API Performance Optimization
Comprehensive performance optimization project that reduced API latency by 85%, from 800ms to 120ms average response time, while handling 1M+ daily requests with 99.9% uptime.
The Problem
Slow Database Queries
Complex aggregation queries taking 5-10 seconds. Missing indexes causing full table scans. N+1 query problems loading related data.
No Caching Strategy
Every request hit the database. Repeated queries for same data. Heavy computational results recalculated on every request.
Inefficient Data Fetching
Over-fetching data from database. Large JSON payloads over the network. No pagination on list endpoints.
Poor Monitoring
No visibility into slow queries. Couldn't identify bottlenecks. No alerting on performance degradation.
Solutions Implemented
Database Query Optimization
Analyzed slow query logs and execution plans. Added strategic indexes on frequently queried columns. Rewrote N+1 queries to use JOINs and eager loading. Denormalized hot data paths for read optimization.
Multi-Layer Caching
Redis for frequently accessed data (user sessions, configs). Application-level caching with LRU eviction. HTTP cache headers for CDN caching. Materialized views for complex aggregations.
Response Optimization
Implemented field selection (GraphQL-style). Added pagination to all list endpoints. Compressed responses with gzip. Lazy loading of related resources. Response streaming for large datasets.
Monitoring & Observability
Application Performance Monitoring (New Relic). Slow query logging and analysis. Real-time metrics dashboards. Automated alerts for performance regressions. Distributed tracing for microservices.
Optimization Journey
Technology Stack
Implementation Timeline
Performance Profiling & Analysis
Set up Application Performance Monitoring. Analyzed slow query logs and identified bottlenecks. Profiled API endpoints to find hot paths. Documented baseline metrics.
Database Optimization
Added strategic indexes on frequently queried columns. Rewrote N+1 queries using JOINs. Implemented database connection pooling. Query execution time reduced from 5s to 200ms average.
Caching Implementation
Deployed Redis cluster for distributed caching. Implemented cache-aside pattern with smart invalidation. Added HTTP cache headers. 70% of requests now served from cache.
Response Optimization & Monitoring
Implemented field selection and pagination. Added gzip compression. Set up real-time dashboards and automated alerts. Payload sizes reduced by 60%.
Results & Impact
Performance Gains
Reduced average API latency from 800ms to 120ms (85% improvement). P99 latency decreased from 3000ms to 400ms. Zero timeout errors after optimization.
Scalability
Successfully handling 1M+ requests daily with headroom to grow. Infrastructure costs reduced by 30% through efficiency gains. Auto-scaling works smoothly without performance degradation.
Business Impact
Met SLA commitments with 99.9% uptime. Customer satisfaction improved significantly. Prevented churn of 3 major enterprise clients who were considering leaving due to performance issues.
Key Learnings
Measure first, optimize second - use profiling tools to identify actual problems. Multi-layer caching strategy provides massive performance gains. Proper indexing is critical for query performance.