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Application performance profiling and bottleneck identification — Node.js profiling, Chrome DevTools, flame graphs, memory leak detection, CPU profiling, React rendering performance. Activate on "profiling", "performance bottleneck", "flame graph", "memory leak", "slow app", "CPU profiling", "heap snapshot", "React re-renders", "EXPLAIN ANALYZE", "event loop lag", "clinic.js", "Core Web Vitals". NOT for infrastructure monitoring or observability (use logging-observability), load testing (use a load-testing skill), or database schema optimization.
allowed-tools
Read,Write,Edit,Bash,Grep,Glob
metadata
{"category":"DevOps & Site Reliability","tags":["performance","profiling","performance-bottleneck","flame-graph"],"pairs-with":[{"skill":"react-performance-optimizer","reason":"React DevTools profiling identifies component re-render bottlenecks for optimization"},{"skill":"caching-strategies","reason":"Profiling reveals cache miss patterns that inform caching architecture decisions"},{"skill":"postgresql-optimization","reason":"Database query profiling with EXPLAIN ANALYZE identifies slow query bottlenecks"},{"skill":"logging-observability","reason":"Performance metrics and traces collected by observability systems feed profiling analysis"}]}
Find where your application actually spends time before touching a line of code. Covers the full stack: Node.js CPU and memory profiling, browser flame graphs, React render profiling, and database query analysis. The discipline here is profile first, optimize second — premature optimization is not a workflow, it is a guess.
0x generates an interactive HTML flame graph. The widest stacks are where time is spent. Look for:
Functions that appear wide near the bottom (called frequently by everything)
Unexpected width in library code (serialization, template engines, parsers)
Idle / [idle] blocks — I/O wait, not CPU (look elsewhere for those)
Clinic.js Suite
npm install -g clinic
# Doctor: overview of what is wrong
clinic doctor -- node src/server.js
# Flame: CPU flame graph (wraps 0x)
clinic flame -- node src/server.js
# Bubbles: event loop utilization
clinic bubbles -- node src/server.js
Clinic Doctor gives you a triage view: CPU, memory, event loop, and handles. Start here when you do not know what kind of bottleneck you have.
Event Loop Utilization (ELU)
const { performance } = require('perf_hooks');
// Sample ELU every 5 secondslet last = performance.eventLoopUtilization();
setInterval(() => {
const current = performance.eventLoopUtilization();
const diff = performance.eventLoopUtilization(current, last);
console.log(`ELU: ${(diff.utilization * 100).toFixed(1)}%`);
last = current;
}, 5000);
ELU above 80% means the event loop is saturated — CPU-bound work or sync blocking. ELU near 0% with slow responses means I/O wait (network, disk, database).
Node.js: Memory Profiling
Heap Snapshots
# Take heap snapshot via CLI
node --inspect src/index.js
# In chrome://inspect → Memory tab → Take Heap Snapshot
Three-snapshot technique for leak detection:
Snapshot after startup (baseline)
Snapshot after N requests (warm)
Snapshot after 2N requests (growth)
Compare Snapshot 3 to Snapshot 2 — objects that grew proportionally to request count are leaking.
Common Leak Patterns
Closure captures — Variables captured in long-lived closures that should have been released:
// LEAK: handler is registered but never removed
emitter.on('data', (chunk) => {
processedData.push(chunk); // processedData grows unbounded
});
// FIX: remove listener when done, or use once()
emitter.once('data', handler);
// orconsthandler = (chunk) => { ... };
emitter.on('data', handler);
// later:
emitter.off('data', handler);
Novice: "This function looks expensive, I'll rewrite it in a more efficient algorithm."
Expert: Rewrote the wrong function. Profiling would have shown that this function is called once per startup and contributes 0.1% of runtime. The actual bottleneck was JSON serialization in the response handler, called 10,000 times per second. Optimization effort must follow measurement, never intuition.
Detection: The "optimized" code is measurably faster in microbenchmark isolation but production p99 latency is unchanged.
Anti-Pattern: Micro-Benchmarking in Isolation
Novice: Writes a benchmark comparing two sorting algorithms on an array of 1000 items, concludes Algorithm B is 2x faster, rewrites production code.
Expert: Micro-benchmarks measure JIT-compiled hot paths under artificial conditions. Real workloads have different data shapes, mixed call patterns, GC pressure, and I/O interspersed. The JIT may optimize the benchmark differently than the real call site. Profile the actual application under real load — or at minimum, profile with realistic data shapes and call patterns embedded in the actual application code path.
The test: Does your benchmark run in a tight loop 10,000 times before measuring? If yes, V8 has JIT-compiled it differently than it will compile the real code, which runs cold at startup and is called with varied inputs.
React Rendering Performance
React Profiler (DevTools)
Open React DevTools → Profiler tab
Click "Record"
Perform the slow interaction
Stop recording
Examine the flame chart — bars represent components, width represents render time
Key columns: "Why did this render?" shows which prop or state change triggered each render.
why-did-you-render
npm install @welldone-software/why-did-you-render
// src/wdyr.js (import before React)importReactfrom'react';
if (process.env.NODE_ENV === 'development') {
const whyDidYouRender = require('@welldone-software/why-did-you-render');
whyDidYouRender(React, { trackAllPureComponents: true });
}
// Mark a specific component for trackingMyExpensiveComponent.whyDidYouRender = true;
This logs to the console every time a component re-renders with the same props — exposing unnecessary renders caused by reference equality failures.
