[opensourcedex]

Elasticsearch is a free, open source search engines project written in Java and released under a custom open-source licence. It has 77,925 GitHub stars, 26,069 forks and 6,059 open issues, and was last pushed 4 hours ago. On this registry it ranks #1 of 8 tracked projects in Search Engines, with 5 head-to-head comparisons available. It gained 16 stars over the last 6 tracked days.

Elasticsearch — The leading distributed search and analytics engine

What is Elasticsearch?

What it is

Elasticsearch is a distributed search and analytics engine built in Java, designed for near real-time search, scalability, and high-performance data querying on production workloads. It serves as the core storage and indexing layer of Elastic’s open stack and supports full-text, structured, and vector-based search.

It solves the problem of efficiently indexing, searching, and analyzing massive volumes of structured and unstructured data across distributed systems. Traditional databases struggle with full-text relevance ranking, horizontal scaling, and low-latency analytics at scale; Elasticsearch addresses these by leveraging distributed architecture, inverted indexes, and Lucene underpinning.

Key capabilities

  • Distributed search indexing with automatic sharding and replication for fault tolerance and scale
  • Near real-time search on large datasets with sub-second latency
  • Native vector search and hybrid search support for generative AI and RAG applications
  • Aggregation framework for complex analytics (e.g., histograms, percentiles, time-series summaries)
  • RESTful HTTP API with JSON input/output for integration with any client or language
  • Schema-less mapping with dynamic field detection and explicit type control
  • Integration with Kibana for visualization, monitoring, and management

Who uses it and how

  • Log aggregation and analysis: Centralize application and infrastructure logs for search, filtering, and alerting
  • Application performance monitoring (APM): Store and query distributed tracing and metrics data
  • Security information and event management (SIEM): Ingest, index, and correlate security logs for threat detection
  • E-commerce product search: Deliver relevance-ranked search with autocomplete, faceting, and typo tolerance
  • RAG systems: Use as a vector store for embedding retrieval in LLM-powered applications

Getting started

Deploy via Elastic Cloud (managed service) or self-host using Docker with the start-local script (curl -fsSL https://elastic.co/start-local | sh). Prebuilt binaries and Docker images are available at elastic.co/downloads/elasticsearch.

When to use it — and when not to

Elasticsearch replaces costly proprietary search and observability platforms but requires operational overhead: users must manage cluster health, scaling, backups, and security (TLS, RBAC, API keys). It is overkill for simple key-value or relational workloads; use a traditional database if full-text search, vector similarity, or distributed analytics are unnecessary. Its free tier lacks advanced security and monitoring features available in paid subscriptions.

project readme (upstream, from github) — read inline

= Elasticsearch

Elasticsearch is a distributed search and analytics engine, scalable data store and vector database optimized for speed and relevance on production-scale workloads. Elasticsearch is the foundation of Elastic's open Stack platform. Search in near real-time over massive datasets, perform vector searches, integrate with generative AI applications, and much more.

Use cases enabled by Elasticsearch include:

... and more!

To learn more about Elasticsearch's features and capabilities, see our https://www.elastic.co/products/elasticsearch[product page].

To access information on https://www.elastic.co/search-labs/blog/categories/ml-research[machine learning innovations] and the latest https://www.elastic.co/search-labs/blog/categories/lucene[Lucene contributions from Elastic], more information can be found in https://www.elastic.co/search-labs[Search Labs].

[[get-started]] == Get started

The simplest way to set up Elasticsearch is to create a managed deployment with https://www.elastic.co/cloud/as-a-service[Elasticsearch Service on Elastic Cloud].

If you prefer to install and manage Elasticsearch yourself, you can download the latest version from https://www.elastic.co/downloads/elasticsearch[elastic.co/downloads/elasticsearch].

=== Run Elasticsearch locally

//// IMPORTANT: This content is replicated in the Elasticsearch repo. See run-elasticsearch-locally.asciidoc. Ensure both files are in sync.

https://github.com/elastic/start-local is the source of truth. ////

[WARNING]

DO NOT USE THESE INSTRUCTIONS FOR PRODUCTION DEPLOYMENTS.

This setup is intended for local development and testing only.

Quickly set up Elasticsearch and Kibana in Docker for local development or testing, using the https://github.com/elastic/start-local?tab=readme-ov-file#-try-elasticsearch-and-kibana-locally[`start-local` script].

ℹ️ For more detailed information about the start-local setup, refer to the https://github.com/elastic/start-local[README on GitHub].

==== Prerequisites

==== Trial license This setup comes with a one-month trial license that includes all Elastic features.

After the trial period, the license reverts to Free and open - Basic. Refer to https://www.elastic.co/subscriptions[Elastic subscriptions] for more information.

==== Run start-local

To set up Elasticsearch and Kibana locally, run the start-local script:

[source,sh]

curl -fsSL https://elastic.co/start-local | sh

// NOTCONSOLE

This script creates an elastic-start-local folder containing configuration files and starts both Elasticsearch and Kibana using Docker.

After running the script, you can access Elastic services at the following endpoints:

The script generates a random password for the elastic user, which is displayed at the end of the installation and stored in the .env file.

