Real-Time Analytics

Real-time analytics in Geode enables instant insights from graph data through streaming aggregations, temporal queries, and incremental computation. Build live dashboards, detect trends, and monitor business metrics with millisecond-latency queries on continuously evolving data. <h3 id="what-are-real-time-graph-analytics" class="position-relative d-flex align-items-center group"> What Are Real-Time Graph 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="what-are-real-time-graph-analytics" aria-haspopup="dialog" aria-label="Share link: What Are Real-Time Graph Analytics?"> 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; letter-spacing: 0.5px;">Share via</div> <div class="hsm-share-grid"> <a id="share-twitter" class="btn btn-outline-secondary w-100" target="_blank" rel="noopener noreferrer"> Twitter </a> <a id="share-linkedin" class="btn btn-outline-secondary w-100" target="_blank" rel="noopener noreferrer"> LinkedIn </a> <a id="share-facebook" class="btn btn-outline-secondary w-100" target="_blank" rel="noopener noreferrer"> Facebook </a> </div> </div> </div> </div> <style> .heading-share-modal { position: fixed; inset: 0; display: flex; justify-content: center; align-items: center; background: rgba(0, 0, 0, 0.6); z-index: 1050; padding: 1rem; backdrop-filter: blur(4px); -webkit-backdrop-filter: blur(4px); } .heading-share-modal[hidden] { display: none !important; } .hsm-dialog { max-width: 420px; width: 100%; background: var(--bs-body-bg, #fff); color: var(--bs-body-color, #212529); border: 1px solid var(--bs-border-color, rgba(0,0,0,0.1)); border-radius: 1rem; box-shadow: 0 25px 50px -12px rgba(0, 0, 0, 0.25); overflow: hidden; animation: hsm-fade-in 0.2s ease-out; } @keyframes hsm-fade-in { from { opacity: 0; transform: scale(0.95); } to { opacity: 1; transform: scale(1); } } [data-bs-theme="dark"] .hsm-dialog { background: #1e293b; border-color: rgba(255,255,255,0.1); color: #f8f9fa; } .hsm-header { display: flex; justify-content: space-between; align-items: center; padding: 1rem 1.5rem; border-bottom: 1px solid var(--bs-border-color, rgba(0,0,0,0.1)); background: rgba(0,0,0,0.02); } [data-bs-theme="dark"] .hsm-header { background: rgba(255,255,255,0.02); border-color: rgba(255,255,255,0.1); } .hsm-close { background: transparent; border: none; color: inherit; opacity: 0.5; padding: 0.25rem 0.5rem; border-radius: 0.25rem; font-size: 1.2rem; line-height: 1; transition: opacity 0.2s; } .hsm-close:hover { opacity: 1; } .hsm-body { padding: 1.5rem; } .hsm-url-group { display: flex !important; align-items: stretch; } .hsm-url-group .form-control { flex: 1; min-width: 0; margin: 0; background: var(--bs-secondary-bg, #f8f9fa); border-color: var(--bs-border-color, #dee2e6); border-top-right-radius: 0; border-bottom-right-radius: 0; height: 42px; } .hsm-url-group .btn { flex: 0 0 auto; margin: 0; margin-left: -1px; border-top-left-radius: 0; border-bottom-left-radius: 0; height: 42px; display: flex; align-items: center; justify-content: center; padding: 0 1.25rem; z-index: 2; } [data-bs-theme="dark"] .hsm-url-group .form-control { background: #0f172a; border-color: #334155; color: #e2e8f0; } .hsm-share-grid { display: flex; flex-direction: column; gap: 0.5rem; } .hsm-share-grid .btn { display: flex; align-items: center; justify-content: center; font-size: 0.9rem; padding: 0.6rem; border-color: var(--bs-border-color); width: 100%; } [data-bs-theme="dark"] .hsm-share-grid .btn { color: #e2e8f0; border-color: #475569; } [data-bs-theme="dark"] .hsm-share-grid .btn:hover { background: #334155; border-color: #cbd5e1; } </style> <script> (function(){ const modal = document.getElementById('headingShareModal'); if(!modal) return; 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>Real-time graph analytics processes graph data as it arrives, computing aggregations, detecting patterns, and updating metrics without batch processing delays. Geode achieves this through: <ul> <li>Streaming aggregations over time windows</li> <li>Incremental materialized views</li> <li>Temporal queries for trend analysis</li> <li>Change data capture for event processing</li> <li>Efficient concurrent query execution</li> </ul> <h3 id="streaming-aggregations" class="position-relative