Zero-Dependency Static Performance Engine • Python 3.7+

Eradicate Latency Bottlenecks
Before They Hit Production

A targeted static analyzer for Angular, React, Node.js, SQL, and AWS. Zero SonarQube bloat — 100% focused on render waste, N+1 query loops, heap memory leaks, and unindexed database bottlenecks.

Quick Start Guide Live Interactive Demo
$ python optimizer.py --frontend ./src/web --backend ./src/api --fail-on critical
┌─────────────────────────────────────────────────────────────┐
│ PERFORMANCE OPTIMIZER v2.5 PRO │
│ Enterprise Multi-Stack Performance Intelligence │
└─────────────────────────────────────────────────────────────┘
Detected Stacks : Angular 18 • React 19 • Node.js • SQL • AWS RDS
Files Analyzed : 142 files in 0.04s

✔ Analysis Complete
Critical : 2 defects   High : 5 defects   Medium : 4 defects
Overall Score : 56 / 100 (Needs Optimization)
Report Export : performance_report.html (Self-contained Interactive Dashboard)
35+
Performance-Only Rules
0
External Dependencies
< 0.05s
Average Full Monorepo Scan
100%
Free & Open Source (MIT)
Supported Frameworks & Stacks

Multi-Stack Architecture Capabilities

Tailored rule engines purpose-built for each runtime ecosystem. Real detection of memory leaks, event loop blocks, and DOM re-render waste.

Full Support (v14-v19+)

Angular UI

Targets change detection cycles, RxJS lifecycle memory leaks, and DOM node recreation.

  • Missing OnPush change detection
  • Observable leaks (missing takeUntil)
  • Un-tracked *ngFor and @for loops
  • Angular 17+ @defer lazy loading
  • detectChanges synchronous loop calls
Full Support (v17-v19+)

React & Next.js

Detects runaway re-render cycles, hook dependency traps, and unmemoized context providers.

  • Array index as key in list render
  • useEffect missing dependency array
  • Context value recreation on render
  • Missing useMemo on heavy data arrays
  • Eager routes without React.lazy()
Express / Seneca / Nest

Node.js Backend

Pinpoints single-threaded event loop blocks, connection leaks, and N+1 query patterns.

  • N+1 database queries inside loops
  • Synchronous fs calls in runtime path
  • EventEmitter memory leak vectors
  • Missing HTTP keep-alive socket reuse
  • Python & Go Engine (v3.0 Roadmap)
SQL & AWS Cloud

SQL & Infrastructure

Flags unindexed foreign keys, deep OFFSET pagination, and under-provisioned serverless memory.

  • Missing foreign key B-tree indexes
  • Deep OFFSET query antipattern
  • SELECT * and unindexed LIKE '%...'
  • Lambda memory CPU throttling (<512MB)
  • Direct RDS access without RDS Proxy
Direct Architectural Comparison

Why Not SonarQube or Lighthouse?

SonarQube is built for code smells, cyclomatic complexity, and unit test coverage. Lighthouse runs client-side inside Chromium. Neither audits full-stack runtime scalability.

Evaluation Criteria Performance Optimizer SonarQube / SonarCloud Google Lighthouse
Detection Purpose 100% Pure Latency & Efficiency Style, Duplication & Security Synthetic Browser Metrics
Angular Change Detection Audit OnPush, @for, detectChanges loop No runtime CD awareness Cannot inspect component code
React Hook & Re-render Analysis useEffect loops, useMemo, Context Basic ESLint rules only Black-box client runtime
Database N+1 Query in Loops O(N) loop query detection Blind to iteration roundtrips No backend / SQL access
Serverless Infrastructure Review Lambda CPU scaling, RDS Proxy No CloudFormation/Serverless rules Zero cloud config visibility
Setup & Footprint 0 Dependencies • Run 1 Python File Heavy JVM daemon, PostgreSQL DB Requires Chrome headless browser
Interactive Diagnostic Studio

Live In-Browser Performance Scanner

Select an architectural scenario below or paste your team's code to test real-time pattern analysis right in your browser:

Preset Scenario:
Source Code Buffer
12 lines TypeScript
Diagnostic Output Analysis Ready
Rules Directory

Comprehensive Performance Catalog

Every rule includes severity weights, priority ranking, and copyable before/after remediation patches.

Integration Guide

Production Setup in 60 Seconds

Zero compilation, zero package installation. Plug directly into your local development flow or CI/CD gate.

01

Local Monorepo Run

Scan your entire repository with a single command:

python optimizer.py \
  --frontend ./frontend \
  --backend ./backend
02

CI/CD Quality Gate

Block Pull Requests introducing critical regressions:

python optimizer.py \
  --project . \
  --fail-on critical \
  --min-score 75 \
  --markdown-summary pr.md
03

Inline Suppression

Suppress accepted design trade-offs directly in code:

// perf-ignore ANG001
@Component({ ... })

// perf-ignore NODE001
fs.readFileSync('init.json');
Open Source Community

Contribute a Rule in 5 Minutes

Performance Optimizer is an open-source project licensed under the MIT License. Adding custom performance rules is straightforward and modular.

3-Step Contribution Workflow

  1. Create or open the appropriate analyzer file in analyzers/ (e.g. react_analyzer.py, node_analyzer.py).
  2. Define your detection logic yielding an Issue(id='RULE001', title='...', severity=Severity.HIGH, ...) with line number and code snippet.
  3. Add a unit test in tests/test_analyzers.py and submit your Pull Request!