The Put Datafeed API can be used to create a new machine learning datafeed
in the cluster. The API accepts a
as a request and returns a
PutDatafeedRequest requires the following argument:
DatafeedConfig object contains all the details about the machine learning datafeed
DatafeedConfig requires the following arguments:
The following arguments are optional:
Sets the delayed data check configuration.
The window must be larger than the Job’s bucket size, but smaller than 24 hours,
and span less than 10,000 buckets.
When executing a
PutDatafeedRequest in the following manner, the client waits
PutDatafeedResponse to be returned before continuing with code execution:
PutDatafeedResponse response = client.machineLearning().putDatafeed(request, RequestOptions.DEFAULT);
Synchronous calls may throw an
IOException in case of either failing to
parse the REST response in the high-level REST client, the request times out
or similar cases where there is no response coming back from the server.
In cases where the server returns a
5xx error code, the high-level
client tries to parse the response body error details instead and then throws
ElasticsearchException and adds the original
ResponseException as a
suppressed exception to it.
PutDatafeedRequest can also be done in an asynchronous fashion so that
the client can return directly. Users need to specify how the response or
potential failures will be handled by passing the request and a listener to the
asynchronous put-datafeed method:
The asynchronous method does not block and returns immediately. Once it is
ActionListener is called back using the
if the execution successfully completed or using the
onFailure method if
it failed. Failure scenarios and expected exceptions are the same as in the
synchronous execution case.
A typical listener for
put-datafeed looks like:
PutDatafeedResponse returns the full representation of
the new machine learning datafeed if it has been successfully created. This will
contain the creation time and other fields initialized using