Optimize HBase reads with bucket caching on Amazon EMR

This article explains how to optimize HBase read performance using bucket caching on Amazon EMR, achieving significant improvements in throughput and latency for large-scale deployments.

• Bucket cache acts as L2 caching mechanism outside JVM heap, reducing garbage collection overhead
• Testing with 7.9TB dataset achieved 138.8% throughput improvement and 57.9% latency reduction
• Cache hit ratios exceeded 95% after 24 hours, reducing S3 requests from 95,000 to under 1,000 per hour
• Persistent bucket cache maintains data across RegionServer restarts with recovery time under 2 minutes
• Configure ZGC garbage collection and cache-aware load balancing for optimal performance
• Monitor L2 cache hit ratio and S3 request patterns using CloudWatch metrics
• Enable compressed block caching and prefetch settings to maximize cache efficiency

The solution provides production-ready guidance for implementing terabyte-scale HBase caching on EMR with persistent storage, significantly reducing latency and S3 costs while maintaining consistent performance during maintenance operations.