Deduplicate data

The Beats framework guarantees at-least-once delivery to ensure that no data is lost when events are sent to outputs that support acknowledgement, such as Elasticsearch, Logstash, Kafka, and Redis. This is great if everything goes as planned. But if Filebeat shuts down during processing, or the connection is lost before events are acknowledged, you can end up with duplicate data.

What causes duplicates in Elasticsearch?

When an output is blocked, the retry mechanism in Filebeat attempts to resend events until they are acknowledged by the output. If the output receives the events, but is unable to acknowledge them, the data might be sent to the output multiple times. Because document IDs are typically set by Elasticsearch after it receives the data from Beats, the duplicate events are indexed as new documents.

How can I avoid duplicates?

Rather than allowing Elasticsearch to set the document ID, set the ID in Beats. The ID is stored in the Beats @metadata._id field and used to set the document ID during indexing. That way, if Beats sends the same event to Elasticsearch more than once, Elasticsearch overwrites the existing document rather than creating a new one.

The @metadata._id field is passed along with the event so that you can use it to set the document ID after the event has been published by Filebeat but before it’s received by Elasticsearch. For example, see Logstash pipeline example.

There are several ways to set the document ID in Beats:

  • add_id processor

    Use the add_id processor when your data has no natural key field, and you can’t derive a unique key from existing fields.

    This example generates a unique ID for each event and adds it to the @metadata._id field:

      - add_id: ~
  • fingerprint processor

    Use the fingerprint processor to derive a unique key from one or more existing fields.

    This example uses the values of field1 and field2 to derive a unique key that it adds to the @metadata._id field:

      - fingerprint:
          fields: ["field1", "field2"]
          target_field: "@metadata._id"
  • decode_json_fields processor

    Use the document_id setting in the decode_json_fields processor when you’re decoding a JSON string that contains a natural key field.

    For this example, assume that the message field contains the JSON string {"myid": "100", "text": "Some text"}. This example takes the value of myid from the JSON string and stores it in the @metadata._id field:

     - decode_json_fields:
         document_id: "myid"
         fields: ["message"]
         max_depth: 1
         target: ""

    The resulting document ID is 100.

  • JSON input settings

    Use the json.document_id input setting if you’re ingesting JSON-formatted data, and the data has a natural key field.

    This example takes the value of key1 from the JSON document and stores it in the @metadata._id field:

    - type: log
        - /path/to/json.log
      json.document_id: "key1"

Logstash pipeline example

For this example, assume that you’ve used one of the approaches described earlier to store the document ID in the Beats @metadata._id field. To preserve the ID when you send Beats data through Logstash en route to Elasticsearch, set the document_id field in the Logstash pipeline:

input {
  beats {
    port => 5044

output {
  if [@metadata][_id] {
    elasticsearch {
      hosts => ["http://localhost:9200"]
      document_id => "%{[@metadata][_id]}" 
      index => "%{[@metadata][beat]}-%{[@metadata][version]}"
  } else {
    elasticsearch {
      hosts => ["http://localhost:9200"]
      index => "%{[@metadata][beat]}-%{[@metadata][version]}"

Sets the document_id field in the Elasticsearch output to the value stored in @metadata._id.

When Elasticsearch indexes the document, it sets the document ID to the specified value, preserving the ID passed from Beats.