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 ```typescript // 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(...