Delete expired ML data

DELETE /_ml/_delete_expired_data/{job_id}
DELETE /_ml/_delete_expired_data

Delete all job results, model snapshots and forecast data that have exceeded their retention days period. Machine learning state documents that are not associated with any job are also deleted. You can limit the request to a single or set of anomaly detection jobs by using a job identifier, a group name, a comma-separated list of jobs, or a wildcard expression. You can delete expired data for all anomaly detection jobs by using _all, by specifying * as the <job_id>, or by omitting the <job_id>.

Required authorization

Cluster privileges: manage_ml

Path Parameters

job_id string Id optional

Identifier for an anomaly detection job. It can be a job identifier, a group name, or a wildcard expression.

Leave it out to use /_ml/_delete_expired_data.

A successful response when deleting expired and unused anomaly detection data.

Request Console 1 / 8 languages

Console Kibana Dev Tools

DELETE _ml/_delete_expired_data?timeout=1h

JavaScript Elasticsearch JavaScript Client

const response = await client.ml.deleteExpiredData({
  timeout: "1h",
});

Python Elasticsearch Python Client

resp = client.ml.delete_expired_data(
    timeout="1h",
)

curl Shell

curl -X DELETE "$ELASTICSEARCH_URL/_ml/_delete_expired_data?timeout=1h" \
  -H "Authorization: ApiKey $ELASTIC_API_KEY"

Java Elasticsearch Java Client

client.ml().deleteExpiredData(d -> d
    .timeout(t -> t
        .time("1h")
    )
);

C# Elasticsearch .NET Client

var response = await client.MachineLearning.DeleteExpiredDataAsync();

PHP Elasticsearch PHP Client

$resp = $client->ml()->deleteExpiredData([
    "timeout" => "1h",
]);

Ruby Elasticsearch Ruby Client

response = client.ml.delete_expired_data(
  timeout: "1h"
)

Response

{
  "deleted": true
}

Required authorization

Path Parameters

Query Parameters { requests_per_second , timeout , }

Request { requests_per_second , timeout , }

Response

Query Parameters

Request