Performance and Optimization Page 2 | Tags

<h2 id="performance-and-optimization-in-geode" class="position-relative d-flex align-items-center group"> Performance and Optimization in 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="performance-and-optimization-in-geode" aria-haspopup="dialog" aria-label="Share link: Performance and Optimization in Geode"> Share link </button> </h2><div id="headingShareModal" class="heading-share-modal" role="dialog" aria-modal="true" aria-labelledby="headingShareTitle" hidden> <div class="hsm-dialog" role="document"> <div class="hsm-header"> <h2 id="headingShareTitle" class="h6 mb-0 fw-bold">Share this section</h2> <button type="button" class="hsm-close" aria-label="Close"> </button> </div> <div class="hsm-body"> <label for="headingShareInput" class="form-label small text-muted mb-1 text-uppercase fw-bold" style="font-size: 0.7rem; letter-spacing: 0.5px;">Permalink</label> <div class="input-group mb-4 hsm-url-group"> <input id="headingShareInput" type="text" class="form-control font-monospace" readonly aria-readonly="true" style="font-size: 0.85rem;" /> <button class="btn btn-primary hsm-copy" type="button" aria-label="Copy" title="Copy"> </button> </div> <div class="small fw-bold mb-2 text-muted text-uppercase" style="font-size: 0.7rem; 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Geode achieves exceptional performance through a combination of modern systems programming (Zig), advanced networking (QUIC protocol), sophisticated query planning, and comprehensive indexing strategies. <h3 id="introduction-to-database-performance" class="position-relative d-flex align-items-center group"> Introduction to 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="introduction-to-database-performance" aria-haspopup="dialog" aria-label="Share link: Introduction to Database Performance"> Share link </button> </h3>Database performance encompasses multiple dimensions: <ul> <li>Query Latency: Time to execute individual queries (milliseconds to seconds)</li> <li>Throughput: Queries per second (QPS) the system can sustain</li> <li>Concurrency: Number of simultaneous connections and transactions</li> <li>Scalability: Performance characteristics as data volume grows</li> <li>Resource Efficiency: CPU, memory, disk I/O, and network utilization</li> </ul> Unlike traditional relational databases optimized for tabular data, graph databases must efficiently traverse relationships, often following unpredictable access patterns. Geode addresses these challenges through careful architectural choices and provides comprehensive tools for monitoring and optimization. <h3 id="geodes-performance-architecture" class="position-relative d-flex align-items-center group"> Geode&rsquo;s Performance Architecture <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="geodes-performance-architecture" aria-haspopup="dialog" aria-label="Share link: Geodes Performance Architecture"> Share link </button> </h3> <h4 id="zero-cost-abstractions-with-zig" class="position-relative d-flex align-items-center group"> Zero-Cost Abstractions with Zig <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="zero-cost-abstractions-with-zig" aria-haspopup="dialog" aria-label="Share link: Zero-Cost Abstractions with Zig"> Share link </button> </h4>Geode is implemented in Zig, a modern systems programming language that provides: Memory Control: Direct control over allocation with no garbage collection pauses <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-zig" data-lang="zig">// Predictable memory allocation var arena = std.heap.ArenaAllocator.init(allocator); defer arena.deinit(); const nodes = try arena.allocator().alloc(Node, count); </code></pre></div>Compile-Time Optimization: Generic code specialized at compile time without runtime overhead Zero-Cost Error Handling: Error handling compiled away in success paths SIMD Support: Vectorized operations for bulk processing These features enable Geode to achieve performance comparable to C/C++ implementations while maintaining memory safety. <h4 id="quic-protocol-networking" class="position-relative d-flex align-items-center group"> QUIC Protocol Networking <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="quic-protocol-networking" aria-haspopup="dialog" aria-label="Share link: QUIC Protocol Networking"> Share link </button> </h4>Geode uses QUIC (Quick UDP Internet Connections) instead of traditional TCP: Benefits over TCP: <ul> <li>0-RTT Connection Resumption: Clients reconnect instantly without handshake</li> <li>Multiplexing Without Head-of-Line Blocking: Multiple queries over single connection</li> <li>Built-in