Flush jobs API
editFlush jobs API
editForces any buffered data to be processed by the job.
Request
editPOST _ml/anomaly_detectors/<job_id>/_flush
Prerequisites
editRequires the manage_ml cluster privilege. This privilege is included in the
machine_learning_admin built-in role.
Description
editThe 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
edit-
<job_id> - (Required, string) Identifier for the anomaly detection job.
Query parameters
edit-
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_interimandstart, 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
editYou can also specify the query parameters (such as advance_time and
calc_interim) in the request body.
Examples
editPOST _ml/anomaly_detectors/total-requests/_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:
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
}