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Profiling captures code-level performance data, helping you identify which functions are consuming the most CPU time and where optimization efforts should be focused.

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What is Profiling?

Profiling samples your application’s call stack at regular intervals, creating a detailed picture of where time is spent:
  • Function-level insights: See which functions are slowest
  • Call hierarchies: Understand the call tree
  • Flame graphs: Visual representation of time spent
  • CPU usage: Identify CPU-intensive operations
Profiling is currently available for Node.js, Python, PHP, Ruby, and other server-side platforms. Browser profiling support is limited.

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Setup (Node.js)

Enable profiling during SDK initialization: 
import * as Sentry from '@sentry/node';
import { nodeProfilingIntegration } from '@sentry/profiling-node';

Sentry.init({
  dsn: 'your-dsn',
  
  // Enable tracing (required for profiling)
  tracesSampleRate: 1.0,
  
  // Enable profiling
  integrations: [
    nodeProfilingIntegration(),
  ],
  
  // Set profiling sample rate
  profilesSampleRate: 1.0, // Profile 100% of sampled transactions
});
Profiling requires Node.js 16.0.0 or higher and the @sentry/profiling-node package.

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Sampling

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Profile Sample Rate

Percentage of sampled transactions to profile: 
Sentry.init({
  dsn: 'your-dsn',
  tracesSampleRate: 0.1,      // Sample 10% of transactions
  profilesSampleRate: 1.0,    // Profile 100% of sampled transactions
  // Result: 10% of transactions are profiled
});

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Dynamic Profiling

Dynamically decide which transactions to profile: 
Sentry.init({
  dsn: 'your-dsn',
  tracesSampleRate: 1.0,
  
  // Conditional profiling
  profilesSampler: (samplingContext) => {
    // Always profile checkout operations
    if (samplingContext.name?.includes('checkout')) {
      return 1.0;
    }
    
    // Profile 10% of other transactions
    return 0.1;
  },
});
In production, use lower profile sample rates (0.01-0.1) to reduce overhead while still collecting useful data.

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Continuous Profiling

Continuously profile your application: 
import * as Sentry from '@sentry/node';
import { nodeProfilingIntegration } from '@sentry/profiling-node';

Sentry.init({
  dsn: 'your-dsn',
  integrations: [
    nodeProfilingIntegration(),
  ],
  profilesSampleRate: 1.0,
});

const profiler = Sentry.getClient()?.getIntegrationByName('ProfilingIntegration')?._profiler;

if (profiler) {
  // Start continuous profiling
  profiler.start();
  
  // Your application runs...
  
  // Stop profiling
  profiler.stop();
}

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Integration with Transactions

Profiles are automatically attached to transactions: 
import * as Sentry from '@sentry/node';

Sentry.startSpan(
  {
    name: 'process_data',
    op: 'function'
  },
  () => {
    // This transaction will be profiled (if sampled)
    processData();
  }
);
The profile shows:
  • Which functions were called during the transaction
  • How long each function took
  • The call hierarchy
  • CPU time distribution

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Manual Profiling

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Start/Stop Profiler

import { getClient } from '@sentry/node';

const client = getClient();
const profilingIntegration = client?.getIntegrationByName('ProfilingIntegration');
const profiler = profilingIntegration?._profiler;

if (profiler) {
  // Start profiling
  profiler.startProfiler();
  
  // Run code to profile
  await expensiveOperation();
  
  // Stop profiling
  profiler.stopProfiler();
}
Most applications should use automatic profiling via profilesSampleRate rather than manual control.

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Understanding Profiles

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Flame Graphs

Profiles are visualized as flame graphs in Sentry:
  • Width: Time spent in the function
  • Height: Call stack depth
  • Color: Different colors for different functions
  • Hover: See function details

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Profile Data

Each profile includes: 
interface Profile {
  // Metadata
  event_id: string;
  version: string;
  platform: string;
  release: string;
  environment: string;
  
  // Runtime information
  runtime: {
    name: string;    // "node"
    version: string; // "20.10.0"
  };
  
  // Profile data
  profile: {
    samples: ThreadCpuSample[];  // CPU samples
    stacks: ThreadCpuStack[];    // Call stacks
    frames: ThreadCpuFrame[];    // Stack frames
    thread_metadata: {};
  };
  
  // Transaction link
  transaction?: {
    name: string;
    id: string;
    trace_id: string;
  };
}

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Performance Overhead

Profiling has minimal overhead:
  • Sampling-based: Only captures at intervals (not every function call)
  • Efficient: Native code for stack capture
  • Configurable: Adjust sample rate to balance detail vs overhead

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Typical Overhead

  • CPU: 1-5% additional CPU usage
  • Memory: ~5-10MB per profile
  • Impact: Negligible for most applications
// Production-friendly configuration
Sentry.init({
  dsn: 'your-dsn',
  tracesSampleRate: 0.1,     // 10% of transactions
  profilesSampleRate: 0.5,   // 50% of sampled transactions
  // Result: 5% of total requests profiled
});

