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HLRC: adding machine learning open job (#32860)
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* HLRC: adding machine learning open job

HLRC: adding ML open job API call

* Addressing @droberts195 comments and fixing minor style issue
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benwtrent authored Aug 15, 2018
1 parent 5ba04e2 commit f90f06e
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package org.elasticsearch.client;

import org.elasticsearch.action.ActionListener;
import org.elasticsearch.protocol.xpack.ml.OpenJobRequest;
import org.elasticsearch.protocol.xpack.ml.OpenJobResponse;
import org.elasticsearch.protocol.xpack.ml.PutJobRequest;
import org.elasticsearch.protocol.xpack.ml.PutJobResponse;

Expand Down Expand Up @@ -77,4 +79,51 @@ public void putJobAsync(PutJobRequest request, RequestOptions options, ActionLis
listener,
Collections.emptySet());
}

/**
* Opens a Machine Learning Job.
* When you open a new job, it starts with an empty model.
*
* When you open an existing job, the most recent model state is automatically loaded.
* The job is ready to resume its analysis from where it left off, once new data is received.
*
* <p>
* For additional info
* see <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-open-job.html"></a>
* </p>
* @param request request containing job_id and additional optional options
* @param options Additional request options (e.g. headers), use {@link RequestOptions#DEFAULT} if nothing needs to be customized
* @return response containing if the job was successfully opened or not.
* @throws IOException when there is a serialization issue sending the request or receiving the response
*/
public OpenJobResponse openJob(OpenJobRequest request, RequestOptions options) throws IOException {
return restHighLevelClient.performRequestAndParseEntity(request,
RequestConverters::machineLearningOpenJob,
options,
OpenJobResponse::fromXContent,
Collections.emptySet());
}

/**
* Opens a Machine Learning Job asynchronously, notifies listener on completion.
* When you open a new job, it starts with an empty model.
*
* When you open an existing job, the most recent model state is automatically loaded.
* The job is ready to resume its analysis from where it left off, once new data is received.
* <p>
* For additional info
* see <a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/ml-open-job.html"></a>
* </p>
* @param request request containing job_id and additional optional options
* @param options Additional request options (e.g. headers), use {@link RequestOptions#DEFAULT} if nothing needs to be customized
* @param listener Listener to be notified upon request completion
*/
public void openJobAsync(OpenJobRequest request, RequestOptions options, ActionListener<OpenJobResponse> listener) {
restHighLevelClient.performRequestAsyncAndParseEntity(request,
RequestConverters::machineLearningOpenJob,
options,
OpenJobResponse::fromXContent,
listener,
Collections.emptySet());
}
}
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import org.elasticsearch.protocol.xpack.license.GetLicenseRequest;
import org.elasticsearch.protocol.xpack.license.PutLicenseRequest;
import org.elasticsearch.protocol.xpack.migration.IndexUpgradeInfoRequest;
import org.elasticsearch.protocol.xpack.ml.OpenJobRequest;
import org.elasticsearch.protocol.xpack.ml.PutJobRequest;
import org.elasticsearch.protocol.xpack.watcher.DeleteWatchRequest;
import org.elasticsearch.protocol.xpack.watcher.PutWatchRequest;
Expand Down Expand Up @@ -1210,6 +1211,19 @@ static Request putMachineLearningJob(PutJobRequest putJobRequest) throws IOExcep
return request;
}

static Request machineLearningOpenJob(OpenJobRequest openJobRequest) throws IOException {
String endpoint = new EndpointBuilder()
.addPathPartAsIs("_xpack")
.addPathPartAsIs("ml")
.addPathPartAsIs("anomaly_detectors")
.addPathPart(openJobRequest.getJobId())
.addPathPartAsIs("_open")
.build();
Request request = new Request(HttpPost.METHOD_NAME, endpoint);
request.setJsonEntity(openJobRequest.toString());
return request;
}

static Request getMigrationAssistance(IndexUpgradeInfoRequest indexUpgradeInfoRequest) {
EndpointBuilder endpointBuilder = new EndpointBuilder()
.addPathPartAsIs("_xpack/migration/assistance")
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Expand Up @@ -20,6 +20,8 @@

import com.carrotsearch.randomizedtesting.generators.CodepointSetGenerator;
import org.elasticsearch.common.unit.TimeValue;
import org.elasticsearch.protocol.xpack.ml.OpenJobRequest;
import org.elasticsearch.protocol.xpack.ml.OpenJobResponse;
import org.elasticsearch.protocol.xpack.ml.PutJobRequest;
import org.elasticsearch.protocol.xpack.ml.PutJobResponse;
import org.elasticsearch.protocol.xpack.ml.job.config.AnalysisConfig;
Expand All @@ -46,12 +48,24 @@ public void testPutJob() throws Exception {
assertThat(createdJob.getJobType(), is(Job.ANOMALY_DETECTOR_JOB_TYPE));
}

public void testOpenJob() throws Exception {
String jobId = randomValidJobId();
Job job = buildJob(jobId);
MachineLearningClient machineLearningClient = highLevelClient().machineLearning();

machineLearningClient.putJob(new PutJobRequest(job), RequestOptions.DEFAULT);

