Langfuse performance tuning

Langfuse Performance Tuning

Overview

Optimize Langfuse tracing for minimal overhead and maximum throughput: benchmark measurement, batch tuning, non-blocking patterns, payload optimization, sampling, and memory management.

Prerequisites

• Existing Langfuse integration
• Performance baseline to compare against
• Understanding of async patterns

Performance Targets

Metric	Target	Critical
Trace creation overhead	< 1ms	< 5ms
Flush latency (batch)	< 100ms	< 500ms
Memory per active trace	< 1KB	< 5KB
CPU overhead	< 1%	< 5%

Instructions

Step 1: Benchmark Current Performance

// scripts/benchmark-langfuse.ts
import { performance } from "perf_hooks";
import { startActiveObservation, updateActiveObservation } from "@langfuse/tracing";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

async function benchmark() {
  const sdk = new NodeSDK({
    spanProcessors: [new LangfuseSpanProcessor()],
  });
  sdk.start();

  const iterations = 1000;

  // Measure trace creation
  const timings: number[] = [];
  for (let i = 0; i < iterations; i++) {
    const start = performance.now();
    await startActiveObservation(`bench-${i}`, async () => {
      updateActiveObservation({ input: { i }, output: { done: true } });
    });
    timings.push(performance.now() - start);
  }

  const sorted = timings.sort((a, b) => a - b);
  console.log("=== Langfuse Performance Benchmark ===");
  console.log(`Iterations: ${iterations}`);
  console.log(`Mean:  ${(sorted.reduce((a, b) => a + b) / sorted.length).toFixed(3)}ms`);
  console.log(`P50:   ${sorted[Math.floor(sorted.length * 0.5)].toFixed(3)}ms`);
  console.log(`P95:   ${sorted[Math.floor(sorted.length * 0.95)].toFixed(3)}ms`);
  console.log(`P99:   ${sorted[Math.floor(sorted.length * 0.99)].toFixed(3)}ms`);

  const flushStart = performance.now();
  await sdk.shutdown();
  console.log(`Flush: ${(performance.now() - flushStart).toFixed(1)}ms`);
}

benchmark();

Step 2: Optimize Batch Configuration

// v4+: Tune OTel span processor
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";

const processor = new LangfuseSpanProcessor({
  exportIntervalMillis: 10000,  // Flush every 10s (default: 5000)
  maxExportBatchSize: 100,      // Larger batches = fewer API calls
  maxQueueSize: 4096,           // Buffer more events before dropping
});

const sdk = new NodeSDK({ spanProcessors: [processor] });
sdk.start();

// v3: Direct configuration
const langfuse = new Langfuse({
  flushAt: 100,           // Larger batches
  flushInterval: 10000,   // Less frequent flushes
  requestTimeout: 30000,  // Allow time for large batches
});

Setting	Low Volume	High Volume	Ultra-High
Batch size	15	50-100	200
Flush interval	5s	10s	30s
Queue size	1024	4096	8192

Step 3: Non-Blocking Trace Wrapper

Ensure tracing never blocks your application's critical path:

import { observe, updateActiveObservation } from "@langfuse/tracing";

// The observe wrapper is already non-blocking for the trace submission.
// But protect against SDK crashes:
function safeObserve<T extends (...args: any[]) => Promise<any>>(
  name: string,
  fn: T
): T {
  return (async (...args: Parameters<T>) => {
    try {
      return await observe({ name }, async () => {
        updateActiveObservation({ input: args });
        const result = await fn(...args);
        updateActiveObservation({ output: result });
        return result;
      })();
    } catch (error) {
      // If tracing throws, run function without tracing
      console.warn(`Tracing failed for ${name}:`, error);
      return fn(...args);
    }
  }) as T;
}

Step 4: Payload Size Optimization

Large trace payloads slow down flush and increase costs:

function truncateForTrace(input: any, maxStringLen = 5000, maxArrayLen = 50): any {
  if (typeof input === "string") {
    return input.length > maxStringLen
      ? input.slice(0, maxStringLen) + `...[truncated ${input.length - maxStringLen} chars]`
      : input;
  }

  if (Array.isArray(input)) {
    return input.slice(0, maxArrayLen).map((item) => truncateForTrace(item));
  }

  if (input instanceof Buffer || input instanceof Uint8Array) {
    return `[Binary: ${input.length} bytes]`;
  }

  if (typeof input === "object" && input !== null) {
    const result: Record<string, any> = {};
    for (const [key, value] of Object.entries(input)) {
      result[key] = truncateForTrace(value);
    }
    return result;
  }

  return input;
}

// Usage
await startActiveObservation("process", async () => {
  updateActiveObservation({
    input: truncateForTrace(largeInput),  // Truncated for trace
  });
  const result = await process(largeInput); // Full input to function
  updateActiveObservation({ output: truncateForTrace(result) });
});

Step 5: Sampling for Ultra-High Volume

When you cannot afford to trace every request:

class TraceSampler {
  private rate: number;
  private windowMs = 60000;
  private maxPerWindow: number;
  private timestamps: number[] = [];

  constructor(rate: number, maxPerMinute: number) {
    this.rate = rate;
    this.maxPerWindow = maxPerMinute;
  }

  shouldSample(isError = false): boolean {
    if (isError) return true; // Always trace errors

    const now = Date.now();
    this.timestamps = this.timestamps.filter((t) => t > now - this.windowMs);

    if (this.timestamps.length >= this.maxPerWindow) return false;
    if (Math.random() > this.rate) return false;

    this.timestamps.push(now);
    return true;
  }
}

const sampler = new TraceSampler(0.1, 1000); // 10%, max 1000/min

async function maybeTrace<T>(name: string, fn: () => Promise<T>, isError = false): Promise<T> {
  if (!sampler.shouldSample(isError)) {
    return fn(); // Skip tracing
  }

  return startActiveObservation(name, async () => {
    updateActiveObservation({ metadata: { sampled: true } });
    return fn();
  });
}

Step 6: Memory Management

// Monitor trace-related memory usage
function logMemoryStats() {
  const mem = process.memoryUsage();
  console.log({
    heapUsedMB: (mem.heapUsed / 1024 / 1024).toFixed(1),
    rssMB: (mem.rss / 1024 / 1024).toFixed(1),
    externalMB: (mem.external / 1024 / 1024).toFixed(1),
  });
}

// Log every minute in production
setInterval(logMemoryStats, 60000);

Optimization Impact Matrix

Optimization	Latency Impact	Throughput Impact	Effort
Increase batch size	High	High	Low
Non-blocking wrapper	High	Medium	Low
Payload truncation	Medium	Medium	Low
Sampling	High	Very High	Medium
Memory monitoring	Low	Low	Low

Error Handling

Issue	Cause	Solution
High P99 latency	Sync flush in hot path	Use non-blocking wrapper
Memory growth	No payload limits	Truncate inputs/outputs
Request timeouts	Batch too large	Reduce batch size or increase timeout
Dropped spans	Queue full	Increase maxQueueSize

Resources

• Event Queuing/Batching
• Advanced SDK Configuration
• Token & Cost Tracking