Speeding up Elasticsearch

We wrote a guide for diagnosing and resolving common performance issues in Elasticsearch clusters. It identifies key problem areas and provides actionable solutions:

Unoptimized Queries: Queries using wildcards or regular expressions on analyzed fields, targeting too many shards, or employing deep pagination (using 'from' and 'size') are computationally expensive. To optimize, use filters where possible, limit the number of shards in queries, and replace deep pagination with 'search_after'.

Disk or Heap Pressure: Issues like garbage collection pauses or disk I/O bottlenecks can impair performance. Solutions include using SSDs, monitoring with '_nodes/stats', tuning garbage collection, and adjusting heap sizing based on node roles.

Misconfigured Refresh Intervals and Merge Policies: Frequent refreshes or default merge settings not aligned with the workload can increase segment count and reduce query performance. For heavy ingest workloads, increasing the 'refresh_interval' to 30 seconds or more and reviewing merge throttling settings is recommended.

The article emphasizes that many Elasticsearch performance issues are addressable with informed configuration and monitoring practices. For teams needing assistance, Dattell offers 24x7 Elasticsearch support and consulting services.