000)) AND ((MetricName = 'http.server.duration'))
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ORDER BY Attributes, TimeUnix ASC
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),RawHist AS (
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ORDER BY AttributesHash ASC
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),Bucketed AS (
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SELECT
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*,
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toUInt64( 0.5 * arraySum(BucketRates)) AS Rank,
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arrayCumSum(BucketRates) as CumRates,
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arrayFirstIndex(x -> if(x > Rank, 1, 0), CumRates) AS BucketLowIdx,
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IF(BucketLowIdx = length(BucketRates),
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arrayElement(ExplicitBounds, length(ExplicitBounds)),
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IF(BucketLowIdx > 1,
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arrayElement(ExplicitBounds, BucketLowIdx - 1) + (arrayElement(ExplicitBounds, BucketLowIdx) - arrayElement(ExplicitBounds, BucketLowIdx - 1)) *
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IF(arrayElement(CumRates, BucketLowIdx) > arrayElement(CumRates, BucketLowIdx - 1),
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(Rank - arrayElement(CumRates, BucketLowIdx - 1)) / (arrayElement(CumRates, BucketLowIdx) - arrayElement(CumRates, BucketLowIdx - 1)), 0),
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arrayElement(ExplicitBounds, BucketLowIdx)
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)) as Rate
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FROM HistRate) SELECT sum(
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any(Source.ExplicitBounds) AS ExplicitBounds,
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sumForEach(MonoBucketCounts) AS TotalBucketCounts,
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arrayCumSum(TotalBucketCounts) AS BucketCumCounts,
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0.5 * arraySum(TotalBucketCounts) AS Rank,
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arrayFirstIndex(x -> if(x > Rank, 1, 0), BucketCumCounts) AS BucketLowIdx,
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CASE
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WHEN BucketLowIdx = 0 THEN 0
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WHEN BucketLowIdx = 1 THEN ExplicitBounds[BucketLowIdx]
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WHEN BucketLowIdx = length(TotalBucketCounts) THEN ExplicitBounds[length(ExplicitBounds)]
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ELSE
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ExplicitBounds[BucketLowIdx - 1] +
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((Rank - ExplicitBounds[BucketLowIdx - 1]) / (ExplicitBounds[BucketLowIdx])) * (ExplicitBounds[BucketLowIdx] - ExplicitBounds[BucketLowIdx - 1])
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END AS Value
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FROM Source
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GROUP BY
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ORDER BY ASC
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),Rates AS (
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SELECT
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\`__hdx_time_bucket\`,
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any(Bucketed.Value) AS \`__hdx_value_high\`,
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any(\`__hdx_value_high\`) OVER (ORDER BY \`__hdx_time_bucket\` ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS \`__hdx_prev_high\`,
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\`__hdx_value_high\` - \`__hdx_prev_high\` AS Rate
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FROM Bucketed
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GROUP BY \`__hdx_time_bucket\`
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) SELECT any(
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toFloat64OrNull(toString(Rate))
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) FROM RawHist WHERE (TimeUnix >= fromUnixTimestamp64Milli(1739318400000) AND TimeUnix <= fromUnixTimestamp64Milli(1765670400000)) LIMIT 10"
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) FROM Rates WHERE (\`__hdx_time_bucket\` >= fromUnixTimestamp64Milli(1739318400000) AND \`__hdx_time_bucket\` <= fromUnixTimestamp64Milli(1765670400000)) LIMIT 10"
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`;
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exports[`renderChartConfig should generate sql for a single sum metric 1`] = `
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@ -1061,87 +1061,107 @@ async function translateMetricChartConfig(
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throw new Error('quantile must be specified for histogram metrics');
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}
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// Render the where clause to limit data selection on the source CTE but also search forward/back one
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// bucket window to ensure that there is enough data to compute a reasonable value on the ends of the
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// series.
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const where = await renderWhere(
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{
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...chartConfig,
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from: {
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...from,
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tableName: metricTables[MetricsDataType.Histogram],
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},
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filters: [
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...(filters ?? []),
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{
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type: 'sql',
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condition: `MetricName = '${metricName}'`,
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},
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],
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includedDataInterval:
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chartConfig.granularity === 'auto' &&
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Array.isArray(chartConfig.dateRange)
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? convertDateRangeToGranularityString(chartConfig.dateRange, 60)
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: chartConfig.granularity,
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// Render the various clauses from the user input so they can be woven into the CTE queries. The dateRange
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// is manipulated to search forward/back one bucket window to ensure that there is enough data to compute
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// a reasonable value on the ends of the series.
