Real-Time Analytics Guide | Geode Database

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. <h3 id="overview" class="position-relative d-flex align-items-center group"> Overview <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="overview" aria-haspopup="dialog" aria-label="Share link: Overview"> Share link </button> </h3><div id="headingShareModal" class="heading-share-modal" role="dialog" aria-modal="true" aria-labelledby="headingShareTitle" hidden> <div class="hsm-dialog" role="document"> <div class="hsm-header"> <h2 id="headingShareTitle" class="h6 mb-0 fw-bold">Share this section</h2> <button type="button" class="hsm-close" aria-label="Close"> </button> </div> <div class="hsm-body"> <label for="headingShareInput" class="form-label small text-muted mb-1 text-uppercase fw-bold" style="font-size: 0.7rem; letter-spacing: 0.5px;">Permalink</label> <div class="input-group mb-4 hsm-url-group"> <input id="headingShareInput" type="text" class="form-control font-monospace" readonly aria-readonly="true" style="font-size: 0.85rem;" /> <button class="btn btn-primary hsm-copy" type="button" aria-label="Copy" title="Copy"> </button> </div> <div class="small fw-bold mb-2 text-muted text-uppercase" style="font-size: 0.7rem; 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const input = modal.querySelector('#headingShareInput'); const copyBtn = modal.querySelector('.hsm-copy'); const twitter = modal.querySelector('#share-twitter'); const linkedin = modal.querySelector('#share-linkedin'); const facebook = modal.querySelector('#share-facebook'); const closeBtn = modal.querySelector('.hsm-close'); let lastFocus=null; let trapBound=false; function buildUrl(id){ return window.location.origin + window.location.pathname + '#' + id; } function isOpen(){ return !modal.hasAttribute('hidden'); } function hydrate(id){ const url=buildUrl(id); input.value=url; const enc=encodeURIComponent(url); const text=encodeURIComponent(document.title); if(twitter) twitter.href=`https://twitter.com/intent/tweet?url=${enc}&text=${text}`; if(linkedin) linkedin.href=`https://www.linkedin.com/sharing/share-offsite/?url=${enc}`; if(facebook) facebook.href=`https://www.facebook.com/sharer/sharer.php?u=${enc}`; } function openModal(id){ lastFocus=document.activeElement; hydrate(id); if(!isOpen()){ modal.removeAttribute('hidden'); } requestAnimationFrame(()=>{ input.focus(); }); trapFocus(); } function closeModal(){ if(!isOpen()) return; modal.setAttribute('hidden',''); if(lastFocus && typeof lastFocus.focus==='function') lastFocus.focus(); } function copyCurrent(){ try{ navigator.clipboard.writeText(input.value).then(()=>feedback(true),()=>fallback()); } catch(e){ fallback(); } } function fallback(){ input.select(); try{ document.execCommand('copy'); feedback(true);}catch(e){ feedback(false);} } function feedback(ok){ if(!copyBtn) return; const icon=copyBtn.querySelector('i'); if(!icon) return; const prev=copyBtn.getAttribute('data-prev')||icon.className; if(!copyBtn.getAttribute('data-prev')) copyBtn.setAttribute('data-prev',prev); icon.className= ok ? 'fa-duotone fa-clipboard-check':'fa-duotone fa-circle-exclamation'; setTimeout(()=>{ icon.className=prev; },1800); } function handleShareClick(e){ e.preventDefault(); const btn=e.currentTarget; const id=btn.getAttribute('data-share-target'); if(id) openModal(id); } function bindShareButtons(){ document.querySelectorAll('.h-share').forEach(btn=>{ if(!btn.dataset.hShareBound){ btn.addEventListener('click', handleShareClick); btn.dataset.hShareBound='1'; } }); } bindShareButtons(); if(document.readyState==='loading'){ document.addEventListener('DOMContentLoaded', bindShareButtons); } else { requestAnimationFrame(bindShareButtons); } document.addEventListener('click', function(e){ const shareBtn=e.target.closest && e.target.closest('.h-share'); if(shareBtn && !shareBtn.dataset.hShareBound){ handleShareClick.call(shareBtn, e); } }, true); document.addEventListener('click', e=>{ if(e.target===modal) closeModal(); if(e.target.closest && e.target.closest('.hsm-close')){ e.preventDefault(); closeModal(); } if(copyBtn && (e.target===copyBtn || (e.target.closest && e.target.closest('.hsm-copy')))) { e.preventDefault(); copyCurrent(); } }); document.addEventListener('keydown', e=>{ if(e.key==='Escape' && isOpen()) closeModal(); }); function trapFocus(){ if(trapBound) return; trapBound=true; modal.addEventListener('keydown', f=>{ if(f.key==='Tab' && isOpen()){ const focusable=[...modal.querySelectorAll('a[href],button,input,textarea,select,[tabindex]:not([tabindex="-1"])')].filter(el=>!el.hasAttribute('disabled')); if(!focusable.length) return; const first=focusable[0]; const last=focusable[focusable.length-1]; if(f.shiftKey && document.activeElement===first){ f.preventDefault(); last.focus(); } else if(!f.shiftKey && document.activeElement===last){ f.preventDefault(); first.focus(); } } }); } if(closeBtn) closeBtn.addEventListener('click', e=>{ e.preventDefault(); closeModal(); }); })(); </script>The Real-Time Analytics system transforms Geode into an intelligent analytics platform by: <ul> <li>Event Stream Processing: Process graph changes in real-time with configurable batching</li> <li>Pattern Detection: Identify recurring patterns, community formation, and behavioral trends</li> <li>Anomaly Detection: Statistical analysis for outlier identification with adaptive baselines</li> <li>Machine Learning Integration: Automatic embedding updates and similarity-based analytics</li> <li>CDC Integration: Change data capture for downstream system synchronization</li> </ul> Performance: >10,000 events/second sustained throughput with <100ms batch processing latency. <h3 id="architecture" class="position-relative d-flex align-items-center group"> Architecture <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="architecture" aria-haspopup="dialog" aria-label="Share link: Architecture"> Share link </button> </h3> <h4 id="core-components" class="position-relative d-flex align-items-center group"> Core Components <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="core-components" aria-haspopup="dialog" aria-label="Share link: Core Components"> Share link </button> </h4> <h5 id="1-realtimeanalyticsengine" class="position-relative d-flex align-items-center group"> 1. RealTimeAnalyticsEngine <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="1-realtimeanalyticsengine" aria-haspopup="dialog" aria-label="Share link: 1. RealTimeAnalyticsEngine"> Share link </button> </h5>Central orchestrator for all real-time analytics operations: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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(); </code></pre></div>Capabilities: <ul> <li>Multi-threaded background event processing</li> <li>Configurable batch processing with overflow protection</li> <li>Comprehensive statistics tracking and monitoring</li> <li>Integration with ML system and CDC</li> </ul> <h5 id="2-analyticsevent-system" class="position-relative d-flex align-items-center group"> 2. AnalyticsEvent System <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="2-analyticsevent-system" aria-haspopup="dialog" aria-label="Share link: 2. AnalyticsEvent System"> Share link </button> </h5>Flexible event structure for capturing diverse analytics data: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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); </code></pre></div>Supported Event Types: <ul> <li><code>node_creation</code>, <code>node_update</code>, <code>node_deletion</code></li> <li><code>edge_creation</code>, <code>edge_update</code>, <code>edge_deletion</code></li> <li><code>graph_query</code>, <code>similarity_search</code></li> <li><code>embedding_update</code>, <code>pattern_detection</code>, <code>anomaly_detection</code></li> <li><code>trend_analysis</code></li> </ul> Property Value Types: <ul> <li><code>string</code>, <code>integer</code>, <code>float</code>, <code>boolean</code></li> <li><code>vector</code> (for ML embeddings)</li> <li><code>array</code> (nested values)</li> <li><code>null</code></li> </ul> <h5 id="3-pattern-detection-engine" class="position-relative d-flex align-items-center group"> 3. Pattern Detection Engine <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="3-pattern-detection-engine" aria-haspopup="dialog" aria-label="Share link: 3. Pattern Detection Engine"> Share link </button> </h5>Advanced pattern recognition for graph behaviors: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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 }); } </code></pre></div>Pattern Types: <ul> <li><code>frequent_connection</code>: Recurring connection patterns</li> <li><code>community_formation</code>: Emerging community structures</li> <li><code>hub_emergence</code>: Nodes becoming central hubs</li> <li><code>cascade_propagation</code>: Information cascade patterns</li> <li><code>temporal_clustering</code>: Time-based clustering</li> <li><code>behavioral_similarity</code>: Similar behavior patterns</li> </ul> <h5 id="4-anomaly-detection-system" class="position-relative d-flex align-items-center group"> 4. Anomaly Detection System <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="4-anomaly-detection-system" aria-haspopup="dialog" aria-label="Share link: 4. Anomaly Detection System"> Share link </button> </h5>Statistical anomaly detection with adaptive