Set an upper bound for the number of timeline events gathered within a single flush period

Author: szegedi

packages/dd-trace/src/profiling/profilers/events.js

@@ -3,6 +3,7 @@
 const { performance, constants, PerformanceObserver } = require('perf_hooks')
 const { END_TIMESTAMP_LABEL, SPAN_ID_LABEL, LOCAL_ROOT_SPAN_ID_LABEL, encodeProfileAsync } = require('./shared')
 const { Function, Label, Line, Location, Profile, Sample, StringTable, ValueType } = require('pprof-format')
+const { availableParallelism, effectiveLibuvThreadCount } = require('../libuv-size')
 
 // perf_hooks uses millis, with fractional part representing nanos. We emit nanos into the pprof file.
 const MS_TO_NS = 1_000_000
@@ -38,6 +39,20 @@ function labelFromStrStr (stringTable, keyStr, valStr) {
   return labelFromStr(stringTable, stringTable.dedup(keyStr), valStr)
 }
 
+function getMaxSamples (options) {
+    const cpuSamplingInterval = (options.samplingInterval || 1e3 / 99) // 99hz
+    const flushInterval = options.flushInterval || 65 * 1e3 // 60 seconds
+    const maxCpuSamples = flushInterval / cpuSamplingInterval
+
+    // The lesser of max parallelism and libuv thread pool size, plus one so we can detect
+    // oversubscription on libuv thread pool, plus another one for GC.
+    const factor = Math.max(1, Math.min(availableParallelism(), effectiveLibuvThreadCount)) + 2
+
+    // Let's not go overboard with too large limit and cap it at 100k. With current defaults, the
+    // value will be 65000/10.1*(4+2) = 38613.
+    return Math.min(100000, Math.floor(maxCpuSamples * factor))
+}
+
 class GCDecorator {
   constructor (stringTable) {
     this.stringTable = stringTable
@@ -181,12 +196,13 @@ const decoratorTypes = {
 
 // Translates performance entries into pprof samples.
 class EventSerializer {
-  constructor () {
+  constructor (maxSamples) {
     this.stringTable = new StringTable()
     this.samples = []
     this.locations = []
     this.functions = []
     this.decorators = {}
+    this.maxSamples = maxSamples
 
     // A synthetic single-frame location to serve as the location for timeline
     // samples. We need these as the profiling backend (mimicking official pprof
@@ -204,6 +220,29 @@ class EventSerializer {
   }
 
   addEvent (item) {
+    if (this.samples.length < this.maxSamples) {
+      const sample = this.createSample(item)
+      if (sample !== undefined) {
+        this.samples.push(sample)
+      }
+    } else {
+      // Choose one sample to be dropped. Using rnd(max + 1) - 1 we allow the
+      // current sample too to be the dropped one, with the equal likelihood as
+      // any other sample.
+      const replacementIndex = Math.floor(Math.random() * (this.maxSamples + 1)) - 1
+      if (replacementIndex != -1) {
+        const sample = this.createSample(item)
+        if (sample !== undefined) {
+          // This will cause the samples to no longer be sorted in their array
+          // by their end time. This is fine as the backend has no ordering
+          // expectations.
+          this.samples[replacementIndex] = sample
+        }
+      }
+    }
+  }
+
+  createSample (item) {
     const { entryType, startTime, duration, _ddSpanId, _ddRootSpanId } = item
     let decorator = this.decorators[entryType]
     if (!decorator) {
@@ -236,7 +275,7 @@ class EventSerializer {
       label
     }
     decorator.decorateSample(sampleInput, item)
-    this.samples.push(new Sample(sampleInput))
+    return new Sample(sampleInput)
   }
 
   createProfile (startDate, endDate) {
@@ -338,7 +377,7 @@ class CompositeEventSource {
   }
 }
 
-function createPossionProcessSamplingFilter (samplingIntervalMillis) {
+function createPoissonProcessSamplingFilter (samplingIntervalMillis) {
   let nextSamplingInstant = performance.now()
   let currentSamplingInstant = 0
   setNextSamplingInstant()
@@ -377,12 +416,13 @@ function createPossionProcessSamplingFilter (samplingIntervalMillis) {
 class EventsProfiler {
   constructor (options = {}) {
     this.type = 'events'
-    this.eventSerializer = new EventSerializer()
+    this.maxSamples = getMaxSamples(options)
+    this.eventSerializer = new EventSerializer(this.maxSamples)
 
     const eventHandler = event => this.eventSerializer.addEvent(event)
     const eventFilter = options.timelineSamplingEnabled
       // options.samplingInterval comes in microseconds, we need millis
-      ? createPossionProcessSamplingFilter((options.samplingInterval ?? 1e6 / 99) / 1000)
+      ? createPoissonProcessSamplingFilter((options.samplingInterval ?? 1e6 / 99) / 1000)
       : _ => true
     const filteringEventHandler = event => {
       if (eventFilter(event)) {
@@ -414,7 +454,7 @@ class EventsProfiler {
       this.stop()
     }
     const thatEventSerializer = this.eventSerializer
-    this.eventSerializer = new EventSerializer()
+    this.eventSerializer = new EventSerializer(this.maxSamples)
     return () => thatEventSerializer.createProfile(startDate, endDate)
   }