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