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README.md

Analytics Engine - Phase 3 Implementation

Overview

The Analytics Engine Phase 3 represents a production-ready, high-performance graph analytics platform with GPU acceleration, comprehensive monitoring, and enterprise-grade capabilities. This implementation transforms the basic analytics engine into a scalable, production-ready system capable of handling large-scale graph analysis workloads.

🏗️ Phase 3 Architecture

graph TB
    subgraph "Client Layer"
        API[Analytics API]
        WebSocket[Real-time WebSocket]
        Dashboard[Analytics Dashboard]
    end
    
    subgraph "API Gateway"
        Routes[Analytics Routes]
        Middleware[Monitoring Middleware]
        Auth[Authentication]
    end
    
    subgraph "Analytics Engine Core"
        Engine[Analytics Engine]
        Cache[Redis Cache]
        Queue[Processing Queue]
    end
    
    subgraph "Phase 3 Components"
        GPU[GPU Acceleration Manager]
        Monitor[Performance Monitor]
        Concurrent[Concurrent Processing Manager]
        Benchmarks[Benchmarking Suite]
        Monitoring[Production Monitoring]
    end
    
    subgraph "Algorithm Libraries"
        GraphAlgo[Graph Algorithms]
        MLAnalytics[ML Analytics]
        NetworkX[NetworkX Integration]
        CuGraph[cuGraph GPU Backend]
    end
    
    subgraph "Data Layer"
        KuzuDB[(Kuzu Graph DB)]
        RedisCache[(Redis Cache)]
        Prometheus[(Prometheus Metrics)]
    end
    
    API --> Routes
    WebSocket --> Routes
    Dashboard --> Routes
    
    Routes --> Middleware
    Middleware --> Engine
    Auth --> Engine
    
    Engine --> GPU
    Engine --> Monitor
    Engine --> Concurrent
    Engine --> Cache
    
    GPU --> CuGraph
    GPU --> NetworkX
    Monitor --> Prometheus
    Concurrent --> GraphAlgo
    Concurrent --> MLAnalytics
    
    GraphAlgo --> NetworkX
    GraphAlgo --> CuGraph
    MLAnalytics --> NetworkX
    
    Engine --> KuzuDB
    Cache --> RedisCache
    Monitor --> Prometheus
    
    style "Phase 3 Components" fill:#e8f5e8
    style "Algorithm Libraries" fill:#fff3e0
    style "Data Layer" fill:#fce4ec
Loading

🚀 Phase 3 Features

GPU Acceleration

  • NVIDIA cuGraph Integration: Up to 500x performance improvement for supported algorithms
  • Automatic Fallback: Seamless CPU fallback when GPU unavailable
  • 60+ Accelerated Algorithms: PageRank, Betweenness Centrality, Community Detection, and more
  • Memory Management: Intelligent GPU memory usage monitoring and optimization

Performance Monitoring

  • Prometheus Integration: 15+ custom metrics for comprehensive monitoring
  • Real-time Metrics: CPU, memory, GPU usage tracking
  • Algorithm Performance: Execution time and resource usage per algorithm
  • Cache Analytics: Hit/miss rates and cache efficiency metrics

Concurrent Processing

  • Thread Pool Management: Optimized I/O-intensive task execution
  • Process Pool Management: CPU-intensive algorithm parallelization
  • Automatic Scaling: Dynamic worker count based on system resources
  • Health Monitoring: Executor status and performance tracking

Production Monitoring

  • Health Checks: Comprehensive component health monitoring
  • Alerting: Performance threshold monitoring and alerting
  • Metrics Export: Prometheus-compatible metrics endpoint
  • System Monitoring: Resource usage and system health tracking

📊 Component Flow Diagram

flowchart TD
    subgraph "Request Processing"
        Request[Analytics Request]
        Validate[Request Validation]
        Route[Route to Engine]
    end
    
    subgraph "Engine Processing"
        Engine[Analytics Engine]
        GPUCheck{GPU Available?}
        AlgoSelect[Algorithm Selection]
        Execute[Execute Algorithm]
    end
    
    subgraph "Execution Paths"
        GPUPath[GPU Execution]
        CPUPath[CPU Execution]
        ConcurrentPath[Concurrent Execution]
    end
    
    subgraph "Monitoring & Metrics"
        PerfMonitor[Performance Monitor]
        Metrics[Collect Metrics]
        Cache[Update Cache]
        Alerts[Check Thresholds]
    end
    
    subgraph "Response"
        Format[Format Results]
        Broadcast[Real-time Broadcast]
        Response[Return Response]
    end
    
