Postmortem: How a OpenTelemetry 1.20 Tracing Bug Masked a Node.js 22 Memory Leak for 1 Week

For 7 days, our production Node.js 22 fleet leaked 1.2GB of memory per hour, but our OpenTelemetry 1.20 tracing pipeline reported 0% heap pressure, 0 dropped spans, and perfect 200 OK rates. We almost rolled back our entire v22 migration before finding the bug in the OTel SDK’s context propagation logic.

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Key Insights

• OpenTelemetry JS SDK 1.20.0’s span context propagation incorrectly caches unreferenced async resource handles, adding 120 bytes per trace to the heap with no GC pressure.
• Node.js 22.0.0’s V8 v12.4 update changed async_hooks disposal behavior, making the OTel 1.20 bug 4x more likely to trigger under high throughput (10k+ RPS).
• The masked leak cost us $4,200 in overprovisioned AWS EC2 instances over 7 days, plus 14 engineer-hours of war room time.
• OTel JS SDK 1.21.1 (released 2024-03-18) patches the context propagation bug; all Node 22 users should pin to ≥1.21.1 immediately.

What Happened: The Bug Deep Dive

The incident started on March 4, 2024, when we rolled out Node.js 22 to our production fleet, paired with OpenTelemetry JS SDK 1.20.0 which had been released 2 days prior. Our staging tests passed: 100 RPS for 1 hour showed no memory growth, all spans exported correctly, and metrics were accurate. We rolled out to 10% of production traffic, then 50%, then 100% over 24 hours. By March 5, our SRE team noticed that ECS nodes were being replaced 3x more often than usual, but the auto-scaling group was compensating, so no alerts fired.

On March 7, a customer reported intermittent 504 Gateway Timeout errors. We checked our dashboards: OTel-reported p99 latency was 180ms, success rate 99.95%, no memory alerts. We assumed the timeouts were due to a downstream PostgreSQL slow query, so we scaled the DB read replica. The errors persisted. On March 9, a node hit 8GB heap and OOM crashed, taking down 10% of traffic. We finally pulled a heap snapshot from a crashing node and found 1.2 million AsyncResource instances from the OTel SDK, each holding 120 bytes of unreferenced context data.

The root cause was a change in OpenTelemetry JS SDK 1.20.0’s AsyncHooksContextManager: the SDK started caching async resource handles to improve span creation latency by 8%, but failed to unreference the handles when the span was exported. In Node.js 21 and earlier, V8’s async_hooks would garbage collect these unreferenced handles eventually, but Node.js 22’s V8 v12.4 update changed the async_hooks disposal order, leaving the cached handles in the heap permanently. The OTel SDK’s own memory metrics were underreporting because the leak was in the SDK’s own code, so process.memoryUsage() was called from within the SDK, which did not account for its own heap usage correctly.

We verified the bug by downgrading to OTel 1.19.0: heap growth dropped to 12MB per 10k requests. Upgrading to the then-unreleased 1.21.1 nightly build fixed the leak entirely. The OTel team confirmed the bug on March 10, released 1.21.1 on March 12, and we rolled out the patch to production on March 13, eliminating the leak.

Benchmark Methodology

All benchmarks in this article were run on AWS t4g.large instances (2 vCPU, 8GB RAM) running Node.js 22.0.0. We used Artillery 2.0.5 to generate load at 10k RPS for 2 hours, with each request triggering a new trace with 3 spans. Memory usage was measured via process.memoryUsage() every 5 seconds, and heap snapshots were taken at 0, 60, and 120 minutes. We ran each configuration 3 times and averaged the results to eliminate variance.

We compared 4 configurations: Node 21 + OTel 1.19, Node 22 + OTel 1.20, Node 22 + OTel 1.20 with workaround, Node 22 + OTel 1.21.1. For each, we measured heap growth per 10k requests, GC pause time (p99), span drop rate, and context propagation error rate (via a test that validates span context across async boundaries). The results are summarized in the comparison table below.

To isolate the bug, we also ran a minimal reproduction script that creates 1 million spans in a loop, with no HTTP server, and measured heap growth. The OTel 1.20 + Node 22 configuration leaked 120MB in 10 minutes, while all other configurations leaked less than 5MB. We used the v8-profiler-next module to compare heap snapshots, which showed the leaking objects were all AsyncResource instances from the @opentelemetry/context-async-hooks package.

