dial9-trace-recipes

name	dial9-trace-recipes
description	Diagnostic recipes for common questions about dial9 Tokio runtime traces. Covers finding long polls, task leaks, worker utilization, blocking calls, wake chains, span analysis, task dumps, time-window debugging, and estimating allocation totals from sampled `Alloc` events. Use when answering specific diagnostic questions about trace data.

Diagnostic Recipes

Concrete code snippets for answering common questions about trace data.

Setup boilerplate

Two APIs depending on what you need:

analyzeTraces(path) returns aggregated results across all files (parallel, fast). Use for diagnostic questions like "what's the worst poll" or "what's the utilization."

const { analyzeTraces } = require('./analyze.js');
const result = await analyzeTraces('/path/to/traces/');
// result.longPolls, result.workerSpans, result.schedDelayHist, result.cpuGroups, etc.

parseTrace(path) yields one ParsedTrace per file. Use when you need raw per-trace data (flamegraphs, field filtering, wake chains).

const { parseTrace, EVENT_TYPES, formatFrame, symbolizeChain, deduplicateSamples } = require('./trace_parser.js');
const { buildWorkerSpans, attachCpuSamples,
        computeSchedulingDelays } = require('./trace_analysis.js');

for await (const trace of parseTrace('/path/to/traces/')) {
  const workerIds = [...new Set(
    trace.events.filter(e => e.eventType !== EVENT_TYPES.QueueSample && e.eventType !== EVENT_TYPES.WakeEvent)
      .map(e => e.workerId)
  )].sort((a, b) => a - b);
  const maxTs = trace.maxTs;
  const minTs = trace.minTs;
  const spans = buildWorkerSpans(trace.events, workerIds, maxTs);
  attachCpuSamples(trace.cpuSamples, spans.workerSpans);
  const schedDelays = computeSchedulingDelays(spans.workerSpans, workerIds, spans.wakesByTask);
}

Which task has the longest poll time?

const { analyzeTraces } = require('./analyze.js');
const { symbolizeChain } = require('./trace_parser.js');
const result = await analyzeTraces('/path/to/traces/');
const worst = result.longPolls[0];
if (worst) {
  console.log(`Longest poll: ${(worst.dur / 1e6).toFixed(2)}ms`);
  console.log(`  Task ID: ${worst.poll.taskId}, Spawn: ${worst.poll.spawnLoc}`);
  if (worst.poll.cpuSamples?.length) {
    for (const s of worst.poll.cpuSamples) {
      const frames = symbolizeChain(s.callchain, result.callframeSymbols);
      console.log(`  CPU: ${require('./trace_parser.js').formatFrame(frames[0]).text}`);
    }
  }
  if (worst.poll.schedSamples?.length) {
    for (const s of worst.poll.schedSamples) {
      const frames = symbolizeChain(s.callchain, result.callframeSymbols);
      console.log(`  Sched: ${require('./trace_parser.js').formatFrame(frames[0]).text}`);
    }
  }
}

Do I have a task leak?

A task leak means tasks are spawned but never terminate, causing the active count to grow monotonically.

const { analyzeTraces } = require('./analyze.js');
const result = await analyzeTraces('/path/to/traces/');
const samples = result.taskTimeline.activeTaskSamples;
if (samples.length > 0) {
  const first = samples[0].count;
  const last = samples[samples.length - 1].count;
  const peak = samples.reduce((m, s) => Math.max(m, s.count), -Infinity);
  console.log(`Active tasks: start=${first}, end=${last}, peak=${peak}`);
  if (last > first * 2 && last === peak) {
    console.log('⚠ Possible task leak');
    const alive = new Map();
    for (const [taskId] of result.taskSpawnTimes) {
      if (!result.taskTerminateTimes.has(taskId)) {
        const loc = result.taskSpawnLocs.get(taskId) || '(unknown)';
        alive.set(loc, (alive.get(loc) || 0) + 1);
      }
    }
    for (const [loc, count] of [...alive.entries()].sort((a, b) => b[1] - a[1])) {
      console.log(`  ${count} tasks from ${loc}`);
    }
  }
}

