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:

• Retrieval Augmented Generation (RAG)

• Vector search

• Full-text search

• Logs

• Metrics

• Application performance monitoring (APM)

• Security logs

... and more!

To learn more about Elasticsearch’s features and capabilities, see our product page.

To access information on machine learning innovations and the latest Lucene contributions from Elastic, more information can be found in Search Labs.

Get started

The simplest way to set up Elasticsearch is to create a managed deployment with Elasticsearch Service on Elastic Cloud.

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

Run Elasticsearch locally

Warning

DO NOT USE THESE INSTRUCTIONS FOR PRODUCTION DEPLOYMENTS. This setup is intended for local development and testing only.

The following commands help you very quickly spin up a single-node Elasticsearch cluster, together with Kibana in Docker. Use this setup for local development or testing.

Prerequisites

If you don’t have Docker installed, download and install Docker Desktop for your operating system.

Set environment variables

Configure the following environment variables.

export ELASTIC_PASSWORD="<ES_PASSWORD>"  # password for "elastic" username
export KIBANA_PASSWORD="<KIB_PASSWORD>"   # Used internally by Kibana, must be at least 6 characters long

Create a Docker network

To run both Elasticsearch and Kibana, you’ll need to create a Docker network:

docker network create elastic-net

Run Elasticsearch

Start the Elasticsearch container with the following command:

docker run -p 127.0.0.1:9200:9200 -d --name elasticsearch --network elastic-net \
  -e ELASTIC_PASSWORD=$ELASTIC_PASSWORD \
  -e "discovery.type=single-node" \
  -e "xpack.security.http.ssl.enabled=false" \
  -e "xpack.license.self_generated.type=trial" \
  docker.elastic.co/elasticsearch/elasticsearch:{version}

Run Kibana (optional)

To run Kibana, you must first set the kibana_system password in the Elasticsearch container.

# configure the Kibana password in the ES container
curl -u elastic:$ELASTIC_PASSWORD \
  -X POST \
  http://localhost:9200/_security/user/kibana_system/_password \
  -d '{"password":"'"$KIBANA_PASSWORD"'"}' \
  -H 'Content-Type: application/json'

Start the Kibana container with the following command:

docker run -p 127.0.0.1:5601:5601 -d --name kibana --network elastic-net \
  -e ELASTICSEARCH_URL=http://elasticsearch:9200 \
  -e ELASTICSEARCH_HOSTS=http://elasticsearch:9200 \
  -e ELASTICSEARCH_USERNAME=kibana_system \
  -e ELASTICSEARCH_PASSWORD=$KIBANA_PASSWORD \
  -e "xpack.security.enabled=false" \
  -e "xpack.license.self_generated.type=trial" \
  docker.elastic.co/kibana/kibana:{version}

Trial license

The service is started with a trial license. The trial license enables all features of Elasticsearch for a trial period of 30 days. After the trial period expires, the license is downgraded to a basic license, which is free forever. If you prefer to skip the trial and use the basic license, set the value of the xpack.license.self_generated.type variable to basic instead. For a detailed feature comparison between the different licenses, refer to our subscriptions page.

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 Elasticsearch language clients and curl.

Using curl

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

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

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 you set in the environment variable.

You’ll use the following connection details:

• Elasticsearch endpoint: http://localhost:9200

• Username: elastic

• Password: $ELASTIC_PASSWORD (Value you set in the environment variable)

For example, to connect with the Python elasticsearch client:

import os
from elasticsearch import Elasticsearch

username = 'elastic'
password = os.getenv('ELASTIC_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.

1. Go to Management > Stack Management > Kibana > Data Views.

2. Select Create data view.

3. Enter a name for the data view and a pattern that matches one or more indices, such as customer.

4. Select Save data view to Kibana.

To start exploring, go to Analytics > Discover.

Upgrade

To upgrade from an earlier version of Elasticsearch, see the Elasticsearch upgrade documentation.

Build from source

Elasticsearch uses 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:archives:windows-zip:assemble

To build distributions for all supported platforms, run:

./gradlew assemble

Distributions are output to distribution/archives.

To run the test suite, see TESTING.

Documentation

For the complete Elasticsearch documentation visit elastic.co.

For information about our documentation processes, see the docs README.

Examples and guides

The elasticsearch-labs repo contains executable Python notebooks, sample apps, and resources to test out Elasticsearch for vector search, hybrid search and generative AI use cases.

Contribute

For contribution guidelines, see CONTRIBUTING.

Questions? Problems? Suggestions?

• To report a bug or request a feature, create a GitHub Issue. Please ensure someone else hasn’t created an issue for the same topic.

• Need help using Elasticsearch? Reach out on the Elastic Forum or Slack. A fellow community member or Elastic engineer will be happy to help you out.