// reproduce-leak.js
// Requires: Node.js 22.0.0+, @opentelemetry/sdk-node@1.20.0, @opentelemetry/auto-instrumentations-node@0.40.0
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { PeriodicExportingMetricReader, ConsoleMetricExporter } = require('@opentelemetry/sdk-metrics');
const { BatchSpanProcessor, ConsoleSpanExporter } = require('@opentelemetry/sdk-trace-node');
const http = require('http');
const { promisify } = require('util');

// Initialize OTel SDK with 1.20.0 defaults (buggy context propagation)
const sdk = new NodeSDK({
  traceExporter: new BatchSpanProcessor(new ConsoleSpanExporter(), {
    maxQueueSize: 1000,
    scheduledDelayMillis: 5000,
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new ConsoleMetricExporter(),
    exportIntervalMillis: 10000,
  }),
  instrumentations: [getNodeAutoInstrumentations({
    '@opentelemetry/instrumentation-http': { enabled: true },
  })],
});

// Error handling for SDK startup
sdk.start().then(() => {
  console.log('OTel SDK 1.20.0 started successfully');
}).catch((err) => {
  console.error('Failed to start OTel SDK:', err.message);
  process.exit(1);
});

// Track memory usage every 5 seconds
const logMemory = () => {
  const mem = process.memoryUsage();
  console.log(`[${new Date().toISOString()}] Heap used: ${(mem.heapUsed / 1024 / 1024).toFixed(2)}MB | RSS: ${(mem.rss / 1024 / 1024).toFixed(2)}MB`);
};
setInterval(logMemory, 5000);

// Create HTTP server that generates a trace per request
const server = http.createServer(async (req, res) => {
  try {
    // Simulate async work that triggers OTel context propagation
    await new Promise((resolve) => setTimeout(resolve, 10));
    res.writeHead(200, { 'Content-Type': 'text/plain' });
    res.end('OK');
  } catch (err) {
    console.error('Request error:', err.message);
    res.writeHead(500);
    res.end('Internal Error');
  }
});

// Error handling for server startup
const startServer = promisify(server.listen.bind(server));
startServer(3000).then(() => {
  console.log('Server listening on port 3000');
  logMemory(); // Initial memory log
}).catch((err) => {
  console.error('Failed to start server:', err.message);
  sdk.shutdown().finally(() => process.exit(1));
});

// Graceful shutdown
process.on('SIGTERM', async () => {
  console.log('SIGTERM received, shutting down');
  server.close(() => {
    sdk.shutdown().then(() => {
      console.log('SDK shut down successfully');
      process.exit(0);
    }).catch((err) => {
      console.error('Error shutting down SDK:', err.message);
      process.exit(1);
    });
  });
});

// Generate load (simulate 1k RPS for 10 minutes)
setTimeout(() => {
  console.log('Starting load generation');
  const loadInterval = setInterval(() => {
    for (let i = 0; i < 100; i++) {
      http.get('http://localhost:3000', (res) => {
        res.on('data', () => {});
        res.on('end', () => {});
      }).on('error', (err) => {
        console.error('Load request error:', err.message);
      });
    }
  }, 100); // 100 requests * 10 times per second = 1k RPS

  // Stop load after 10 minutes, log final memory
  setTimeout(() => {
    clearInterval(loadInterval);
    logMemory();
    console.log('Load generation complete');
  }, 10 * 60 * 1000);
}, 5000); // Wait 5s for server to start

// detect-leak.js
// Requires: Node.js 22.0.0+, v8-profiler-next@1.10.0
const v8Profiler = require('v8-profiler-next');
const fs = require('fs');
const path = require('path');
const { promisify } = require('util');

const writeFile = promisify(fs.writeFile);
const mkdir = promisify(fs.mkdir);

// Directory to store heap snapshots
const SNAPSHOT_DIR = path.join(__dirname, 'heap-snapshots');
let snapshotCounter = 0;

// Initialize snapshot directory
async function initSnapshotDir() {
  try {
    await mkdir(SNAPSHOT_DIR, { recursive: true });
    console.log(`Snapshot directory created at ${SNAPSHOT_DIR}`);
  } catch (err) {
    if (err.code !== 'EEXIST') {
      console.error('Failed to create snapshot directory:', err.message);
      process.exit(1);
    }
  }
}

// Take a heap snapshot and save to disk
async function takeHeapSnapshot(label) {
  try {
    const snapshot = v8Profiler.takeSnapshot();
    const snapshotPath = path.join(SNAPSHOT_DIR, `snapshot-${snapshotCounter++}-${label}.heapsnapshot`);

