IMPORTANT: No additional bug fixes or documentation updates will be released for this version. For the latest information, see the current release documentation.

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Quick start guide

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Quick start guide

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This guide helps you learn how to:

Run Elasticsearch and Kibana (using Elastic Cloud or in a local Docker dev environment),
add simple (non-timestamped) dataset to Elasticsearch,
run basic searches.

If you’re interested in using Elasticsearch with Python, check out Elastic Search Labs. This is the best place to explore AI-powered search use cases, such as working with embeddings, vector search, and retrieval augmented generation (RAG).

Tutorial: this walks you through building a complete search solution with Elasticsearch, from the ground up.
elasticsearch-labs repository: it contains a range of Python notebooks and example apps.

Run Elasticsearch

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The simplest way to set up Elasticsearch is to create a managed deployment with Elasticsearch Service on Elastic Cloud. If you prefer to manage your own test environment, install and run Elasticsearch using Docker.

Get a free trial.
Log into Elastic Cloud.
Click Create deployment.
Give your deployment a name.
Click Create deployment and download the password for the elastic user.
Click Continue to open Kibana, the user interface for Elastic Cloud.
Click Explore on my own.

Refer to our quickstart local dev instructions to quickly spin up a local development environment in Docker. If you don’t need Kibana, you’ll only need one docker run command to start Elasticsearch. Please note that this setup is not suitable for production use.

Send requests to Elasticsearch

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You send data and other requests to Elasticsearch using REST APIs. This lets you interact with Elasticsearch using any client that sends HTTP requests, such as curl. You can also use Kibana’s Console to send requests to Elasticsearch.

Use Kibana

Open Kibana’s main menu ("☰" near Elastic logo) and go to Dev Tools > Console.

Run the following test API request in Console:

$response = $client->info();

response = client.info
puts response

res, err := es.Info()
fmt.Println(res, err)

const response = await client.info()
console.log(response)

GET /

Use curl

To communicate with Elasticsearch using curl or another client, you need your cluster’s endpoint.

Open Kibana’s main menu and click Manage this deployment.
From your deployment menu, go to the Elasticsearch page. Click Copy endpoint.
To submit an example API request, run the following curl command in a new terminal session. Replace <password> with the password for the elastic user. Replace <elasticsearch_endpoint> with your endpoint.
```
curl -u elastic:<password> <elasticsearch_endpoint>/
```

Use Kibana

Open Kibana’s main menu ("☰" near Elastic logo) and go to Dev Tools > Console.

Run the following test API request in Console:

$response = $client->info();

response = client.info
puts response

res, err := es.Info()
fmt.Println(res, err)

const response = await client.info()
console.log(response)

GET /

Use curl

To submit an example API request, run the following curl command in a new terminal session.

curl -u elastic:$ELASTIC_PASSWORD https://localhost:9200

Add data

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You add data to Elasticsearch as JSON objects called documents. Elasticsearch stores these documents in searchable indices.

Add a single document

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Submit the following indexing request to add a single document to the books index. The request automatically creates the index.

response = client.index(
  index: 'books',
  body: {
    name: 'Snow Crash',
    author: 'Neal Stephenson',
    release_date: '1992-06-01',
    page_count: 470
  }
)
puts response

const response = await client.index({
  index: 'books',
  document: {
    name: 'Snow Crash',
    author: 'Neal Stephenson',
    release_date: '1992-06-01',
    page_count: 470,
  }
})
console.log(response)

POST books/_doc
{"name": "Snow Crash", "author": "Neal Stephenson", "release_date": "1992-06-01", "page_count": 470}

The response includes metadata that Elasticsearch generates for the document including a unique _id for the document within the index.

Expand to see example response

{
  "_index": "books",
  "_id": "O0lG2IsBaSa7VYx_rEia",
  "_version": 1,
  "result": "created",
  "_shards": {
    "total": 2,
    "successful": 2,
    "failed": 0
  },
  "_seq_no": 0,
  "_primary_term": 1
}

Add multiple documents

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

response = client.bulk(
  body: [
    {
      index: {
        _index: 'books'
      }
    },
    {
      name: 'Revelation Space',
      author: 'Alastair Reynolds',
      release_date: '2000-03-15',
      page_count: 585
    },
    {
      index: {
        _index: 'books'
      }
    },
    {
      name: '1984',
      author: 'George Orwell',
      release_date: '1985-06-01',
      page_count: 328
    },
    {
      index: {
        _index: 'books'
      }
    },
    {
      name: 'Fahrenheit 451',
      author: 'Ray Bradbury',
      release_date: '1953-10-15',
      page_count: 227
    },
    {
      index: {
        _index: 'books'
      }
    },
    {
      name: 'Brave New World',
      author: 'Aldous Huxley',
      release_date: '1932-06-01',
      page_count: 268
    },
    {
      index: {
        _index: 'books'
      }
    },
    {
      name: 'The Handmaids Tale',
      author: 'Margaret Atwood',
      release_date: '1985-06-01',
      page_count: 311
    }
  ]
)
puts response

const response = await client.bulk({
  operations: [
    { index: { _index: 'books' } },
    {
      name: 'Revelation Space',
      author: 'Alastair Reynolds',
      release_date: '2000-03-15',
      page_count: 585,
    },
    { index: { _index: 'books' } },
    {
      name: '1984',
      author: 'George Orwell',
      release_date: '1985-06-01',
      page_count: 328,
    },
    { index: { _index: 'books' } },
    {
      name: 'Fahrenheit 451',
      author: 'Ray Bradbury',
      release_date: '1953-10-15',
      page_count: 227,
    },
    { index: { _index: 'books' } },
    {
      name: 'Brave New World',
      author: 'Aldous Huxley',
      release_date: '1932-06-01',
      page_count: 268,
    },
    { index: { _index: 'books' } },
    {
      name: 'The Handmaids Tale',
      author: 'Margaret Atwood',
      release_date: '1985-06-01',
      page_count: 311,
    }
  ]
})
console.log(response)