[CAUTION]

This setup is for local testing only. HTTPS is disabled, and Basic authentication is used for Elasticsearch. For security, Elasticsearch and Kibana are accessible only through localhost.

==== API access

An API key for Elasticsearch is generated and stored in the .env file as ES_LOCAL_API_KEY. Use this key to connect to Elasticsearch with a https://www.elastic.co/guide/en/elasticsearch/client/index.html[programming language client] or the https://www.elastic.co/guide/en/elasticsearch/reference/current/rest-apis.html[REST API].

From the elastic-start-local folder, check the connection to Elasticsearch using curl:

[source,sh]

source .env curl $ES_LOCAL_URL -H "Authorization: ApiKey ${ES_LOCAL_API_KEY}"

To use the password for the elastic user, set and export the ES_LOCAL_PASSWORD environment variable. For example:

[source,sh]

source .env export ES_LOCAL_PASSWORD

// NOTCONSOLE

=== Send requests to Elasticsearch

You send data and other requests to Elasticsearch through REST APIs. You can interact with Elasticsearch using any client that sends HTTP requests, such as the https://www.elastic.co/guide/en/elasticsearch/client/index.html[Elasticsearch language clients] and https://curl.se[curl].

==== Using curl

Here's an example curl command to create a new Elasticsearch index, using basic auth:

[source,sh]

curl -u elastic:$ES_LOCAL_PASSWORD
-X PUT
http://localhost:9200/my-new-index
-H 'Content-Type: application/json'

// NOTCONSOLE

==== Using a language client

To connect to your local dev Elasticsearch cluster with a language client, you can use basic authentication with the elastic username and the password stored in the ES_LOCAL_PASSWORD environment variable.

You'll use the following connection details:

  • Elasticsearch endpoint: http://localhost:9200
  • Username: elastic
  • Password: $ES_LOCAL_PASSWORD (Value you set in the environment variable)

For example, to connect with the Python elasticsearch client:

[source,python]

import os from elasticsearch import Elasticsearch

username = 'elastic' password = os.getenv('ES_LOCAL_PASSWORD') # Value you set in the environment variable

client = Elasticsearch( "http://localhost:9200", basic_auth=(username, password) )

print(client.info())

==== Using the Dev Tools Console

Kibana's developer console provides an easy way to experiment and test requests. To access the console, open Kibana, then go to Management > Dev Tools.

Add data

You index data into Elasticsearch by sending JSON objects (documents) through the REST APIs. Whether you have structured or unstructured text, numerical data, or geospatial data, Elasticsearch efficiently stores and indexes it in a way that supports fast searches.

For timestamped data such as logs and metrics, you typically add documents to a data stream made up of multiple auto-generated backing indices.

To add a single document to an index, submit an HTTP post request that targets the index.

POST /customer/_doc/1 { "firstname": "Jennifer", "lastname": "Walters" }

This request automatically creates the customer index if it doesn't exist, adds a new document that has an ID of 1, and stores and indexes the firstname and lastname fields.

The new document is available immediately from any node in the cluster. You can retrieve it with a GET request that specifies its document ID:

GET /customer/_doc/1

To add multiple documents in one request, use the _bulk API. Bulk data must be newline-delimited JSON (NDJSON). Each line must end in a newline character (\n), including the last line.

PUT customer/_bulk { "create": { } } { "firstname": "Monica","lastname":"Rambeau"} { "create": { } } { "firstname": "Carol","lastname":"Danvers"} { "create": { } } { "firstname": "Wanda","lastname":"Maximoff"} { "create": { } } { "firstname": "Jennifer","lastname":"Takeda"}

Search

Indexed documents are available for search in near real-time. The following search matches all customers with a first name of Jennifer in the customer index.

GET customer/_search { "query" : { "match" : { "firstname": "Jennifer" } }

}

Explore

You can use Discover in Kibana to interactively search and filter your data. From there, you can start creating visualizations and building and sharing dashboards.

To get started, create a data view that connects to one or more Elasticsearch indices, data streams, or index aliases.

. Go to Management > Stack Management > Kibana > Data Views. . Select Create data view. . Enter a name for the data view and a pattern that matches one or more indices, such as customer. . Select Save data view to Kibana.

To start exploring, go to Analytics > Discover.

[[upgrade]] == Upgrade

To upgrade from an earlier version of Elasticsearch, see the https://www.elastic.co/guide/en/elasticsearch/reference/current/setup-upgrade.html[Elasticsearch upgrade documentation].

[[build-source]] == Build from source

Elasticsearch uses https://gradle.org[Gradle] for its build system.

To build a distribution for your local OS and print its output location upon completion, run:

./gradlew localDistro

To build a distribution for another platform, run the related command:

./gradlew :distribution:archives:linux-tar:assemble ./gradlew :distribution:archives:darwin-tar:assemble ./gradlew :distribution:

readme truncated — read the full docs on github

Frequently asked questions

Is Elasticsearch free to use?

Elasticsearch is open source. There is no licence fee and no seat count — you can self-host it or, where the project offers one, pay a vendor for a managed version instead.

What does Elasticsearch do?

The leading distributed search and analytics engine

What is Elasticsearch written in?

Elasticsearch is primarily written in Java. Its source is publicly available at https://github.com/elastic/elasticsearch, and it has 77,925 GitHub stars.