Elasticsearch Cheat Sheet

Elasticsearch REST API essentials for indexing, searching, filtering, and aggregating documents with mappings and analyzers.

2 PagesIntermediateMar 15, 2026

REST API Basics

Creating an index and indexing documents.

bash

# Create an index with an explicit mappingPUT /products{  "mappings": {    "properties": {      "name": { "type": "text" },      "price": { "type": "float" }    }  }}# Index a documentPOST /products/_doc/1{ "name": "Laptop", "price": 999.99 }# Get and deleteGET /products/_doc/1DELETE /products

Search Queries

Bool queries, filters, and sorting.

bash

GET /products/_search{  "query": {    "bool": {      "must": [{ "match": { "name": "laptop" } }],      "filter": [{ "range": { "price": { "lte": 1500 } } }]    }  },  "sort": [{ "price": "asc" }],  "size": 10}# Simple query string searchGET /products/_search?q=name:laptop

Core Concepts

Building blocks of the search engine.

Index- a collection of documents, roughly analogous to a database table
Shard- a horizontal partition of an index, enabling scale-out
Replica- a copy of a shard for high availability and extra read throughput
Mapping- defines each field's type and how it is indexed
Analyzer- tokenizes and normalizes text so it can be searched
Inverted index- the core structure mapping terms to the documents containing them

Aggregations

Computing metrics and buckets over search results.

bash

GET /products/_search{  "size": 0,  "aggs": {    "avg_price": { "avg": { "field": "price" } },    "by_category": { "terms": { "field": "category.keyword" } }  }}

Pro Tip

Use the .keyword sub-field (e.g. category.keyword) for exact-match filtering, sorting, and aggregations — plain "text" fields are analyzed/tokenized and won't behave as you expect for those operations.

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