TLS 1.3: Security without performance penalty</li> <li>Better Loss Recovery: Faster recovery from packet loss</li> </ul> Performance Impact: <ul> <li>Lower latency for short queries (workload dependent)</li> <li>Higher throughput under packet loss (workload dependent)</li> <li>Connection pooling effectiveness improved</li> </ul> <h4 id="query-planning-and-optimization" class="position-relative d-flex align-items-center group"> Query Planning and 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-planning-and-optimization" aria-haspopup="dialog" aria-label="Share link: Query Planning and Optimization"> Share link </button> </h4>Geode’s query planner analyzes GQL queries and generates optimized execution plans: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Complex query analyzed by planner MATCH (u:User {country: 'USA'})-[:PURCHASED]->(p:Product) WHERE p.price > 100 RETURN u.name, count(p) AS purchase_count ORDER BY purchase_count DESC LIMIT 10; </code></pre></div>Planner optimizations: <ol> <li>Index Selection: Chooses best index for filtering</li> <li>Join Ordering: Orders traversals to minimize intermediate results</li> <li>Predicate Pushdown: Applies filters as early as possible</li> <li>Cardinality Estimation: Uses statistics to estimate result sizes</li> </ol> <h3 id="performance-monitoring-and-profiling" class="position-relative d-flex align-items-center group"> Performance Monitoring and 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-monitoring-and-profiling" aria-haspopup="dialog" aria-label="Share link: Performance Monitoring and Profiling"> Share link </button> </h3> <h4 id="explain-query-plan-visualization" class="position-relative d-flex align-items-center group"> EXPLAIN: Query Plan Visualization <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="explain-query-plan-visualization" aria-haspopup="dialog" aria-label="Share link: EXPLAIN: Query Plan Visualization"> Share link </button> </h4>Use EXPLAIN to see how Geode will execute a query without running it: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">EXPLAIN MATCH (u:User)-[:FOLLOWS]->(friend)-[:LIKES]->(p:Post) WHERE u.id = $user_id RETURN p.title, count(friend) AS friend_likes ORDER BY friend_likes DESC; </code></pre></div>Output: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-fallback" data-lang="fallback">QueryPlan: 1. IndexSeek: User.id = $user_id (cost: 1) 2. Expand: (User)-[:FOLLOWS]->(friend) (est. 150 rows) 3. Expand: (friend)-[:LIKES]->(Post) (est. 3000 rows) 4. Aggregate: count(friend) GROUP BY p.title (est. 500 groups) 5. Sort: friend_likes DESC (est. 500 rows) 6. Limit: 10 Estimated Cost: 3152 </code></pre></div>Interpreting plans: <ul> <li>IndexSeek: Fast lookup using index (best case)</li> <li>IndexScan: Scanning index range</li> <li>NodeScan: Full table scan (avoid if possible)</li> <li>Expand: Relationship traversal</li> <li>Filter: Post-traversal filtering (push down if possible)</li> </ul> <h4 id="profile-runtime-performance-analysis" class="position-relative d-flex align-items-center group"> PROFILE: Runtime 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="profile-runtime-performance-analysis" aria-haspopup="dialog" aria-label="Share link: PROFILE: Runtime Performance Analysis"> Share link </button> </h4>PROFILE executes the query and provides detailed timing information: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">PROFILE MATCH (u:User {id: $user_id})-[:PURCHASED*1..3]->(p:Product) RETURN p.name, count(*) AS purchase_paths; </code></pre></div>Output: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-fallback" data-lang="fallback">Execution Profile: IndexSeek (User.id): 0.05ms, 1 row VariableLengthExpand: 24.3ms, 1,247 rows - Path length 1: 12 rows (0.8ms) - Path length 2: 234 rows (8.2ms) - Path length 3: 1,001 rows (15.3ms) Aggregate: 3.1ms, 87 groups Total: 27.45ms </code></pre></div>Key metrics: <ul> <li>Time per operation: Identify bottlenecks</li> <li>Row counts: Detect unexpected cardinality</li> <li>Cache hit rates: Memory efficiency</li> <li>Index usage: Verify indexes are used</li> </ul> <h4 id="python-client-profiling" class="position-relative d-flex align-items-center group"> Python Client 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="python-client-profiling" aria-haspopup="dialog" aria-label="Share link: Python Client Profiling"> Share link </button> </h4><div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">from geode_client import Client import asyncio async def profile_query(): client = Client(host="localhost", port=3141) async with client.connection() as conn: result, _ = await