ruvnet--ruflo/v3/implementation/adrs/ADR-037-performance-optimization-plugin.md

ADR-037: Performance Optimization Plugin

Status: Proposed Date: 2026-01-24 Category: Advanced Development Tool Author: Plugin Architecture Team Version: 1.0.0 Deciders: Plugin Architecture Team, Performance Engineering Team Supersedes: None

Context

Application performance optimization requires deep understanding of runtime behavior, resource utilization patterns, and the complex interactions between system components. Traditional profiling tools provide raw data but lack the intelligence to suggest optimizations or predict performance impacts. AI-powered performance analysis can provide actionable insights and automated optimization suggestions.

Decision

Create a Performance Optimization Plugin that leverages RuVector WASM packages for intelligent performance analysis, bottleneck detection, and automated optimization suggestions with support for web, server, and native applications.

Plugin Name

@claude-flow/plugin-performance-optimizer

Description

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.

Key WASM Packages

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

MCP Tools

1. perf/bottleneck-detect

Detect performance bottlenecks using AI analysis.

{
  name: 'perf/bottleneck-detect',
  description: 'Detect performance bottlenecks using GNN-based dependency analysis',
  inputSchema: {
    type: 'object',
    properties: {
      traceData: {
        type: 'object',
        description: 'Performance trace data (OpenTelemetry, Chrome DevTools, etc.)',
        properties: {
          format: { type: 'string', enum: ['otlp', 'chrome_devtools', 'jaeger', 'zipkin'] },
          spans: { type: 'array' },
          metrics: { type: 'object' }
        }
      },
      analysisScope: {
        type: 'array',
        items: {
          type: 'string',
          enum: ['cpu', 'memory', 'io', 'network', 'database', 'render', 'all']
        },
        default: ['all']
      },
      threshold: {
        type: 'object',
        properties: {
          latencyP95: { type: 'number' },
          throughput: { type: 'number' },
          errorRate: { type: 'number' }
        }
      }
    },
    required: ['traceData']
  }
}

2. perf/memory-analyze

Analyze memory usage patterns and detect leaks.

{
  name: 'perf/memory-analyze',
  description: 'Analyze memory patterns and detect potential leaks',
  inputSchema: {
    type: 'object',
    properties: {
      heapSnapshot: { type: 'string', description: 'Heap snapshot file path' },
      timeline: { type: 'array', description: 'Memory timeline data points' },
      analysis: {
        type: 'array',
        items: {
          type: 'string',
          enum: ['leak_detection', 'retention_analysis', 'allocation_hotspots', 'gc_pressure']
        }
      },
      compareBaseline: { type: 'string', description: 'Baseline snapshot for comparison' }
    }
  }
}

3. perf/query-optimize

Detect and optimize database query patterns.

{
  name: 'perf/query-optimize',
  description: 'Detect N+1 queries and suggest optimizations',
  inputSchema: {
    type: 'object',
    properties: {
      queries: {
        type: 'array',
        items: {
          type: 'object',
          properties: {
            sql: { type: 'string' },
            duration: { type: 'number' },
            stackTrace: { type: 'string' },
            resultSize: { type: 'number' }
          }
        }
      },
      patterns: {
        type: 'array',
        items: { type: 'string', enum: ['n_plus_1', 'missing_index', 'full_scan', 'large_result', 'slow_join'] }
      },
      suggestIndexes: { type: 'boolean', default: true }
    },
    required: ['queries']
  }
}

4. perf/bundle-optimize

Analyze and optimize JavaScript bundle size.

{
  name: 'perf/bundle-optimize',
  description: 'Analyze bundle size and suggest optimizations',
  inputSchema: {
    type: 'object',
    properties: {
      bundleStats: { type: 'string', description: 'Webpack/Vite stats file path' },
      analysis: {
        type: 'array',
        items: {
          type: 'string',
          enum: ['tree_shaking', 'code_splitting', 'duplicate_deps', 'large_modules', 'dynamic_import']
        }
      },
      targets: {
        type: 'object',
        properties: {
          maxSize: { type: 'number', description: 'Max bundle size in KB' },
          maxChunks: { type: 'number' }
        }
      }
    },
    required: ['bundleStats']
  }
}

5. perf/config-optimize

Suggest optimal configuration based on workload patterns.