baselines: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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 }); } </code></pre></div>Severity Classification: <ul> <li>Low: 3-5 standard deviations from baseline</li> <li>Medium: 5-10 standard deviations</li> <li>High: 10-20 standard deviations</li> <li>Critical: >20 standard deviations</li> </ul> <h3 id="pattern-detection" class="position-relative d-flex align-items-center group"> Pattern Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="pattern-detection" aria-haspopup="dialog" aria-label="Share link: Pattern Detection"> Share link </button> </h3> <h4 id="connection-pattern-detection" class="position-relative d-flex align-items-center group"> Connection Pattern Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="connection-pattern-detection" aria-haspopup="dialog" aria-label="Share link: Connection Pattern Detection"> Share link </button> </h4>Identify recurring connection patterns in graph: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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" </code></pre></div>Pattern Analysis: <ul> <li>Frequency tracking with configurable time windows</li> <li>Confidence scoring based on statistical significance</li> <li>Temporal pattern evolution tracking</li> </ul> <h4 id="community-formation-detection" class="position-relative d-flex align-items-center group"> Community Formation Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="community-formation-detection" aria-haspopup="dialog" aria-label="Share link: Community Formation Detection"> Share link </button> </h4>Detect emerging communities in real-time: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div>Community Metrics: <ul> <li>Triangle count (3-node cycles)</li> <li>Clique detection (fully connected subgraphs)</li> <li>Modularity scores for community strength</li> </ul> <h4 id="hub-emergence-detection" class="position-relative d-flex align-items-center group"> Hub Emergence Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="hub-emergence-detection" aria-haspopup="dialog" aria-label="Share link: Hub Emergence Detection"> Share link </button> </h4>Identify nodes becoming central hubs: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div>Hub Metrics: <ul> <li>Degree centrality growth rate</li> <li>Betweenness centrality changes</li> <li>Temporal connection velocity</li> </ul> <h4 id="cascade-propagation-detection" class="position-relative d-flex align-items-center group"> Cascade Propagation Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="cascade-propagation-detection" aria-haspopup="dialog" aria-label="Share link: Cascade Propagation Detection"> Share link </button> </h4>Detect information cascades spreading through graph: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div>Cascade Metrics: <ul> <li>Propagation depth (number of hops)</li> <li>Propagation velocity (hops per time unit)</li> <li>Breadth (number of nodes at each level)</li> </ul> <h3 id="anomaly-detection" class="position-relative d-flex align-items-center group"> Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Anomaly Detection"> Share link </button> </h3> <h4 id="statistical-anomaly-detection" class="position-relative d-flex align-items-center group"> Statistical Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="statistical-anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Statistical Anomaly Detection"> Share link </button> </h4>Z-score based anomaly detection with adaptive baselines:  <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># 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()) </code></pre></div>Detection Algorithm: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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); } </code></pre></div> <h4 id="behavioral-anomaly-detection" class="position-relative d-flex align-items-center group"> Behavioral Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="behavioral-anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Behavioral Anomaly Detection"> Share link </button> </h4>Detect unusual user or system behavior: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div> <h4 id="network-anomaly-detection" class="position-relative d-flex align-items-center group"> Network Anomaly Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="network-anomaly-detection" aria-haspopup="dialog" aria-label="Share link: Network Anomaly Detection"> Share link </button> </h4>Identify unusual network patterns: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div> <h3 id="machine-learning-integration" class="position-relative d-flex align-items-center group"> Machine Learning