    Request --> Validate
    Validate --> Route
    Route --> Engine
    
    Engine --> GPUCheck
    GPUCheck -->|Yes| AlgoSelect
    GPUCheck -->|No| AlgoSelect
    
    AlgoSelect --> Execute
    Execute --> GPUPath
    Execute --> CPUPath
    Execute --> ConcurrentPath
    
    GPUPath --> PerfMonitor
    CPUPath --> PerfMonitor
    ConcurrentPath --> PerfMonitor
    
    PerfMonitor --> Metrics
    Metrics --> Cache
    Cache --> Alerts
    
    Alerts --> Format
    Format --> Broadcast
    Broadcast --> Response
    
    style "Engine Processing" fill:#e8f5e8
    style "Execution Paths" fill:#fff3e0
    style "Monitoring & Metrics" fill:#fce4ec
Loading

🔧 Component Details

GPU Acceleration Manager

classDiagram
    class GPUAccelerationManager {
        +gpu_available: bool
        +cugraph_enabled: bool
        +supported_algorithms: List[str]
        +initialize()
        +is_gpu_available() bool
        +is_gpu_enabled() bool
        +get_acceleration_status() Dict
        +get_performance_estimate(algorithm, size) Dict
        +get_memory_usage() Dict
        +_detect_nvidia_gpu() bool
        +_check_cugraph_availability() bool
        +_get_supported_algorithms() List[str]
    }
    
    class NetworkXBackend {
        +execute_algorithm(name, graph, params)
        +get_supported_algorithms()
    }
    
    class CuGraphBackend {
        +execute_algorithm(name, graph, params)
        +get_supported_algorithms()
        +estimate_speedup(algorithm, size)
    }
    
    GPUAccelerationManager --> NetworkXBackend
    GPUAccelerationManager --> CuGraphBackend
Loading

Key Features:

  • Automatic GPU detection and cuGraph availability checking
  • Performance estimation for GPU vs CPU execution
  • Memory usage monitoring and optimization
  • Fallback mechanisms for unsupported operations

Performance Monitor

classDiagram
    class PerformanceMonitor {
        +cache_stats: Dict
        +process: psutil.Process
        +start_time: float
        +monitor_algorithm(name, backend, category)
        +monitor_graph_operation(operation, size)
        +record_cache_hit()
        +record_cache_miss()
        +update_graph_size(nodes, edges)
        +get_system_metrics() Dict
        +get_performance_summary() Dict
        +reset_cache_stats()
    }
    
    class PrometheusMetrics {
        +ALGORITHM_EXECUTION_TIME
        +CACHE_HIT_RATE
        +SYSTEM_CPU_USAGE
        +SYSTEM_MEMORY_USAGE
        +GPU_MEMORY_USAGE
    }
    
    PerformanceMonitor --> PrometheusMetrics
Loading

Metrics Collected:

  • Algorithm execution times and resource usage
  • Cache hit/miss rates and efficiency
  • System resource utilization (CPU, memory, disk)
  • GPU memory usage and utilization
  • Graph size and complexity metrics

Concurrent Processing Manager

classDiagram
    class ConcurrentProcessingManager {
        +max_thread_workers: int
        +max_process_workers: int
        +thread_executor: ThreadPoolExecutor
        +process_executor: ProcessPoolExecutor
        +initialize()
        +shutdown()
        +run_cpu_intensive_task(func, args)
        +run_io_intensive_task(func, args)
        +run_parallel_tasks(tasks, type)
        +run_parallel_algorithms(algorithms, graph)
        +get_executor_status() Dict
        +health_check() Dict
    }
    
    class ThreadPoolExecutor {
        +max_workers: int
        +submit(fn, args)
        +shutdown(wait)
    }
    
    class ProcessPoolExecutor {
        +max_workers: int
        +submit(fn, args)
        +shutdown(wait)
    }
    
    ConcurrentProcessingManager --> ThreadPoolExecutor
    ConcurrentProcessingManager --> ProcessPoolExecutor
Loading

Capabilities:

  • Optimal worker count calculation based on system resources
  • Intelligent task routing (CPU vs I/O intensive)
  • Parallel algorithm execution with resource management
  • Health monitoring and status reporting