conn.query(""" PROFILE MATCH (p:Product) WHERE p.category = $cat RETURN p.name, p.price """, {"cat": "Electronics"}) # Access profiling information print(f"Execution time: {result.profile.total_time_ms}ms") print(f"Rows returned: {result.profile.rows_returned}") for stage in result.profile.stages: print(f"{stage.name}: {stage.time_ms}ms ({stage.rows} rows)") </code></pre></div> <h3 id="performance-optimization-techniques" class="position-relative d-flex align-items-center group"> Performance Optimization Techniques <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-techniques" aria-haspopup="dialog" aria-label="Share link: Performance Optimization Techniques"> Share link </button> </h3> <h4 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> </h4>B-Tree Indexes for Equality and Range Queries: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Create indexes on frequently filtered properties CREATE INDEX user_email ON User(email); CREATE INDEX product_price ON Product(price); CREATE INDEX order_date ON Order(date); -- Index usage MATCH (u:User {email: $email}) -- Uses user_email index RETURN u; MATCH (p:Product) WHERE p.price BETWEEN 100 AND 500 -- Uses product_price index RETURN p; </code></pre></div>Composite Indexes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Index multiple properties together CREATE INDEX product_category_price ON Product(category, price); -- Efficient for queries filtering both MATCH (p:Product {category: 'Electronics'}) WHERE p.price > 100 RETURN p; </code></pre></div>Full-Text Search Indexes: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">CREATE TEXT INDEX product_description ON Product(description) WITH (analyzer = 'english', tokenizer = 'standard'); -- Fast text search MATCH (p:Product) WHERE text_search(p.description, 'wireless bluetooth speaker') RETURN p.name, text_rank(p.description, 'wireless bluetooth speaker') AS relevance ORDER BY relevance DESC; </code></pre></div>Vector Indexes (HNSW): <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">CREATE VECTOR INDEX product_embeddings ON Product(embedding) WITH (metric = 'cosine', dimensions = 768, m = 16); -- Fast similarity search MATCH (p:Product) WITH p, vector_similarity(p.embedding, $query_vector, 'cosine') AS similarity WHERE similarity > 0.7 RETURN p.name, similarity ORDER BY similarity DESC LIMIT 10; </code></pre></div> <h4 id="query-optimization-patterns" class="position-relative d-flex align-items-center group"> Query Optimization Patterns <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-patterns" aria-haspopup="dialog" aria-label="Share link: Query Optimization Patterns"> Share link </button> </h4>Pattern 1: Filter Before Traversal <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- BAD: Traverse then filter MATCH (u:User)-[:PURCHASED]->(p:Product) WHERE u.country = 'USA' RETURN p.name; -- GOOD: Filter then traverse MATCH (u:User {country: 'USA'})-[:PURCHASED]->(p:Product) RETURN p.name; </code></pre></div>Pattern 2: Limit Early in Pipeline <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- BAD: Process all then limit MATCH (u:User)-[:FRIEND]->(friend)-[:LIKES]->(p:Post) RETURN p.title ORDER BY p.created_at DESC LIMIT 10; -- GOOD: Limit intermediate results MATCH (u:User)-[:FRIEND]->(friend) WITH friend LIMIT 100 -- Limit before expensive traversal MATCH (friend)-[:LIKES]->(p:Post) RETURN p.title ORDER BY p.created_at DESC LIMIT 10; </code></pre></div>Pattern 3: Avoid Cartesian Products <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- BAD: Creates cartesian product MATCH (u:User), (p:Product) WHERE u.preference = p.category RETURN u.name, p.name; -- GOOD: Use relationship or index MATCH (u:User)-[:INTERESTED_IN]->(cat:Category)<-[:IN_CATEGORY]-(p:Product) RETURN u.name, p.name; </code></pre></div>Pattern 4: Use Aggregation Efficiently <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- BAD: Collect all then count MATCH (u:User)-[:PURCHASED]->(p:Product) WITH u, collect(p) AS products RETURN u.name, size(products) AS purchase_count; -- GOOD: Count directly MATCH (u:User)-[:PURCHASED]->(p:Product) RETURN u.name, count(p) AS purchase_count; </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>Connection pooling improves client performance by reusing connections: Python Client with Pooling: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">from geode_client import Client, ConnectionPool # Create connection pool pool = ConnectionPool( host="localhost", port=3141, min_connections=5, max_connections=50, max_idle_time=300 # seconds ) async def query_with_pool(): # Reuses existing connection from pool async with