Integration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="machine-learning-integration" aria-haspopup="dialog" aria-label="Share link: Machine Learning Integration"> Share link </button> </h3> <h4 id="automatic-embedding-updates" class="position-relative d-flex align-items-center group"> Automatic Embedding Updates <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="automatic-embedding-updates" aria-haspopup="dialog" aria-label="Share link: Automatic Embedding Updates"> Share link </button> </h4>Real-time embedding updates based on graph changes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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); }, } } </code></pre></div>GQL Example: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div> <h4 id="similarity-based-analytics" class="position-relative d-flex align-items-center group"> Similarity-Based Analytics <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="similarity-based-analytics" aria-haspopup="dialog" aria-label="Share link: Similarity-Based Analytics"> Share link </button> </h4>Real-time similarity monitoring for recommendations: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div> <h4 id="integration-with-graph-embeddings" class="position-relative d-flex align-items-center group"> Integration with Graph Embeddings <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="integration-with-graph-embeddings" aria-haspopup="dialog" aria-label="Share link: Integration with Graph Embeddings"> Share link </button> </h4>Seamless integration with ML Graph Embeddings system: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Configure ML integration export GEODE_ANALYTICS_ML_ENABLED=true export GEODE_ANALYTICS_EMBEDDING_UPDATE_THRESHOLD=0.1 export GEODE_ANALYTICS_SIMILARITY_THRESHOLD=0.8 </code></pre></div>Supported Algorithms: <ul> <li>Node2Vec: Biased random walk updates</li> <li>GraphSAGE: Neighbor aggregation updates</li> <li>DeepWalk: Structural embedding updates</li> </ul> <h3 id="cdc-integration" class="position-relative d-flex align-items-center group"> CDC Integration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="cdc-integration" aria-haspopup="dialog" aria-label="Share link: CDC Integration"> Share link </button> </h3> <h4 id="change-data-capture-setup" class="position-relative d-flex align-items-center group"> Change Data Capture Setup <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="change-data-capture-setup" aria-haspopup="dialog" aria-label="Share link: Change Data Capture Setup"> Share link </button> </h4>Configure CDC integration for downstream synchronization: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// 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); </code></pre></div> <h4 id="webhook-configuration" class="position-relative d-flex align-items-center group"> Webhook Configuration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="webhook-configuration" aria-haspopup="dialog" aria-label="Share link: Webhook Configuration"> Share link </button> </h4>Configure webhooks for real-time event streaming: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># 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 </code></pre></div> <h4 id="event-streaming-pipeline" class="position-relative d-flex align-items-center group"> Event Streaming Pipeline <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="event-streaming-pipeline" aria-haspopup="dialog" aria-label="Share link: Event Streaming Pipeline"> Share link </button> </h4>Real-time event streaming with backpressure handling: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># 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 </code></pre></div>Streaming Characteristics: <ul> <li>Event ordering guarantees (within partition)</li> <li>Backpressure handling with configurable queue limits</li> <li>Graceful overflow protection</li> <li>Fault tolerance with automatic recovery</li> </ul> <h3 id="configuration" class="position-relative d-flex align-items-center group"> Configuration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="configuration" aria-haspopup="dialog" aria-label="Share link: Configuration"> Share link </button> </h3> <h4 id="analytics-engine-configuration" class="position-relative d-flex align-items-center group"> Analytics Engine Configuration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="analytics-engine-configuration" aria-haspopup="dialog" aria-label="Share link: Analytics Engine Configuration"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">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 }; </code></pre></div> <h4 id="pattern-detection-configuration" class="position-relative d-flex align-items-center group"> Pattern Detection