A Kibana‑observed daily aggregation query on 70 million documents took 5.2 seconds, but by tweaking three Elasticsearch settings—eager_global_ordinals, doc_values_format v2, and index.sort—the same query consistently runs in about 80 milliseconds.
The article explains Elasticsearch’s internal Doc Values column‑store structure, why it outperforms the inverted index for sorting and aggregation, how it is generated, stored, compressed, and used in queries, and provides practical mapping examples.
This article explains Elasticsearch aggregation queries, covering metric, bucket, and pipeline aggregations, the role of doc_values and fielddata, multi‑field mapping, practical query examples such as terms, date_histogram, range, nested and pipeline aggregations, as well as sorting techniques and performance optimization tips.
This article explains Elasticsearch's logical and physical design, how documents are structured and indexed, the role of shards and replicas, and provides practical examples of indexing, searching, aggregations, and retrieving documents using RESTful APIs.
