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Update Datafeed API

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Update Datafeed API

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The Update Datafeed API can be used to update a machine learning datafeed in the cluster. The API accepts a UpdateDatafeedRequest object as a request and returns a PutDatafeedResponse.

Update Datafeed Request

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A UpdateDatafeedRequest requires the following argument:

UpdateDatafeedRequest request = new UpdateDatafeedRequest(datafeedUpdateBuilder.build());

The updated configuration of the machine learning datafeed

Updated Datafeed Arguments

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A DatafeedUpdate requires an existing non-null datafeedId and 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");

	Mandatory, non-null `datafeedId` referencing an existing machine learning datafeed
	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 `match_all` query.
	Optional, the time interval behind real time that data is queried.
	Optional, allows the use of script fields.
	Optional, the `size` parameter used in the searches.
	Optional, the `jobId` that references the job that the datafeed should be associated with after the update.

Synchronous Execution

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When executing a UpdateDatafeedRequest in the following manner, the client waits for the 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 4xx or 5xx error code, the high-level client tries to parse the response body error details instead and then throws a generic ElasticsearchException and adds the original ResponseException as a suppressed exception to it.

Asynchronous Execution

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Executing a 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:

client.machineLearning().updateDatafeedAsync(request, RequestOptions.DEFAULT, listener);

The UpdateDatafeedRequest to execute and the ActionListener to use when the execution completes

The asynchronous method does not block and returns immediately. Once it is completed the ActionListener is called back using the onResponse method 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:

ActionListener<PutDatafeedResponse> listener = new ActionListener<PutDatafeedResponse>() {
    @Override
    public void onResponse(PutDatafeedResponse response) {
        
    }

    @Override
    public void onFailure(Exception e) {
        
    }
};

	Called when the execution is successfully completed.
	Called when the whole `UpdateDatafeedRequest` fails.

Response

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The returned PutDatafeedResponse returns the full representation of the updated machine learning datafeed if it has been successfully updated.

DatafeedConfig updatedDatafeed = response.getResponse();

The updated datafeed

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