Run Elasticsearch locally

To try out Elasticsearch on your own machine, we recommend using Docker and running both Elasticsearch and Kibana. Docker images are available from the Elastic Docker registry.

For other installation options, see the Elasticsearch installation documentation.

Kibana enables you to easily send requests to Elasticsearch and analyze, visualize, and manage data interactively.

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

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.

response = client.index(
  index: 'customer',
  id: 1,
  body: {
    firstname: 'Jennifer',
    lastname: 'Walters'
  }
)
puts response

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:

$params = [
    'index' => 'customer',
    'id' => '1',
];
$response = $client->get($params);

resp = client.get(index="customer", id="1")
print(resp)

response = client.get(
  index: 'customer',
  id: 1
)
puts response

res, err := es.Get("customer", "1", es.Get.WithPretty())
fmt.Println(res, err)

const response = await client.get({
  index: 'customer',
  id: '1'
})
console.log(response)

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.

response = client.bulk(
  index: 'customer',
  body: [
    {
      create: {}
    },
    {
      firstname: 'Monica',
      lastname: 'Rambeau'
    },
    {
      create: {}
    },
    {
      firstname: 'Carol',
      lastname: 'Danvers'
    },
    {
      create: {}
    },
    {
      firstname: 'Wanda',
      lastname: 'Maximoff'
    },
    {
      create: {}
    },
    {
      firstname: 'Jennifer',
      lastname: 'Takeda'
    }
  ]
)
puts response

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

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.

response = client.search(
  index: 'customer',
  body: {
    query: {
      match: {
        firstname: 'Jennifer'
      }
    }
  }
)
puts response

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

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.

To start exploring, go to Analytics > Discover.