Measure first, optimize second. Always.
Measure first, optimize second. Always.
Donald Knuth: "Premature optimization is the root of all evil." The forgotten half: "we should not pass up our critical 3%." The key word is critical — find it with data, not guessing.
The Performance Engineering Commandment
Never optimize without a measurement showing (a) there is a performance problem and (b) which part of code is responsible. Optimizing the wrong thing wastes time and makes code worse to maintain.
The Performance Optimization Cycle
01
Baseline: measure current performance (p50/p95/p99 latency, throughput, error rate)
02
Set goal: "reduce p99 latency from 2s to 500ms for checkout endpoint"
03
Profile: identify the actual bottleneck using profiling tools (not guessing)
04
Hypothesize: "removing this N+1 query should eliminate 1.5s of DB time"
05
Implement: make the targeted change — one change at a time
06
Measure: compare new vs old baseline. Improvement? By how much?
07
Repeat: Amdahl's Law — fixing 50% of runtime only gives 2× speedup. Profile again for next bottleneck.
Baseline: measure current performance (p50/p95/p99 latency, throughput, error rate)
Set goal: "reduce p99 latency from 2s to 500ms for checkout endpoint"
Profile: identify the actual bottleneck using profiling tools (not guessing)
Hypothesize: "removing this N+1 query should eliminate 1.5s of DB time"
Implement: make the targeted change — one change at a time
Measure: compare new vs old baseline. Improvement? By how much?
Repeat: Amdahl's Law — fixing 50% of runtime only gives 2× speedup. Profile again for next bottleneck.
| Tool | Platform | What It Shows | When to Use |
|---|---|---|---|
| clinic.js (doctor, flame) | Node.js | Event loop delays, CPU flame graph | Node.js CPU or event loop bottlenecks |
| 0x (zero-ex) | Node.js | Interactive flame graph from V8 | Identifying hot functions |
| py-spy | Python | Low-overhead sampling profiler | Python production CPU profiling (no code changes) |
| async-profiler | JVM | CPU + allocation + lock profiling | Production JVM profiling (fixes safepoint bias) |
| EXPLAIN ANALYZE | PostgreSQL | Query execution plan with timing | Database query optimization |
| clinic doctor | Node.js | Specifically detects event loop blocking | When event loop is blocked by sync code |
Reading Flame Graphs
X-axis = % of time, Y-axis = call stack depth. Wide boxes at the top = hot functions consuming the most time. The flatness at top means that function is "on-CPU" most. Read the widest boxes — those are your optimization targets.
1// Common Node.js performance optimizations23// ❌ Anti-pattern: Sequential DB calls (100ms + 100ms = 200ms)4async function getDashboardSlow(userId: string) {5const user = await db.users.findById(userId); // 50ms6const orders = await db.orders.getByUserId(userId); // 50ms7const analytics = await db.analytics.getUserStats(userId); // 100ms8return { user, orders, analytics }; // Total: 200ms9}1011// ✅ Parallel DB calls (max(50, 50, 100) = 100ms)Promise.all: 3 independent queries start simultaneously. Total = max(50, 50, 100) = 100ms vs 200ms12async function getDashboardFast(userId: string) {13const [user, orders, analytics] = await Promise.all([14db.users.findById(userId),15db.orders.getByUserId(userId),16db.analytics.getUserStats(userId),17]);18return { user, orders, analytics }; // Total: 100ms19}2021// ❌ Anti-pattern: Serializing huge datasets blocks event loopJSON.stringify on 100k objects blocks the event loop for seconds — all other requests stall22app.get('/export', async (req, res) => {23const data = await db.getAllRows(); // 100k rows24res.json(data); // JSON.stringify blocks event loop for 2s!25});26Streaming: serialize row-by-row, never holding full dataset in memory or blocking the loop27// ✅ Streaming: serialize row-by-row, event loop never blocks28app.get('/export', async (req, res) => {29res.setHeader('Content-Type', 'application/json');30res.write('[');31let first = true;3233for await (const row of db.streamAllRows()) {34if (!first) res.write(',');35res.write(JSON.stringify(row)); // one row at a time36first = false;37}3839res.write(']');40res.end();Worker threads: CPU-intensive work runs in a separate thread, event loop stays free for requests41});4243// ✅ CPU-intensive work → Worker Thread (never blocks event loop)44import { Worker } from 'worker_threads';4546function runInWorker(data: unknown): Promise<unknown> {47return new Promise((resolve, reject) => {48const worker = new Worker('./heavy-computation.js', { workerData: data });49worker.on('message', resolve);50worker.on('error', reject);51});52}5354const result = await runInWorker({ imageBuffer: req.file.buffer });
| Bottleneck | Symptoms | Diagnosis | Solutions |
|---|---|---|---|
| CPU-bound | High CPU%, latency scales with rate | CPU flame graph | Optimize hot functions, horizontal scale, Worker Threads for CPU tasks |
| I/O-bound (DB) | Low CPU%, high latency, slow DB log | EXPLAIN ANALYZE, slow query log | Indexes, query rewrite, read replicas, caching |
| Memory/GC | High GC activity, increasing memory, OOM | Heap snapshot, allocation profiler | Fix leaks, reduce allocation, increase heap limit |
| Event loop blocking | Event loop lag > 10ms, serial handling | clinic doctor | Worker Threads for CPU work, stream large payloads |
| Lock contention | High p99 vs p50 ratio, threads waiting | Thread dump, lock profiler | Reduce critical section, use async patterns |
Amdahl's Law
If 10% of code can't be parallelized, adding infinite CPUs gives max 10× speedup. Applied: fixing a bottleneck that accounts for 50% of runtime gives at most 2× total speedup. Profile to find the LARGEST bottleneck first.
Performance questions test engineering discipline. The right answer always starts with "measure first." Engineers who jump to solutions before profiling are a red flag.
Common questions:
Strong answers include:
Red flags:
Quick check · Performance Tuning: Profiling, Bottlenecks, and Optimization
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Key takeaways
From the books
Systems Performance: Enterprise and the Cloud — Brendan Gregg (2020)
Chapter 2: Methodologies
The USE Method: for every resource, check Utilization, Saturation, and Errors. Systematic approach finds bottlenecks faster than intuition-based debugging.
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