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performance-profiler

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.

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UpdatedJune 12, 2026 at 05:21

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alirezarezvani

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name	performance-profiler
description	Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.

Performance Profiler

Tier: POWERFUL
Category: Engineering
Domain: Performance Engineering

Overview

Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks; generates flamegraphs; analyzes bundle sizes; optimizes database queries; detects memory leaks; and runs load tests with k6 and Artillery. Always measures before and after.

Core Capabilities

CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go
Memory profiling — heap snapshots, leak detection, GC pressure
Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer
Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection
Load testing — k6 scripts, Artillery scenarios, ramp-up patterns
Before/after measurement — establish baseline, profile, optimize, verify

When to Use

App is slow and you don't know where the bottleneck is
P99 latency exceeds SLA before a release
Memory usage grows over time (suspected leak)
Bundle size increased after adding dependencies
Preparing for a traffic spike (load test before launch)
Database queries taking >100ms

Quick Start

# Analyze a project for performance risk indicators
python3 scripts/performance_profiler.py /path/to/project

# JSON output for CI integration
python3 scripts/performance_profiler.py /path/to/project --json

# Custom large-file threshold
python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256

Golden Rule: Measure First

# Establish baseline BEFORE any optimization
# Record: P50, P95, P99 latency | RPS | error rate | memory usage

# Wrong: "I think the N+1 query is slow, let me fix it"
# Right: Profile → confirm bottleneck → fix → measure again → verify improvement

Node.js Profiling

→ See references/profiling-recipes.md for details

References

references/profiling-recipes.md — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots
references/optimization-playbook.md — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices

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name	performance-profiler
description	Systematic performance profiling for Node.js, Python, and Go applications. Identifies CPU, memory, and I/O bottlenecks, generates flamegraphs, analyzes bundle sizes, optimizes database queries, runs load tests with k6 and Artillery. Always measures before and after. Use when investigating a slow endpoint, planning a performance budget, or hunting a memory leak in production.

Performance Profiler

Tier: POWERFUL
Category: Engineering
Domain: Performance Engineering

Overview

Core Capabilities

CPU profiling — flamegraphs for Node.js, py-spy for Python, pprof for Go
Memory profiling — heap snapshots, leak detection, GC pressure
Bundle analysis — webpack-bundle-analyzer, Next.js bundle analyzer
Database optimization — EXPLAIN ANALYZE, slow query log, N+1 detection
Load testing — k6 scripts, Artillery scenarios, ramp-up patterns
Before/after measurement — establish baseline, profile, optimize, verify

When to Use

App is slow and you don't know where the bottleneck is
P99 latency exceeds SLA before a release
Memory usage grows over time (suspected leak)
Bundle size increased after adding dependencies
Preparing for a traffic spike (load test before launch)
Database queries taking >100ms

Quick Start

# Analyze a project for performance risk indicators
python3 scripts/performance_profiler.py /path/to/project

# JSON output for CI integration
python3 scripts/performance_profiler.py /path/to/project --json

# Custom large-file threshold
python3 scripts/performance_profiler.py /path/to/project --large-file-threshold-kb 256

Golden Rule: Measure First

# Establish baseline BEFORE any optimization
# Record: P50, P95, P99 latency | RPS | error rate | memory usage

# Wrong: "I think the N+1 query is slow, let me fix it"
# Right: Profile → confirm bottleneck → fix → measure again → verify improvement

Node.js Profiling

→ See references/profiling-recipes.md for details

References

references/profiling-recipes.md — Node.js/Python/Go profiling commands, flamegraph generation, heap snapshots
references/optimization-playbook.md — before/after measurement template, quick-win optimization checklist (DB/Node/bundle/API), common pitfalls, best practices