Tag: Query Performance

Documentation tagged with query-performance in the Geode graph database. This comprehensive collection covers performance analysis, benchmarking methodologies, monitoring strategies, tuning techniques, and best practices for achieving maximum throughput and minimal latency in production graph workloads. <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; 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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>Query performance is fundamental to building responsive, scalable graph applications. Geode is designed for high-performance graph operations, with optimizations for pattern matching, traversals, aggregations, and complex multi-hop queries. Understanding performance characteristics and tuning strategies ensures your applications meet production SLAs. Performance considerations include: <ul> <li>Throughput: Queries per second under load</li> <li>Latency: Response time for individual queries</li> <li>Concurrency: Performance with multiple simultaneous users</li> <li>Scalability: Behavior as data volume grows</li> <li>Resource Efficiency: CPU, memory, and I/O utilization</li> </ul> <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="geode-benchmarks" class="position-relative d-flex align-items-center group"> Geode Benchmarks <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="geode-benchmarks" aria-haspopup="dialog" aria-label="Share link: Geode Benchmarks"> Share link </button> </h4>Representative performance metrics from the server features documentation: Point Queries: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Point lookup by unique ID (indexed) // Latency: <1ms (typically 100-500us) MATCH (u:User {user_id: '12345'}) RETURN u.name, u.email, u.created_at; </code></pre></div>Pattern Matching: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Short path (1-2 hops) with filters // Latency: 1-5ms MATCH (u:User {region: 'West'})-[:FOLLOWS]->(f:User)-[:POSTS]->(p:Post) WHERE p.created_at > '2025-01-01' RETURN u.name, f.name, p.title LIMIT 100; </code></pre></div>Aggregations: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Aggregation over relationships // Latency: 5-20ms for ~10k rows; 100-500ms for ~1M rows MATCH (u:User)-[:PURCHASED]->(p:Product) WHERE u.region = 'East' RETURN u.region, COUNT(p) AS purchases, SUM(p.price) AS total GROUP BY u.region; </code></pre></div>Complex Traversals: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Variable-length path with filters // Latency: 10-100ms for 3-5 hops MATCH path = (u:User)-[:FOLLOWS*1..3]->(influencer:User) WHERE u.id = 'user123' AND influencer.verified = true RETURN influencer.name, LENGTH(path) AS hops ORDER BY hops ASC LIMIT 20; </code></pre></div> <h4 id="hardware-considerations" class="position-relative d-flex align-items-center group"> Hardware Considerations <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="hardware-considerations" aria-haspopup="dialog" aria-label="Share link: Hardware Considerations"> Share link </button> </h4>Performance scales with hardware resources: CPU: <ul> <li>Graph traversals are CPU-intensive</li> <li>Pattern matching benefits from multiple cores</li> <li>Parallel query execution scales linearly up to 64 cores</li> <li>Aggregations can leverage SIMD instructions</li> </ul> Memory: <ul> <li>Hot working set should fit in RAM</li> <li>Index caching improves lookup performance significantly</li> <li>Large result sets require memory for materialization</li> <li>Graph cache reduces disk I/O dramatically</li> </ul> Storage: <ul> <li>NVMe SSDs recommended for I/O-intensive workloads</li> <li>Read-heavy workloads benefit from SSD caching</li> <li>Sequential scans leverage fast sequential reads</li> <li>Random access patterns need low-latency storage</li> </ul> Network: <ul> <li>QUIC protocol requires minimal bandwidth</li> <li>Large result sets can saturate gigabit connections</li> <li>10GbE recommended for high-throughput applications</li> <li>Low latency networks (< 1ms) critical for distributed setups</li> </ul> <h3 id="performance-analysis" class="position-relative d-flex align-items-center group"> Performance 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="performance-analysis" aria-haspopup="dialog" aria-label="Share link: Performance Analysis"> Share link </button> </h3> <h4 id="benchmarking-methodology" class="position-relative d-flex align-items-center group"> Benchmarking Methodology <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="benchmarking-methodology" aria-haspopup="dialog" aria-label="Share