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Example: Enrich your data based on exact values

Serverless

Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Serverless interfaces and procedures might differ from classic Elastic Stack deployments.

Stack

The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.

This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.

match enrich policies match enrich data to incoming documents based on an exact value, such as a email address or ID, using a term query.

The following example creates a match enrich policy that adds user name and contact information to incoming documents based on an email address. It then adds the match enrich policy to a processor in an ingest pipeline.

Use the create index API or index API to create a source index.

The following index API request creates a source index and indexes a new document to that index.

JSON
PUT /users/_doc/1?refresh=wait_for
{
  "email": "mardy.brown@example.com",
  "first_name": "Mardy",
  "last_name": "Brown",
  "city": "New Orleans",
  "county": "Orleans",
  "state": "LA",
  "zip": 70116,
  "web": "mardy.example.com"
}

Use the create enrich policy API to create an enrich policy with the match policy type. This policy must include:

  • One or more source indices
  • A match_field, the field from the source indices used to match incoming documents
  • Enrich fields from the source indices you’d like to append to incoming documents
JSON
PUT /_enrich/policy/users-policy
{
  "match": {
    "indices": "users",
    "match_field": "email",
    "enrich_fields": ["first_name", "last_name", "city", "zip", "state"]
  }
}

Use the execute enrich policy API to create an enrich index for the policy.

JSON
POST /_enrich/policy/users-policy/_execute

Use the create or update pipeline API to create an ingest pipeline. In the pipeline, add an enrich processor that includes:

  • Your enrich policy.
  • The field of incoming documents used to match documents from the enrich index.
  • The target_field used to store appended enrich data for incoming documents. This field contains the match_field and enrich_fields specified in your enrich policy.
JSON
PUT /_ingest/pipeline/user_lookup
{
  "processors" : [
    {
      "enrich" : {
        "description": "Add 'user' data based on 'email'",
        "policy_name": "users-policy",
        "field" : "email",
        "target_field": "user",
        "max_matches": "1"
      }
    }
  ]
}

Use the ingest pipeline to index a document. The incoming document should include the field specified in your enrich processor.

JSON
PUT /my-index-000001/_doc/my_id?pipeline=user_lookup
{
  "email": "mardy.brown@example.com"
}

To verify the enrich processor matched and appended the appropriate field data, use the get API to view the indexed document.

JSON
GET /my-index-000001/_doc/my_id

The API returns the following response:

JSON
{
  "found": true,
  "_index": "my-index-000001",
  "_id": "my_id",
  "_version": 1,
  "_seq_no": 55,
  "_primary_term": 1,
  "_source": {
    "user": {
      "email": "mardy.brown@example.com",
      "first_name": "Mardy",
      "last_name": "Brown",
      "zip": 70116,
      "city": "New Orleans",
      "state": "LA"
    },
    "email": "mardy.brown@example.com"
  }
}