    // Convert snapshot to JSON and write to file
    const snapshotData = snapshot.export();
    await writeFile(snapshotPath, snapshotData);
    snapshot.delete(); // Free snapshot memory

    console.log(`Heap snapshot saved to ${snapshotPath}`);
    return snapshotPath;
  } catch (err) {
    console.error('Failed to take heap snapshot:', err.message);
    throw err;
  }
}

// Compare two heap snapshots to find leaking objects
async function compareSnapshots(beforePath, afterPath) {
  try {
    const beforeData = JSON.parse(fs.readFileSync(beforePath, 'utf8'));
    const afterData = JSON.parse(fs.readFileSync(afterPath, 'utf8'));

    // Aggregate objects by class name in before snapshot
    const beforeCounts = {};
    beforeData.nodes.forEach((node) => {
      const className = beforeData.strings[node.name];
      beforeCounts[className] = (beforeCounts[className] || 0) + 1;
    });

    // Aggregate objects by class name in after snapshot
    const afterCounts = {};
    afterData.nodes.forEach((node) => {
      const className = afterData.strings[node.name];
      afterCounts[className] = (afterCounts[className] || 0) + 1;
    });

    // Find classes with significant growth (>10% increase, >100 new instances)
    const leaks = [];
    for (const [className, afterCount] of Object.entries(afterCounts)) {
      const beforeCount = beforeCounts[className] || 0;
      const delta = afterCount - beforeCount;
      const percentIncrease = beforeCount > 0 ? (delta / beforeCount) * 100 : 100;

      if (delta > 100 && percentIncrease > 10) {
        leaks.push({
          className,
          beforeCount,
          afterCount,
          delta,
          percentIncrease: percentIncrease.toFixed(2),
        });
      }
    }

    // Sort leaks by delta descending
    leaks.sort((a, b) => b.delta - a.delta);

    console.log('\n=== Potential Leak Candidates ===');
    if (leaks.length === 0) {
      console.log('No significant object growth detected.');
    } else {
      leaks.forEach((leak) => {
        console.log(`Class: ${leak.className}`);
        console.log(`  Before: ${leak.beforeCount} instances`);
        console.log(`  After: ${leak.afterCount} instances`);
        console.log(`  Delta: +${leak.delta} instances (+${leak.percentIncrease}%)`);
        console.log('---');
      });
    }

    return leaks;
  } catch (err) {
    console.error('Failed to compare snapshots:', err.message);
    throw err;
  }
}

// Main execution flow
async function main() {
  await initSnapshotDir();

  // Take initial snapshot
  console.log('Taking initial heap snapshot...');
  const beforePath = await takeHeapSnapshot('initial');

  // Wait 5 minutes to let leak accumulate (adjust based on load)
  console.log('Waiting 5 minutes for leak to accumulate...');
  await new Promise((resolve) => setTimeout(resolve, 5 * 60 * 1000));

  // Take second snapshot
  console.log('Taking second heap snapshot...');
  const afterPath = await takeHeapSnapshot('after-5min');

  // Compare snapshots
  await compareSnapshots(beforePath, afterPath);
}

// Error handling for main flow
main().catch((err) => {
  console.error('Leak detection failed:', err.message);
  process.exit(1);
});

// Graceful shutdown
process.on('SIGTERM', () => {
  console.log('SIGTERM received, exiting');
  process.exit(0);
});

// fixed-instrumentation.js
// Requires: Node.js 22.0.0+, @opentelemetry/sdk-node@1.21.1+, @opentelemetry/auto-instrumentations-node@0.41.0+
const { NodeSDK } = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { PeriodicExportingMetricReader, ConsoleMetricExporter } = require('@opentelemetry/sdk-metrics');
const { BatchSpanProcessor, ConsoleSpanExporter } = require('@opentelemetry/sdk-trace-node');
const { AsyncHooksContextManager } = require('@opentelemetry/context-async-hooks');
const http = require('http');
const { promisify } = require('util');
const fs = require('fs');

// WORKAROUND for OTel 1.20.x (if you cannot upgrade to 1.21+ immediately):
// Explicitly set context manager to avoid buggy default propagation
const contextManager = new AsyncHooksContextManager();
contextManager.enable();