Task spawn rate by location

const { analyzeTraces } = require('./analyze.js');
const result = await analyzeTraces('/path/to/traces/');
const spawnCounts = new Map();
for (const [, loc] of result.taskSpawnLocs) {
  spawnCounts.set(loc || '(unknown)', (spawnCounts.get(loc || '(unknown)') || 0) + 1);
}
for (const [loc, count] of [...spawnCounts.entries()].sort((a, b) => b[1] - a[1])) {
  console.log(`  ${count} from ${loc}`);
}

Flamegraph for a specific spawn location

Requires per-trace iteration (see parseTrace boilerplate above).

const targetLoc = 'src/main.rs:42:5'; // adjust to your spawn location
const targetSamples = trace.cpuSamples.filter(s => s.spawnLoc === targetLoc);
console.log(`${targetSamples.length} CPU samples for tasks from ${targetLoc}`);

const groups = deduplicateSamples(targetSamples, trace.callframeSymbols);
console.log('Top hotspots:');
for (const g of groups.slice(0, 10)) {
  console.log(`  ${g.count} samples (${(g.count/targetSamples.length*100).toFixed(1)}%) — ${g.leaf}`);
}

Note: spawnLoc is set on samples by attachCpuSamples().

What's happening at a specific time?

const targetMs = 1500; // 1.5 seconds into the trace
const targetNs = minTs + targetMs * 1e6;
const windowNs = 10 * 1e6; // ±10ms window

for (const w of workerIds) {
  const polls = spans.workerSpans[w].polls.filter(p =>
    p.end >= targetNs - windowNs && p.start <= targetNs + windowNs
  );
  console.log(`Worker ${w}: ${polls.length} polls in window`);
  for (const p of polls) {
    const dur = (p.end - p.start) / 1e6;
    const rel = (p.start - minTs) / 1e6;
    console.log(`  ${rel.toFixed(1)}ms +${dur.toFixed(2)}ms task=${p.taskId} spawn=${p.spawnLoc}`);
  }
}

// Check queue depth at that time
if (spans.queueSamples.length > 0) {
  const nearestQueue = spans.queueSamples.reduce((best, s) =>
    Math.abs(s.t - targetNs) < Math.abs(best.t - targetNs) ? s : best
  );
  console.log(`Queue depth near target: global=${nearestQueue.global}`);
}

Are long poll times hurting my application?

const { analyzeTraces } = require('./analyze.js');
const result = await analyzeTraces('/path/to/traces/');
console.log(`${result.longPolls.length} long polls (>1ms)`);
// Poll duration by spawn location
for (const [loc, h] of result.pollDurationByLoc) {
  console.log(`  ${loc}: p50=${(h.percentile(50)/1e3).toFixed(1)}µs p99=${(h.percentile(99)/1e3).toFixed(1)}µs max=${(h.max/1e6).toFixed(2)}ms`);
}
// Scheduling delay correlation
if (result.schedDelayHist) {
  console.log(`Scheduling delays: p99=${(result.schedDelayHist.percentile(99)/1e6).toFixed(2)}ms max=${(result.schedDelayHist.max/1e6).toFixed(2)}ms`);
}

Worker utilization

const { analyzeTraces } = require('./analyze.js');
const result = await analyzeTraces('/path/to/traces/');
for (const w of result.workerIds) {
  const ws = result.workerSpans[w];
  console.log(`Worker ${w}: ${(ws.utilization * 100).toFixed(1)}% active, avg CPU ratio ${ws.avgCpuRatio.toFixed(3)}`);
}

Blocking call detection

Scheduling samples (source=1) capture stack traces when the OS deschedules a worker thread.

const { analyzeTraces } = require('./analyze.js');
const { formatFrame } = require('./trace_parser.js');
const result = await analyzeTraces('/path/to/traces/');
console.log(`${result.offCpuSampleCount} off-CPU samples`);
for (const g of result.schedGroups.slice(0, 10)) {
  console.log(`  ${g.count} samples — ${g.leaf}`);
  if (g === result.schedGroups[0]) {
    for (const f of g.frames) console.log(`    ${formatFrame(f).text}`);
  }
}