POST /_bulk
{ "index" : { "_index" : "books" } }
{"name": "Revelation Space", "author": "Alastair Reynolds", "release_date": "2000-03-15", "page_count": 585}
{ "index" : { "_index" : "books" } }
{"name": "1984", "author": "George Orwell", "release_date": "1985-06-01", "page_count": 328}
{ "index" : { "_index" : "books" } }
{"name": "Fahrenheit 451", "author": "Ray Bradbury", "release_date": "1953-10-15", "page_count": 227}
{ "index" : { "_index" : "books" } }
{"name": "Brave New World", "author": "Aldous Huxley", "release_date": "1932-06-01", "page_count": 268}
{ "index" : { "_index" : "books" } }
{"name": "The Handmaids Tale", "author": "Margaret Atwood", "release_date": "1985-06-01", "page_count": 311}

You should receive a response indicating there were no errors.

Expand to see example response

{
  "errors": false,
  "took": 29,
  "items": [
    {
      "index": {
        "_index": "books",
        "_id": "QklI2IsBaSa7VYx_Qkh-",
        "_version": 1,
        "result": "created",
        "_shards": {
          "total": 2,
          "successful": 2,
          "failed": 0
        },
        "_seq_no": 1,
        "_primary_term": 1,
        "status": 201
      }
    },
    {
      "index": {
        "_index": "books",
        "_id": "Q0lI2IsBaSa7VYx_Qkh-",
        "_version": 1,
        "result": "created",
        "_shards": {
          "total": 2,
          "successful": 2,
          "failed": 0
        },
        "_seq_no": 2,
        "_primary_term": 1,
        "status": 201
      }
    },
    {
      "index": {
        "_index": "books",
        "_id": "RElI2IsBaSa7VYx_Qkh-",
        "_version": 1,
        "result": "created",
        "_shards": {
          "total": 2,
          "successful": 2,
          "failed": 0
        },
        "_seq_no": 3,
        "_primary_term": 1,
        "status": 201
      }
    },
    {
      "index": {
        "_index": "books",
        "_id": "RUlI2IsBaSa7VYx_Qkh-",
        "_version": 1,
        "result": "created",
        "_shards": {
          "total": 2,
          "successful": 2,
          "failed": 0
        },
        "_seq_no": 4,
        "_primary_term": 1,
        "status": 201
      }
    },
    {
      "index": {
        "_index": "books",
        "_id": "RklI2IsBaSa7VYx_Qkh-",
        "_version": 1,
        "result": "created",
        "_shards": {
          "total": 2,
          "successful": 2,
          "failed": 0
        },
        "_seq_no": 5,
        "_primary_term": 1,
        "status": 201
      }
    }
  ]
}

Search data

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Indexed documents are available for search in near real-time.

Search all documents

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Run the following command to search the books index for all documents:

response = client.search(
  index: 'books'
)
puts response

const response = await client.search({
  index: 'books'
})
console.log(response)

GET books/_search

The _source of each hit contains the original JSON object submitted during indexing.

`match` query

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You can use the match query to search for documents that contain a specific value in a specific field. This is the standard query for performing full-text search, including fuzzy matching and phrase searches.

Run the following command to search the books index for documents containing brave in the name field:

response = client.search(
  index: 'books',
  body: {
    query: {
      match: {
        name: 'brave'
      }
    }
  }
)
puts response

const response = await client.search({
  index: 'books',
  query: {
    match: {
      name: 'brave'
    }
  }
})
console.log(response)

GET books/_search
{
  "query": {
    "match": {
      "name": "brave"
    }
  }
}

Next steps

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Now that Elasticsearch is up and running and you’ve learned the basics, you’ll probably want to test out larger datasets, or index your own data.

Learn more about search queries

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Search your data. Jump here to learn about exact value search, full-text search, vector search, and more, using the search API.

Add more data

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Learn how to install sample data using Kibana. This is a quick way to test out Elasticsearch on larger workloads.
Learn how to use the upload data UI in Kibana to add your own CSV, TSV, or JSON files.
Use the bulk API to ingest your own datasets to Elasticsearch.

Elasticsearch programming language clients

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Check out our client library to work with your Elasticsearch instance in your preferred programming language.
If you’re using Python, check out Elastic Search Labs for a range of examples that use the Elasticsearch Python client. This is the best place to explore AI-powered search use cases, such as working with embeddings, vector search, and retrieval augmented generation (RAG).
- This extensive, hands-on tutorial walks you through building a complete search solution with Elasticsearch, from the ground up.
- elasticsearch-labs contains a range of executable Python notebooks and example apps.

« Run Elasticsearch locally in Docker (without security) Set up Elasticsearch »

Quick start guide

Quick start guide

Run Elasticsearch

Send requests to Elasticsearch

Add data

Add a single document

Add multiple documents

Search data

Search all documents

match query

Next steps

Learn more about search queries

Add more data

Elasticsearch programming language clients

`match` query