d-flex align-items-center group"> Streaming Aggregations <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="streaming-aggregations" aria-haspopup="dialog" aria-label="Share link: Streaming Aggregations"> Share link </button> </h3> <h4 id="time-window-queries" class="position-relative d-flex align-items-center group"> Time Window Queries <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="time-window-queries" aria-haspopup="dialog" aria-label="Share link: Time Window Queries"> Share link </button> </h4>Compute metrics over sliding time windows: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Events in last 5 minutes MATCH (e:Event) WHERE e.timestamp > datetime().minusMinutes(5) RETURN COUNT(e) AS recent_events, COUNT(DISTINCT e.user_id) AS active_users; // Rolling hourly metrics for last 24 hours MATCH (e:Event) WHERE e.timestamp > datetime().minusHours(24) WITH date.truncate('hour', e.timestamp) AS hour, COUNT(e) AS event_count, COUNT(DISTINCT e.user_id) AS unique_users ORDER BY hour DESC RETURN hour, event_count, unique_users; // Minute-by-minute breakdown for real-time monitoring MATCH (t:Transaction) WHERE t.timestamp > datetime().minusMinutes(15) WITH date.truncate('minute', t.timestamp) AS minute, COUNT(t) AS tx_count, SUM(t.amount) AS total_amount, AVG(t.amount) AS avg_amount ORDER BY minute ASC RETURN minute, tx_count, total_amount, avg_amount; </code></pre></div> <h4 id="cumulative-aggregations" class="position-relative d-flex align-items-center group"> Cumulative Aggregations <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="cumulative-aggregations" aria-haspopup="dialog" aria-label="Share link: Cumulative Aggregations"> Share link </button> </h4>Track running totals and cumulative metrics: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Daily cumulative revenue MATCH (t:Transaction) WHERE t.timestamp > datetime().minusDays(30) WITH date.truncate('day', t.timestamp) AS day, SUM(t.amount) AS daily_revenue ORDER BY day ASC WITH day, daily_revenue, SUM(daily_revenue) OVER (ORDER BY day) AS cumulative_revenue RETURN day, daily_revenue, cumulative_revenue; // Running user count over time MATCH (u:User) WITH date.truncate('day', u.created_at) AS day, COUNT(u) AS new_users ORDER BY day ASC WITH day, new_users, SUM(new_users) OVER (ORDER BY day) AS total_users RETURN day, new_users, total_users; </code></pre></div> <h3 id="live-dashboard-queries" class="position-relative d-flex align-items-center group"> Live Dashboard Queries <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="live-dashboard-queries" aria-haspopup="dialog" aria-label="Share link: Live Dashboard Queries"> Share link </button> </h3> <h4 id="key-performance-indicators" class="position-relative d-flex align-items-center group"> Key Performance Indicators <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="key-performance-indicators" aria-haspopup="dialog" aria-label="Share link: Key Performance Indicators"> Share link </button> </h4>Real-time KPI tracking for dashboards: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Real-time business metrics MATCH (t:Transaction) WHERE t.timestamp > datetime().minusHours(1) WITH COUNT(t) AS last_hour_tx, SUM(t.amount) AS last_hour_revenue, AVG(t.amount) AS avg_transaction_value, COUNT(DISTINCT t.user_id) AS active_buyers MATCH (u:User) WHERE u.created_at > datetime().minusHours(1) WITH last_hour_tx, last_hour_revenue, avg_transaction_value, active_buyers, COUNT(u) AS new_users MATCH (p:Product)-[:PURCHASED]->() WHERE p.stock_quantity < 10 WITH last_hour_tx, last_hour_revenue, avg_transaction_value, active_buyers, new_users, COUNT(p) AS low_stock_products RETURN { transactions_last_hour: last_hour_tx, revenue_last_hour: last_hour_revenue, avg_transaction_value: avg_transaction_value, active_buyers: active_buyers, new_users_last_hour: new_users, low_stock_alerts: low_stock_products } AS dashboard_metrics; </code></pre></div> <h4 id="top-n-real-time-rankings" class="position-relative d-flex align-items-center group"> Top-N Real-Time Rankings <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="top-n-real-time-rankings" aria-haspopup="dialog" aria-label="Share link: Top-N Real-Time Rankings"> Share link </button> </h4>Track trending items and top