Run Elasticsearch locallyedit

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.

Starting in Elasticsearch 8.0, security is enabled by default. The first time you start Elasticsearch, TLS encryption is configured automatically, a password is generated for the elastic user, and a Kibana enrollment token is created so you can connect Kibana to your secured cluster.

For other installation options, see the Elasticsearch installation documentation.

Start Elasticsearchedit

  1. Install and start Docker Desktop. Go to Preferences > Resources > Advanced and set Memory to at least 4GB.
  2. Start an Elasticsearch container:

    docker network create elastic
    docker pull docker.elastic.co/elasticsearch/elasticsearch:8.4.3
    docker run --name elasticsearch --net elastic -p 9200:9200 -p 9300:9300 -e "discovery.type=single-node" -t docker.elastic.co/elasticsearch/elasticsearch:8.4.3

    When you start Elasticsearch for the first time, the generated elastic user password and Kibana enrollment token are output to the terminal.

    You might need to scroll back a bit in the terminal to view the password and enrollment token.

  3. Copy the generated password and enrollment token and save them in a secure location. These values are shown only when you start Elasticsearch for the first time. You’ll use these to enroll Kibana with your Elasticsearch cluster and log in.

Start Kibanaedit

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

  1. In a new terminal session, start Kibana and connect it to your Elasticsearch container:

    docker pull docker.elastic.co/kibana/kibana:8.4.3
    docker run --name kibana --net elastic -p 5601:5601 docker.elastic.co/kibana/kibana:8.4.3

    When you start Kibana, a unique URL is output to your terminal.

  2. To access Kibana, open the generated URL in your browser.

    1. Paste the enrollment token that you copied when starting Elasticsearch and click the button to connect your Kibana instance with Elasticsearch.
    2. Log in to Kibana as the elastic user with the password that was generated when you started Elasticsearch.

Send requests to Elasticsearchedit

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.

Add dataedit

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:

$params = [
    'index' => 'customer',
    'id' => '1',
];
$response = $client->get($params);
var getResponse = client.Get<Customer>(1, g => g
    .Index("customer")
);
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.

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

Searchedit

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" }
  }
}

Exploreedit

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.