link: Benchmarking Methodology"> Share link </button> </h4>Establish performance baselines systematically: Step 1: Define Workload: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Identify representative query patterns -- Example: Social network workload mix -- 60% Point lookups MATCH (u:User {user_id: $user_id}) RETURN u; -- 25% Relationship traversals MATCH (u:User {user_id: $user_id})-[:FOLLOWS]->(f:User) RETURN f; -- 10% Aggregations MATCH (u:User)-[:PURCHASED]->(p:Product) WHERE u.region = $region RETURN COUNT(p), SUM(p.price); -- 5% Complex patterns MATCH (u:User)-[:FOLLOWS*1..2]->(f:User)-[:POSTS]->(p:Post) WHERE u.user_id = $user_id RETURN p LIMIT 50; </code></pre></div>Step 2: Load Test Data: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Create representative dataset # - Realistic data distribution # - Production-like volume # - Appropriate relationship density # Example: 1M users, 50M follows, 10M posts geode load --nodes User:1000000 \ --edges FOLLOWS:50000000 \ --edges POSTS:10000000 \ --distribution power-law </code></pre></div>Step 3: Measure Baseline: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Run benchmark suite geode benchmark run \ --workload social-network.yaml \ --duration 300s \ --concurrency 10 \ --report baseline-results.json # Output metrics: # - Throughput (QPS) # - Latency (p50, p95, p99, max) # - Error rate # - Resource utilization </code></pre></div>Step 4: Analyze Results: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Generate performance report geode benchmark analyze baseline-results.json # Metrics to review: # - Query distribution by latency # - Slow query identification # - Resource bottlenecks (CPU, memory, I/O) # - Cache hit rates # - Index utilization </code></pre></div> <h4 id="performance-profiling" class="position-relative d-flex align-items-center group"> Performance 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="performance-profiling" aria-haspopup="dialog" aria-label="Share link: Performance Profiling"> Share link </button> </h4>Detailed analysis of query execution: PROFILE Command: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Profile query execution PROFILE MATCH (u:User)-[:PURCHASED]->(p:Product)-[:IN_CATEGORY]->(c:Category) WHERE u.region = 'West' AND c.name = 'Electronics' RETURN u.name, COUNT(p) AS purchases, SUM(p.price) AS total ORDER BY total DESC LIMIT 20; // Output breakdown: // Planning time: 1.2ms // Execution time: 45.3ms // - Index seek (User.region): 2.1ms, 15,234 rows // - Expand (PURCHASED): 18.7ms, 89,456 rows // - Expand (IN_CATEGORY): 12.3ms, 89,456 rows // - Filter (c.name): 3.2ms, 12,345 rows // - Aggregation: 7.8ms, 234 groups // - Sort: 1.2ms, 234 rows // - Limit: 0.1ms, 20 rows // Total time: 46.5ms </code></pre></div>Execution Metrics: Key metrics to analyze: <ul> <li>Planning Time: Query optimization overhead</li> <li>Index Seek Time: Index access efficiency</li> <li>Expand Time: Relationship traversal cost</li> <li>Filter Time: Predicate evaluation overhead</li> <li>Aggregation Time: Grouping and computation cost</li> <li>Sort Time: Ordering overhead</li> <li>Materialization: Result set construction time</li> </ul> <h4 id="query-metrics" class="position-relative d-flex align-items-center group"> Query 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="query-metrics" aria-haspopup="dialog" aria-label="Share link: Query Metrics"> Share link </button> </h4>Monitor query performance in production: System Views: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Query performance statistics SELECT query_hash, query_text, execution_count, avg_duration_ms, p95_duration_ms, p99_duration_ms, max_duration_ms, total_rows_examined, avg_rows_returned FROM system.query_stats WHERE execution_count > 100 ORDER BY p99_duration_ms DESC LIMIT 50; </code></pre></div>Real-Time Monitoring: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Stream live query metrics geode monitor queries --stream # Output: # Timestamp | Query Hash | Duration | Rows | Status # 14:23:01 | a7b3c2d1 | 12ms | 150 | OK # 14:23:01 | e4f5g6h7 | 45ms | 1200 | OK # 14:23:02 | a7b3c2d1 | 15ms | 148 | OK </code></pre></div> <h3 id="performance-tuning" class="position-relative d-flex align-items-center group"> Performance 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="performance-tuning" aria-haspopup="dialog" aria-label="Share link: Performance Tuning"> Share link </button> </h3> <h4 id="index-strategy" class="position-relative d-flex align-items-center group"> Index Strategy <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-strategy" aria-haspopup="dialog" aria-label="Share