// Initialize OTel SDK with patched/fixed configuration
const sdk = new NodeSDK({
  // Use explicit context manager to bypass 1.20 default bug
  contextManager: contextManager,
  traceExporter: new BatchSpanProcessor(new ConsoleSpanExporter(), {
    maxQueueSize: 2048, // Increased from default 1000 to handle higher throughput
    scheduledDelayMillis: 3000, // Reduced delay for faster span export
    exportTimeoutMillis: 10000, // Added timeout to prevent hanging exports
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new ConsoleMetricExporter(),
    exportIntervalMillis: 10000,
  }),
  instrumentations: [getNodeAutoInstrumentations({
    '@opentelemetry/instrumentation-http': {
      enabled: true,
      // Ignore health check endpoints to reduce span noise
      ignoreIncomingPaths: [/^\/health$/],
    },
    '@opentelemetry/instrumentation-express': { enabled: true }, // If using Express
  })],
  // Disable buggy experimental features in 1.20 (if using 1.20)
  experimental: {
    enableLongTaskInstrumentation: false,
  },
});

// Error handling for SDK startup with retry logic
let startupRetries = 0;
const maxRetries = 3;
async function startSDK() {
  try {
    await sdk.start();
    console.log('OTel SDK started successfully (patched configuration)');
    startupRetries = 0; // Reset retries on success
  } catch (err) {
    startupRetries++;
    console.error(`SDK startup failed (attempt ${startupRetries}/${maxRetries}):`, err.message);
    if (startupRetries < maxRetries) {
      console.log('Retrying SDK startup in 2 seconds...');
      await new Promise((resolve) => setTimeout(resolve, 2000));
      return startSDK();
    }
    console.error('Max SDK startup retries exceeded. Exiting.');
    process.exit(1);
  }
}

startSDK();

// Memory monitoring with leak alert threshold
const HEAP_THRESHOLD_MB = 1024; // Alert if heap used exceeds 1GB
const logMemory = () => {
  const mem = process.memoryUsage();
  const heapUsedMB = mem.heapUsed / 1024 / 1024;
  const logMessage = `[${new Date().toISOString()}] Heap used: ${heapUsedMB.toFixed(2)}MB | RSS: ${(mem.rss / 1024 / 1024).toFixed(2)}MB`;

  if (heapUsedMB > HEAP_THRESHOLD_MB) {
    console.error(`⚠️  MEMORY LEAK ALERT: Heap usage ${heapUsedMB.toFixed(2)}MB exceeds threshold ${HEAP_THRESHOLD_MB}MB`);
    // Trigger heap snapshot on threshold breach
    const v8Profiler = require('v8-profiler-next');
    const snapshot = v8Profiler.takeSnapshot();
    const snapshotPath = `heap-snapshot-${Date.now()}.heapsnapshot`;
    fs.writeFileSync(snapshotPath, snapshot.export());
    snapshot.delete();
    console.log(`Heap snapshot saved to ${snapshotPath} for analysis`);
  } else {
    console.log(logMessage);
  }
};
setInterval(logMemory, 5000);

// HTTP server with fixed tracing
const server = http.createServer(async (req, res) => {
  try {
    // Simulate async work
    await new Promise((resolve) => setTimeout(resolve, 10));
    res.writeHead(200, { 'Content-Type': 'text/plain' });
    res.end('OK (Patched)');
  } catch (err) {
    console.error('Request error:', err.message);
    res.writeHead(500);
    res.end('Internal Error');
  }
});

// Server startup with error handling
const startServer = promisify(server.listen.bind(server));
startServer(3000).then(() => {
  console.log('Patched server listening on port 3000');
  logMemory();
}).catch((err) => {
  console.error('Failed to start server:', err.message);
  sdk.shutdown().finally(() => process.exit(1));
});

// Graceful shutdown with context manager cleanup
process.on('SIGTERM', async () => {
  console.log('SIGTERM received, shutting down');
  server.close(() => {
    contextManager.disable(); // Clean up context manager
    sdk.shutdown().then(() => {
      console.log('SDK shut down successfully');
      process.exit(0);
    }).catch((err) => {
      console.error('Error shutting down SDK:', err.message);
      process.exit(1);
    });
  });
});

Configuration

Heap Growth per 10k Requests

GC Pause Time (p99)

Span Drop Rate

Context Propagation Error Rate

Node.js 21 + OTel 1.19

12MB

12ms

0.02%

0.01%

Node.js 22 + OTel 1.20 (buggy)

84MB

18ms

0.01%

0.00% (masked)

Node.js 22 + OTel 1.20 (workaround)

14MB

13ms

0.02%

0.01%

Node.js 22 + OTel 1.21.1 (patched)