{
  name: 'perf/config-optimize',
  description: 'Suggest optimal configurations using SONA learning',
  inputSchema: {
    type: 'object',
    properties: {
      workloadProfile: {
        type: 'object',
        properties: {
          type: { type: 'string', enum: ['web', 'api', 'batch', 'stream', 'hybrid'] },
          metrics: { type: 'object' },
          constraints: { type: 'object' }
        }
      },
      configSpace: {
        type: 'object',
        description: 'Configuration parameters to optimize',
        additionalProperties: {
          type: 'object',
          properties: {
            type: { type: 'string' },
            range: { type: 'array' },
            current: {}
          }
        }
      },
      objective: { type: 'string', enum: ['latency', 'throughput', 'cost', 'balanced'] }
    },
    required: ['workloadProfile', 'configSpace']
  }
}

Use Cases

1. Production Debugging: Quickly identify bottlenecks in production traces
2. Memory Profiling: Detect memory leaks before they cause outages
3. Database Optimization: Find N+1 queries and suggest batch alternatives
4. Frontend Performance: Optimize bundle size and loading performance
5. Auto-Tuning: Automatically optimize configuration parameters

Architecture

+------------------+     +----------------------+     +------------------+
| Trace Collectors |---->| Performance Plugin   |---->| Recommendations  |
| (OTLP/DevTools)  |     | (Analysis Engine)    |     | (Actionable)     |
+------------------+     +----------------------+     +------------------+
                                   |
                         +---------+---------+
                         |         |         |
                    +----+---+ +---+----+ +--+-----+
                    | Sparse | |  GNN   | | FPGA   |
                    |Traces  | |Chains  | |Xformer |
                    +--------+ +--------+ +--------+
                                   |
                              +----+----+
                              |  SONA   |
                              |Config   |
                              +---------+

Trace Processing Pipeline

Raw Traces --> Sparse Encoding --> GNN Analysis --> Pattern Match --> Recommendations
     |              |                   |                |               |
     v              v                   v                v               v
[spans]       [efficient repr]    [critical path]  [similar issues]  [fixes]
[metrics]     [feature extract]   [bottlenecks]    [prior solutions] [priority]

Performance Targets

Metric	Target	Baseline (Traditional)	Improvement
Trace analysis	<5s for 1M spans	~2min (Jaeger UI)	24x
Memory analysis	<30s for 1GB heap	~5min (Chrome DevTools)	10x
Query pattern detection	<1s for 10K queries	~10min (manual review)	600x
Bundle analysis	<10s for 10MB bundle	~1min (webpack-bundle-analyzer)	6x
Config optimization	<1min convergence	~days (manual tuning)	1440x+

Security Considerations

Input Validation (CRITICAL)

All MCP tool inputs MUST be validated using Zod schemas:

// perf/bottleneck-detect input validation
const BottleneckDetectSchema = z.object({
  traceData: z.object({
    format: z.enum(['otlp', 'chrome_devtools', 'jaeger', 'zipkin']),
    spans: z.array(z.unknown()).max(1_000_000), // Max 1M spans
    metrics: z.record(z.string(), z.unknown()).optional()
  }),
  analysisScope: z.array(z.enum(['cpu', 'memory', 'io', 'network', 'database', 'render', 'all'])).default(['all']),
  threshold: z.object({
    latencyP95: z.number().min(0).max(86400000).optional(), // Max 24 hours in ms
    throughput: z.number().min(0).optional(),
    errorRate: z.number().min(0).max(1).optional()
  }).optional()
});

// perf/memory-analyze input validation
const MemoryAnalyzeSchema = z.object({
  heapSnapshot: z.string().max(500).optional(),
  timeline: z.array(z.unknown()).max(100000).optional(),
  analysis: z.array(z.enum([
    'leak_detection', 'retention_analysis', 'allocation_hotspots', 'gc_pressure'
  ])).optional(),
  compareBaseline: z.string().max(500).optional()
});

// perf/query-optimize input validation
const QueryOptimizeSchema = z.object({
  queries: z.array(z.object({
    sql: z.string().max(10000), // Max 10KB query
    duration: z.number().min(0).max(86400000),
    stackTrace: z.string().max(50000).optional(),
    resultSize: z.number().int().min(0).optional()
  })).min(1).max(10000),
  patterns: z.array(z.enum(['n_plus_1', 'missing_index', 'full_scan', 'large_result', 'slow_join'])).optional(),
  suggestIndexes: z.boolean().default(true)
});

WASM Security Constraints

Constraint	Value	Rationale
Memory Limit	2GB max	Handle large heap snapshots
CPU Time Limit	300 seconds for analysis	Allow thorough analysis
No Network Access	Enforced by WASM sandbox	Prevent trace data exfiltration
No Process Access	Cannot read other process memory	Isolation from production
Read-Only Traces	Analysis only, no modification	Data integrity

Sensitive Data in Traces (HIGH)

// Trace data may contain sensitive information
// MUST sanitize before processing

const SENSITIVE_TRACE_PATTERNS = [
  /password[=:][^\s&]+/gi,
  /token[=:][^\s&]+/gi,
  /authorization:\s*bearer\s+[^\s]+/gi,
  /cookie:\s*[^\n]+/gi,
  /session[_-]?id[=:][^\s&]+/gi
];

function sanitizeTraceData(trace: TraceData): TraceData {
  const sanitized = JSON.parse(JSON.stringify(trace));

  // Sanitize span attributes
  for (const span of sanitized.spans) {
    for (const [key, value] of Object.entries(span.attributes || {})) {
      if (typeof value === 'string') {
        span.attributes[key] = sanitizeString(value);
      }
    }
  }

  return sanitized;
}

Identified Security Risks

Risk ID	Severity	Description	Mitigation
PERF-SEC-001	HIGH	Credentials in trace/query data	Automatic sanitization, pattern detection
PERF-SEC-002	HIGH	SQL injection via query analysis	Parse-only, never execute queries
PERF-SEC-003	MEDIUM	DoS via malformed heap snapshots	Size limits, format validation
PERF-SEC-004	MEDIUM	Path traversal in bundle stats paths	Path validation
PERF-SEC-005	LOW	Information leakage via performance patterns	Access controls, audit logging

Query Analysis Safety

// CRITICAL: Query analysis MUST NOT execute queries

// BAD - dangerous
const result = await db.query(userProvidedSql);

// GOOD - parse and analyze only
const parsedQuery = sqlParser.parse(userProvidedSql);
const analysis = analyzeQueryPlan(parsedQuery);
return analysis; // Never execute

Risk Assessment

Risk	Likelihood	Impact	Mitigation
False positive bottlenecks	Medium	Low	Confidence scores, correlation with metrics
Incomplete instrumentation	Medium	Medium	Partial analysis support, instrumentation guides
Config regression	Low	High	A/B testing, rollback capabilities
High overhead in production	Low	Medium	Sampling, adaptive collection

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

Implementation Notes

Phase 1: Core Analysis

• Trace ingestion and sparse encoding
• Basic bottleneck detection
• Query pattern analysis

Phase 2: Advanced Intelligence

• GNN-based dependency chain analysis
• Memory leak pattern detection
• Bundle optimization suggestions

Phase 3: Auto-Optimization

• SONA-based configuration tuning
• Historical pattern learning
• Automated fix suggestions

Dependencies

{
  "dependencies": {
    "ruvector-sparse-inference-wasm": "^0.1.0",
    "ruvector-gnn-wasm": "^0.1.0",
    "micro-hnsw-wasm": "^0.2.0",
    "ruvector-fpga-transformer-wasm": "^0.1.0",
    "sona": "^0.1.0",
    "@opentelemetry/api": "^1.7.0"
  }
}

Consequences

Positive

• Faster root cause analysis for performance issues
• Proactive identification of potential problems
• Data-driven optimization recommendations

Negative

• Requires comprehensive tracing instrumentation
• May produce false positives in complex systems
• Learning period needed for optimal suggestions

Neutral

• Can provide value even with partial instrumentation

Related ADRs

ADR	Relationship
ADR-004: Plugin Architecture	Foundation - Defines plugin structure
ADR-017: RuVector Integration	Dependency - Provides WASM packages
ADR-035: Code Intelligence	Related - Code analysis integration
ADR-036: Test Intelligence	Related - Test performance metrics
ADR-039: Cognitive Kernel	Related - Cognitive load optimization

References

• OpenTelemetry: https://opentelemetry.io/
• Chrome DevTools Protocol: https://chromedevtools.github.io/devtools-protocol/
• ADR-017: RuVector Integration
• ADR-004: Plugin Architecture

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Last Updated: 2026-01-24