-- Wrap any query in EXPLAIN (ANALYZE, BUFFERS) to see execution plan
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT u.*, COUNT(o.id) as order_count
FROM users u
LEFTJOIN orders o ON o.user_id = u.id
WHERE u.created_at > NOW() -INTERVAL'30 days'GROUPBY u.id;
Read the output bottom-up. Each node shows:
actual time=X..Y — startup time to first row, total time for all rows
rows=N — actual rows returned
loops=N — how many times this node executed
Red flags:
Seq Scan on large tables — missing index
rows=1000 estimated vs rows=1 actual — stale statistics, run ANALYZE
Hash Join with large hash batches — memory pressure, tune work_mem
Nested Loop on large outer result — cartesian product risk
Finding Slow Queries in Production
-- Enable pg_stat_statements extensionCREATE EXTENSION IF NOTEXISTS pg_stat_statements;
-- Top 10 slowest queries by total timeSELECT
query,
calls,
total_exec_time /1000AS total_seconds,
mean_exec_time AS mean_ms,
rowsFROM pg_stat_statements
ORDERBY total_exec_time DESC
LIMIT 10;
Browser Profiling
See references/browser-profiling.md for the full Chrome Performance tab workflow, Core Web Vitals measurement, and layout thrashing diagnosis.
Bottleneck Classification Rules
When the user provides profiling data, classify and rank bottlenecks using these rules. Process signals in priority order — higher-priority signals override lower ones.
Priority 1: Database (check first — it's the bottleneck 70% of the time)
Signal
Classification
Any query >500ms
Critical — type: database. Next step: Run EXPLAIN ANALYZE on the query. Look for sequential scans on large tables (missing index) and N+1 patterns (same query repeated with different IDs).
Multiple queries >100ms per request
High — type: database. Next step: Aggregate query count per endpoint. If >5 queries per request, look for N+1 or missing JOINs. Consider a query count budget per endpoint.
Query count >20 per page load
High — type: database. Even if individual queries are fast, connection overhead and round-trip latency compound. Next step: Batch with WHERE id IN (...) or use a DataLoader pattern.
Priority 2: Event Loop (Node.js-specific — the most underdiagnosed bottleneck)
Signal
Classification
ELU >0.8
Critical — type: cpu. The event loop is saturated. Next step: Run clinic flame or --prof to find synchronous hot paths. Common culprits: JSON.parse on large payloads, synchronous crypto, regex backtracking.
ELU >0.5 with slow p99 latency
High — type: cpu. Event loop contention is causing tail latency. Next step: Look for blocking operations that run infrequently but hold the loop when they do (large sorts, template rendering, PDF generation).
ELU <0.2 with slow responses
This is NOT a CPU problem.type: io. Next step: The app is waiting on something external (DB, API calls, file system). Trace outbound requests with clinic bubbleprof or add timing logs to external calls.
Priority 3: Memory
Signal
Classification
Heap growth rate >10MB/min sustained
Critical — type: memory. Memory leak will OOM the process. Next step: Take two heap snapshots 5 minutes apart, compare in Chrome DevTools, look for growing object counts (retained size). Common suspects: event listener accumulation, closures capturing request objects, unbounded caches.
Heap growth proportional to request rate (resets on GC)
Medium — type: memory. Not a leak, just high allocation pressure. Next step: Check for unnecessary object creation in hot paths (cloning large objects, building strings with concatenation). Reduce allocation, don't chase GC.
suspects array from heap analysis
List each suspect with its retained size. High if any single object retains >50MB. Next step: Trace the retainer tree to find why it's not being collected.
Priority 4: React Rendering (frontend)
Signal
Classification
Component render time >16ms
High — type: rendering. Dropping frames. Next step: Check if the component re-renders on every parent render (missing React.memo or unstable props). Profile with React DevTools "Why did this render?"
>5 re-renders per user interaction
Medium — type: rendering. Next step: Check for state updates that trigger cascading re-renders. Move state closer to where it's used, or split context providers.
Large component tree (>500 components mounted)
Medium — type: rendering. Next step: Virtualize lists (react-window), lazy-load off-screen components, check for unnecessary mount/unmount cycles.
Priority 5: CPU (non-event-loop)
Signal
Classification
Single function >30% of selfTime in CPU profile
High — type: cpu. Hot function dominates. Next step: Read the function. If it's in your code, optimize it. If it's in a library, check if you're calling it unnecessarily or with pathologically large input.
Flame graph shows wide, flat profile (no single hot function)
Medium — type: cpu. Death by a thousand cuts. Next step: Look for patterns — are many functions doing similar work? This often means redundant computation (computing the same derived value multiple times per request).
Output Ranking
After classifying all signals, rank the bottleneck list by:
Severity (critical first)
Actionability (clear next step ranks higher than vague "investigate further")
Estimated impact — "Adding an index will reduce this query from 800ms to 5ms" is more useful than "This might help"
Always include estimatedImpact as a concrete prediction: "Eliminating N+1 queries should reduce request count from 47 to 3, cutting endpoint latency by ~200ms" — not "should improve performance."