📈 Performance Benchmarking

Benchmarking Suite Architecture

flowchart TD
    subgraph "Benchmark Categories"
        Centrality[Centrality Algorithms]
        Community[Community Detection]
        Clustering[ML Clustering]
        Anomaly[Anomaly Detection]
    end
    
    subgraph "Comparison Types"
        GPUvsCPU[GPU vs CPU]
        ConcurrentvsSeq[Concurrent vs Sequential]
        AlgorithmComp[Algorithm Comparison]
    end
    
    subgraph "Graph Sizes"
        Small[100-500 nodes]
        Medium[500-1000 nodes]
        Large[1000-2000 nodes]
        XLarge[2000+ nodes]
    end
    
    subgraph "Metrics Collection"
        ExecTime[Execution Time]
        MemUsage[Memory Usage]
        CPUUsage[CPU Usage]
        GPUUsage[GPU Usage]
        Throughput[Throughput]
    end
    
    Centrality --> GPUvsCPU
    Community --> ConcurrentvsSeq
    Clustering --> AlgorithmComp
    Anomaly --> GPUvsCPU
    
    Small --> ExecTime
    Medium --> MemUsage
    Large --> CPUUsage
    XLarge --> GPUUsage
    
    ExecTime --> Throughput
    MemUsage --> Throughput
    CPUUsage --> Throughput
    GPUUsage --> Throughput
Loading

Performance Targets

Algorithm Category CPU Baseline GPU Target Concurrent Target
PageRank 1.0x 50-500x 2-4x
Betweenness Centrality 1.0x 100-1000x 4-8x
Community Detection 1.0x 10-100x 2-6x
ML Clustering 1.0x 5-50x 3-8x

🔍 Monitoring and Observability

Prometheus Metrics

graph LR
    subgraph "Request Metrics"
        ReqCount[analytics_requests_total]
        ReqDuration[analytics_request_duration_seconds]
    end
    
    subgraph "Algorithm Metrics"
        AlgoOps[analytics_operations_total]
        AlgoDuration[analytics_operation_duration_seconds]
    end
    
    subgraph "System Metrics"
        CPUUsage[analytics_system_cpu_percent]
        MemUsage[analytics_system_memory_percent]
        GPUMem[analytics_gpu_memory_percent]
    end
    
    subgraph "Cache Metrics"
        CacheHits[analytics_cache_hits_total]
        CacheMisses[analytics_cache_misses_total]
    end
    
    subgraph "Graph Metrics"
        GraphNodes[analytics_graph_nodes_total]
        GraphEdges[analytics_graph_edges_total]
    end
    
    subgraph "Health Metrics"
        ComponentHealth[analytics_component_health]
        SystemInfo[analytics_system_info]
    end
Loading

Health Check System

flowchart TD
    HealthCheck[Health Check Request]
    
    subgraph "Component Checks"
        GPUCheck[GPU Acceleration]
        PerfCheck[Performance Monitor]
        ConcCheck[Concurrent Processing]
        CacheCheck[Cache System]
        DBCheck[Database Connection]
    end
    
    subgraph "System Checks"
        ResourceCheck[System Resources]
        MetricsCheck[Metrics Collection]
        AlertCheck[Alert Thresholds]
    end
    
    subgraph "Status Response"
        Overall[Overall Status]
        Details[Component Details]
        Metrics[Current Metrics]
        Recommendations[Recommendations]
    end
    
    HealthCheck --> GPUCheck
    HealthCheck --> PerfCheck
    HealthCheck --> ConcCheck
    HealthCheck --> CacheCheck
    HealthCheck --> DBCheck
    
    GPUCheck --> ResourceCheck
    PerfCheck --> MetricsCheck
    ConcCheck --> AlertCheck
    
    ResourceCheck --> Overall
    MetricsCheck --> Details
    AlertCheck --> Metrics
    
    Overall --> Recommendations
Loading

🚀 API Endpoints

Phase 3 Endpoints

Endpoint Method Description Response
/analytics/phase3/status GET Phase 3 capabilities overview System status and features
/analytics/gpu/status GET GPU acceleration status GPU availability and performance
/analytics/performance/metrics GET Real-time performance data Current system metrics
/analytics/benchmarks/run POST Execute performance benchmarks Benchmark results
/analytics/monitoring/health GET Comprehensive health checks Component health status
/analytics/monitoring/prometheus GET Prometheus metrics endpoint Metrics in Prometheus format

Enhanced Analytics Endpoints

Endpoint Method Description New Features
/analytics/centrality POST Centrality analysis GPU acceleration, concurrent processing
/analytics/communities POST Community detection Advanced algorithms, performance monitoring
/analytics/clustering POST ML clustering analysis Spectral clustering, anomaly detection
/analytics/anomalies POST Anomaly detection Isolation Forest, feature analysis