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const cteChartConfig = {
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...chartConfig,
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from: {
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...from,
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tableName: metricTables[MetricsDataType.Histogram],
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},
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metadata,
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);
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filters: [
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...(filters ?? []),
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{
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type: 'sql',
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condition: `MetricName = '${metricName}'`,
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},
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],
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includedDataInterval:
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chartConfig.granularity === 'auto' &&
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Array.isArray(chartConfig.dateRange)
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? convertDateRangeToGranularityString(chartConfig.dateRange, 60)
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: chartConfig.granularity,
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} as ChartConfigWithOptDateRangeEx;
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const timeBucketSelect = isUsingGranularity(cteChartConfig)
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? timeBucketExpr({
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interval: cteChartConfig.granularity,
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timestampValueExpression: cteChartConfig.timestampValueExpression,
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dateRange: cteChartConfig.dateRange,
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})
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: chSql``;
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const where = await renderWhere(cteChartConfig, metadata);
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const groupBy = (await renderGroupBy(chartConfig, metadata)) || chSql``;
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return {
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...restChartConfig,
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with: [
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{
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name: 'HistRate',
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name: 'Source',
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sql: chSql`
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SELECT
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*,
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cityHash64(mapConcat(ScopeAttributes, ResourceAttributes, Attributes)) AS AttributesHash,
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length(BucketCounts) as CountLength,
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any(BucketCounts) OVER (ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS PrevBucketCounts,
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any(CountLength) OVER (ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS PrevCountLength,
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any(AttributesHash) OVER (ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS PrevAttributesHash,
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IF(AggregationTemporality = 1,
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BucketCounts,
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IF(AttributesHash = PrevAttributesHash AND CountLength = PrevCountLength,
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arrayMap((prev, curr) -> IF(curr < prev, curr, toUInt64(toInt64(curr) - toInt64(prev))), PrevBucketCounts, BucketCounts),
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BucketCounts)) as BucketRates
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sumForEach(BucketCounts) OVER (PARTITION BY AttributesHash ORDER BY AttributesHash, TimeUnix ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW),
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deltaSumForEach(BucketCounts) OVER (PARTITION BY AttributesHash ORDER BY AttributesHash, TimeUnix ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)
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) AS MonoBucketCounts
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FROM ${renderFrom({ from: { ...from, tableName: metricTables[MetricsDataType.Histogram] } })}
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WHERE ${where}
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ORDER BY Attributes, TimeUnix ASC
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ORDER BY AttributesHash ASC
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`,
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},
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{
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name: 'RawHist',
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name: 'Bucketed',
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sql: chSql`
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SELECT
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*,
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toUInt64( ${{ Float64: level }} * arraySum(BucketRates)) AS Rank,
|
||||
arrayCumSum(BucketRates) as CumRates,
|
||||
arrayFirstIndex(x -> if(x > Rank, 1, 0), CumRates) AS BucketLowIdx,
|
||||
IF(BucketLowIdx = length(BucketRates),
|
||||
arrayElement(ExplicitBounds, length(ExplicitBounds)),
|
||||
IF(BucketLowIdx > 1,
|
||||
arrayElement(ExplicitBounds, BucketLowIdx - 1) + (arrayElement(ExplicitBounds, BucketLowIdx) - arrayElement(ExplicitBounds, BucketLowIdx - 1)) *
|
||||
IF(arrayElement(CumRates, BucketLowIdx) > arrayElement(CumRates, BucketLowIdx - 1),
|
||||
(Rank - arrayElement(CumRates, BucketLowIdx - 1)) / (arrayElement(CumRates, BucketLowIdx) - arrayElement(CumRates, BucketLowIdx - 1)), 0),
|
||||
arrayElement(ExplicitBounds, BucketLowIdx)
|
||||
)) as Rate
|
||||
FROM HistRate`,
|
||||
${timeBucketSelect.sql ? chSql`${timeBucketSelect},` : ''}
|
||||
any(Source.ExplicitBounds) AS ExplicitBounds,
|
||||
sumForEach(MonoBucketCounts) AS TotalBucketCounts,
|
||||
arrayCumSum(TotalBucketCounts) AS BucketCumCounts,
|
||||
${{ Float64: level }} * arraySum(TotalBucketCounts) AS Rank,
|
||||
arrayFirstIndex(x -> if(x > Rank, 1, 0), BucketCumCounts) AS BucketLowIdx,
|
||||
CASE
|
||||
WHEN BucketLowIdx = 0 THEN 0
|
||||
WHEN BucketLowIdx = 1 THEN ExplicitBounds[BucketLowIdx]
|
||||
WHEN BucketLowIdx = length(TotalBucketCounts) THEN ExplicitBounds[length(ExplicitBounds)]
|
||||
ELSE
|
||||
ExplicitBounds[BucketLowIdx - 1] +
|
||||
((Rank - ExplicitBounds[BucketLowIdx - 1]) / (ExplicitBounds[BucketLowIdx])) * (ExplicitBounds[BucketLowIdx] - ExplicitBounds[BucketLowIdx - 1])
|
||||
END AS Value
|
||||
FROM Source
|
||||
GROUP BY ${groupBy}
|
||||
ORDER BY ${groupBy} ASC
|
||||
`,
|
||||
},
|
||||
{
|
||||
name: 'Rates',
|
||||
sql: chSql`
|
||||
SELECT
|
||||
\`__hdx_time_bucket\`,
|
||||
any(Bucketed.Value) AS \`__hdx_value_high\`,
|
||||
any(\`__hdx_value_high\`) OVER (ORDER BY \`__hdx_time_bucket\` ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS \`__hdx_prev_high\`,
|
||||
\`__hdx_value_high\` - \`__hdx_prev_high\` AS Rate
|
||||
FROM Bucketed
|
||||
GROUP BY \`__hdx_time_bucket\`
|
||||
`,
|
||||
},
|
||||
],
|
||||
select: [
|
||||
{
|
||||
..._selectRest,
|
||||
aggFn: 'sum',
|
||||
aggFn: 'any', // groupings happen further in the CTE stack, just need to grab the value
|
||||
aggCondition: '', // clear up the condition since the where clause is already applied at the upstream CTE
|
||||
valueExpression: 'Rate',
|
||||
},
|
||||
],
|
||||
from: {
|
||||
databaseName: '',
|
||||
tableName: 'RawHist',
|
||||
tableName: 'Rates',
|
||||
},
|
||||
where: '', // clear up the condition since the where clause is already applied at the upstream CTE
|
||||
timestampValueExpression: '`__hdx_time_bucket`',
|
||||
};
|
||||
}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue