A dedicated performance course run by Matt Gregory from Elastic, an absolute legend with deep Elasticsearch expert. Contents Cool takeaways Tuning for Index Speed Increase the refresh interval Index architecting Bulk Hardware settings to improve performance Disable swapping Indexing Buffer size Best practices and scaling Disable replics for initial loads Use auto-generated IDs Use Cross Cluster Replication Thread Pools Memory Locking Transforms Tuning for search API settings and data modelling to improve search performance Search as few fields as possible One big copy_to field as opposed to individual text multi field Consider mapping identifiers as keywords Document modeling Consider mapping numeric fields as keyword Hardware settings to improve search Warm Up Global Ordinals Warm up filesystem Cache Use index sorting to speed up search Ways to improve searches must and should clauses filter and must not clauses node query cache shard request cache Aggregation performance Search rounded dates Force merge read only indices Search profiler and Explain API Search profiler Search profiler API ID Query section Timing breakdown Collection section Collectors reasons Rewrite section Explain and Tasks API Explain API Score Field length normalization and coordindation Other Query Parameters API Settings to improve indexing performance Hardware settings to improve performance Best Practices and scaling Transforms Cool takeaways Increase the refresh_interval from default 1s to something higher, like 10s.

Revised 2024 edition based on Elasticsearch 8.1. Recently the opportunity to attend the latest revision of the 4-day Elasticsearch engineer course, which I did in-person about 5 years ago in Sydney. Elasticsearch has often been an integral part of the data solutions I’ve been involved with and I’m quite fond of it. This time round the course only runs in a virtual class room format (using strigo.io) with our awesome trainers Krishna Shah and Kiju Kim.