Implement unified performance monitoring and metrics framework

[avrabe]

Summary

Create a comprehensive performance monitoring and metrics framework that unifies monitoring across all components (caching, storage, transport, components) and works consistently in both native and WASM environments.

Background

The current framework has ad-hoc monitoring in different components (e.g., studio-mcp caching metrics), but lacks a unified approach. A standardized monitoring framework is essential for:

• Production deployment and operations
• Performance optimization and bottleneck identification
• SLA monitoring and alerting
• Capacity planning and resource management
• Debug and troubleshooting support

Implementation Tasks

Core Metrics Infrastructure

• Create pulseengine-mcp-metrics crate with trait-based abstractions
• Design unified metrics collection interface
• Implement metrics aggregation and storage
• Add configurable metrics export formats (Prometheus, StatsD, JSON)

Standard Metric Types

• Counter - Monotonically increasing values (requests, errors)
• Gauge - Point-in-time values (memory usage, active connections)
• Histogram - Distribution of values (request duration, payload size)
• Summary - Statistical summaries with quantiles
• Timer - High-precision timing measurements

Component-Specific Metrics

Transport Layer Metrics

• Request/response counts and rates
• Connection establishment and teardown timing
• Message size distributions
• Error rates by transport type
• Network latency and throughput

Storage Backend Metrics

• Read/write operation counts and latencies
• Storage utilization and capacity
• Cache hit/miss ratios
• Data integrity check results
• Backup operation timing and success rates

Caching Framework Metrics

• Cache hit/miss ratios by cache type
• Memory utilization and eviction rates
• Cache invalidation frequency and causes
• Query response time improvements
• Cache size and entry count distributions

Component Runtime Metrics

• Component load/unload timing
• Memory usage per component
• CPU utilization and execution time
• Inter-component communication latency
• Resource allocation and cleanup timing

WASM-Specific Monitoring

• WASM runtime performance metrics
• Component instantiation and execution timing
• Host function call frequency and latency
• Memory allocation patterns in WASM context
• WASI interface operation timing

Health Monitoring

• Component health status tracking
• Automatic health check execution
• Dependency health monitoring
• Service degradation detection
• Automated recovery attempts and success rates

Alerting and Notification

• Configurable alerting rules and thresholds
• Integration with notification systems (email, Slack, webhooks)
• Alert escalation and suppression
• Performance regression detection
• Anomaly detection for unusual patterns

Configuration System

#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct MetricsConfig {
    /// Enable/disable metrics collection
    pub enabled: bool,
    /// Metrics collection interval
    pub collection_interval: Duration,
    /// Export configuration
    pub exporters: Vec<MetricsExporter>,
    /// Retention policy for historical metrics
    pub retention: RetentionPolicy,
    /// Sampling rate for high-volume metrics
    pub sampling_rate: f64,
    /// Component-specific metric configuration
    pub components: HashMap<String, ComponentMetricsConfig>,
}

#[derive(Clone, Debug)]
pub enum MetricsExporter {
    Prometheus { endpoint: String, port: u16 },
    StatsD { host: String, port: u16 },
    File { path: String, format: FileFormat },
    Console { format: ConsoleFormat },
}

Integration Points

Framework Integration

• Add metrics middleware to MCP server framework
• Automatic metric collection for all MCP operations
• Configurable metric inclusion/exclusion rules
• Zero-overhead compilation for disabled metrics

Observability Stack Integration

• Prometheus metrics export with standard labels
• OpenTelemetry tracing integration
• Structured logging correlation with metrics
• Jaeger/Zipkin distributed tracing support

Development Tools

• Metrics dashboard for development
• Performance profiling integration
• Benchmark result correlation
• Load testing metrics collection

Performance Considerations

• Minimal overhead metrics collection
• Async metrics export to avoid blocking
• Configurable sampling for high-frequency events
• Memory-efficient metric storage
• Batch export for network efficiency

WASM Compatibility

• Feature-flagged implementation for WASM targets
• Component Model metrics interfaces
• Host-side metrics aggregation for WASM components
• Browser-compatible metrics visualization

Example Usage

// Instrument a function with timing
#[timed_metric("mcp.tool.execution_time")]
async fn execute_tool(name: &str, args: Value) -> Result<ToolResult> {
    // Increment counter
    metrics::counter\!("mcp.tool.calls", "tool_name" => name).increment();
    
    let result = do_tool_execution(name, args).await;
    
    match &result {
        Ok(_) => metrics::counter\!("mcp.tool.success", "tool_name" => name).increment(),
        Err(_) => metrics::counter\!("mcp.tool.errors", "tool_name" => name).increment(),
    }
    
    result
}

// Record gauge value
metrics::gauge\!("mcp.cache.memory_usage").set(cache.memory_usage() as f64);

// Record histogram
metrics::histogram\!("mcp.request.size").record(request_size as f64);

Acceptance Criteria

• Unified metrics collection across all framework components
• Multiple export formats supported (Prometheus, StatsD, etc.)
• WASM-compatible metrics implementation
• Minimal performance overhead (<1% in production)
• Comprehensive documentation and examples
• Integration with popular observability tools
• Health monitoring and alerting capabilities

Related Issues

• Generalized Caching Framework (Extract and generalize caching framework from studio-mcp #28)
• Storage Backend Abstraction (Create trait-based storage backend abstraction with WASM compatibility #29)
• WASM32-WASIP2 Target Support (Add WASM32-WASIP2 target support for WebAssembly deployment #26)

References

• Prometheus metrics best practices
• OpenTelemetry metrics specification
• Rust metrics ecosystem