n8n/packages/testing/performance

Performance Benchmarks

Microbenchmarks for measuring and tracking performance of critical code paths.

When to Use Benchmarks

Good fit:

• Hot paths executed thousands of times (expression evaluation, data transforms)
• Comparing implementation approaches (current vs proposed)
• Detecting regressions in critical code

Not a good fit:

• API endpoint latency (use load testing - k6, artillery)
• Database query performance (use query analysis tools)
• Frontend rendering (use browser profiling)
• One-off operations (startup time, migrations)

Rule of thumb: If it runs millions of times per day across all users, benchmark it.

Commands

pnpm --filter=@n8n/performance bench          # Run benchmarks
pnpm --filter=@n8n/performance bench:baseline  # Save baseline for local comparison
pnpm --filter=@n8n/performance bench:compare   # Compare against baseline (>10% = fail)

CI Regression Detection

Benchmarks run automatically on PRs that touch packages/testing/performance/** or packages/workflow/src/**. CodSpeed counts CPU instructions instead of wall-clock time, producing deterministic results regardless of runner load. It comments on PRs with results and regression warnings.

You can also trigger benchmarks manually for any branch via Actions > Test: Benchmarks > Run workflow.

Local vs CI

	Local (bench)	CI
Measurement	Wall-clock time (Hz, ms)	CPU instruction count
Noise	15-30% variance	Near-zero variance
Best for	Quick sanity checks, comparing approaches	Automated regression detection

Local benchmarks are useful for eyeballing performance during development. Use bench:baseline + bench:compare for before/after comparisons on the same machine in the same session.

Adding a Benchmark

// benchmarks/my-feature/thing.bench.ts
import { bench, describe } from 'vitest';

// Setup runs once, not measured
const data = createTestData();

describe('My Feature', () => {
  bench('operation name', () => {
    doTheThing(data);
  });
});

Reading Results

name                hz      min    max   mean    p99    rme   samples
my operation    20,000   0.04   0.20   0.05   0.10  ±0.5%   10000

Column	Meaning
hz	Operations per second (higher = faster)
mean	Average time per operation in ms
p99	99th percentile - worst case latency
rme	Margin of error - lower = more reliable
samples	Number of iterations run

Current Benchmarks

Area	What it measures	Why it matters
Expression Engine	={{ }} evaluation speed	Runs for every node parameter

Tips

1. Keep benchmarks focused - one thing per bench, not workflows
2. Use realistic data sizes - 100 items is typical, 10k is stress test
3. Compare approaches - benchmark both before deciding
4. Don't over-benchmark - only critical hot paths need this