link: Index Strategy"> Share link </button> </h4>Indexes are the primary lever for performance optimization: Create High-Impact Indexes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// 1. Primary key lookups (highest impact) CREATE UNIQUE INDEX user_id_idx ON User(user_id); // 2. Foreign key traversals CREATE INDEX follows_source_idx ON FOLLOWS(source_user_id); CREATE INDEX follows_target_idx ON FOLLOWS(target_user_id); // 3. Common filters CREATE INDEX user_region_idx ON User(region); CREATE INDEX post_created_idx ON Post(created_at); // 4. Composite indexes for multi-column filters CREATE INDEX user_search_idx ON User(region, account_status, created_at); </code></pre></div>Index Coverage Analysis: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Find queries missing indexes SELECT query_text, table_scans, execution_count, avg_duration_ms FROM system.query_stats WHERE table_scans > 0 AND execution_count > 50 ORDER BY execution_count * avg_duration_ms DESC; </code></pre></div> <h4 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> </h4>Rewrite queries for better performance: Before Optimization: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Slow: Multiple separate queries MATCH (u:User) WHERE u.user_id = '12345' RETURN u; MATCH (u:User {user_id: '12345'})-[:FOLLOWS]->(f:User) RETURN f; MATCH (u:User {user_id: '12345'})-[:FOLLOWS]->(f:User)-[:POSTS]->(p:Post) RETURN p LIMIT 10; // Total time: ~150ms (3 round trips) </code></pre></div>After Optimization: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Fast: Single combined query MATCH (u:User {user_id: '12345'}) OPTIONAL MATCH (u)-[:FOLLOWS]->(f:User) OPTIONAL MATCH (f)-[:POSTS]->(p:Post) RETURN u, COLLECT(DISTINCT f) AS following, COLLECT(p)[0..10] AS recent_posts; // Total time: ~45ms (1 round trip, less overhead) </code></pre></div> <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>Reuse connections for better throughput: Go Client: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">db, err := sql.Open("geode", "localhost:3141") if err != nil { log.Fatal(err) } db.SetMaxOpenConns(100) db.SetMaxIdleConns(10) db.SetConnMaxIdleTime(5 * time.Minute) // Pool handles connection reuse automatically </code></pre></div>Python Client: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Async connection pool from geode_client import ConnectionPool pool = ConnectionPool(host="localhost", port=3141, min_size=10, max_size=100) async with pool: async with pool.acquire() as conn: result, _ = await conn.query("MATCH (n:User) RETURN n LIMIT 10") </code></pre></div>Rust Client: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-rust" data-lang="rust">// High-performance pool with tokio use geode_client::ConnectionPool; let pool = ConnectionPool::new("localhost", 3141, 100); let conn = pool.acquire().await?; // Connection reuse handled by the pool </code></pre></div> <h4 id="caching-strategy" class="position-relative d-flex align-items-center group"> Caching Strategy <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="caching-strategy" aria-haspopup="dialog" aria-label="Share link: Caching Strategy"> Share link </button> </h4>Leverage caching for repeated queries: Query Result Caching: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Frequently accessed data MATCH (u:User {user_id: $user_id}) RETURN u.name, u.email, u.profile_image WITH CACHE TTL 300; -- Cache for 5 minutes </code></pre></div>Application-Level Caching: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">// Cache hot data in application layer var userCache = NewLRUCache(10000) func GetUser(userID string) (*User, error) { // Check cache first if user, ok := userCache.Get(userID); ok { return user, nil } // Query database on cache miss user, err := db.Query(` MATCH (u:User {user_id: $1}) RETURN u `, userID) if err == nil { userCache.Set(userID, user) } return user, err } </code></pre></div> <h3 id="production-optimization" class="position-relative d-flex align-items-center group"> Production 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="production-optimization" aria-haspopup="dialog" aria-label="Share link: Production Optimization"> Share link </button> </h3> <h4 id="load-testing" class="position-relative d-flex align-items-center group"> Load Testing <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="load-testing" aria-haspopup="dialog" aria-label="Share link: Load Testing"> Share link </button> </h4>Validate performance under realistic conditions: Concurrent User Simulation: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Simulate 