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

Create project

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

1. Navigate to cloud.elastic.co and create a new account or log in to your existing account.

2. Within Fully-managed projects, choose Create project.

3. Choose the Elasticsearch project type.

4. Select a configuration for your project, based on your use case.

   • General purpose. For general search use cases across various data types.
   • Optimized for Vectors. For search use cases using vectors and near real-time retrieval.

5. Provide a name for the project and optionally edit the project settings, such as the cloud platform region. Select Create project to continue.

6. Once the project is ready, select Continue.

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

Create API key

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

1. Scroll to Add an API Key and select New.

2. In Create an API key, enter a name for your key and its expiration. Select Create API Key to finish.

The API key is displayed as a set of values, including id, name, expiration, api_key, and encoded. Store this information securely—it is displayed only once.

You will use the encoded value when sending API requests.

Copy URL

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

1. Scroll to Copy your connection details.

2. Find the value for Elasticsearch Endpoint.

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

Test connection

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.

Ingest data

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" : [ ... ]
}

Search data

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
        }
      }
    ]
  }
}

Continue on your own

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.

Explore

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.

Build

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.