📋 Testing and Validation

Test Categories

mindmap
  root((Testing Strategy))
    Integration Tests
      Component Integration
      API Endpoint Testing
      Database Integration
      Cache Integration
    Performance Tests
      Benchmark Execution
      GPU vs CPU Comparison
      Concurrent Processing
      Memory Usage
    Validation Tests
      GPU Acceleration
      Performance Monitoring
      Error Handling
      Fallback Mechanisms
    Load Tests
      High Volume Requests
      Concurrent Users
      Resource Limits
      Stress Testing
Loading

Validation Checklist

  • GPU Acceleration: Hardware detection, cuGraph integration, fallback mechanisms
  • Performance Monitoring: Metrics collection, Prometheus integration, health checks
  • Concurrent Processing: Thread/process pools, parallel execution, resource management
  • Analytics Engine: Algorithm integration, real-time updates, error handling
  • API Endpoints: All Phase 3 endpoints functional and documented
  • Benchmarking: Performance testing suite, comparison capabilities
  • Monitoring: Production monitoring, alerting, observability
  • Documentation: Comprehensive documentation with Mermaid diagrams

🔧 Configuration

Environment Variables

Variable Default Description
GPU_ACCELERATION_ENABLED true Enable GPU acceleration
CUGRAPH_BACKEND_ENABLED true Enable cuGraph backend
MAX_THREAD_WORKERS auto Maximum thread pool workers
MAX_PROCESS_WORKERS auto Maximum process pool workers
PROMETHEUS_ENABLED true Enable Prometheus metrics
PERFORMANCE_MONITORING_ENABLED true Enable performance monitoring
BENCHMARK_MODE false Enable benchmark mode

Performance Tuning

# GPU Configuration
gpu:
  memory_limit: "8GB"
  algorithms: ["pagerank", "betweenness", "louvain"]
  fallback_threshold: 1000  # nodes

# Concurrent Processing
concurrent:
  thread_workers: 32
  process_workers: 8
  task_timeout: 300  # seconds

# Monitoring
monitoring:
  metrics_interval: 30  # seconds
  health_check_interval: 60  # seconds
  alert_thresholds:
    cpu_percent: 80
    memory_percent: 85
    gpu_memory_percent: 90

📚 Usage Examples

GPU-Accelerated Centrality Analysis

from server.analytics.engine import AnalyticsEngine
from server.analytics.models import CentralityRequest, CentralityType

# Initialize engine with GPU acceleration
engine = AnalyticsEngine(kuzu_conn, redis_url)
await engine.initialize()

# Create centrality request
request = CentralityRequest(
    centrality_type=CentralityType.PAGERANK,
    limit=100,
    use_gpu=True  # Enable GPU acceleration
)

# Execute with automatic GPU acceleration
response = await engine.analyze_centrality(request)

print(f"Execution time: {response.execution_time:.3f}s")
print(f"GPU accelerated: {response.gpu_accelerated}")
print(f"Speedup factor: {response.speedup_factor}x")

Performance Monitoring

from server.analytics.performance_monitor import performance_monitor

# Monitor algorithm execution
with performance_monitor.monitor_algorithm("pagerank", "cugraph", "large"):
    result = await run_pagerank_algorithm(graph_data)

# Get performance summary
summary = performance_monitor.get_performance_summary()
print(f"Cache hit rate: {summary['cache_statistics']['hit_rate']:.2%}")
print(f"Average execution time: {summary['algorithm_performance']['avg_time']:.3f}s")

Concurrent Processing

from server.analytics.concurrent_processing import concurrent_manager

# Initialize concurrent processing
await concurrent_manager.initialize()

# Run algorithms in parallel
algorithms = [
    {"name": "pagerank", "func": calculate_pagerank, "params": {}},
    {"name": "betweenness", "func": calculate_betweenness, "params": {}},
    {"name": "communities", "func": detect_communities, "params": {}}
]

results = await concurrent_manager.run_parallel_algorithms(algorithms, graph_data)

for algo_name, result in results.items():
    print(f"{algo_name}: {result['execution_type']} execution")

🎯 Performance Achievements

Quantitative Results

  • 5 Advanced Centrality Algorithms: PageRank, Betweenness, Closeness, Eigenvector, Degree
  • 3 Community Detection Methods: Louvain, Greedy Modularity, Label Propagation
  • 3 ML Clustering Algorithms: Spectral, K-means, Hierarchical
  • 1 Anomaly Detection Method: Isolation Forest
  • 8 Phase 3 API Endpoints: Complete Phase 3 functionality
  • 15+ Prometheus Metrics: Comprehensive monitoring
  • 60+ GPU-Accelerated Algorithms: NVIDIA cuGraph integration
  • Up to 500x Performance Improvement: GPU acceleration potential

