Geode’s real-time analytics system provides enterprise-grade streaming analytics capabilities with integrated machine learning, pattern detection, anomaly detection, and change data capture (CDC). This guide covers the complete analytics platform for building intelligent, responsive graph applications.

Overview

The Real-Time Analytics system transforms Geode into an intelligent analytics platform by:

  • Event Stream Processing: Process graph changes in real-time with configurable batching
  • Pattern Detection: Identify recurring patterns, community formation, and behavioral trends
  • Anomaly Detection: Statistical analysis for outlier identification with adaptive baselines
  • Machine Learning Integration: Automatic embedding updates and similarity-based analytics
  • CDC Integration: Change data capture for downstream system synchronization

Performance: >10,000 events/second sustained throughput with <100ms batch processing latency.

Architecture

Core Components

1. RealTimeAnalyticsEngine

Central orchestrator for all real-time analytics operations:

// Initialize analytics engine
const config = AnalyticsConfig{
    .enable_ml_integration = true,
    .enable_pattern_detection = true,
    .enable_anomaly_detection = true,
    .batch_size = 100,
    .processing_interval_ms = 1000,
    .max_event_queue_size = 10000,
};

var analytics_engine = try RealTimeAnalyticsEngine.init(allocator, config);
defer analytics_engine.deinit();

// Start background processing
try analytics_engine.start();

Capabilities:

  • Multi-threaded background event processing
  • Configurable batch processing with overflow protection
  • Comprehensive statistics tracking and monitoring
  • Integration with ML system and CDC
2. AnalyticsEvent System

Flexible event structure for capturing diverse analytics data:

// Create analytics event
var event = AnalyticsEvent.init(allocator, .node_creation);
defer event.deinit();

event.node_id = 123;
try event.setProperty("user_type", AnalyticsValue{ .string = "premium" });
try event.setProperty("activity_score", AnalyticsValue{ .float = 85.5 });
try event.setProperty("tags", AnalyticsValue{ .array = tags_array });

// Queue event for processing
try analytics_engine.queueEvent(event);

Supported Event Types:

  • node_creation, node_update, node_deletion
  • edge_creation, edge_update, edge_deletion
  • graph_query, similarity_search
  • embedding_update, pattern_detection, anomaly_detection
  • trend_analysis

Property Value Types:

  • string, integer, float, boolean
  • vector (for ML embeddings)
  • array (nested values)
  • null
3. Pattern Detection Engine

Advanced pattern recognition for graph behaviors:

// Configure pattern detector
const pattern_config = PatternDetectionConfig{
    .min_pattern_frequency = 3,
    .pattern_time_window_ms = 30000,  // 30 seconds
    .max_patterns_tracked = 500,
};

var pattern_detector = PatternDetector.init(allocator, pattern_config);
defer pattern_detector.deinit();

// Detect patterns in events
if (try pattern_detector.detectPattern(&event)) |detected_pattern| {
    std.log.info("Pattern detected: {} with confidence {d:.2}", .{
        detected_pattern.pattern_type,
        detected_pattern.confidence
    });
}

Pattern Types:

  • frequent_connection: Recurring connection patterns
  • community_formation: Emerging community structures
  • hub_emergence: Nodes becoming central hubs
  • cascade_propagation: Information cascade patterns
  • temporal_clustering: Time-based clustering
  • behavioral_similarity: Similar behavior patterns
4. Anomaly Detection System

Statistical anomaly detection with adaptive baselines:

// Configure anomaly detector
const anomaly_config = AnomalyDetectionConfig{
    .statistical_threshold = 2.5,  // Z-score threshold
    .minimum_baseline_samples = 50,
    .baseline_update_frequency = 1000,
    .anomaly_cooldown_ms = 5000,
};

var anomaly_detector = AnomalyDetector.init(allocator, anomaly_config);
defer anomaly_detector.deinit();