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Practical Examples

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Express.js API

import * as Sentry from '@sentry/node';
import { nodeProfilingIntegration } from '@sentry/profiling-node';
import express from 'express';

Sentry.init({
  dsn: 'your-dsn',
  integrations: [
    Sentry.httpIntegration(),
    nodeProfilingIntegration(),
  ],
  tracesSampleRate: 1.0,
  profilesSampleRate: 1.0,
});

const app = express();

// Profiling is automatic for instrumented requests
app.get('/api/heavy-operation', async (req, res) => {
  // This will be profiled
  const result = await heavyComputation();
  res.json(result);
});

app.listen(3000);

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Background Job

import * as Sentry from '@sentry/node';
import { nodeProfilingIntegration } from '@sentry/profiling-node';

Sentry.init({
  dsn: 'your-dsn',
  integrations: [nodeProfilingIntegration()],
  tracesSampleRate: 1.0,
  profilesSampleRate: 1.0,
});

async function processJob(job) {
  // Create a transaction for profiling
  await Sentry.startSpan(
    {
      name: `job.${job.type}`,
      op: 'queue.process'
    },
    async () => {
      // Job processing is profiled
      await job.process();
    }
  );
}

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Critical Path Profiling

Sentry.init({
  dsn: 'your-dsn',
  integrations: [nodeProfilingIntegration()],
  tracesSampleRate: 1.0,
  
  // Only profile critical operations
  profilesSampler: (samplingContext) => {
    const criticalOps = ['checkout', 'payment', 'data_export'];
    
    if (criticalOps.some(op => samplingContext.name?.includes(op))) {
      return 1.0; // Profile all critical operations
    }
    
    return 0.01; // Profile 1% of others
  },
});

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Analyzing Profiles

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Finding Slow Functions

  1. Look for wide bars: Functions that take a lot of time
  2. Check self-time: Time spent in the function itself (not children)
  3. Identify hot paths: Call paths that appear frequently
  4. Compare with baseline: Look for regressions

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Common Issues to Look For

  • Synchronous I/O: Blocking operations
  • Inefficient algorithms: O(n²) loops
  • Unnecessary computation: Repeated calculations
  • Large object processing: JSON parsing, serialization
  • Deep call stacks: Excessive function calls

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Best Practices

  1. Start with low sample rates: 0.01-0.1 in production
  2. Profile consistently: Keep profiling enabled
  3. Compare over time: Look for regressions
  4. Focus on hot paths: Optimize frequently-called code
  5. Combine with tracing: Use profiles with performance monitoring
  6. Monitor overhead: Ensure profiling doesn’t impact users

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Profiling vs Other Tools

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Profiling vs Tracing

  • Tracing: Shows what operations ran and their duration
  • Profiling: Shows where time was spent at the code level
// Tracing: Operation took 500ms
Sentry.startSpan({ name: 'process_data' }, () => {
  processData(); // 500ms
});

// Profiling: Shows that calculateSum() took 300ms of the 500ms
// and processArray() took 200ms

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Profiling vs Debugging

  • Debugging: Step-by-step execution
  • Profiling: Statistical sampling in production

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Troubleshooting

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Profiling Not Working

// Check Node.js version
console.log('Node version:', process.version);
// Must be >= 16.0.0

// Check integration
const client = Sentry.getClient();
const profiling = client?.getIntegrationByName('ProfilingIntegration');

if (!profiling) {
  console.error('Profiling integration not found');
}

// Check sample rates
console.log('Traces sample rate:', client.getOptions().tracesSampleRate);
console.log('Profiles sample rate:', client.getOptions().profilesSampleRate);

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High Memory Usage

// Reduce sample rates
Sentry.init({
  dsn: 'your-dsn',
  tracesSampleRate: 0.1,
  profilesSampleRate: 0.1,
  // Only 1% of requests profiled
});

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Missing Profiles

Profiles require:
  1. Profiling integration installed
  2. Transaction sampling enabled
  3. Profile sampling enabled
  4. Node.js >= 16.0.0

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Platform Support

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Node.js

import { nodeProfilingIntegration } from '@sentry/profiling-node';

Sentry.init({
  integrations: [nodeProfilingIntegration()],
  profilesSampleRate: 1.0,
});

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Other Platforms

  • Python: sentry_sdk.profiler
  • PHP: Native profiling support
  • Ruby: sentry-ruby with profiling
  • Browser: Limited support (experimental)
Check the platform-specific documentation for profiling setup details.

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Next Steps

Performance

Performance monitoring overview

Tracing

Combine profiling with tracing

Spans

Understand span timing

Session Replay

Visual debugging with replay

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