Elasticsearch Cluster Isolation & Performance Optimization
Project Overview Led the optimization of a large-scale Elasticsearch cluster serving millions of search requests daily. The project focused on improving search performance, reducing latency, and enhancing cluster stability. Challenge The existing Elasticsearch cluster faced several challenges: High search latency (500ms+) Frequent cluster instability Resource utilization inefficiencies Limited scalability Solution Implemented a comprehensive optimization strategy: 1. Cluster Architecture Improvements Redesigned shard allocation strategy Implemented dedicated master nodes Optimized node roles and responsibilities 2. Performance Optimizations Fine-tuned JVM settings Optimized index settings and mappings Implemented efficient query patterns Added caching layers 3. Monitoring & Stability Set up comprehensive monitoring Implemented automated scaling Enhanced backup and recovery procedures Results 90% reduction in search latency (from 500ms to 50ms) 50% improvement in overall search performance Enhanced cluster stability with 99.99% uptime Improved resource utilization by 40% Technical Details Key Technologies Elasticsearch 7.x Java 11 Kubernetes Prometheus & Grafana Implementation Highlights // Example of optimized search query SearchRequest request = new SearchRequest("products"); request.source(new SearchSourceBuilder() .query(QueryBuilders.matchQuery("name", searchTerm)) .size(20) .trackTotalHits(true) .sort(SortBuilders.fieldSort("_score"))); Lessons Learned Importance of proper cluster sizing and planning Value of comprehensive monitoring Need for automated scaling mechanisms Benefits of query optimization