Qualitative Improvements

  • Production-Ready Architecture: Enterprise-grade monitoring and health checks
  • Scalable Design: Concurrent processing and resource optimization
  • Comprehensive Observability: Prometheus metrics and real-time monitoring
  • Robust Error Handling: Graceful fallbacks and error recovery
  • Extensive Documentation: Complete API documentation with examples
  • Automated Testing: Comprehensive test suite with validation

🔮 Future Enhancements

Phase 4 Roadmap

  • Distributed Processing: Multi-node cluster support
  • Advanced ML Models: Graph neural networks and deep learning
  • Real-time Streaming: Continuous graph updates and analysis
  • Advanced Visualization: Interactive graph visualization components
  • Cloud Integration: AWS/GCP/Azure deployment templates
  • Advanced Security: Role-based access control and audit logging

This documentation represents the complete Phase 3 implementation of the Analytics Engine, providing production-ready graph analytics capabilities with GPU acceleration, comprehensive monitoring, and enterprise-grade features.

Analytics Integration Layer

Production-ready analytics integration for GraphMemory-IDE with advanced graph algorithms, gateway orchestration, and service discovery.

Overview

The Analytics Integration Layer provides a comprehensive, scalable analytics platform for GraphMemory-IDE with:

  • 🔍 Advanced Graph Analytics: Centrality analysis, community detection, path analysis, similarity computation
  • 🔄 Gateway Orchestration: Load balancing, caching, circuit breakers, and unified API
  • 📡 Service Discovery: Health monitoring, automatic failover, and dynamic service registration
  • ⚡ Performance Optimized: Async processing, intelligent caching, and concurrent execution
  • 🔒 Production Ready: Comprehensive testing, error handling, and monitoring

Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Analytics Gateway                        │
│  ┌─────────────────┐  ┌─────────────────┐  ┌─────────────── │
│  │ Load Balancer   │  │ Circuit Breaker │  │ Cache Layer   │
│  └─────────────────┘  └─────────────────┘  └─────────────── │
└─────────────────────────────────────────────────────────────┘
                                 │
            ┌────────────────────┼────────────────────┐
            │                    │                    │
   ┌────────▼────────┐  ┌────────▼────────┐  ┌────────▼────────┐
   │ Analytics       │  │ Service         │  │ Kuzu GraphDB    │
   │ Engine          │  │ Registry        │  │ Integration     │
   └─────────────────┘  └─────────────────┘  └─────────────────┘

Quick Start

Installation

# Install dependencies
pip install -r requirements.txt

# Setup environment
cp .env.example .env

# Initialize analytics services
python -c "
import asyncio
from server.analytics.service_registry import initialize_service_registry
asyncio.run(initialize_service_registry())
"

Basic Usage

import asyncio
from server.analytics.kuzu_analytics import KuzuAnalyticsEngine, GraphAnalyticsType
from server.analytics.gateway import get_analytics_gateway

async def main():
    # Initialize analytics engine
    engine = KuzuAnalyticsEngine(kuzu_connection)
    
    # Execute centrality analysis
    result = await engine.execute_graph_analytics(
        GraphAnalyticsType.CENTRALITY,
        {
            "entity_type": "Entity",
            "algorithm": "degree_centrality",
            "limit": 50
        }
    )
    
    print(f"Found {len(result['entities'])} entities")
    print(f"Execution time: {result['execution_time_ms']:.2f}ms")

asyncio.run(main())

API Reference

Analytics Engine

KuzuAnalyticsEngine

The core analytics engine providing graph algorithms and analysis capabilities.

Methods:

execute_graph_analytics(analytics_type, parameters, use_cache=True)

Execute graph analytics operations with caching and optimization.

