Elasticsearch Cheat Sheet
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This section documents the current Elasticsearch 9.x line (with 8.19 as the final 8.x release) as published at the Elasticsearch documentation, which is the reference these pages are written and verified against. No specific patch version is pinned. Some capabilities (Kibana-only UIs, the ML/NLP model-management workflow, cross-cluster replication, and parts of the paid / serverless-only surface) are linked, not documented in depth. This content was generated with the assistance of AI and should be verified against the official documentation before being relied on in production, as Elasticsearch iterates quickly. This section’s bibliography lists the reference material consulted while preparing these pages. |
A single-page, colour-coded summary of the Elasticsearch essentials — the curl / Kibana Dev Tools
request shape with ?pretty and ?filter_path, PUT create-index with settings and mappings,
text vs. keyword and the multi-field idiom, the character filters / tokenizer / token filters
analysis chain, the document CRUD and _bulk NDJSON skeletons with if_seq_no / if_primary_term
optimistic concurrency control, _search in query vs. filter context, the match / multi_match /
term / range / bool query skeletons, function_score / sort / search_after with a
point-in-time, the metrics / terms / date_histogram aggregation skeleton, nested vs. join,
top-level knn and a retriever, an ES|QL one-liner, the _cat/health?v / _cat/indices?v /
_cat/shards?v one-liners, an alias swap and the ILM phase strip, a snapshot / restore skeleton,
and a "query vs. filter context" decision strip — handy for quick memorization or as a printable
desk reference.
For the full explanation, every option, and additional examples, see the dedicated pages:
Getting started — What Elasticsearch is & how to run it.
Data model, mapping & analysis — Documents, indices & the inverted index, Mapping & field types, and Text analysis: analyzers, tokenizers & token filters.
Searching & analytics — The search API, paging & sorting, Full-text queries, Term-level queries, Compound queries, relevance & scoring, Aggregations, Joins & relationships, Search extras: highlighting, suggesters, collapse, percolation & more like this, Geospatial data & queries, Vector & semantic search, and Query languages & scripting.
Distributed model & operations — Cluster, nodes & shards, Index lifecycle & scaling, Performance & the storage/caching model, Administration, monitoring & snapshots, Securing a cluster, and Clients & REST API conventions.