Wake chain analysis

function traceWakeChain(taskId, wakesByTask, taskSpawnLocs, depth = 0, seen = new Set()) {
  if (seen.has(taskId)) return;
  seen.add(taskId);
  const wakes = wakesByTask[taskId];
  if (!wakes || wakes.length === 0) return;
  const lastWake = wakes[wakes.length - 1];
  const loc = taskSpawnLocs.get(taskId) || '(unknown)';
  console.log(`${'  '.repeat(depth)}Task ${taskId} (${loc}) woken by task ${lastWake.wakerTaskId}`);
  if (depth < 5) traceWakeChain(lastWake.wakerTaskId, wakesByTask, taskSpawnLocs, depth + 1, seen);
}

// Example: trace a task's wake chain
const taskId = 42; // replace with a task ID from your trace
traceWakeChain(taskId, spans.wakesByTask, trace.taskSpawnLocs);

Span duration percentiles by name

Requires Dial9TokioLayer in the subscriber.

const { analyzeTraces } = require('./analyze.js');
const result = await analyzeTraces('/path/to/traces/');
for (const [name, h] of result.spanStats) {
  console.log(`${name}: count=${h.count} p50=${(h.percentile(50)/1e3).toFixed(1)}µs p99=${(h.percentile(99)/1e3).toFixed(1)}µs max=${(h.max/1e3).toFixed(1)}µs`);
}

What spans happened inside a long poll?

const { buildSpanData } = require('./trace_analysis.js');
const { allSpans } = buildSpanData(trace.customEvents);

// Find the longest poll
let worst = null;
for (const w of workerIds) {
  for (const p of spans.workerSpans[w].polls) {
    const dur = p.end - p.start;
    if (!worst || dur > worst.dur) worst = { dur, poll: p, worker: w };
  }
}

// Find spans that overlap with that poll (via segments on the same worker)
const inner = allSpans.filter(s =>
  s.segments.some(seg => seg.workerId === worst.worker && seg.start >= worst.poll.start && seg.end <= worst.poll.end)
);
console.log(`Longest poll: ${(worst.dur / 1e6).toFixed(2)}ms on worker ${worst.worker}`);
console.log(`Contains ${inner.length} spans:`);
const byName = {};
for (const s of inner) byName[s.spanName] = (byName[s.spanName] || 0) + 1;
for (const [name, count] of Object.entries(byName)) {
  console.log(`  ${name}: ${count}`);
}

Filter spans by field value

const { buildSpanData } = require('./trace_analysis.js');
const { allSpans } = buildSpanData(trace.customEvents);

const matches = allSpans.filter(s => s.fields.request_id === 'abc-123');
console.log(`${matches.length} spans for request abc-123:`);
for (const s of matches) {
  console.log(`  ${s.spanName} ${(( s.end - s.start) / 1e3).toFixed(1)}µs`);
}

Detect tight loops (many spans per poll)

const { buildSpanData } = require('./trace_analysis.js');
const { allSpans } = buildSpanData(trace.customEvents);

for (const w of workerIds) {
  for (const p of spans.workerSpans[w].polls) {
    const inner = allSpans.filter(s =>
      s.segments.some(seg => seg.workerId === w && seg.start >= p.start && seg.end <= p.end)
    );
    if (inner.length > 10) {
      const byName = {};
      for (const s of inner) byName[s.spanName] = (byName[s.spanName] || 0) + 1;
      const summary = Object.entries(byName).map(([n, c]) => `${n}×${c}`).join(', ');
      console.log(`Worker ${w} poll at +${((p.start - minTs) / 1e6).toFixed(1)}ms: ${inner.length} spans (${summary}), poll duration ${((p.end - p.start) / 1e6).toFixed(2)}ms`);
    }
  }
}

What else was happening during a slow span?

Find a slow span and see what other spans and polls overlap on the same and other workers.

const { buildSpanData } = require('./trace_analysis.js');
const { allSpans } = buildSpanData(trace.customEvents);

// Find the slowest query_metric span
const slowest = allSpans
  .filter(s => s.spanName === 'query_metric')
  .sort((a, b) => (b.end - b.start) - (a.end - a.start))[0];

if (slowest) {
  console.log(`Slowest query_metric: ${((slowest.end - slowest.start) / 1e6).toFixed(2)}ms`);
  console.log(`Fields: ${JSON.stringify(slowest.fields)}`);

  // What other spans overlapped?
  const overlapping = allSpans.filter(s => s.start < slowest.end && s.end > slowest.start && s !== slowest);
  const byName = {};
  for (const s of overlapping) byName[s.spanName] = (byName[s.spanName] || 0) + 1;
  for (const [name, count] of Object.entries(byName)) {
    console.log(`  ${name}: ${count}`);
  }
}

Where does a specific span rank among its peers?