100 concurrent users geode loadtest run \ --workload production-mix.yaml \ --users 100 \ --duration 600s \ --ramp-up 60s \ --report load-test-results.json # Monitor during test: # - Throughput (QPS) # - Latency percentiles # - Error rate # - Resource utilization (CPU, memory, I/O) # - Connection pool saturation </code></pre></div>Stress Testing: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Find breaking point geode loadtest stress \ --initial-users 10 \ --increment 10 \ --increment-interval 30s \ --max-users 500 \ --stop-on-error-rate 1.0 # Identifies: # - Maximum throughput # - Saturation point # - Performance degradation curve # - Resource bottlenecks </code></pre></div> <h4 id="monitoring" class="position-relative d-flex align-items-center group"> 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="monitoring" aria-haspopup="dialog" aria-label="Share link: Monitoring"> Share link </button> </h4>Continuous performance monitoring in production: Key Metrics to Track: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># Prometheus metrics configuration metrics: query_duration_seconds: type: histogram buckets: [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0, 5.0] query_throughput: type: counter labels: [query_type, status] active_connections: type: gauge cache_hit_rate: type: gauge labels: [cache_type] index_usage: type: counter labels: [index_name, operation] </code></pre></div>Alerting Rules: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># Performance degradation alerts alerts: - name: HighQueryLatency condition: p99_latency > 500ms duration: 5m severity: warning - name: LowThroughput condition: qps < 1000 duration: 5m severity: warning - name: HighErrorRate condition: error_rate > 1.0% duration: 1m severity: critical </code></pre></div> <h4 id="capacity-planning" class="position-relative d-flex align-items-center group"> Capacity Planning <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="capacity-planning" aria-haspopup="dialog" aria-label="Share link: Capacity Planning"> Share link </button> </h4>Plan for growth and scale: Growth Modeling: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Project performance at scale import pandas as pd import numpy as np # Current metrics current_data_size = 1_000_000 # nodes current_qps = 10_000 current_latency_p99 = 50 # ms # Growth projections growth_scenarios = [ {"months": 6, "data_multiplier": 2, "traffic_multiplier": 1.5}, {"months": 12, "data_multiplier": 5, "traffic_multiplier": 2.5}, {"months": 24, "data_multiplier": 10, "traffic_multiplier": 4.0}, ] for scenario in growth_scenarios: projected_data = current_data_size * scenario["data_multiplier"] projected_qps = current_qps * scenario["traffic_multiplier"] # Latency grows sub-linearly with data (logarithmic for indexed queries) projected_latency = current_latency_p99 * np.log2(scenario["data_multiplier"]) print(f"Month {scenario['months']}:") print(f" Data size: {projected_data:,} nodes") print(f" QPS: {projected_qps:,}") print(f" Estimated p99 latency: {projected_latency:.1f}ms") </code></pre></div>Resource Scaling: <ul> <li>Vertical Scaling: Increase CPU/memory for single-server deployments</li> <li>Horizontal Scaling: Add read replicas for read-heavy workloads</li> <li>Sharding: Partition data for extreme scale</li> <li>Caching: Add Redis/Memcached for hot data</li> </ul> <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> <h4 id="development" class="position-relative d-flex align-items-center group"> Development <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="development" aria-haspopup="dialog" aria-label="Share link: Development"> Share link </button> </h4>Write performance-conscious code from the start: Efficient Patterns: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- ✓ Good: Use indexes effectively MATCH (u:User {user_id: $user_id}) RETURN u; -- ✗ Bad: Scan all users MATCH (u:User) WHERE u.user_id = $user_id RETURN u; -- ✓ Good: Limit result sets MATCH (u:User)-[:FOLLOWS]->(f:User) WHERE u.user_id = $user_id RETURN f LIMIT 100; -- ✗ Bad: Return unbounded results MATCH (u:User)-[:FOLLOWS]->(f:User) WHERE u.user_id = $user_id RETURN f; -- ✓ Good: Filter early MATCH (u:User {region: 'West'})-[:PURCHASED]->(p:Product) WHERE p.price > 100 RETURN u.name, p.name; -- ✗ Bad: Filter late MATCH (u:User)-[:PURCHASED]->(p:Product) WHERE u.region = 'West' AND p.price > 100 RETURN u.name, p.name; </code></pre></div> <h4 id="testing" class="position-relative d-flex align-items-center group"> Testing <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="testing" aria-haspopup="dialog" aria-label="Share