Parameters:

  • analytics_type (GraphAnalyticsType): Type of analytics to perform
  • parameters (Dict[str, Any]): Algorithm-specific parameters
  • use_cache (bool): Enable result caching (default: True)

Returns: Dict[str, Any] - Analysis results with metadata

Example:

# Centrality Analysis
result = await engine.execute_graph_analytics(
    GraphAnalyticsType.CENTRALITY,
    {
        "entity_type": "Entity",
        "algorithm": "degree_centrality",  # or "betweenness_centrality", "pagerank"
        "limit": 25
    }
)

# Community Detection
result = await engine.execute_graph_analytics(
    GraphAnalyticsType.COMMUNITY_DETECTION,
    {
        "entity_type": "Entity",
        "similarity_threshold": 0.7,
        "min_cluster_size": 3
    }
)

# Path Analysis
result = await engine.execute_graph_analytics(
    GraphAnalyticsType.PATH_ANALYSIS,
    {
        "source_node": "entity_123",
        "target_node": "entity_456",  # Optional
        "max_hops": 6
    }
)
get_graph_metrics(force_refresh=False)

Get comprehensive graph metrics with caching.

Parameters:

  • force_refresh (bool): Force metric recalculation (default: False)

Returns: GraphMetrics - Graph statistics and metrics

Example:

metrics = await engine.get_graph_metrics()
print(f"Nodes: {metrics.node_count}")
print(f"Edges: {metrics.edge_count}")
print(f"Density: {metrics.density:.4f}")
print(f"Clustering: {metrics.clustering_coefficient:.4f}")
find_shortest_paths(source_node, target_node=None, max_hops=6)

Find shortest paths between nodes.

Example:

# Single target
paths = await engine.find_shortest_paths("A", "B", max_hops=5)

# All reachable nodes
paths = await engine.find_shortest_paths("A", max_hops=4)
detect_knowledge_clusters(entity_type="Entity", similarity_threshold=0.7)

Detect clusters of related entities.

analyze_entity_importance(entity_type="Entity", algorithm="degree_centrality")

Analyze entity importance using centrality measures.

Analytics Gateway

AnalyticsGateway

Unified API gateway with load balancing, caching, and orchestration.

execute_request(service, operation, data=None, **kwargs)

Execute analytics request through the gateway.

Parameters:

  • service (str): Target service type
  • operation (str): Operation to perform
  • data (Dict): Request data
  • headers (Dict): HTTP headers
  • timeout_seconds (int): Request timeout
  • priority (str): Request priority ("low", "normal", "high")
  • use_cache (bool): Enable caching

Returns: GatewayResponse - Response with metadata

Example:

from server.analytics.gateway import get_analytics_gateway

gateway = await get_analytics_gateway(service_registry)

# Execute analytics request
response = await gateway.execute_request(
    service="analytics_engine",
    operation="centrality",
    data={
        "entity_type": "Entity",
        "algorithm": "degree_centrality"
    },
    priority="high",
    timeout_seconds=30
)

print(f"Status: {response.status}")
print(f"Data: {response.data}")
print(f"Execution time: {response.execution_time_ms}ms")
execute_batch_requests(requests, user_id=None)

Execute multiple requests concurrently.

Example:

requests = [
    {
        "service": "analytics_engine",
        "operation": "centrality",
        "data": {"entity_type": "Entity"}
    },
    {
        "service": "analytics_engine", 
        "operation": "clustering",
        "data": {"min_cluster_size": 3}
    }
]

responses = await gateway.execute_batch_requests(requests)
for response in responses:
    print(f"Operation: {response.operation}, Status: {response.status}")
get_gateway_stats()

Get comprehensive gateway statistics.

Example:

stats = await gateway.get_gateway_stats()
print(f"Total requests: {stats['gateway_stats']['total_requests']}")
print(f"Cache hits: {stats['gateway_stats']['cached_responses']}")
print(f"Average response time: {stats['gateway_stats']['average_response_time']:.2f}ms")

Service Registry

AnalyticsServiceRegistry

Service discovery and health monitoring system.

register_service(service_id, service_name, service_type, endpoint_url, **kwargs)

Register a new analytics service.

Example:

from server.analytics.service_registry import get_service_registry, ServiceType

registry = await get_service_registry()

await registry.register_service(
    service_id="custom_analytics",
    service_name="Custom Analytics Service",
    service_type=ServiceType.ANALYTICS_ENGINE,
    endpoint_url="http://localhost:8001",
    capabilities=["custom_algorithm", "specialized_analysis"],
    tags=["custom", "experimental"]
)
discover_services(service_type=None, capabilities=None, healthy_only=True)

Discover available services.

Example:

# Find all healthy analytics engines
services = await registry.discover_services(
    service_type=ServiceType.ANALYTICS_ENGINE,
    healthy_only=True
)

# Find services with specific capabilities
services = await registry.discover_services(
    capabilities=["centrality", "clustering"]
)
get_registry_status()

Get comprehensive registry status.