Given a span, show its percentile rank compared to all spans of the same name.

const { buildSpanData } = require('./trace_analysis.js');
const { allSpans } = buildSpanData(trace.customEvents);

function spanPercentile(span) {
  const peers = allSpans.filter(s => s.spanName === span.spanName).map(s => s.end - s.start);
  peers.sort((a, b) => a - b);
  const dur = span.end - span.start;
  const rank = peers.filter(d => d <= dur).length;
  const pct = (rank / peers.length * 100).toFixed(1);
  const p50 = peers[Math.floor(peers.length * 0.5)];
  const p90 = peers[Math.floor(peers.length * 0.9)];
  const p99 = peers[Math.floor(peers.length * 0.99)];
  console.log(`${span.spanName} duration: ${(dur / 1e3).toFixed(1)}µs (P${pct} of ${peers.length})`);
  console.log(`  p0=${(peers[0] / 1e3).toFixed(1)}µs p50=${(p50 / 1e3).toFixed(1)}µs p90=${(p90 / 1e3).toFixed(1)}µs p99=${(p99 / 1e3).toFixed(1)}µs p100=${(peers[peers.length - 1] / 1e3).toFixed(1)}µs`);
}

// Example: rank the slowest query_metric
const slowest = allSpans
  .filter(s => s.spanName === 'query_metric')
  .sort((a, b) => (b.end - b.start) - (a.end - a.start))[0];
if (slowest) spanPercentile(slowest);

Trace a request across workers

const { buildSpanData } = require('./trace_analysis.js');
const { allSpans } = buildSpanData(trace.customEvents);

const requestId = 'abc-123'; // replace with your request ID
const matches = allSpans.filter(s => s.fields.request_id === requestId);
matches.sort((a, b) => a.start - b.start);
for (const s of matches) {
  const workers = [...new Set(s.segments.map(seg => seg.workerId))].join(',');
  console.log(`  +${((s.start - minTs) / 1e6).toFixed(3)}ms worker=${workers} ${s.spanName} ${((s.end - s.start) / 1e3).toFixed(1)}µs`);
}

What is a task waiting on? (task dumps)

Task dumps capture async backtraces at yield points. Use them to see what futures a task is awaiting during idle periods.

const { parseTrace, symbolizeChain, formatFrame } = require('./trace_parser.js');
const { buildWorkerSpans } = require('./trace_analysis.js');

for await (const trace of parseTrace('/path/to/traces/')) {
  const workerIds = [...new Set(trace.events.filter(e => e.workerId !== undefined).map(e => e.workerId))].sort((a,b)=>a-b);
  const spans = buildWorkerSpans(trace.events, workerIds, trace.maxTs);

  for (const [taskId, dumps] of trace.taskDumps) {
    const taskPolls = [];
    for (const w of workerIds) {
      for (const p of spans.workerSpans[w].polls) {
        if (p.taskId === taskId) taskPolls.push(p);
      }
    }
    taskPolls.sort((a, b) => a.start - b.start);

    const loc = trace.taskSpawnLocs?.get(taskId) || '(unknown)';
    for (const dump of dumps) {
      // dump.timestamp matches the pollStart of the following poll;
      // the idle period is the gap between the preceding poll's end and this timestamp
      const pollIdx = taskPolls.findIndex(p => p.start === dump.timestamp);
      const idleStart = pollIdx > 0 ? taskPolls[pollIdx - 1].end : trace.minTs;
      const idleDur = (dump.timestamp - idleStart) / 1e6;

      const frames = symbolizeChain(dump.callchain, trace.callframeSymbols);
      const leaf = frames[0] ? formatFrame(frames[0]).text : '(unknown)';
      console.log(`Task ${taskId} (${loc}) idle ${idleDur.toFixed(1)}ms awaiting: ${leaf}`);
    }
  }
}

Investigating block_in_place gaps

When tokio::task::block_in_place is called, a worker's OS thread temporarily stops being a worker. The analysis layer detects these intervals as "block-in-place gaps" — periods where worker attribution is unknowable.

trace.blockInPlaceGaps is an array of {workerId, fromTid, toTid, startNs, endNs}.