Configuration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="pattern-detection-configuration" aria-haspopup="dialog" aria-label="Share link: Pattern Detection Configuration"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">pub const PatternDetectionConfig = struct { min_pattern_frequency: u32 = 5, pattern_time_window_ms: i64 = 60000, // 1 minute max_patterns_tracked: u32 = 1000, }; </code></pre></div> <h4 id="anomaly-detection-configuration" class="position-relative d-flex align-items-center group"> Anomaly Detection Configuration <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="anomaly-detection-configuration" aria-haspopup="dialog" aria-label="Share link: Anomaly Detection Configuration"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">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, }; </code></pre></div> <h4 id="environment-variables" class="position-relative d-flex align-items-center group"> Environment Variables <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="environment-variables" aria-haspopup="dialog" aria-label="Share link: Environment Variables"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># 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 </code></pre></div> <h3 id="performance-characteristics" class="position-relative d-flex align-items-center group"> Performance Characteristics <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="performance-characteristics" aria-haspopup="dialog" aria-label="Share link: Performance Characteristics"> Share link </button> </h3> <h4 id="throughput-metrics" class="position-relative d-flex align-items-center group"> Throughput Metrics <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="throughput-metrics" aria-haspopup="dialog" aria-label="Share link: Throughput Metrics"> Share link </button> </h4>Empirical performance data: <table> <thead> <tr> <th>Operation</th> <th>Throughput</th> <th>Latency</th> <th>Notes</th> </tr> </thead> <tbody> <tr> <td>Event Processing</td> <td>>10,000 events/sec</td> <td><10ms/event</td> <td>Sustained throughput</td> </tr> <tr> <td>Batch Processing</td> <td>1000 events/batch</td> <td><100ms</td> <td>Configurable batch size</td> </tr> <tr> <td>Pattern Detection</td> <td>>1000 patterns/sec</td> <td><1ms/event</td> <td>Common patterns</td> </tr> <tr> <td>Anomaly Detection</td> <td>>2000 checks/sec</td> <td><500μs/event</td> <td>Statistical analysis</td> </tr> <tr> <td>ML Integration</td> <td>>500 updates/sec</td> <td><2ms/update</td> <td>Embedding updates</td> </tr> </tbody> </table> <h4 id="memory-usage" class="position-relative d-flex align-items-center group"> Memory Usage <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="memory-usage" aria-haspopup="dialog" aria-label="Share link: Memory Usage"> Share link </button> </h4>Memory footprint for different components: <ul> <li>Base Engine: ~5MB for core analytics engine</li> <li>Event Queue: ~1KB per queued event (configurable limit)</li> <li>Pattern Storage: ~500KB for 1000 tracked patterns</li> <li>Anomaly Baselines: ~100KB for 1000 metric baselines</li> <li>Statistics: ~50KB for comprehensive analytics statistics</li> </ul> Total Memory: ~6-10MB for typical configuration <h4 id="scalability-limits" class="position-relative d-flex align-items-center group"> Scalability Limits <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="scalability-limits" aria-haspopup="dialog" aria-label="Share link: Scalability Limits"> Share link </button> </h4>Maximum supported scale: <ul> <li>Event Queue: Up to 100,000 queued events</li> <li>Pattern Tracking: Up to 10,000 concurrent patterns</li> <li>Anomaly Metrics: Up to 10,000 baseline metrics</li> <li>Processing Threads: Up to 8 background threads</li> <li>Memory Bounds: Configurable limits with overflow protection</li> </ul> <h3 id="production-deployment" class="position-relative d-flex align-items-center group"> Production Deployment <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="production-deployment" aria-haspopup="dialog" aria-label="Share link: Production Deployment"> Share link </button> </h3> <h4 id="monitoring-and-alerting" class="position-relative d-flex align-items-center group"> Monitoring and Alerting <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="monitoring-and-alerting" aria-haspopup="dialog" aria-label="Share link: Monitoring and Alerting"> Share link </button> </h4>Monitor key analytics metrics: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># 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 </code></pre></div>Alert Thresholds: <ul> <li>Event queue depth > 80% of max_queue_size</li> <li>Processing latency > 200ms</li> <li>Anomaly detection rate > 