performers: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Trending products (last hour) MATCH (p:Product)<-[purchase:PURCHASED]-() WHERE purchase.timestamp > datetime().minusHours(1) WITH p, COUNT(purchase) AS purchase_count, SUM(purchase.amount) AS revenue, COUNT(DISTINCT purchase.user_id) AS unique_buyers ORDER BY purchase_count DESC LIMIT 10 RETURN p.product_id, p.name, purchase_count, revenue, unique_buyers; // Most active users (last 24 hours) MATCH (u:User)-[activity:PERFORMED|PURCHASED|VIEWED]->() WHERE activity.timestamp > datetime().minusHours(24) WITH u, COUNT(activity) AS activity_count, SIZE([a IN COLLECT(activity) WHERE type(a) = 'PURCHASED']) AS purchases, SIZE([a IN COLLECT(activity) WHERE type(a) = 'VIEWED']) AS views ORDER BY activity_count DESC LIMIT 20 RETURN u.user_id, u.name, activity_count, purchases, views; </code></pre></div> <h3 id="trend-detection" class="position-relative d-flex align-items-center group"> Trend 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="trend-detection" aria-haspopup="dialog" aria-label="Share link: Trend Detection"> Share link </button> </h3> <h4 id="growth-rate-analysis" class="position-relative d-flex align-items-center group"> Growth Rate Analysis <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="growth-rate-analysis" aria-haspopup="dialog" aria-label="Share link: Growth Rate Analysis"> Share link </button> </h4>Detect accelerating or declining trends: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Compare current week to previous week MATCH (e:Event) WHERE e.timestamp > datetime().minusDays(14) WITH CASE WHEN e.timestamp > datetime().minusDays(7) THEN 'current_week' ELSE 'previous_week' END AS period, COUNT(e) AS event_count WITH MAX(CASE WHEN period = 'current_week' THEN event_count ELSE 0 END) AS current, MAX(CASE WHEN period = 'previous_week' THEN event_count ELSE 0 END) AS previous RETURN current, previous, ((current - previous) * 100.0 / previous) AS growth_rate_pct; // Hourly growth compared to same hour yesterday WITH datetime().hour AS current_hour MATCH (e_today:Event) WHERE e_today.timestamp > datetime().minusHours(1) WITH current_hour, COUNT(e_today) AS today_count MATCH (e_yesterday:Event) WHERE e_yesterday.timestamp > datetime().minusHours(25) AND e_yesterday.timestamp < datetime().minusHours(24) AND e_yesterday.timestamp.hour = current_hour WITH today_count, COUNT(e_yesterday) AS yesterday_count RETURN today_count, yesterday_count, ((today_count - yesterday_count) * 100.0 / yesterday_count) AS change_pct; </code></pre></div> <h4 id="anomaly-detection-in-real-time" class="position-relative d-flex align-items-center group"> Anomaly Detection in Real-Time <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-in-real-time" aria-haspopup="dialog" aria-label="Share link: Anomaly Detection in Real-Time"> Share link </button> </h4>Identify unusual spikes or drops: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Detect transaction volume anomalies MATCH (t:Transaction) WHERE t.timestamp > datetime().minusHours(24) WITH date.truncate('hour', t.timestamp) AS hour, COUNT(t) AS hourly_count WITH AVG(hourly_count) AS avg_count, STDDEV(hourly_count) AS stddev_count, COLLECT({hour: hour, count: hourly_count}) AS hourly_data UNWIND hourly_data AS data WITH data.hour AS hour, data.count AS count, avg_count, stddev_count, (data.count - avg_count) / stddev_count AS z_score WHERE ABS(z_score) > 2 // More than 2 standard deviations RETURN hour, count, avg_count, z_score, CASE WHEN z_score > 2 THEN 'spike' WHEN z_score < -2 THEN 'drop' END AS anomaly_type ORDER BY ABS(z_score) DESC; </code></pre></div> <h3 id="incremental-materialized-views" class="position-relative d-flex align-items-center group"> Incremental Materialized Views <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="incremental-materialized-views" aria-haspopup="dialog" aria-label="Share link: Incremental Materialized Views"> Share link </button> </h3> <h4 id="pre-computed-aggregations" class="position-relative d-flex align-items-center group"> Pre-Computed Aggregations <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="pre-computed-aggregations" aria-haspopup="dialog" aria-label="Share link: Pre-Computed Aggregations"> Share link </button> </h4>Maintain real-time summary tables: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Create materialized view for user statistics CREATE MATERIALIZED VIEW user_activity_stats AS SELECT u.user_id, COUNT(DISTINCT e) AS total_events, COUNT(DISTINCT p) AS total_purchases, SUM(p.amount) AS lifetime_value, MAX(e.timestamp) AS last_activity, MIN(e.timestamp) AS first_activity FROM (u:User) LEFT JOIN (u)-[e:PERFORMED]->() LEFT JOIN (u)-[p:PURCHASED]->() GROUP BY u.user_id; // Query materialized view (fast, pre-computed) MATCH (stats:user_activity_stats {user_id: $user_id}) RETURN stats.total_events, stats.total_purchases, stats.lifetime_value, stats.last_activity; // View is automatically updated when new events arrive INSERT (u:User {user_id: 'user_123'}) INSERT (e:Event {event_id: 'evt_456'}) INSERT (u)-[:PERFORMED {timestamp: datetime()}]->(e); // user_activity_stats for user_123 immediately reflects the new event </code></pre></div> <h4 id="category-aggregations" class="position-relative d-flex align-items-center group"> Category Aggregations <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="category-aggregations" aria-haspopup="dialog" aria-label="Share link: Category Aggregations"> Share link </button> </h4>Real-time category performance metrics: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Materialized category statistics CREATE MATERIALIZED VIEW category_metrics AS SELECT c.category_id, c.name AS category_name, COUNT(DISTINCT p) AS product_count, COUNT(DISTINCT purchase) AS total_purchases, SUM(purchase.amount) AS total_revenue, AVG(purchase.amount) AS avg_sale_price FROM (c:Category) LEFT JOIN (c)<-[:IN_CATEGORY]-(p:Product) LEFT JOIN (p)<-[purchase:PURCHASED]-() WHERE purchase.timestamp > datetime().minusDays(7) GROUP BY c.category_id, c.name; // Fast category performance queries MATCH (cm:category_metrics) ORDER BY cm.total_revenue DESC LIMIT 10 RETURN cm.category_name, cm.product_count, cm.total_purchases, cm.total_revenue, cm.avg_sale_price; </code></pre></div> <h3 id="temporal-pattern-analysis" class="position-relative d-flex align-items-center group"> Temporal Pattern Analysis <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="temporal-pattern-analysis" aria-haspopup="dialog" aria-label="Share link: Temporal Pattern Analysis"> Share link </button> </h3> <h4 id="session-analytics" class="position-relative d-flex align-items-center group"> Session 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="session-analytics" aria-haspopup="dialog" aria-label="Share link: Session Analytics"> Share link </button> </h4>Analyze user sessions in real-time: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Current active sessions MATCH (u:User)-[:PERFORMED]->(e:Event) WHERE e.timestamp > datetime().minusMinutes(30) WITH u, MAX(e.timestamp) AS last_activity WHERE duration.between(last_activity, datetime()).minutes < 5 // Active in last 5 min RETURN COUNT(DISTINCT u) AS active_users_now; // Session duration distribution MATCH (u:User)-[:PERFORMED]->(e:Event) WHERE e.timestamp > datetime().minusHours(24) WITH u, MIN(e.timestamp) AS session_start, MAX(e.timestamp) AS session_end, COUNT(e) AS event_count WITH duration.between(session_start, session_end).minutes AS session_duration_min WITH CASE WHEN session_duration_min < 1 THEN '0-1 min' WHEN session_duration_min < 5 THEN '1-5 min' WHEN session_duration_min < 15 THEN '5-15 min' WHEN session_duration_min < 30 THEN '15-30 min' ELSE '30+ min' END AS duration_bucket, COUNT(*) AS session_count RETURN duration_bucket, session_count ORDER BY duration_bucket; </code></pre></div> <h4 id="conversion-funnels" class="position-relative d-flex align-items-center group"> Conversion Funnels <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="conversion-funnels" aria-haspopup="dialog" aria-label="Share link: Conversion Funnels"> Share link </button> </h4>Track real-time conversion rates: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// E-commerce funnel (last hour) MATCH (view:Event {event_type: 'page_view'}) WHERE view.timestamp > datetime().minusHours(1) AND view.page_type = 'product' WITH COUNT(DISTINCT view.user_id) AS viewed_users MATCH (cart:Event {event_type: 'add_to_cart'}) WHERE cart.timestamp > datetime().minusHours(1) WITH viewed_users, COUNT(DISTINCT cart.user_id) AS cart_users MATCH (purchase:Transaction) WHERE purchase.timestamp > datetime().minusHours(1) WITH viewed_users, cart_users, COUNT(DISTINCT purchase.user_id) AS purchased_users RETURN { step: 'viewed', users: viewed_users, conversion_rate: 100.0 } AS funnel_step UNION ALL RETURN { step: 'added_to_cart', users: cart_users, conversion_rate: (cart_users * 100.0 / viewed_users) } AS funnel_step UNION ALL RETURN { step: 'purchased', users: purchased_users, conversion_rate: (purchased_users * 100.0 / cart_users) } AS funnel_step; </code></pre></div> <h3 id="geolocation-analytics" class="position-relative d-flex align-items-center group"> Geolocation 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="geolocation-analytics" aria-haspopup="dialog" aria-label="Share link: Geolocation Analytics"> Share link </button> </h3> <h4 id="regional-performance" class="position-relative d-flex align-items-center group"> Regional Performance <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="regional-performance" aria-haspopup="dialog" aria-label="Share link: Regional Performance"> Share link </button> </h4>Real-time metrics by region: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Sales by region (last 24 hours) MATCH (t:Transaction)-[:FROM_USER]->(u:User) WHERE t.timestamp > datetime().minusHours(24) WITH u.country AS country, COUNT(t) AS transaction_count, SUM(t.amount) AS revenue, AVG(t.amount) AS avg_order_value, COUNT(DISTINCT u) AS unique_customers ORDER BY revenue DESC RETURN country, transaction_count, revenue, avg_order_value, unique_customers; // Active users by city (current hour) MATCH (u:User)-[:PERFORMED]->(e:Event) WHERE e.timestamp > datetime().minusHours(1) WITH u.city AS city, COUNT(DISTINCT u) AS active_users, COUNT(e) AS total_events ORDER BY active_users DESC LIMIT 20 RETURN city, active_users, total_events; </code></pre></div> <h3 id="cohort-analysis" class="position-relative d-flex align-items-center group"> Cohort Analysis <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="cohort-analysis" aria-haspopup="dialog" aria-label="Share link: Cohort Analysis"> Share link </button> </h3> <h4 id="retention-tracking" class="position-relative d-flex align-items-center group"> Retention Tracking <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="retention-tracking" aria-haspopup="dialog" aria-label="Share link: Retention Tracking"> Share link </button> </h4>Real-time cohort retention metrics: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Weekly cohort retention MATCH (u:User) WHERE u.created_at > datetime().minusDays(90) WITH date.truncate('week', u.created_at) AS cohort_week, u WITH cohort_week, COLLECT(u) AS cohort_users // Check activity in each subsequent week MATCH (cu:User)-[:PERFORMED]->(e:Event) WHERE cu IN cohort_users WITH cohort_week, SIZE(cohort_users) AS cohort_size, date.truncate('week', e.timestamp) AS activity_week, COUNT(DISTINCT cu) AS active_users WITH cohort_week, cohort_size, duration.between(cohort_week, activity_week).weeks AS weeks_since_join, active_users WHERE weeks_since_join >= 0 RETURN cohort_week, cohort_size, weeks_since_join, active_users, (active_users * 100.0 / cohort_size) AS retention_rate ORDER BY cohort_week, weeks_since_join; </code></pre></div> <h3 id="performance-optimization" class="position-relative d-flex align-items-center group"> Performance Optimization <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-optimization" aria-haspopup="dialog" aria-label="Share link: Performance Optimization"> Share link </button> </h3> <h4 id="query-caching" class="position-relative d-flex align-items-center group"> Query Caching <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="query-caching" aria-haspopup="dialog" aria-label="Share link: Query Caching"> Share link </button> </h4>Cache frequently accessed analytics: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Cache dashboard query results (application layer) CREATE CACHED QUERY dashboard_summary REFRESH EVERY 30 SECONDS AS MATCH (t:Transaction) WHERE t.timestamp > datetime().minusHours(24) RETURN COUNT(t) AS tx_24h, SUM(t.amount) AS revenue_24h, AVG(t.amount) AS avg_tx_value; // Subsequent calls return