# @claude-flow/plugin-performance-optimizer

[![npm version](https://img.shields.io/npm/v/@claude-flow/plugin-performance-optimizer.svg)](https://www.npmjs.com/package/@claude-flow/plugin-performance-optimizer)
[![npm downloads](https://img.shields.io/npm/dm/@claude-flow/plugin-performance-optimizer.svg)](https://www.npmjs.com/package/@claude-flow/plugin-performance-optimizer)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

A comprehensive performance optimization plugin combining sparse inference for efficient trace analysis with graph neural networks for dependency chain optimization. The plugin enables intelligent bottleneck detection, memory leak identification, N+1 query detection, and bundle size optimization while providing explainable recommendations based on historical performance patterns.

## Features

- **Bottleneck Detection**: Identify performance bottlenecks using GNN-based dependency analysis
- **Memory Analysis**: Detect memory leaks, retention chains, and GC pressure points
- **Query Optimization**: Detect N+1 queries, missing indexes, and slow joins
- **Bundle Optimization**: Analyze and optimize JavaScript bundle size with tree shaking and code splitting
- **Configuration Optimization**: Learn optimal configurations from workload patterns using SONA

## Installation

### npm

```bash
npm install @claude-flow/plugin-performance-optimizer
```

### CLI

```bash
npx claude-flow plugins install --name @claude-flow/plugin-performance-optimizer
```

## Quick Start

```typescript
import { PerfOptimizerPlugin } from '@claude-flow/plugin-performance-optimizer';

// Initialize the plugin
const plugin = new PerfOptimizerPlugin();
await plugin.initialize();

// Detect performance bottlenecks
const bottlenecks = await plugin.detectBottlenecks({
  traceData: {
    format: 'otlp',
    spans: traceSpans,
    metrics: performanceMetrics
  },
  analysisScope: ['cpu', 'memory', 'database'],
  threshold: {
    latencyP95: 500,  // 500ms
    throughput: 1000,
    errorRate: 0.01
  }
});

console.log('Detected bottlenecks:', bottlenecks);
```

## MCP Tools

### 1. `perf/bottleneck-detect`

Detect performance bottlenecks using GNN-based dependency analysis.

```typescript
// Example usage via MCP
const result = await mcp.call('perf/bottleneck-detect', {
  traceData: {
    format: 'chrome_devtools',
    spans: chromeTraceSpans,
    metrics: { renderTime: 150, scriptTime: 200 }
  },
  analysisScope: ['cpu', 'render', 'network'],
  threshold: {
    latencyP95: 100,
    throughput: 60
  }
});
```

**Returns:** List of identified bottlenecks with severity, location, and recommended fixes.

### 2. `perf/memory-analyze`

Analyze memory usage patterns and detect potential leaks.

```typescript
const result = await mcp.call('perf/memory-analyze', {
  heapSnapshot: '/path/to/heap-snapshot.heapsnapshot',
  timeline: memoryTimelineData,
  analysis: ['leak_detection', 'retention_analysis', 'gc_pressure'],
  compareBaseline: '/path/to/baseline-snapshot.heapsnapshot'
});
```

**Returns:** Memory analysis report with leak candidates, retention chains, and optimization suggestions.

### 3. `perf/query-optimize`

Detect N+1 queries and suggest database optimizations.

```typescript
const result = await mcp.call('perf/query-optimize', {
  queries: [
    { sql: 'SELECT * FROM users WHERE id = ?', duration: 5, resultSize: 1 },
    { sql: 'SELECT * FROM orders WHERE user_id = ?', duration: 3, resultSize: 10 }
  ],
  patterns: ['n_plus_1', 'missing_index', 'slow_join'],
  suggestIndexes: true
});
```

**Returns:** Detected query anti-patterns with suggested batch alternatives and index recommendations.

### 4. `perf/bundle-optimize`

Analyze and optimize JavaScript bundle size.

```typescript
const result = await mcp.call('perf/bundle-optimize', {
  bundleStats: '/path/to/webpack-stats.json',
  analysis: ['tree_shaking', 'code_splitting', 'duplicate_deps', 'large_modules'],
  targets: {
    maxSize: 250,  // 250KB
    maxChunks: 10
  }
});
```

**Returns:** Bundle analysis with optimization recommendations for tree shaking, code splitting, and dependency deduplication.