pool.acquire() as client: result, _ = await client.query("MATCH (n:User) RETURN count(n)") return result.bindings[0]['count'] # Pool improves throughput; benchmark your workload for _ in range(10000): await query_with_pool() </code></pre></div>Go Client Connection Management: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-go" data-lang="go">import "geodedb.com/geode" // Connection pool configured automatically db, err := sql.Open("geode", "quic://localhost:3141") if err != nil { log.Fatal(err) } // Set pool parameters db.SetMaxOpenConns(50) db.SetMaxIdleConns(10) db.SetConnMaxLifetime(time.Minute * 5) // Queries automatically use pool rows, err := db.Query("MATCH (u:User) RETURN u.name") </code></pre></div> <h4 id="batch-operations" class="position-relative d-flex align-items-center group"> Batch 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="batch-operations" aria-haspopup="dialog" aria-label="Share link: Batch Operations"> Share link </button> </h4>Batch multiple operations to reduce round-trip latency: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">async def insert_users_batch(users): client = Client(host="localhost", port=3141) async with client.connection() as conn: # Batch insert with transaction await conn.begin() try: for batch in chunk(users, 1000): await conn.execute(""" UNWIND $users AS user_data CREATE (u:User { id: user_data.id, name: user_data.name, email: user_data.email }) """, {"users": batch}) await conn.commit() except Exception: await conn.rollback() raise # Much faster than individual INSERTs await insert_users_batch(user_list) # 10,000 users in 2-3 seconds </code></pre></div> <h3 id="performance-best-practices" class="position-relative d-flex align-items-center group"> Performance 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="performance-best-practices" aria-haspopup="dialog" aria-label="Share link: Performance Best Practices"> Share link </button> </h3> <h4 id="schema-design-for-performance" class="position-relative d-flex align-items-center group"> Schema Design for 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="schema-design-for-performance" aria-haspopup="dialog" aria-label="Share link: Schema Design for Performance"> Share link </button> </h4><ol> <li>Denormalize Strategically: Store frequently accessed computed values</li> </ol> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Store aggregate counts for fast access CREATE (u:User { name: 'Alice', follower_count: 1523, -- Precomputed following_count: 892 }); </code></pre></div><ol start="2"> <li>Use Appropriate Relationship Direction:</li> </ol> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Model relationships in query direction -- If you frequently query "who follows X", use: (follower:User)-[:FOLLOWS]->(followee:User) -- Not: (followee:User)-[:FOLLOWED_BY]->(follower:User) </code></pre></div><ol start="3"> <li>Partition Large Graphs: Use domain-specific nodes as hubs</li> </ol> <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">-- Use year nodes to partition time-series data (year:Year {year: 2024})-[:HAS_MONTH]->(month:Month {month: 3}) -[:CONTAINS]->(event:Event {date: '2024-03-15'}) </code></pre></div> <h4 id="monitoring-and-metrics" class="position-relative d-flex align-items-center group"> Monitoring and 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="monitoring-and-metrics" aria-haspopup="dialog" aria-label="Share link: Monitoring and Metrics"> Share link </button> </h4>Built-in Metrics (exposed via Prometheus): <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Query Geode metrics endpoint curl http://localhost:9090/metrics </code></pre></div>Key metrics to monitor: <ul> <li><code>geode_queries_total</code>: Total queries executed</li> <li><code>geode_query_duration_seconds</code>: Query latency distribution</li> <li><code>geode_connections_active</code>: Active client connections</li> <li><code>geode_cache_hits_total</code>: Cache effectiveness</li> <li><code>geode_index_seeks_total</code>: Index usage frequency</li> <li><code>geode_transactions_active</code>: Concurrent transactions</li> </ul> Python monitoring integration: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python">from prometheus_client import start_http_server, Counter, Histogram # Custom application metrics query_counter = Counter('app_geode_queries', 'Geode queries executed') query_duration = Histogram('app_geode_query_duration', 'Query execution time') @query_duration.time() async def execute_query(query, params): query_counter.inc() client = Client(host="localhost", port=3141) async with client.connection() as conn: return await