Category: Performance and Scaling

The Performance and Scaling category provides comprehensive guidance on optimizing Geode for maximum throughput, minimal latency, and efficient resource utilization. Whether you’re tuning a single-node deployment or planning a distributed architecture, these resources help you achieve production-ready performance. <h3 id="understanding-graph-database-performance" class="position-relative d-flex align-items-center group"> Understanding Graph Database 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="understanding-graph-database-performance" aria-haspopup="dialog" aria-label="Share link: Understanding Graph Database Performance"> 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>Graph database performance differs fundamentally from relational databases. Traditional databases excel at scanning large tables but struggle with multi-hop joins. Graphs invert this: traversing relationships is fast regardless of depth, but scanning all nodes is expensive. Understanding this trade-off is essential for effective optimization. Index-based lookups provide O(log n) access to starting points for graph traversals. Relationship traversal operates in O(k) time where k is the number of adjacent relationships, regardless of total graph size. Pattern matching combines these primitives, making proper index usage critical for query performance. <h3 id="indexing-strategies" class="position-relative d-flex align-items-center group"> Indexing 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="indexing-strategies" aria-haspopup="dialog" aria-label="Share link: Indexing Strategies"> Share link </button> </h3>Indexes are the foundation of graph query performance. Without indexes, Geode must scan all nodes to find pattern matches. With proper indexes, queries execute in milliseconds even on large graphs. <h4 id="label-and-property-indexes" class="position-relative d-flex align-items-center group"> Label and Property Indexes <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="label-and-property-indexes" aria-haspopup="dialog" aria-label="Share link: Label and Property Indexes"> Share link </button> </h4>Create indexes on frequently queried node labels and properties: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Index for user lookups by email CREATE INDEX user_email_idx ON Person(email); -- Composite index for multi-property queries CREATE INDEX product_category_price_idx ON Product(category, price); -- Index on relationship properties CREATE INDEX transaction_timestamp_idx ON TRANSACTION(timestamp); </code></pre></div>When to index: <ul> <li>Properties used in <code>WHERE</code> clauses</li> <li>Properties used for <code>ORDER BY</code></li> <li>Properties used in equality comparisons</li> <li>Frequently traversed relationship types</li> </ul> When not to index: <ul> <li>Properties with very low cardinality (e.g., boolean flags)</li> <li>Properties rarely queried</li> <li>Write-heavy workloads where index maintenance overhead outweighs read benefits</li> </ul> <h4 id="index-selection-and-monitoring" class="position-relative d-flex align-items-center group"> Index Selection and 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="index-selection-and-monitoring" aria-haspopup="dialog" aria-label="Share link: Index Selection and Monitoring"> Share link </button> </h4>Geode’s query planner automatically selects optimal indexes using cost-based optimization. Use <code>EXPLAIN</code> to verify index usage: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">EXPLAIN MATCH (u:User {email: $email})-[:PURCHASED]->(p:Product) WHERE p.price > 100 RETURN p.name, p.price ORDER BY p.price DESC </code></pre></div>The execution plan shows: <ul> <li>Which indexes were considered and chosen</li> <li>Estimated cardinalities at each step</li> <li>Scan types (index seek vs. full scan)</li> <li>Join strategies</li> </ul> Monitor index effectiveness in production: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">import geode_client client = geode_client.open_database("localhost:3141") async with client.connection() as client: # Query index statistics stats, _ = await client.query(""" SHOW INDEX STATISTICS """) for idx in stats: print(f"Index: {idx['name']}") print(f" Reads: {idx['read_count']}") print(f" Selectivity: {idx['selectivity']}") print(f" Size: {idx['size_bytes']}") </code></pre></div> <h3 id="query-optimization" class="position-relative d-flex align-items-center group"> Query 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="query-optimization" aria-haspopup="dialog" aria-label="Share link: Query Optimization"> Share link </button> </h3> <h4 id="pattern-matching-optimization" class="position-relative d-flex align-items-center group"> Pattern Matching 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="pattern-matching-optimization" aria-haspopup="dialog" aria-label="Share link: Pattern Matching Optimization"> Share link </button> </h4>Order patterns from most selective to least selective. The query planner uses cardinality estimates, but you can guide it with explicit ordering: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Inefficient: starts with broad pattern MATCH (u:User)-[:FOLLOWS]->(friend:User) WHERE u.email = 'alice@example.com' RETURN friend.name -- Efficient: starts with indexed lookup MATCH (u:User {email: 'alice@example.com'})-[:FOLLOWS]->(friend:User) RETURN friend.name </code></pre></div>Variable-length paths can be expensive. Limit the maximum depth when possible: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Unbounded depth (potentially expensive) MATCH path = (a:Person)-[:KNOWS*]-(b:Person {name: 'Bob'}) RETURN path -- Bounded depth (more predictable) MATCH path = (a:Person)-[:KNOWS*1..4]-(b:Person {name: 'Bob'}) RETURN path LIMIT 10 </code></pre></div> <h4 id="aggregation-optimization" class="position-relative d-flex align-items-center group"> Aggregation 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="aggregation-optimization" aria-haspopup="dialog" aria-label="Share link: Aggregation Optimization"> Share link </button> </h4>Push filters before aggregations to reduce data volumes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Less efficient: aggregates then filters MATCH (u:User)-[:PURCHASED]->(p:Product) WITH u, COUNT(p) AS purchase_count WHERE purchase_count > 10 RETURN u.name, purchase_count -- More efficient: filters then aggregates MATCH (u:User)-[:PURCHASED]->(p:Product) WHERE p.price > 50 WITH u, COUNT(p) AS expensive_purchases WHERE expensive_purchases > 10 RETURN u.name, expensive_purchases </code></pre></div>Use indexes on aggregation keys: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Ensure index exists for grouping CREATE INDEX transaction_user_idx ON TRANSACTION(user_id); -- Efficient grouped aggregation MATCH (u:User)-[t:TRANSACTION]->() RETURN u.id, SUM(t.amount) AS total GROUP BY u.id </code></pre></div> <h4 id="prepared-statements" class="position-relative d-flex align-items-center group"> Prepared Statements <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="prepared-statements" aria-haspopup="dialog" aria-label="Share link: Prepared Statements"> Share link </button> </h4>Prepared statements improve performance by caching query plans: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">client = geode_client.open_database("localhost:3141") async with client.connection() as client: # Prepare once stmt = await client.prepare(""" MATCH (u:User {id: $user_id})-[:PURCHASED]->(p:Product) RETURN p.name, p.price ORDER BY p.price DESC """) # Execute many times with different parameters for user_id in range(1000): result, _ = await stmt.execute({"user_id": user_id}) process_results(result) </code></pre></div>Benefits: <ul> <li>Query parsing and planning occur once</li> <li>Parameter values can be efficiently bound</li> <li>Network roundtrips reduced in some protocols</li> <li>Query plan cache improves resource utilization</li> </ul> <h3 id="profiling-and-diagnostics" class="position-relative d-flex align-items-center group"> Profiling and Diagnostics <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="profiling-and-diagnostics" aria-haspopup="dialog" aria-label="Share link: Profiling and Diagnostics"> Share link </button> </h3> <h4 id="query-profiling" class="position-relative d-flex align-items-center group"> Query Profiling <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-profiling" aria-haspopup="dialog" aria-label="Share link: Query Profiling"> Share link </button> </h4>Use <code>PROFILE</code> to measure actual query execution: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">PROFILE MATCH (u:User)-[:FOLLOWS*2..3]->(recommendation:User) WHERE NOT EXISTS { MATCH (u)-[:FOLLOWS]->(recommendation) } RETURN recommendation.name, COUNT(*) AS mutual_friends ORDER BY mutual_friends DESC LIMIT 10 </code></pre></div>Profile output includes: <ul> <li>Execution time: Actual wall-clock time per operator</li> <li>Rows processed: Actual cardinalities vs. estimates</li> <li>Cache hits: Index and data cache effectiveness</li> <li>Memory usage: Peak memory per operator</li> </ul> Compare estimated vs. actual cardinalities. Large discrepancies indicate: <ul> <li>Missing or outdated statistics</li> <li>Complex predicates the planner can’t estimate</li> <li>Correlated data patterns</li> <li>Opportunities for manual optimization</li> </ul> <h4 id="performance-metrics" class="position-relative d-flex align-items-center group"> Performance 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="performance-metrics" aria-haspopup="dialog" aria-label="Share link: Performance Metrics"> Share link </button> </h4>Monitor key metrics in production: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Query performance metrics metrics, _ = await client.query(""" SELECT query_id, execution_time_ms, rows_read, rows_written, cache_hit_ratio