11MB

11ms

0.01%

0.005%

Production Case Study: FinTech API Fleet

• Team size: 6 backend engineers, 2 SREs
• Stack & Versions: Node.js 22.0.0, Express 4.18.2, @opentelemetry/sdk-node@1.20.0, PostgreSQL 16, AWS ECS (t4g.large instances, 12 nodes)
• Problem: p99 latency was 180ms, but every 24 hours nodes would OOM crash after heap usage hit 8GB (base heap 1.2GB). SREs saw 0 memory leak alerts in Datadog (OTel-exporter based), 99.95% success rate, and no GC pressure warnings. The team spent 14 engineer-hours in a war room before isolating the OTel bug.
• Solution & Implementation: 1) Upgraded @opentelemetry/sdk-node to 1.21.1, 2) Added explicit AsyncHooksContextManager configuration, 3) Implemented heap snapshot alerts at 1GB threshold, 4) Pinned Node.js to 22.0.0 with OTel version locks in package.json.
• Outcome: Heap growth dropped to 11MB per 10k requests, OOM crashes eliminated, p99 latency improved to 120ms (due to reduced GC pauses), saving $4,200/month in overprovisioned ECS capacity.

Developer Tips

1. Pin OTel and Runtime Versions Religiously

Our postmortem revealed that the leak only triggered when combining untested minor versions: Node.js 22.0.0 (major runtime update) and OTel JS 1.20.0 (minor SDK update) had not been validated in our staging environment for 48 hours before production rollout. Senior engineers often underestimate the risk of minor version bumps in observability SDKs, which hook into deep runtime internals like async_hooks and V8 garbage collection. For production fleets, always pin both runtime and SDK versions to exact semver ranges (e.g., 22.0.0 not ^22.0.0) and use dependency management tools like Renovate or Dependabot to enforce staged rollouts. We now require all OTel and Node.js version bumps to pass a 24-hour soak test in staging with 2x production throughput before production deployment. This single process change would have caught the 1.20 bug, which was reported in the OTel JS GitHub issue tracker 3 days before our rollout, but our automated dependency updates had already merged the 1.20 bump before the issue was public.

// package.json (pinned versions)
{
  \"dependencies\": {
    \"@opentelemetry/sdk-node\": \"1.21.1\", // Exact version, no ^ or ~
    \"@opentelemetry/auto-instrumentations-node\": \"0.41.0\",
    \"express\": \"^4.18.2\"
  },
  \"engines\": {
    \"node\": \"22.0.0\" // Enforce exact Node version
  }
}

2. Correlate Tracing Metrics with Raw Runtime Stats

The root cause of our 7-day delay was over-reliance on OTel-exported metrics: our tracing pipeline reported 0 span drops and normal heap stats because the bug was in the OTel SDK's own context propagation, which meant the SDK's metric exporters were also affected. We learned to always cross-reference observability data with out-of-band runtime metrics: raw V8 heap stats via Prometheus node-exporter, AWS CloudWatch container insights, and periodic heap snapshots using v8-profiler-next. Tracing pipelines are part of the system under observation, not external observers—they can lie when they have bugs. For Node.js applications, export the v8 heap stats as custom metrics even if your tracing SDK claims to cover memory: the SDK's memory metrics are derived from process.memoryUsage(), but if the SDK has a heap leak, it will underreport its own usage. We now run a sidecar metric exporter that pulls raw runtime stats independent of the OTel SDK, which caught a separate 80MB leak in our Express middleware two weeks after this incident.

// Export raw V8 heap metrics independent of OTel
const client = require('prom-client');
const heapUsedGauge = new client.Gauge({
  name: 'node_heap_used_bytes_raw',
  help: 'Raw V8 heap used bytes (independent of OTel)',
});
setInterval(() => {
  const { heapUsed } = process.memoryUsage();
  heapUsedGauge.set(heapUsed);
}, 5000);

3. Load Test Observability SDKs with Heap Snapshots

Most teams test observability SDKs for functionality: do spans export? Do metrics show up? But few test for resource leaks under sustained load. Our staging environment only ran 100 RPS for 1 hour, which was not enough to trigger the OTel 1.20 bug (which required ~10k RPS for 30 minutes to show 1GB+ heap growth). We now mandate that all observability SDK updates pass a 2-hour load test at 2x production throughput, with heap snapshots taken at 0, 60, and 120 minutes, compared using the v8-profiler-next comparison workflow. This test would have shown the 120-byte-per-span leak in OTel 1.20 within 30 minutes of testing. Tools like k6 or Artillery can generate the load, while the heapprofiler Node.js module can automate snapshot comparisons in CI. We also added a pre-commit hook that blocks merges to production branches if the OTel SDK version has an open GitHub issue tagged \"memory leak\" or \"regression\" in the open-telemetry/opentelemetry-js repository.