• fromTid: the OS thread that was the worker before the handoff (now running the blocking closure).
• toTid: the OS thread that took over the worker's scheduler responsibilities.

To investigate what code triggered block_in_place, look at CPU samples on fromTid during the gap:

const { parseTrace, symbolizeChain, formatFrame } = require('./trace_parser.js');

for await (const trace of parseTrace('/path/to/traces/')) {
  for (const gap of trace.blockInPlaceGaps) {
    console.log(`\nWorker ${gap.workerId}: block_in_place gap ${((gap.endNs - gap.startNs) / 1e6).toFixed(2)}ms`);
    console.log(`  fromTid=${gap.fromTid} → toTid=${gap.toTid}`);

    // Stacks on fromTid during the gap — this is the blocking closure
    const blockingSamples = trace.cpuSamples.filter(s =>
      s.tid === gap.fromTid && s.timestamp >= gap.startNs && s.timestamp < gap.endNs
    );
    if (blockingSamples.length > 0) {
      console.log(`  Blocking closure stacks (${blockingSamples.length} samples):`);
      for (const s of blockingSamples.slice(0, 5)) {
        const frames = symbolizeChain(s.callchain, trace.callframeSymbols);
        console.log(`    ${formatFrame(frames[0]).text}`);
      }
    }

    // Stacks on toTid during the gap — usually scheduler internals
    const replacementSamples = trace.cpuSamples.filter(s =>
      s.tid === gap.toTid && s.timestamp >= gap.startNs && s.timestamp < gap.endNs
    );
    if (replacementSamples.length > 0) {
      console.log(`  Replacement thread stacks (${replacementSamples.length} samples):`);
      for (const s of replacementSamples.slice(0, 5)) {
        const frames = symbolizeChain(s.callchain, trace.callframeSymbols);
        console.log(`    ${formatFrame(frames[0]).text}`);
      }
    }
  }
}

Note: block_in_place is not inherently a problem — it's a legitimate way to run blocking code on a worker thread. The gap detection helps you understand what happened during the interval, not flag it as an issue.

Estimating allocation totals from sampled Alloc events

Critical: Each Alloc event's size is the raw bytes of one sampled allocation, not a scaled estimate. If you sum raw size values you will undercount, often by orders of magnitude. Always scale by inverse Poisson sampling probability before aggregating.

Memory profiling samples allocations at a configured rate R (mean bytes between samples — MemoryProfilingConfig.sample_rate_bytes, default 512 KiB). The sampling probability for one allocation of size s is 1 - exp(-s/R). The Horvitz–Thompson estimator weights each sample by 1 / P(sample) to produce unbiased totals:

total_bytes ≈ Σ over sampled events  s_i / (1 - exp(-s_i / R))
total_count ≈ Σ over sampled events       1 / (1 - exp(-s_i / R))

The same formula handles all size regimes:

• For s << R (tiny allocs): each sample contributes ~R bytes.
• For s ≈ R: each sample contributes ~1.58 s bytes.
• For s >> R (huge allocs): each sample contributes ~s bytes.

R is recorded in segment metadata as memory.sample_rate_bytes. Read it from trace.segmentMetadata after parsing:

const meta = Object.fromEntries(trace.segmentMetadata || []);
const SAMPLE_RATE_BYTES = Number(meta['memory.sample_rate_bytes']) || 512 * 1024;

If analysing traces from older versions that lack this field, pass R explicitly (a CLI flag or a constant per service).

