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Elastic Docs ›Elasticsearch Guide [master] ›REST APIs ›Machine learning anomaly detection APIs

Flush jobs API

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Flush jobs API

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Forces any buffered data to be processed by the job.

Request

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POST _ml/anomaly_detectors/<job_id>/_flush

Prerequisites

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Requires the manage_ml cluster privilege. This privilege is included in the machine_learning_admin built-in role.

Description

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The flush jobs API is only applicable when sending data for analysis using the post data API. Depending on the content of the buffer, then it might additionally calculate new results.

Both flush and close operations are similar, however the flush is more efficient if you are expecting to send more data for analysis. When flushing, the job remains open and is available to continue analyzing data. A close operation additionally prunes and persists the model state to disk and the job must be opened again before analyzing further data.

Path parameters

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<job_id>
(Required, string) Identifier for the anomaly detection job.

Query parameters

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advance_time
(string) Optional. Specifies to advance to a particular time value. Results are generated and the model is updated for data from the specified time interval.
calc_interim
(Boolean) Optional. If true, calculates the interim results for the most recent bucket or all buckets within the latency period.
end
(string) Optional. When used in conjunction with calc_interim and start, specifies the range of buckets on which to calculate interim results.
skip_time
(string) Optional. Specifies to skip to a particular time value. Results are not generated and the model is not updated for data from the specified time interval.
start
(string) Optional. When used in conjunction with calc_interim, specifies the range of buckets on which to calculate interim results.

Request body

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You can also specify the query parameters (such as advance_time and calc_interim) in the request body.

Examples

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resp = client.ml.flush_job(
    job_id="low_request_rate",
    calc_interim=True,
)
print(resp)
const response = await client.ml.flushJob({
  job_id: "low_request_rate",
  calc_interim: true,
});
console.log(response);
POST _ml/anomaly_detectors/low_request_rate/_flush
{
  "calc_interim": true
}

When the operation succeeds, you receive the following results:

{
  "flushed": true,
  "last_finalized_bucket_end": 1455234900000
}

The last_finalized_bucket_end provides the timestamp (in milliseconds-since-the-epoch) of the end of the last bucket that was processed.

If you want to flush the job to a specific timestamp, you can use the advance_time or skip_time parameters. For example, to advance to 11 AM GMT on January 1, 2018:

resp = client.ml.flush_job(
    job_id="total-requests",
    advance_time="1514804400000",
)
print(resp)
const response = await client.ml.flushJob({
  job_id: "total-requests",
  advance_time: 1514804400000,
});
console.log(response);
POST _ml/anomaly_detectors/total-requests/_flush
{
  "advance_time": "1514804400000"
}

When the operation succeeds, you receive the following results:

{
  "flushed": true,
  "last_finalized_bucket_end": 1514804400000
}
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