10 anomalies/minute</li> <li>Error rate > 1%</li> </ul> <h4 id="scaling-strategies" class="position-relative d-flex align-items-center group"> Scaling Strategies <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="scaling-strategies" aria-haspopup="dialog" aria-label="Share link: Scaling Strategies"> Share link </button> </h4>Scale analytics system for high-volume workloads: Horizontal Scaling: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># Multiple analytics engine instances analytics_cluster: instances: 4 load_balancer: round_robin event_distribution: partitioned </code></pre></div>Vertical Scaling: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Increase processing capacity export GEODE_ANALYTICS_BATCH_SIZE=500 export GEODE_ANALYTICS_PROCESSING_THREADS=8 export GEODE_ANALYTICS_MAX_QUEUE_SIZE=50000 </code></pre></div> <h4 id="operational-procedures" class="position-relative d-flex align-items-center group"> Operational Procedures <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="operational-procedures" aria-haspopup="dialog" aria-label="Share link: Operational Procedures"> Share link </button> </h4>Health Checks: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># 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 # } </code></pre></div>Backup Procedures: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># 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 </code></pre></div> <h3 id="use-cases" class="position-relative d-flex align-items-center group"> Use Cases <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="use-cases" aria-haspopup="dialog" aria-label="Share link: Use Cases"> Share link </button> </h3> <h4 id="real-time-fraud-detection" class="position-relative d-flex align-items-center group"> Real-Time Fraud Detection <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="real-time-fraud-detection" aria-haspopup="dialog" aria-label="Share link: Real-Time Fraud Detection"> Share link </button> </h4>Detect fraudulent patterns in financial transactions: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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" </code></pre></div> <h4 id="social-network-monitoring" class="position-relative d-flex align-items-center group"> Social Network Monitoring <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="social-network-monitoring" aria-haspopup="dialog" aria-label="Share link: Social Network Monitoring"> Share link </button> </h4>Monitor social network dynamics: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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) </code></pre></div> <h4 id="e-commerce-recommendations" class="position-relative d-flex align-items-center group"> E-Commerce Recommendations <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="e-commerce-recommendations" aria-haspopup="dialog" aria-label="Share link: E-Commerce Recommendations"> Share link </button> </h4>Real-time product recommendations: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- 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 </code></pre></div> <h3 id="related-documentation" class="position-relative d-flex align-items-center group"> Related Documentation <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="related-documentation" aria-haspopup="dialog" aria-label="Share link: Related Documentation"> Share link </button> </h3><ul> <li><a href="/docs/query/performance-tuning/" >Query Performance Tuning</a> - Optimize analytics queries</li> <li><a href="/docs/architecture/distributed-architecture/" >Distributed Architecture</a> - Distributed analytics</li> <li><a href="/docs/configuration/server-configuration/" >Server Configuration</a> - Analytics configuration</li> <li><a href="/docs/guides/troubleshooting/" >Troubleshooting Guide</a> - Common analytics issues</li> </ul> <h3 id="summary" class="position-relative d-flex align-items-center group"> Summary <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="summary" aria-haspopup="dialog" aria-label="Share link: Summary"> Share link </button> </h3>Geode’s Real-Time Analytics system provides: <ul> <li>High-Throughput Processing: >10,000 events/second sustained</li> <li>Pattern Detection: Six pattern types with confidence scoring</li> <li>Anomaly Detection: Statistical Z-score analysis with adaptive baselines</li> <li>ML Integration: Automatic embedding updates and similarity analytics</li> <li>CDC Support: Change data capture for downstream synchronization</li> <li>Scalability: Up to 100,000 queued events with overflow protection</li> <li>Flexibility: Comprehensive configuration and tuning options</li> </ul> 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.