### 5. `perf/config-optimize`

Suggest optimal configurations based on workload patterns using SONA learning.

```typescript
const result = await mcp.call('perf/config-optimize', {
  workloadProfile: {
    type: 'api',
    metrics: { requestsPerSecond: 1000, avgLatency: 50 },
    constraints: { maxMemory: '4GB', maxCpu: 4 }
  },
  configSpace: {
    poolSize: { type: 'number', range: [10, 100], current: 25 },
    cacheSize: { type: 'number', range: [100, 1000], current: 200 }
  },
  objective: 'latency'
});
```

**Returns:** Optimized configuration values with expected performance improvements.

## Configuration Options

```typescript
interface PerfOptimizerConfig {
  // WASM memory limit (default: 2GB)
  memoryLimit: number;

  // Analysis timeout in seconds (default: 300)
  analysisTimeout: number;

  // Enable SONA learning for configuration optimization
  enableSONALearning: boolean;

  // Supported trace formats
  supportedFormats: ('otlp' | 'chrome_devtools' | 'jaeger' | 'zipkin')[];

  // Performance thresholds for alerting
  thresholds: {
    latencyP95: number;
    throughput: number;
    errorRate: number;
  };
}
```

## Performance Targets

| Metric | Target | Improvement vs Baseline |
|--------|--------|------------------------|
| Trace analysis (1M spans) | <5s | 24x faster |
| Memory analysis (1GB heap) | <30s | 10x faster |
| Query pattern detection (10K queries) | <1s | 600x faster |
| Bundle analysis (10MB) | <10s | 6x faster |
| Config optimization | <1min convergence | 1440x+ faster |

## Security Considerations

- **Trace Data Sanitization**: Automatically sanitizes sensitive data (passwords, tokens, cookies) from trace data before processing
- **Query Parse-Only**: SQL queries are parsed and analyzed but never executed
- **WASM Sandboxing**: All analysis runs in isolated WASM sandbox with 2GB memory limit and no network access
- **Path Validation**: Bundle stats paths are validated to prevent path traversal attacks
- **Input Validation**: All inputs validated with Zod schemas to prevent injection attacks
- **No Code Execution**: Performance suggestions are recommendations only - no automatic code modification

### WASM Security Constraints

| Constraint | Value | Rationale |
|------------|-------|-----------|
| Memory Limit | 2GB max | Handle large trace datasets |
| CPU Time Limit | 300 seconds | Allow deep performance analysis |
| No Network Access | Enforced | Prevent data exfiltration |
| No File System Write | Enforced | Read-only analysis mode |
| Sandboxed Paths | Validated prefixes only | Prevent path traversal |

### Input Limits

| Input | Limit |
|-------|-------|
| Max spans per trace | 1,000,000 |
| Max query size | 10KB |
| Max queries per batch | 10,000 |
| Max heap snapshot size | 1GB |
| Max bundle stats size | 50MB |
| CPU time limit | 300 seconds |

### Rate Limiting

```typescript
const rateLimits = {
  'perf/bottleneck-detect': { requestsPerMinute: 10, maxConcurrent: 2 },
  'perf/memory-analyze': { requestsPerMinute: 5, maxConcurrent: 1 },
  'perf/query-optimize': { requestsPerMinute: 30, maxConcurrent: 3 },
  'perf/bundle-optimize': { requestsPerMinute: 10, maxConcurrent: 2 },
  'perf/config-optimize': { requestsPerMinute: 5, maxConcurrent: 1 }
};
```

## Dependencies

- `ruvector-sparse-inference-wasm` - Efficient sparse performance trace processing
- `ruvector-gnn-wasm` - Dependency chain analysis and critical path detection
- `micro-hnsw-wasm` - Similar performance pattern matching
- `ruvector-fpga-transformer-wasm` - Fast transformer inference for trace analysis
- `sona` - Learning optimal configurations from historical data

## Supported Formats

| Category | Formats |
|----------|---------|
| Tracing | OpenTelemetry, Jaeger, Zipkin, Chrome DevTools |
| Profiling | Chrome CPU Profile, Node.js Profile, pprof |
| Memory | Chrome Heap Snapshot, Node.js Heap |
| Bundles | Webpack Stats, Vite Stats, Rollup |

## Related Plugins

| Plugin | Description | Use Case |
|--------|-------------|----------|
| [@claude-flow/plugin-code-intelligence](../code-intelligence) | Code analysis | Identify code causing performance issues |
| [@claude-flow/plugin-test-intelligence](../test-intelligence) | Test optimization | Performance regression test selection |
| [@claude-flow/plugin-financial-risk](../financial-risk) | Risk analysis | Trading system latency optimization |

## License

MIT License

Copyright (c) 2026 Claude Flow

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.