The Update Datafeed API can be used to update a machine learning datafeed
in the cluster. The API accepts a
as a request and returns a
UpdateDatafeedRequest requires the following argument:
DatafeedUpdate requires an existing non-null
allows updating various settings.
DatafeedUpdate.Builder datafeedUpdateBuilder = new DatafeedUpdate.Builder(datafeedId) .setAggregations(aggs) .setIndices("index_1", "index_2") .setChunkingConfig(ChunkingConfig.newAuto()) .setFrequency(TimeValue.timeValueSeconds(30)) .setQuery(QueryBuilders.matchAllQuery()) .setQueryDelay(TimeValue.timeValueMinutes(1)) .setScriptFields(scriptFields) .setScrollSize(1000) .setJobId("update-datafeed-job");
Optional, set the datafeed Aggregations for data gathering
Optional, the indices that contain the data to retrieve and feed into the job
Optional, specifies how data searches are split into time chunks.
Optional, the interval at which scheduled queries are made while the datafeed runs in real time.
Optional, a query to filter the search results by. Defaults to the
Optional, the time interval behind real time that data is queried.
Optional, allows the use of script fields.
When executing a
UpdateDatafeedRequest in the following manner, the client waits
PutDatafeedResponse to be returned before continuing with code execution:
PutDatafeedResponse response = client.machineLearning().updateDatafeed(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.
UpdateDatafeedRequest 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 update-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
update-datafeed looks like:
PutDatafeedResponse returns the full representation of
the updated machine learning datafeed if it has been successfully updated.