Total bytes allocated by call site

const { parseTrace, symbolizeChain, formatFrame } = require('./trace_parser.js');

// Read R from segment metadata (falls back to default for old traces)
let SAMPLE_RATE_BYTES = 512 * 1024;

function inverseProb(size, rate) {
  // 1 / (1 - exp(-size/rate)). For very small size/rate, this approaches
  // rate/size; for size >> rate, approaches 1.
  return 1.0 / (1.0 - Math.exp(-size / rate));
}

const byCallSite = new Map();  // leaf symbol -> { bytes, count }

for await (const trace of parseTrace('/path/to/traces/')) {
  // `allocEvents` may be absent on traces from older toolkit caches —
  // memory profiling events were added to the directory cache after
  // initial release. Defensive `?? []` keeps the recipe portable.
  for (const ev of (trace.allocEvents ?? [])) {
    const frames = symbolizeChain(ev.callchain, trace.callframeSymbols);
    const leaf = frames[0] ? formatFrame(frames[0]).text : '(unknown)';
    const w = inverseProb(ev.size, SAMPLE_RATE_BYTES);
    const cur = byCallSite.get(leaf) ?? { bytes: 0, count: 0 };
    // Weight EACH sample individually before summing — sum-then-unbias
    // under-reports skewed groups.
    cur.bytes += ev.size * w;
    cur.count += w;
    byCallSite.set(leaf, cur);
  }
}

const top = [...byCallSite.entries()]
  .sort((a, b) => b[1].bytes - a[1].bytes)
  .slice(0, 20);
for (const [leaf, { bytes, count }] of top) {
  console.log(`${(bytes / 1024 / 1024).toFixed(1)} MiB  ${count.toFixed(0)} allocs  ${leaf}`);
}

Total bytes allocated per task (from poll-correlated allocs)

Alloc.tid matches the worker's TID; cross-reference with poll spans to attribute allocations to the task running at the time:

const { parseTrace, symbolizeChain, formatFrame } = require('./trace_parser.js');
const { buildWorkerSpans } = require('./trace_analysis.js');

const SAMPLE_RATE_BYTES = 512 * 1024;
const inverseProb = (s, r) => 1.0 / (1.0 - Math.exp(-s / r));

const byTask = new Map();  // taskId -> bytes (estimated)

for await (const trace of parseTrace('/path/to/traces/')) {
  const workerIds = [...new Set(trace.events.filter(e => e.workerId !== undefined).map(e => e.workerId))].sort((a,b)=>a-b);
  const { workerSpans } = buildWorkerSpans(trace.events, workerIds, trace.maxTs);

  // Index polls by tid for binary search.
  const pollsByTid = new Map();
  for (const wid of workerIds) {
    const polls = workerSpans[wid].polls;
    if (polls.length === 0) continue;
    const tid = polls[0].tid;  // worker's OS tid
    pollsByTid.set(tid, polls);
  }

  // `allocEvents` may be absent on traces from older toolkit caches —
  // memory profiling events were added to the directory cache after
  // initial release. Defensive `?? []` keeps the recipe portable.
  for (const ev of (trace.allocEvents ?? [])) {
    const polls = pollsByTid.get(ev.tid);
    if (!polls) continue;
    // Find the poll containing ev.timestamp (linear scan; for big
    // traces use binary search).
    const poll = polls.find(p => p.start <= ev.timestamp && ev.timestamp < p.end);
    if (!poll) continue;
    const w = inverseProb(ev.size, SAMPLE_RATE_BYTES);
    byTask.set(poll.taskId, (byTask.get(poll.taskId) ?? 0) + ev.size * w);
  }
}

const top = [...byTask.entries()].sort((a, b) => b[1] - a[1]).slice(0, 20);
for (const [taskId, bytes] of top) {
  console.log(`Task ${taskId}: ~${(bytes / 1024 / 1024).toFixed(1)} MiB`);
}

Common pitfalls

• Summing Alloc.size directly. Will report ~2 KiB for a workload that allocated 1 MiB if all allocations were 1 byte each. Always weight by 1 / (1 - exp(-size / R)).
• Sum-then-unbias. Computing (Σ s_i) / (1 - exp(-mean(s) / R)) uses the group's mean size as if every allocation were that size; for skewed groups (mix of small and huge) this can be wrong by orders of magnitude. Weight each sample individually.
• Hard-coding R. The default may change between releases. Pull the rate from your deployment config or from segment metadata.
• Counting Alloc events as allocation count. That's the sampled count, not the actual count. Use the Σ 1 / (1 - exp(-s_i / R)) formula above.
• Forgetting track_liveset. Live-set bytes (current allocation footprint) requires pairing Alloc with Free events and only works when MemoryProfilingConfig.track_liveset(true) was set at install time.
• Ring buffer overflow causing false leaks. When the free queue overflows (visible as MemoryProfileOverflowEvent in the trace or totalDroppedFrees > 0 in the analysis summary), dropped frees leave their matching allocations permanently in the liveset. These appear as leaks but are actually freed memory the profiler couldn't record. If you see overflow warnings, increase ring_capacity in MemoryProfilingConfig and re-record. Until then, treat leak counts as upper bounds.