cached results (sub-millisecond) GET CACHED QUERY dashboard_summary; </code></pre></div> <h4 id="approximate-aggregations" class="position-relative d-flex align-items-center group"> Approximate Aggregations <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="approximate-aggregations" aria-haspopup="dialog" aria-label="Share link: Approximate Aggregations"> Share link </button> </h4>Use sampling for faster approximate results: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Sample 10% of data for faster estimates MATCH (e:Event) WHERE e.timestamp > datetime().minusDays(30) AND rand() < 0.1 // 10% sample WITH COUNT(e) AS sampled_count, COUNT(DISTINCT e.user_id) AS sampled_users RETURN sampled_count * 10 AS estimated_total_events, sampled_users * 10 AS estimated_unique_users; </code></pre></div> <h3 id="integration-with-streaming-platforms" class="position-relative d-flex align-items-center group"> Integration with Streaming Platforms <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-streaming-platforms" aria-haspopup="dialog" aria-label="Share link: Integration with Streaming Platforms"> Share link </button> </h3> <h4 id="kafka-integration" class="position-relative d-flex align-items-center group"> Kafka 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="kafka-integration" aria-haspopup="dialog" aria-label="Share link: Kafka Integration"> Share link </button> </h4>Process streaming events from Kafka: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Python consumer example from geode_client import Client from kafka import KafkaConsumer import json client = Client("geodedb://localhost:3141") consumer = KafkaConsumer( 'user-events', bootstrap_servers=['localhost:9092'], value_deserializer=lambda m: json.loads(m.decode('utf-8')) ) for message in consumer: event = message.value # Insert event into Geode in real-time client.execute(""" MERGE (u:User {user_id: $user_id}) CREATE (e:Event { event_id: $event_id, event_type: $event_type, timestamp: $timestamp }) CREATE (u)-[:PERFORMED]->(e) """, { 'user_id': event['user_id'], 'event_id': event['event_id'], 'event_type': event['event_type'], 'timestamp': event['timestamp'] }) </code></pre></div> <h3 id="best-practices" class="position-relative d-flex align-items-center group"> Best Practices <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="best-practices" aria-haspopup="dialog" aria-label="Share link: Best Practices"> Share link </button> </h3><ol> <li>Use Time Windows: Limit analytical queries to recent time ranges for better performance</li> <li>Materialize Common Aggregations: Pre-compute frequently accessed metrics</li> <li>Index Temporal Properties: Ensure timestamp fields are indexed for fast filtering</li> <li>Batch Small Updates: Group related updates into transactions for better throughput</li> <li>Sample Large Datasets: Use sampling for approximate results on massive datasets</li> <li>Cache Dashboard Queries: Cache results for 30-60 seconds for high-traffic dashboards</li> <li>Partition by Time: Organize data by time periods for efficient pruning</li> <li>Monitor Query Performance: Track slow queries and optimize hot paths</li> <li>Use Prepared Statements: Reduce parsing overhead for repeated analytics queries</li> <li>Leverage CDC: Use change streams for event-driven analytics pipelines</li> </ol> <h3 id="integration-with-geode-features" class="position-relative d-flex align-items-center group"> Integration with Geode Features <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-geode-features" aria-haspopup="dialog" aria-label="Share link: Integration with Geode Features"> Share link </button> </h3>Real-time analytics leverages: <ul> <li>MVCC: Non-blocking concurrent reads for consistent analytics</li> <li>Indexes: Fast temporal filtering with indexed timestamp fields</li> <li>Materialized Views: Incrementally maintained aggregations</li> <li>CDC: Change data capture for event streaming</li> <li>Telemetry: Real-time performance metrics via Prometheus</li> <li>QUIC Protocol: Low-latency query execution</li> </ul> Browse the tagged content below to discover documentation, tutorials, and guides for building real-time analytics applications with Geode.

Popular

Related Articles

Fraud and Anomaly Detection