Configuration

Environment Variables

# Analytics Configuration
ANALYTICS_CACHE_TTL=600              # Cache TTL in seconds
ANALYTICS_MAX_CONCURRENT=100         # Max concurrent operations
ANALYTICS_TIMEOUT=30                 # Default timeout in seconds

# Gateway Configuration  
GATEWAY_WORKERS=4                    # Number of worker processes
GATEWAY_CACHE_SIZE=1000             # Max cached responses
GATEWAY_CIRCUIT_BREAKER_THRESHOLD=5  # Failure threshold

# Service Registry Configuration
REGISTRY_HEALTH_CHECK_INTERVAL=30    # Health check interval in seconds
REGISTRY_CACHE_TTL=60               # Discovery cache TTL

# Database Configuration
KUZU_DATABASE_PATH=/data/kuzu        # Kuzu database path
KUZU_BUFFER_SIZE=1024               # Buffer size in MB

Configuration File

Create config/analytics.yaml:

analytics:
  engine:
    cache_ttl: 600
    max_concurrent_operations: 100
    default_timeout: 30
    algorithms:
      centrality:
        default_algorithm: "degree_centrality"
        max_limit: 1000
      clustering:
        default_min_size: 3
        max_clusters: 100
      
  gateway:
    workers: 4
    cache:
      enabled: true
      max_size: 1000
      ttl: 300
    circuit_breaker:
      failure_threshold: 5
      recovery_timeout: 60
    load_balancing:
      algorithm: "round_robin"
      
  services:
    health_check_interval: 30
    discovery_cache_ttl: 60
    auto_registration: true
    
  monitoring:
    metrics_enabled: true
    performance_tracking: true
    error_reporting: true

Performance Optimization

Caching Strategy

The analytics layer implements multi-level caching:

  1. Query Result Caching: Analytics results cached by query signature
  2. Gateway Response Caching: HTTP responses cached by request hash
  3. Service Discovery Caching: Service lists cached with TTL

Best Practices:

# Enable caching for expensive operations
result = await engine.execute_graph_analytics(
    GraphAnalyticsType.CENTRALITY,
    parameters,
    use_cache=True  # Default: True
)

# Disable caching for real-time data
result = await engine.execute_graph_analytics(
    GraphAnalyticsType.TEMPORAL_ANALYSIS,
    parameters,
    use_cache=False  # For real-time data
)

Concurrency Control

# High-priority requests bypass queues
response = await gateway.execute_request(
    service="analytics_engine",
    operation="urgent_analysis",
    data=data,
    priority="high"  # Immediate execution
)

# Batch processing for efficiency
responses = await gateway.execute_batch_requests(requests)

Memory Management

# Clear cache when memory is limited
engine.query_cache.clear()

# Monitor memory usage
stats = await engine.get_analytics_stats()
print(f"Cache size: {stats['cache_size']}")

Error Handling

Common Error Patterns

from server.analytics.exceptions import AnalyticsError, ServiceUnavailableError

try:
    result = await engine.execute_graph_analytics(
        GraphAnalyticsType.CENTRALITY,
        parameters
    )
except AnalyticsError as e:
    logger.error(f"Analytics failed: {e}")
    # Fallback to cached result or default
except ServiceUnavailableError as e:
    logger.warning(f"Service unavailable: {e}")
    # Try alternative service or queue request
except Exception as e:
    logger.error(f"Unexpected error: {e}")
    # General error handling

Circuit Breaker Pattern

The gateway automatically implements circuit breakers:

# Circuit breaker prevents cascade failures
try:
    response = await gateway.execute_request(
        service="unreliable_service",
        operation="test"
    )
except HTTPException as e:
    if e.status_code == 503:
        print("Service temporarily unavailable (circuit breaker open)")

Monitoring and Observability

Metrics Collection

# Get comprehensive analytics stats
stats = await engine.get_analytics_stats()
print(f"Total queries: {stats['analytics_stats']['total_queries']}")
print(f"Cache hit rate: {stats['analytics_stats']['cache_hits'] / stats['analytics_stats']['total_queries']:.2%}")

# Get gateway performance metrics
gateway_stats = await gateway.get_gateway_stats()
print(f"Request rate: {gateway_stats['gateway_stats']['successful_requests']} RPS")
print(f"Error rate: {gateway_stats['gateway_stats']['failed_requests'] / gateway_stats['gateway_stats']['total_requests']:.2%}")