Heap flamegraph: where are allocations happening and how big are they?

Build a weighted flamegraph tree where each node accumulates estimated bytes and estimated allocation count. The ratio bytes / allocs gives the average allocation size for that call path — useful for finding code that makes many small allocations vs. few large ones.

const { parseTrace, symbolizeChain, formatFrame } = require('./trace_parser.js');
const { buildFlamegraphTree } = require('./trace_analysis.js');

const R = 512 * 1024; // sample rate bytes

for await (const trace of parseTrace('/path/to/traces/')) {
  if (!trace.allocEvents || trace.allocEvents.length === 0) continue;

  // Strip dial9 allocator hook frames (always the top 2 frames)
  const HOOK_PATTERNS = [
    'memory_profiling::hook::on_alloc',
    'memory_profiling::allocator::Dial9Allocator',
    '__rustc::__rust_alloc',
    '__rustc::__rust_realloc',
  ];
  function stripHook(chain) {
    let i = 0;
    while (i < chain.length) {
      const entry = trace.callframeSymbols.get(chain[i]);
      const frames = Array.isArray(entry) ? entry : [entry];
      const name = frames.map(f => f && f.symbol || '').join(' ');
      if (HOOK_PATTERNS.some(p => name.includes(p))) { i++; continue; }
      break;
    }
    return i > 0 ? chain.slice(i) : chain;
  }

  // Build weighted samples: weight = estimated bytes, allocWeight = estimated count
  const samples = trace.allocEvents
    .map(a => ({ ...a, callchain: stripHook(a.callchain) }))
    .filter(a => a.callchain.length > 0)
    .map(a => {
      const s = a.size;
      const invP = s <= 0 ? 1 : 1 / (1 - Math.exp(-s / R));
      return { callchain: a.callchain, weight: s * invP, allocWeight: invP };
    });

  // buildFlamegraphTree accumulates weight into node.count and allocWeight into node.allocCount
  const tree = buildFlamegraphTree(samples, trace.callframeSymbols);

  // Walk the tree to find interesting nodes
  function walk(node, depth) {
    if (depth > 0 && node.allocCount > 0) {
      const avgSize = node.count / node.allocCount;
      const pct = (node.count / tree.count * 100).toFixed(1);
      console.log(`${'  '.repeat(depth)}${node.name}: ~${(node.count/1024/1024).toFixed(1)} MiB (${pct}%), ~${Math.round(node.allocCount)} allocs, avg ${Math.round(avgSize)} B`);
    }
    for (const child of [...node.children.values()].sort((a, b) => b.count - a.count)) {
      if (child.count / tree.count > 0.05) walk(child, depth + 1); // only >5%
    }
  }
  walk(tree, 0);
}

Answering "how big are allocations in X?": Find the node for X in the tree and compute node.count / node.allocCount. This is the average allocation size for all code paths through that frame.

Answering "what's making lots of small allocations?": Sort nodes by node.allocCount descending and look for nodes where node.count / node.allocCount is small (e.g., < 1 KB). These are high-frequency small-allocation sites.

// Find top allocation-count nodes with small avg size
// (assumes `tree` from the snippet above { ... } )
const nodes = [];
function collect(node) {
  if (node.allocCount > 0) nodes.push(node);
  for (const child of node.children.values()) collect(child);
}
collect(tree);

const smallAllocHotspots = nodes
  .filter(n => n.allocCount > 10 && n.count / n.allocCount < 1024)
  .sort((a, b) => b.allocCount - a.allocCount)
  .slice(0, 10);

for (const n of smallAllocHotspots) {
  console.log(`${n.name}: ~${Math.round(n.allocCount)} allocs, avg ${Math.round(n.count / n.allocCount)} B`);
}