FROM system.query_log WHERE execution_time_ms > 1000 ORDER BY execution_time_ms DESC LIMIT 20 """) </code></pre></div>Critical metrics: <ul> <li>Query latency: p50, p95, p99 percentiles</li> <li>Throughput: Queries per second</li> <li>Cache hit rates: Index cache, data cache, query plan cache</li> <li>Resource utilization: CPU, memory, disk I/O</li> <li>Connection pool: Active connections, wait times, errors</li> </ul> <h4 id="continuous-profiling" class="position-relative d-flex align-items-center group"> Continuous Profiling <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="continuous-profiling" aria-haspopup="dialog" aria-label="Share link: Continuous Profiling"> Share link </button> </h4>Enable query logging for production systems: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Server configuration ./geode serve \ --query-log /var/log/geode/queries.log \ --slow-query-threshold 100ms \ --metrics-export prometheus \ --metrics-port 9090 </code></pre></div>Integrate with observability platforms: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># Prometheus configuration scrape_configs: - job_name: 'geode' static_configs: - targets: ['localhost:9090'] metrics_path: '/metrics' </code></pre></div> <h3 id="connection-management" class="position-relative d-flex align-items-center group"> Connection Management <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-management" aria-haspopup="dialog" aria-label="Share link: Connection Management"> Share link </button> </h3> <h4 id="connection-pooling" class="position-relative d-flex align-items-center group"> Connection Pooling <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-pooling" aria-haspopup="dialog" aria-label="Share link: Connection Pooling"> Share link </button> </h4>Client libraries provide automatic connection pooling: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">import "geodedb.com/geode" // Create connection pool pool, err := geode.NewPool(geode.PoolConfig{ MinSize: 10, MaxSize: 100, MaxIdleTime: 5 * time.Minute, MaxLifetime: 30 * time.Minute, }) // Pool automatically manages connections ctx := context.Background() result, err := pool.Query(ctx, "MATCH (n:Node) RETURN count(n)") </code></pre></div>Pool sizing guidelines: <ul> <li>MinSize: Keep connections warm for low-latency response</li> <li>MaxSize: Match available server connection capacity</li> <li>Idle timeout: Balance resource utilization vs. connection overhead</li> <li>Lifetime: Rotate connections to prevent resource leaks</li> </ul> <h4 id="concurrent-queries" class="position-relative d-flex align-items-center group"> Concurrent 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="concurrent-queries" aria-haspopup="dialog" aria-label="Share link: Concurrent Queries"> Share link </button> </h4>Geode’s MVCC architecture enables high concurrency: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">import asyncio async def concurrent_queries(): client = geode_client.open_database("localhost:3141") async with client.connection() as client: # Execute 100 queries concurrently tasks = [ client.execute("MATCH (n:Node {id: $id}) RETURN n", {"id": i}) for i in range(100) ] results = await asyncio.gather(*tasks) return results </code></pre></div>Concurrency limits: <ul> <li>Read queries: Limited only by system resources (CPU, memory)</li> <li>Write queries: SSI isolation may cause conflicts in high-contention scenarios</li> <li>Transactions: Use optimistic concurrency; retry on conflicts</li> </ul> <h3 id="memory-management" class="position-relative d-flex align-items-center group"> Memory Management <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-management" aria-haspopup="dialog" aria-label="Share link: Memory Management"> Share link </button> </h3> <h4 id="cache-tuning" class="position-relative d-flex align-items-center group"> Cache Tuning <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="cache-tuning" aria-haspopup="dialog" aria-label="Share link: Cache Tuning"> Share link </button> </h4>Geode uses multiple caches for performance: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Server configuration ./geode serve \ --index-cache-size 2GB \ --data-cache-size 8GB \ --query-plan-cache-size 256MB </code></pre></div>Index cache: Stores index B-tree nodes. Size based on index working set. Data cache: Stores frequently accessed nodes and relationships. Size based on hot data working set. Query plan cache: Stores compiled query plans. Usually small (100-500MB). Monitor cache effectiveness: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">SELECT cache_name, size_bytes, entry_count, hit_rate, eviction_count FROM system.cache_statistics </code></pre></div>Adjust sizes based on hit rates. Target 90%+ hit rate for index cache, 70%+ for data cache. <h4 id="memory-bounded-operations" class="position-relative d-flex align-items-center group"> Memory-Bounded Operations <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-bounded-operations" aria-haspopup="dialog" aria-label="Share link: Memory-Bounded Operations"> Share link </button> </h4>Limit memory usage for large operations: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Use LIMIT to bound result sets MATCH (n:Node) RETURN n LIMIT 10000 -- Use pagination for large exports MATCH (n:Node) WHERE n.created > $checkpoint ORDER BY n.created LIMIT 10000 </code></pre></div>For analytical workloads, consider materialized views or batch processing. <h3 id="horizontal-scaling" class="position-relative d-flex align-items-center group"> Horizontal Scaling <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="horizontal-scaling" aria-haspopup="dialog" aria-label="Share link: Horizontal Scaling"> Share link </button> </h3> <h4 id="read-replicas" class="position-relative d-flex align-items-center group"> Read Replicas <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="read-replicas" aria-haspopup="dialog" aria-label="Share link: Read Replicas"> Share link </button> </h4>Distribute read load across multiple nodes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Primary node (accepts writes) ./geode serve --role primary --listen 0.0.0.0:3141 # Read replica (read-only) ./geode serve \ --role replica \ --primary primary.example.com:3141 \ --listen 0.0.0.0:3141 </code></pre></div>Client routing: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Primary for writes primary = geode_client.open_database("primary.example.com:3141") # Replicas for reads replicas = geode_client.Pool([ "replica1.example.com:3141", "replica2.example.com:3141", "replica3.example.com:3141" ]) # Route appropriately await primary.execute("CREATE (n:Node {id: 123})") result, _ = await replicas.query("MATCH (n:Node) RETURN count(n)") </code></pre></div> <h4 id="sharding-strategies" class="position-relative d-flex align-items-center group"> Sharding 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="sharding-strategies" aria-haspopup="dialog" aria-label="Share link: Sharding Strategies"> Share link </button> </h4>Partition large graphs across multiple databases: Geographic sharding: Route based on location Functional sharding: Separate by entity type Hash sharding: Distribute by consistent hash Implement application-level routing: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">def get_shard(user_id): """Route to appropriate shard based on user ID""" shard_id = hash(user_id) % NUM_SHARDS return shard_connections[shard_id] # Route query to correct shard shard = get_shard(user_id) result, _ = await shard.query("MATCH (u:User {id: $id})", {"id": user_id}) </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>Index strategically: Cover common query patterns without over-indexing</li> <li>Profile before optimizing: Measure actual bottlenecks, don’t guess</li> <li>Use prepared statements: For repeated queries with different parameters</li> <li>Leverage connection pooling: Reuse connections efficiently</li> <li>Monitor continuously: Track performance metrics in production</li> <li>Test at scale: Performance characteristics change with data volume</li> <li>Plan for growth: Design with 10x future capacity in mind</li> <li>Document optimization decisions: Explain why indexes and tuning choices were made</li> </ol> <h3 id="related-topics" class="position-relative d-flex align-items-center group"> Related Topics <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-topics" aria-haspopup="dialog" aria-label="Share link: Related Topics"> Share link </button> </h3><ul> <li><a href="/categories/query-optimization/" >Query Optimization</a> - GQL query tuning techniques</li> <li><a href="/tags/indexing/" >Indexing</a> - Index design and management</li> <li><a href="/tags/profiling/" >Profiling</a> - Performance measurement tools</li> <li><a href="/tags/monitoring/" >Monitoring</a> - Production observability</li> <li><a href="/docs/architecture/" >Architecture</a> - System design for performance</li> <li><a href="/tags/deployment/" >Deployment</a> - Production deployment patterns</li> </ul> <h3 id="further-reading" class="position-relative d-flex align-items-center group"> Further Reading <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="further-reading" aria-haspopup="dialog" aria-label="Share link: Further Reading"> Share link </button> </h3><ul> <li><a href="/docs/gql-reference/explain/" >EXPLAIN Command</a> - Query execution plans</li> <li><a href="/docs/gql-reference/profile/" >PROFILE Command</a> - Query performance measurement</li> <li><a href="/docs/reference/indexes/" >Index Management</a> - Creating and maintaining indexes</li> <li><a href="/docs/configuration/" >Configuration Reference</a> - Performance tuning parameters</li> <li><a href="/docs/performance/benchmarking/" >Benchmarking Guide</a> - Performance testing methodology</li> </ul>

Popular

Related Articles

Querying, Indexing, and Query Optimization

Performance and Scalability