// Detect anomalies
var anomaly_result = try anomaly_detector.detectAnomaly(&event);
defer anomaly_result.deinit();

if (anomaly_result.is_anomaly) {
    std.log.warn("Anomaly detected: {} severity, confidence: {d:.2}", .{
        anomaly_result.severity,
        anomaly_result.confidence
    });
}

Severity Classification:

  • Low: 3-5 standard deviations from baseline
  • Medium: 5-10 standard deviations
  • High: 10-20 standard deviations
  • Critical: >20 standard deviations

Pattern Detection

Connection Pattern Detection

Identify recurring connection patterns in graph:

-- GQL query generating analytics events
MATCH (u:User)-[:FOLLOWS]->(influencer:User)
WHERE influencer.follower_count > 10000
RETURN u, influencer;

-- Analytics engine detects pattern:
-- "frequent_connection: User -> Influencer with confidence 0.92"

Pattern Analysis:

  • Frequency tracking with configurable time windows
  • Confidence scoring based on statistical significance
  • Temporal pattern evolution tracking

Community Formation Detection

Detect emerging communities in real-time:

-- Series of connection events
CREATE (alice:User {id: 1})-[:KNOWS]->(bob:User {id: 2});
CREATE (bob)-[:KNOWS]->(charlie:User {id: 3});
CREATE (charlie)-[:KNOWS]->(alice);

-- Pattern detector identifies:
-- "community_formation: Triangle pattern detected (3 nodes)"
-- Confidence: 0.87, frequency: 5 occurrences in past 60 seconds

Community Metrics:

  • Triangle count (3-node cycles)
  • Clique detection (fully connected subgraphs)
  • Modularity scores for community strength

Hub Emergence Detection

Identify nodes becoming central hubs:

-- Multiple users connecting to same node
CREATE (user1:User)-[:FOLLOWS]->(influencer:User {id: 123});
CREATE (user2:User)-[:FOLLOWS]->(influencer);
CREATE (user3:User)-[:FOLLOWS]->(influencer);

-- Pattern detector tracks:
-- "hub_emergence: Node 123 gained 3 new followers in 10 seconds"
-- Degree centrality increasing from 10 -> 13

Hub Metrics:

  • Degree centrality growth rate
  • Betweenness centrality changes
  • Temporal connection velocity

Cascade Propagation Detection

Detect information cascades spreading through graph:

-- Information spreading pattern
MATCH (source:Post {id: 'post-123'})
MATCH (source)<-[:SHARED]-(user1:User)
MATCH (user1)<-[:FOLLOWS]-(user2:User)
CREATE (source)<-[:SHARED]-(user2);

-- Pattern detector identifies:
-- "cascade_propagation: 2-hop cascade from post-123"
-- Propagation velocity: 5 hops/minute

Cascade Metrics:

  • Propagation depth (number of hops)
  • Propagation velocity (hops per time unit)
  • Breadth (number of nodes at each level)

Anomaly Detection

Statistical Anomaly Detection

Z-score based anomaly detection with adaptive baselines:

# Python client example
import asyncio
from geode_client import Client

client = Client(host="localhost", port=3141)

async def simulate_activity():
    async with client.connection() as conn:
        # Normal activity: 100 requests/minute baseline
        for i in range(100):
            await conn.query("MATCH (u:User {id: $id}) RETURN u", {"id": i})

        # Anomaly: Sudden spike to 500 requests/minute
        # Analytics engine detects:
        # "anomaly_detection: Query rate anomaly detected"
        # Z-score: 8.5, Severity: HIGH, Confidence: 0.94

asyncio.run(simulate_activity())

Detection Algorithm:

// Z-score calculation
const z_score = abs(current_value - baseline.mean) / sqrt(baseline.variance);

if (z_score > config.statistical_threshold) {
    result.is_anomaly = true;
    result.confidence = min(z_score / 10.0, 1.0);
    result.severity = classifySeverity(z_score);
}

Behavioral Anomaly Detection

Detect unusual user or system behavior:

-- Normal behavior: User updates profile monthly
MATCH (u:User {id: 123})
SET u.last_updated = timestamp();

-- Anomaly: User updating profile 20 times/hour
-- Analytics engine flags:
-- "behavioral_anomaly: Unusual update frequency for User 123"
-- Historical frequency: 1/month, Current: 20/hour
-- Severity: CRITICAL

Network Anomaly Detection

Identify unusual network patterns:

-- Normal: User creates 0-5 relationships/day
CREATE (u:User {id: 456})-[:FOLLOWS]->(target:User);

-- Anomaly: User creates 100 relationships/hour
-- Analytics engine detects:
-- "network_anomaly: Potential bot activity for User 456"
-- Connection rate 20x baseline, Severity: HIGH

Machine Learning Integration

Automatic Embedding Updates

Real-time embedding updates based on graph changes:

// Automatic embedding updates
fn handleMLIntegration(self: *RealTimeAnalyticsEngine, event: *AnalyticsEvent) !void {
    switch (event.event_type) {
        .node_creation, .node_update => {
            // Update Node2Vec embeddings
            try ml_manager.generateEmbedding(node_id, .node2vec);
        },
        .edge_creation, .edge_update => {
            // Update GraphSAGE embeddings
            try ml_manager.generateEmbedding(source_node, .graphsage);
        },
        .similarity_search => {
            // Refine embeddings based on search patterns
            try ml_manager.generateEmbedding(node_id, .node2vec);
        },
    }
}

GQL Example:

-- Creating relationship triggers embedding update
CREATE (alice:User {id: 1})-[:FOLLOWS]->(bob:User {id: 2});

-- Analytics engine automatically:
-- 1. Queues event: edge_creation
-- 2. Updates GraphSAGE embeddings for alice and bob
-- 3. Recalculates similarity scores
-- 4. Updates recommendation cache

Similarity-Based Analytics

Real-time similarity monitoring for recommendations:

-- Similarity search generates analytics events
MATCH (u:User {id: 123})
MATCH (similar:User)
WHERE vector_cosine(u.embedding, similar.embedding) > 0.8
RETURN similar.id, similar.name;

-- Analytics engine tracks:
-- - Similarity search patterns
-- - Popular similarity thresholds
-- - Clustering behavior
-- - Recommendation effectiveness

Integration with Graph Embeddings

Seamless integration with ML Graph Embeddings system:

# Configure ML integration
export GEODE_ANALYTICS_ML_ENABLED=true
export GEODE_ANALYTICS_EMBEDDING_UPDATE_THRESHOLD=0.1
export GEODE_ANALYTICS_SIMILARITY_THRESHOLD=0.8

Supported Algorithms:

  • Node2Vec: Biased random walk updates
  • GraphSAGE: Neighbor aggregation updates
  • DeepWalk: Structural embedding updates

CDC Integration

Change Data Capture Setup

Configure CDC integration for downstream synchronization:

// Initialize CDC integration
var cdc_integration = CDCAnalyticsIntegration.init(&analytics_engine);

// Handle CDC events
const cdc_event = CDCEvent{
    .event_type = "INSERT",
    .table_name = "nodes",
    .node_id = 456,
    .timestamp = std.time.milliTimestamp(),
};

try cdc_integration.handleCDCEvent(cdc_event);

Webhook Configuration

Configure webhooks for real-time event streaming:

# cdc-config.yaml
webhooks:
  - name: analytics-webhook
    endpoint: https://analytics.example.com/events
    headers:
      Authorization: 'Bearer ${ANALYTICS_TOKEN}'
      X-Tenant-ID: 'tenant-123'
    retry:
      max_attempts: 5
      base_delay_ms: 100
      max_delay_ms: 30000
    filters:
      event_types:
        - node_creation
        - edge_creation
        - pattern_detection
        - anomaly_detection

Event Streaming Pipeline

Real-time event streaming with backpressure handling:

# Configure streaming
export GEODE_ANALYTICS_STREAM_ENABLED=true
export GEODE_ANALYTICS_BATCH_SIZE=100
export GEODE_ANALYTICS_PROCESSING_INTERVAL_MS=1000
export GEODE_ANALYTICS_MAX_QUEUE_SIZE=10000

Streaming Characteristics:

  • Event ordering guarantees (within partition)
  • Backpressure handling with configurable queue limits
  • Graceful overflow protection
  • Fault tolerance with automatic recovery

Configuration

Analytics Engine Configuration 

pub const AnalyticsConfig = struct {
    // Feature toggles
    enable_ml_integration: bool = true,
    enable_pattern_detection: bool = true,
    enable_anomaly_detection: bool = true,
    enable_trend_analysis: bool = true,

    // Performance tuning
    batch_size: u32 = 100,
    processing_interval_ms: u32 = 1000,
    max_event_queue_size: u32 = 10000,

    // ML settings
    embedding_update_threshold: f32 = 0.1,
    similarity_threshold: f32 = 0.8,

    // Time windows
    anomaly_detection_window: u32 = 3600,   // 1 hour
    trend_analysis_window: u32 = 86400,     // 24 hours
};

Pattern Detection Configuration 

pub const PatternDetectionConfig = struct {
    min_pattern_frequency: u32 = 5,
    pattern_time_window_ms: i64 = 60000,    // 1 minute
    max_patterns_tracked: u32 = 1000,
};

Anomaly Detection Configuration 

pub const AnomalyDetectionConfig = struct {
    statistical_threshold: f64 = 3.0,       // Z-score threshold
    minimum_baseline_samples: u32 = 100,
    baseline_update_frequency: u32 = 1000,
    anomaly_cooldown_ms: i64 = 5000,
};

Environment Variables 

# Core analytics settings
export GEODE_ANALYTICS_ENABLED=true
export GEODE_ANALYTICS_BATCH_SIZE=100
export GEODE_ANALYTICS_PROCESSING_INTERVAL_MS=1000
export GEODE_ANALYTICS_MAX_QUEUE_SIZE=10000

# ML integration
export GEODE_ANALYTICS_ML_ENABLED=true
export GEODE_ANALYTICS_EMBEDDING_UPDATE_THRESHOLD=0.1
export GEODE_ANALYTICS_SIMILARITY_THRESHOLD=0.8

# Pattern detection
export GEODE_ANALYTICS_PATTERN_ENABLED=true
export GEODE_ANALYTICS_PATTERN_FREQUENCY=5
export GEODE_ANALYTICS_PATTERN_WINDOW_MS=60000

# Anomaly detection
export GEODE_ANALYTICS_ANOMALY_ENABLED=true
export GEODE_ANALYTICS_ANOMALY_THRESHOLD=3.0
export GEODE_ANALYTICS_ANOMALY_BASELINE_SAMPLES=100

# Time windows
export GEODE_ANALYTICS_ANOMALY_WINDOW=3600
export GEODE_ANALYTICS_TREND_WINDOW=86400

Performance Characteristics

Throughput Metrics

Empirical performance data:

OperationThroughputLatencyNotes
Event Processing>10,000 events/sec<10ms/eventSustained throughput
Batch Processing1000 events/batch<100msConfigurable batch size
Pattern Detection>1000 patterns/sec<1ms/eventCommon patterns
Anomaly Detection>2000 checks/sec<500μs/eventStatistical analysis
ML Integration>500 updates/sec<2ms/updateEmbedding updates

Memory Usage

Memory footprint for different components:

  • Base Engine: ~5MB for core analytics engine
  • Event Queue: ~1KB per queued event (configurable limit)
  • Pattern Storage: ~500KB for 1000 tracked patterns
  • Anomaly Baselines: ~100KB for 1000 metric baselines
  • Statistics: ~50KB for comprehensive analytics statistics

Total Memory: ~6-10MB for typical configuration

Scalability Limits

Maximum supported scale:

  • Event Queue: Up to 100,000 queued events
  • Pattern Tracking: Up to 10,000 concurrent patterns
  • Anomaly Metrics: Up to 10,000 baseline metrics
  • Processing Threads: Up to 8 background threads
  • Memory Bounds: Configurable limits with overflow protection

Production Deployment

Monitoring and Alerting

Monitor key analytics metrics:

# Get analytics statistics
curl http://localhost:9090/metrics | grep geode_analytics

# Key metrics:
# - geode_analytics_events_processed_total
# - geode_analytics_events_queued
# - geode_analytics_patterns_detected_total
# - geode_analytics_anomalies_detected_total
# - geode_analytics_processing_latency_ms

Alert Thresholds:

  • Event queue depth > 80% of max_queue_size
  • Processing latency > 200ms
  • Anomaly detection rate > 10 anomalies/minute
  • Error rate > 1%

Scaling Strategies

Scale analytics system for high-volume workloads:

Horizontal Scaling:

# Multiple analytics engine instances
analytics_cluster:
  instances: 4
  load_balancer: round_robin
  event_distribution: partitioned

Vertical Scaling:

# Increase processing capacity
export GEODE_ANALYTICS_BATCH_SIZE=500
export GEODE_ANALYTICS_PROCESSING_THREADS=8
export GEODE_ANALYTICS_MAX_QUEUE_SIZE=50000

Operational Procedures

Health Checks:

# Check analytics engine status
curl http://localhost:8080/health/analytics

# Response:
# {
#   "status": "healthy",
#   "event_queue_depth": 127,
#   "processing_latency_ms": 45,
#   "patterns_tracked": 342,
#   "baselines_active": 89
# }

Backup Procedures:

# Backup analytics configuration and patterns
geode analytics backup --dest /backups/analytics-$(date +%Y%m%d)

# Restore from backup
geode analytics restore --source /backups/analytics-20260123

Use Cases

Real-Time Fraud Detection

Detect fraudulent patterns in financial transactions:

-- Normal transaction pattern
MATCH (u:User {id: 123})-[:TRANSACTED]->(merchant:Merchant)
WHERE merchant.risk_score < 5
RETURN merchant;

-- Anomaly: Unusual transaction pattern
-- Analytics engine detects:
-- 1. High-frequency transactions (10x baseline)
-- 2. New merchant connections (5 in 1 minute)
-- 3. Unusual amounts (3 std deviations)
-- Alert: "Potential fraud detected for User 123"

Social Network Monitoring

Monitor social network dynamics:

-- Community formation tracking
MATCH (u:User)-[:FOLLOWS]->(influencer:User)
WHERE influencer.verified = true

-- Pattern detection identifies:
-- - Emerging communities around influencers
-- - Viral content propagation patterns
-- - Bot-like behavior (excessive following)
-- - Trending topics (hashtag clusters)

E-Commerce Recommendations

Real-time product recommendations:

-- User browses product
MATCH (u:User {id: 456})-[:VIEWED]->(p:Product {id: 789});

-- Analytics engine:
-- 1. Updates user embedding (GraphSAGE)
-- 2. Finds similar products (cosine similarity)
-- 3. Detects browsing patterns
-- 4. Updates recommendation cache
-- Result: Real-time personalized recommendations

Summary

Geode’s Real-Time Analytics system provides:

  • High-Throughput Processing: >10,000 events/second sustained
  • Pattern Detection: Six pattern types with confidence scoring
  • Anomaly Detection: Statistical Z-score analysis with adaptive baselines
  • ML Integration: Automatic embedding updates and similarity analytics
  • CDC Support: Change data capture for downstream synchronization
  • Scalability: Up to 100,000 queued events with overflow protection
  • Flexibility: Comprehensive configuration and tuning options

Use real-time analytics to build intelligent, responsive graph applications with automatic pattern detection, anomaly identification, and machine learning integration. Monitor system health with comprehensive metrics and scale horizontally or vertically as needed.