conn.execute(query, params) # Start metrics server start_http_server(8000) </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>Estimate resource requirements: Memory: <ul> <li>Base: <code>nodes × 256 bytes + relationships × 128 bytes</code></li> <li>Indexes: <code>20-50% overhead per indexed property</code></li> <li>HNSW: <code>vectors × dimensions × 4 × (1 + m × 16)</code></li> <li>Working set: Keep hot data in RAM</li> </ul> Storage: <ul> <li>Graph data: <code>nodes × 512 bytes + rels × 256 bytes</code></li> <li>WAL: 10-20% of database size</li> <li>Indexes: 30-100% of data size (varies by index type)</li> </ul> CPU: <ul> <li>2-4 cores: Development and testing</li> <li>8-16 cores: Production workloads (benchmark dependent)</li> <li>32+ cores: High-throughput workloads (benchmark dependent)</li> </ul> Example calculation (1M nodes, 5M relationships): <ul> <li>Memory: ~1.5GB graph + ~2GB indexes = 3.5GB minimum</li> <li>Storage: ~2.5GB graph + ~2GB indexes = 5GB total</li> <li>CPU: 8 cores for moderate workloads; benchmark for your workload</li> </ul> <h3 id="troubleshooting-performance-issues" class="position-relative d-flex align-items-center group"> Troubleshooting 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="troubleshooting-performance-issues" aria-haspopup="dialog" aria-label="Share link: Troubleshooting Performance Issues"> Share link </button> </h3> <h4 id="slow-queries" class="position-relative d-flex align-items-center group"> Slow 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="slow-queries" aria-haspopup="dialog" aria-label="Share link: Slow Queries"> Share link </button> </h4>Symptom: Queries taking seconds to complete Diagnosis: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-gql" data-lang="gql">PROFILE [your slow query] </code></pre></div>Common causes: <ol> <li>Missing indexes → Add appropriate index</li> <li>Cartesian products → Restructure query with relationships</li> <li>Excessive intermediate results → Add LIMIT clauses</li> <li>Full table scans → Filter earlier in pipeline</li> </ol> <h4 id="high-memory-usage" class="position-relative d-flex align-items-center group"> High Memory Usage <button type="button" class="h-share btn btn-link p-0 text-decoration-none link-secondary opacity-50 hover-opacity-100 transition-all ms-1" data-share-target="high-memory-usage" aria-haspopup="dialog" aria-label="Share link: High Memory Usage"> Share link </button> </h4>Symptom: Server memory consumption growing over time Diagnosis: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-bash" data-lang="bash"># Check Geode memory stats curl http://localhost:9090/metrics | grep memory </code></pre></div>Solutions: <ol> <li>Tune cache size configuration</li> <li>Review index memory usage</li> <li>Close idle connections</li> <li>Implement result pagination</li> </ol> <h4 id="connection-pool-exhaustion" class="position-relative d-flex align-items-center group"> Connection Pool Exhaustion <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-pool-exhaustion" aria-haspopup="dialog" aria-label="Share link: Connection Pool Exhaustion"> Share link </button> </h4>Symptom: “Too many connections” errors Solutions: <div class="highlight"><pre tabindex="0" class="chroma"><code class="language-python" data-lang="python"># Increase pool size pool = ConnectionPool( max_connections=100, # Increased from 50 max_idle_time=600 # Keep connections longer ) # Or decrease connection timeout in app async with pool.acquire(timeout=5.0) as client: # Query with timeout </code></pre></div> <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="/tags/query-optimization/" >Query Optimization</a> : Deep dive into query tuning</li> <li><a href="/tags/indexing/" >Indexing</a> : Comprehensive indexing guide</li> <li><a href="/tags/profiling/" >Profiling</a> : Advanced profiling techniques</li> <li><a href="/tags/scaling/" >Scaling</a> : Horizontal and vertical scaling strategies</li> <li><a href="/tags/monitoring/" >Monitoring</a> : Production monitoring and alerting</li> <li><a href="/tags/concurrency/" >Concurrency</a> : Managing concurrent access</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>Geode Performance Guide: <code>/docs/performance/overview/</code></li> <li>Query Optimization Cookbook: <code>/docs/performance/query-optimization/</code></li> <li>Index Selection Guide: <code>/docs/indexing/choosing-indexes/</code></li> <li>Prometheus Integration: <code>/docs/operations/monitoring-with-prometheus/</code></li> <li>Capacity Planning: <code>/docs/operations/capacity-planning/</code></li> </ul>

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