Health Monitoring

# Check service health
registry = await get_service_registry()
status = await registry.get_registry_status()

print(f"Total services: {status['total_services']}")
print(f"Healthy services: {status['services_by_health']['healthy']}")
print(f"Unhealthy services: {status['services_by_health']['unhealthy']}")

Performance Benchmarking

# Run comprehensive benchmarks
python server/tests/benchmark_analytics.py

# Run load testing with Locust
locust -f server/tests/locust_performance_test.py --host=http://localhost:8000

# Run unit performance tests
pytest server/tests/test_performance_analytics.py -v

Testing

Unit Tests

# Run analytics integration tests
pytest server/tests/test_analytics_integration.py -v

# Run with coverage
pytest server/tests/test_analytics_integration.py --cov=server.analytics --cov-report=html

Performance Tests

# Quick performance check
python server/tests/benchmark_analytics.py

# Comprehensive load testing
locust -f server/tests/locust_performance_test.py --host=http://localhost:8000 -u 10 -r 2 -t 60s

Integration Tests

# Test end-to-end workflows
pytest server/tests/test_analytics_integration.py::TestEndToEndIntegration -v

# Test error handling
pytest server/tests/test_analytics_integration.py::TestEndToEndIntegration::test_error_handling_and_recovery -v

Deployment

Docker Configuration

# Dockerfile
FROM python:3.11-slim

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy application
COPY . .

# Expose ports
EXPOSE 8000

# Start analytics services
CMD ["python", "-m", "server.analytics.main"]

Docker Compose

# docker-compose.yml
version: '3.8'

services:
  analytics-engine:
    build: .
    ports:
      - "8000:8000"
    environment:
      - ANALYTICS_CACHE_TTL=600
      - GATEWAY_WORKERS=4
    depends_on:
      - kuzu-db
      - redis
      
  kuzu-db:
    image: kuzudb/kuzu:latest
    volumes:
      - kuzu_data:/var/lib/kuzu
      
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
      
volumes:
  kuzu_data:

Kubernetes Deployment

# k8s/analytics-deployment.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: analytics-engine
spec:
  replicas: 3
  selector:
    matchLabels:
      app: analytics-engine
  template:
    metadata:
      labels:
        app: analytics-engine
    spec:
      containers:
      - name: analytics-engine
        image: graphmemory/analytics:latest
        ports:
        - containerPort: 8000
        env:
        - name: ANALYTICS_CACHE_TTL
          value: "600"
        - name: GATEWAY_WORKERS
          value: "4"
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /analytics/health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /analytics/ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 5

Production Checklist

  • Environment Configuration: All environment variables set
  • Database Setup: Kuzu database initialized and accessible
  • Cache Configuration: Redis/memory cache configured
  • Monitoring Setup: Metrics collection and alerting configured
  • Load Testing: Performance validated under expected load
  • Health Checks: All health endpoints responding
  • Circuit Breakers: Failure thresholds configured
  • Logging: Structured logging with appropriate levels
  • Security: Authentication and authorization implemented
  • Backup Strategy: Data backup and recovery procedures

Troubleshooting

Common Issues

Issue: High memory usage

# Solution: Check cache sizes and clear if needed
stats = await engine.get_analytics_stats()
if stats['cache_size'] > 1000:
    engine.query_cache.clear()

Issue: Slow query performance

# Solution: Enable caching and check query optimization
result = await engine.execute_graph_analytics(
    analytics_type,
    parameters,
    use_cache=True  # Ensure caching is enabled
)

Issue: Service discovery failures

# Solution: Check service health and re-register if needed
services = await registry.discover_services(healthy_only=False)
for service in services:
    if service.health_status != ServiceHealth.HEALTHY:
        await registry._check_service_health(service)

Debug Mode

import logging
logging.getLogger('server.analytics').setLevel(logging.DEBUG)

# Enable verbose output
result = await engine.execute_graph_analytics(
    GraphAnalyticsType.CENTRALITY,
    parameters,
    debug=True  # If supported
)

Contributing

Development Setup

# Clone repository
git clone https://github.com/your-org/graphmemory-ide.git
cd graphmemory-ide

# Setup virtual environment
python -m venv .venv
source .venv/bin/activate

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
pytest server/tests/test_analytics_integration.py -v

# Run linting
mypy server/analytics/
black server/analytics/
isort server/analytics/

Code Style

  • Follow PEP 8 style guidelines
  • Use type hints for all function signatures
  • Write comprehensive docstrings
  • Add unit tests for new functionality
  • Update documentation for API changes

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For support and questions:

Analytics Integration Layer v1.0 - Production-ready analytics for GraphMemory-IDE