Configure Sampling

Sentry's tracing functionality helps you monitor application performance by capturing distributed traces, attaching attributes, and span performance across your application. However, Capturing traces for every transaction can generate significant volumes of data. Sampling allows you to control the amount of spans that are sent to Sentry from your application.

Sampling Configuration Options

The JavaScript SDK provides two main options for controlling the sampling rate:

1. Uniform Sample Rate (tracesSampleRate) This option sets a fixed percentage of transactions to be captured:

Sentry.init({
  dsn: "https://examplePublicKey@o0.ingest.sentry.io/0",
  integrations: [Sentry.browserTracingIntegration()],
  // Capture 25% of all transactions
  tracesSampleRate: 0.25,
});

With tracesSampleRate set to 0.25, approximately 25% of transactions will be recorded and sent to Sentry. This provides an even cross-section of transactions regardless of where in your app they occur.

2. Sampling Function (tracesSampler)

For more granular control, you can use the tracesSampler function. This approach allows you to:

• Apply different sampling rates to different types of transactions
• Filter out specific transactions entirely
• Make sampling decisions based on transaction data
• Control the inheritance of sampling decisions in distributed traces

Sentry.init({
  dsn: "https://examplePublicKey@o0.ingest.sentry.io/0",
  integrations: [Sentry.browserTracingIntegration()],
  tracesSampler: (samplingContext) => {
    // Access transaction details from the sampling context
    const { name, attributes, inheritOrSampleWith } = samplingContext;

    // Skip health checks entirely
    if (name.includes("healthcheck")) {
      return 0;
    }

    // Capture all auth-related transactions
    if (name.includes("auth")) {
      return 1;
    }

    // Sample only 1% of comment-related transactions
    if (name.includes("comment")) {
      return 0.01;
    }

    // For everything else, inherit parent sampling decision or use 0.5
    return inheritOrSampleWith(0.5);
  },
});

Trace Sampler Examples

1. Prioritizing Critical User Flows

tracesSampler: (samplingContext) => {
  const { name, attributes } = samplingContext;

  // Sample all checkout transactions
  if (name.includes("/checkout") || attributes?.flow === "checkout") {
    return 1.0;
  }

  // Sample 50% of login transactions
  if (name.includes("/login") || attributes?.flow === "login") {
    return 0.5;
  }

  // Sample 10% of everything else
  return 0.1;
};

2. Handling Different Environments

tracesSampler: (samplingContext) => {
  // Sample all transactions in development
  if (process.env.NODE_ENV === "development") {
    return 1.0;
  }

  // Sample 5% in production
  if (process.env.NODE_ENV === "production") {
    return 0.05;
  }

  // Sample 20% in staging
  return 0.2;
};

3. Controlling Sampling Based on User or Transaction Properties

tracesSampler: (samplingContext) => {
  const { attributes, inheritOrSampleWith } = samplingContext;

  // Always sample for premium users
  if (attributes?.userTier === "premium") {
    return 1.0;
  }

  // Sample more transactions for users experiencing errors
  if (attributes?.hasRecentErrors === true) {
    return 0.8;
  }

  // Sample less for high-volume, low-value paths
  if (attributes?.path?.includes("/api/metrics")) {
    return 0.01;
  }

  // Default sampling rate
  return inheritOrSampleWith(0.2);
};

The Sampling Context Object

When the tracesSampler function is called, it receives a samplingContext object with valuable information to help make sampling decisions:

typescriptCopyinterface SamplingContext {
  // Name of the span/transaction
  name: string;
  
  // Initial attributes of the span/transaction
  attributes: SpanAttributes | undefined;
  
  // Whether the parent span was sampled (undefined if no incoming trace)
  parentSampled: boolean | undefined;
  
  // Sample rate from incoming trace (undefined if no incoming trace)
  parentSampleRate: number | undefined;
  
  // Utility function to inherit parent decision or fallback
  inheritOrSampleWith: (fallbackRate: number) => number;
}

The sampling context contains:

• name: The name of the transaction/span
• attributes: Initial tags and attributes set on the transaction
• parentSampled: Whether the parent transaction was sampled (for distributed tracing)
• parentSampleRate: The sample rate used in the parent transaction
• inheritOrSampleWith: A utility function to handle inheritance logic (recommended)

Inheritance in Distributed Tracing

In distributed systems, trace information is propagated between services. The inheritOrSampleWith function simplifies handling parent sampling decisions:

tracesSampler: (samplingContext) => {
  const { name, inheritOrSampleWith } = samplingContext;

  // Apply specific rules first
  if (name.includes("critical-path")) {
    return 1.0; // Always sample
  }

  // Otherwise inherit parent sampling decision or fall back to 0.1
  return inheritOrSampleWith(0.1);
};

This approach ensures consistent sampling decisions across your entire distributed trace. All transactions in a given trace will share the same sampling decision, preventing broken or incomplete traces.

Note: The inheritOrSampleWith helper was introduced in version 9 of the SDK. For earlier versions, you can implement similar logic manually using the parentSampled property.

Sampling Decision Precedence

When multiple sampling mechanisms could apply, Sentry follows this order of precedence:

• If tracesSampler is defined, its decision is used (can consider parent sampling)
• If no tracesSampler but parent sampling is available, parent decision is used
• If neither of the above, tracesSampleRate is used
• If none of the above are set, no transactions are sampled (0%)

How Sampling Propagates in Distributed Traces

Sentry uses a "head-based" sampling approach:

• A sampling decision is made in the originating service (the "head")
• This decision is propagated to all downstream services via HTTP headers

The two key headers are:

• sentry-trace: Contains trace ID, span ID, and sampling decision
• baggage: Contains additional trace metadata including sample rate

Sentry automatically attaches these headers to outgoing HTTP requests when using the browserTracingIntegration. For other communication channels like WebSockets, you can manually propagate trace information:

// Extract trace data from the current scope
const traceData = Sentry.getTraceData();
const sentryTraceHeader = traceData["sentry-trace"];
const sentryBaggageHeader = traceData["baggage"];

// Add to your custom request (example using WebSocket)
webSocket.send(
  JSON.stringify({
    message: "Your data here",
    metadata: {
      sentryTrace: sentryTraceHeader,
      baggage: sentryBaggageHeader,
    },
  }),
);

Conclusion

Effective sampling is key to getting the most value from Sentry's performance monitoring while minimizing overhead. The tracesSampler function gives you precise control over which transactions to record, allowing you to focus on the most important parts of your application.

By implementing a thoughtful sampling strategy, you'll get the performance insights you need without overwhelming your systems or your Sentry quota.