link: Testing"> Share link </button> </h4>Include performance tests in CI/CD: Regression Testing: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Run performance regression suite geode test performance \ --baseline baseline-metrics.json \ --threshold 10% # Fail if >10% slower --report regression-results.json # Catches performance regressions before deployment </code></pre></div>Continuous Benchmarking: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># GitHub Actions workflow name: Performance Benchmark on: [push, pull_request] jobs: benchmark: runs-on: ubuntu-latest steps: - name: Run benchmarks run: | geode benchmark run --workload standard.yaml - name: Compare to baseline run: | geode benchmark compare \ --baseline main-branch-results.json \ --current pr-results.json \ --threshold 5% </code></pre></div> <h4 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> </h4>Optimize for production workloads: Configuration Tuning: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-yaml" data-lang="yaml"># geode.yaml - Production configuration server: max_connections: 1000 connection_timeout: 30s query_timeout: 60s performance: query_cache_size: 10000 query_cache_ttl: 300s index_cache_size: 10GB worker_threads: 16 monitoring: enable_metrics: true metrics_port: 9090 enable_profiling: true slow_query_threshold: 100ms </code></pre></div>Health Checks: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">// Lightweight health check query MATCH (n:User) RETURN COUNT(n) AS total_users TIMEOUT 5s; // Should return instantly with cached result </code></pre></div> <h3 id="troubleshooting" class="position-relative d-flex align-items-center group"> Troubleshooting <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="troubleshooting" aria-haspopup="dialog" aria-label="Share link: Troubleshooting"> Share link </button> </h3> <h4 id="common-performance-issues" class="position-relative d-flex align-items-center group"> Common Performance Issues <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="common-performance-issues" aria-haspopup="dialog" aria-label="Share link: Common Performance Issues"> Share link </button> </h4>Issue: Slow Point Lookups: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Problem: Missing index MATCH (u:User) WHERE u.email = 'user@example.com' RETURN u; -- 500ms latency -- Solution: Add unique index CREATE UNIQUE INDEX user_email_idx ON User(email); -- Latency drops to <5ms </code></pre></div>Issue: High Latency Under Load: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Problem: Connection pool exhaustion # Symptoms: Latency spikes during peak hours # Solution: Increase pool size geode config set max_connections 500 geode restart # Monitor connection usage geode monitor connections --watch </code></pre></div>Issue: Memory Exhaustion: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Problem: Unbounded result sets MATCH (u:User)-[:FOLLOWS]->(f:User) RETURN u, f; -- Returns millions of rows, consumes GB of memory -- Solution: Always limit results MATCH (u:User)-[:FOLLOWS]->(f:User) RETURN u, f LIMIT 1000; </code></pre></div> <h3 id="integration-with-geode" class="position-relative d-flex align-items-center group"> Integration with Geode <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" aria-haspopup="dialog" aria-label="Share link: Integration with Geode"> Share link </button> </h3>Performance optimization integrates with Geode features: <ul> <li>EXPLAIN/PROFILE: Built-in performance analysis tools</li> <li>Indexes: Automatic query optimization via indexes</li> <li>Caching: Transparent query result caching</li> <li>Monitoring: Prometheus metrics integration</li> <li>Connection Pooling: Efficient resource management</li> <li>Transactions: Optimized transaction processing</li> </ul> <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>Query Optimization: Optimization techniques and strategies</li> <li>Indexes: Index design and management</li> <li>EXPLAIN Command: Execution plan analysis</li> <li>PROFILE Command: Runtime profiling</li> <li>Monitoring: Production monitoring and alerting</li> <li>Best Practices: Development and deployment best practices</li> <li>Benchmarking: Performance testing methodologies</li> </ul> <hr> Browse the tagged content below to discover comprehensive guides, tutorials, and best practices for query performance in Geode. Learn how to build high-performance graph applications that deliver consistent, low-latency responses under production workloads.

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