Get started

[preview] This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.

Follow along to set up your Elasticsearch project and get started with some sample documents. Then, choose how to continue with your own data.

Use your Elastic Cloud account to create a fully-managed Elasticsearch project:

You should now see Get started with Elasticsearch, and you’re ready to continue.

Create an API key, which will enable you to access the Elasticsearch API to ingest and search data.

After creation, you’ll see your API key displayed as an encoded string. Store this encoded API key securely. It is displayed only once and cannot be retrieved later. You will use this encoded API key when sending API requests.

Next, copy the URL of your API endpoint. You’ll send all Elasticsearch API requests to this URL.

Store this value along with your encoded API key. You’ll use both values in the next step.

We’ll use the curl command to test your connection and make additional API requests. (See Install curl if you need to install this program.)

curl will need access to your Elasticsearch Endpoint and encoded API key. Within your terminal, assign these values to the ES_URL and API_KEY environment variables.

For example:

export ES_URL="https://dda7de7f1d264286a8fc9741c7741690.es.us-east-1.aws.elastic.cloud:443"
export API_KEY="ZFZRbF9Jb0JDMEoxaVhoR2pSa3Q6dExwdmJSaldRTHFXWEp4TFFlR19Hdw=="

Then run the following command to test your connection:

curl "${ES_URL}" \
  -H "Authorization: ApiKey ${API_KEY}" \
  -H "Content-Type: application/json"

You should receive a response similar to the following:

{
  "name" : "serverless",
  "cluster_name" : "dda7de7f1d264286a8fc9741c7741690",
  "cluster_uuid" : "ws0IbTBUQfigmYAVMztkZQ",
  "version" : { ... },
  "tagline" : "You Know, for Search"
}

Now you’re ready to ingest and search some sample documents.

To ingest data, you must create an index and store some documents. This process is also called "indexing".

You can index multiple documents using a single POST request to the _bulk API endpoint. The request body specifies the documents to store and the indices in which to store them.

Elasticsearch will automatically create the index and map each document value to one of its data types. Include the ?pretty option to receive a human-readable response.

Run the following command to index some sample documents into the books index:

curl -X POST "${ES_URL}/_bulk?pretty" \
  -H "Authorization: ApiKey ${API_KEY}" \
  -H "Content-Type: application/json" \
  -d '
{ "index" : { "_index" : "books" } }
{"name": "Snow Crash", "author": "Neal Stephenson", "release_date": "1992-06-01", "page_count": 470}
{ "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:

{
  "errors" : false,
  "took" : 1260,
  "items" : [ ... ]
}

To search, send a POST request to the _search endpoint, specifying the index to search. Use the Elasticsearch query DSL to construct your request body.

Run the following command to search the books index for documents containing snow:

curl -X POST "${ES_URL}/books/_search?pretty" \
  -H "Authorization: ApiKey ${API_KEY}" \
  -H "Content-Type: application/json" \
  -d '
{
  "query": {
    "query_string": {
      "query": "snow"
    }
  }
}
'

You should receive a response with the results:

{
  "took" : 24,
  "timed_out" : false,
  "_shards" : {
    "total" : 1,
    "successful" : 1,
    "skipped" : 0,
    "failed" : 0
  },
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.5904956,
    "hits" : [
      {
        "_index" : "books",
        "_id" : "Z3hf_IoBONQ5TXnpLdlY",
        "_score" : 1.5904956,
        "_source" : {
          "name" : "Snow Crash",
          "author" : "Neal Stephenson",
          "release_date" : "1992-06-01",
          "page_count" : 470
        }
      }
    ]
  }
}

Congratulations! You’ve set up an Elasticsearch project, and you’ve ingested and searched some sample data. Now you’re ready to continue on your own.

Want to explore the sample documents or your own data?

By creating a data view, you can explore data using several UI tools, such as Discover or Dashboards. Or, use Elasticsearch aggregations to explore your data using the API. Find more information in Explore your data.

Ready to build your own solution?

To learn more about sending and syncing data to Elasticsearch, or the search API and its query DSL, check Ingest your data and REST APIs.