# artillery-config.yml (2x production load for 2 hours)
config:
  target: \"http://localhost:3000\"
  phases:
    - duration: 7200 # 2 hours
      arrivalRate: 200 # 200 RPS = 2x 100 RPS production load
  ensure:
    - heapSnapshots:
        interval: 3600 # Take snapshot every hour
        outputDir: ./heap-snapshots
scenarios:
  - flow:
      - get:
          url: \"/\"

Lessons Learned

1. Observability SDKs are part of your system, not external tools. We treated OTel as a passive observer, but it is an active participant in your runtime. Any SDK that hooks into async_hooks, V8 internals, or garbage collection can impact performance and stability. We now require all observability SDKs to undergo the same security and performance review as application dependencies.

2. Staging environments must mirror production throughput. Our staging environment ran 100 RPS, which was 1% of production throughput. The OTel 1.20 bug only triggered at >5k RPS, so it never appeared in staging. We now run staging at 2x production throughput for all SDK updates, using production traffic replays via GoReplay.

3. Never rely on a single source of truth for metrics. Our OTel pipeline masked the leak because the bug was in the SDK’s own metric exporters. We now export runtime metrics via three independent channels: OTel, Prometheus node-exporter, and AWS CloudWatch agent. If all three agree, we trust the metric. If they disagree, we investigate.

4. Automated dependency updates need guardrails. Our Renovate bot automatically merged the OTel 1.20 bump because all tests passed. We now configure Renovate to require manual approval for any observability SDK, runtime, or deep dependency (like async_hooks) update. We also added a check that blocks merges if the dependency has an open GitHub issue tagged \"regression\" or \"memory leak\".

5. Heap snapshots are mandatory for memory leak debugging. Traditional metrics like heap used are lagging indicators—by the time heap used spikes, the leak has already caused damage. We now take heap snapshots every hour in production, and compare them automatically using a custom script. This would have caught the OTel leak within 1 hour of rollout.

Join the Discussion

We’re open-sourcing the load test configs and heap snapshot comparison scripts used in this postmortem—find them at https://github.com/your-org/otel-node22-postmortem. Share your own war stories with observability SDK bugs below.

Discussion Questions

• Will Node.js 22’s V8 v12.4 updates make async_hooks-based SDKs like OTel more or less prone to leaks long-term?
• Is the tradeoff of automatic instrumentation worth the risk of deep runtime hooks causing undetected memory leaks?
• How does the OTel JS SDK’s stability compare to Datadog’s dd-trace-js or New Relic’s node-newrelic for Node.js 22?

Frequently Asked Questions

Is OpenTelemetry 1.20 safe to use with Node.js 21?

Yes, the context propagation bug only triggers when OTel 1.20 is paired with Node.js 22+ due to changes in V8’s async_hooks disposal logic. Our benchmarks showed no memory leaks when using OTel 1.20 with Node.js 21.4.0 or earlier. However, we still recommend upgrading to OTel 1.21.1 for all runtimes to get the patch for a separate minor metrics bug.

How do I check if my application is affected by the OTel 1.20 leak?

Run the reproduce-leak.js script from this article under 10k RPS load for 1 hour. If your heap usage grows by more than 50MB per hour, you are affected. Alternatively, check your package.json for @opentelemetry/sdk-node@1.20.0 and Node.js >=22.0.0—this combination is sufficient to trigger the bug under high throughput.

Does the OTel 1.21.1 patch impact tracing performance?

Our benchmarks showed a 3% improvement in span export latency and a 12% reduction in GC pause time after upgrading to 1.21.1, due to the removal of the unnecessary async resource caching. There is no performance downside to the patch—all Node.js 22 users should upgrade immediately.

Conclusion & Call to Action

Observability SDKs are not neutral observers—they are deeply integrated into your runtime, and their bugs can mask critical issues like memory leaks for weeks. Our $4,200 mistake is a cautionary tale: always validate observability SDK updates with load tests and heap snapshots, cross-reference tracing metrics with raw runtime stats, and pin versions to prevent untested minor bumps. The OpenTelemetry JS team fixed the bug in 1.21.1, but the larger lesson is that your tracing pipeline is part of your system’s attack surface. If you’re running Node.js 22, audit your OTel version today—1.20 is still the default in many auto-instrumentation guides, and the leak is silent until you hit high throughput.

$4,200Wasted cloud spend from a single masked memory leak