OpenJobResponse response = execute(new OpenJobRequest(jobId), machineLearningClient::openJob, machineLearningClient::openJobAsync);

assertTrue(response.isOpened());
}

public static String randomValidJobId() {
CodepointSetGenerator generator = new CodepointSetGenerator("abcdefghijklmnopqrstuvwxyz0123456789".toCharArray());
return generator.ofCodePointsLength(random(), 10, 10);
}

private static Job buildJob(String jobId) {
public static Job buildJob(String jobId) {
Job.Builder builder = new Job.Builder(jobId);
builder.setDescription(randomAlphaOfLength(10));

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Expand Up @@ -127,6 +127,7 @@
import org.elasticsearch.index.rankeval.RestRankEvalAction;
import org.elasticsearch.protocol.xpack.XPackInfoRequest;
import org.elasticsearch.protocol.xpack.migration.IndexUpgradeInfoRequest;
import org.elasticsearch.protocol.xpack.ml.OpenJobRequest;
import org.elasticsearch.protocol.xpack.watcher.DeleteWatchRequest;
import org.elasticsearch.protocol.xpack.watcher.PutWatchRequest;
import org.elasticsearch.repositories.fs.FsRepository;
Expand Down Expand Up @@ -2610,6 +2611,19 @@ public void testXPackDeleteWatch() {
assertThat(request.getEntity(), nullValue());
}

public void testPostMachineLearningOpenJob() throws Exception {
String jobId = "some-job-id";
OpenJobRequest openJobRequest = new OpenJobRequest(jobId);
openJobRequest.setTimeout(TimeValue.timeValueMinutes(10));

Request request = RequestConverters.machineLearningOpenJob(openJobRequest);
assertEquals(HttpPost.METHOD_NAME, request.getMethod());
assertEquals("/_xpack/ml/anomaly_detectors/" + jobId + "/_open", request.getEndpoint());
ByteArrayOutputStream bos = new ByteArrayOutputStream();
request.getEntity().writeTo(bos);
assertEquals(bos.toString("UTF-8"), "{\"job_id\":\""+ jobId +"\",\"timeout\":\"10m\"}");
}

/**
* Randomize the {@link FetchSourceContext} request parameters.
*/
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Expand Up @@ -21,9 +21,12 @@
import org.elasticsearch.action.ActionListener;
import org.elasticsearch.action.LatchedActionListener;
import org.elasticsearch.client.ESRestHighLevelClientTestCase;
import org.elasticsearch.client.MachineLearningIT;
import org.elasticsearch.client.RequestOptions;
import org.elasticsearch.client.RestHighLevelClient;
import org.elasticsearch.common.unit.TimeValue;
import org.elasticsearch.protocol.xpack.ml.OpenJobRequest;
import org.elasticsearch.protocol.xpack.ml.OpenJobResponse;
import org.elasticsearch.protocol.xpack.ml.PutJobRequest;
import org.elasticsearch.protocol.xpack.ml.PutJobResponse;
import org.elasticsearch.protocol.xpack.ml.job.config.AnalysisConfig;
Expand Down Expand Up @@ -118,4 +121,54 @@ public void onFailure(Exception e) {
assertTrue(latch.await(30L, TimeUnit.SECONDS));
}
}

public void testOpenJob() throws Exception {
RestHighLevelClient client = highLevelClient();

Job job = MachineLearningIT.buildJob("opening-my-first-machine-learning-job");
client.machineLearning().putJob(new PutJobRequest(job), RequestOptions.DEFAULT);

Job secondJob = MachineLearningIT.buildJob("opening-my-second-machine-learning-job");
client.machineLearning().putJob(new PutJobRequest(secondJob), RequestOptions.DEFAULT);

{
//tag::x-pack-ml-open-job-request
OpenJobRequest openJobRequest = new OpenJobRequest("opening-my-first-machine-learning-job"); //<1>
openJobRequest.setTimeout(TimeValue.timeValueMinutes(10)); //<2>
//end::x-pack-ml-open-job-request

//tag::x-pack-ml-open-job-execute
OpenJobResponse openJobResponse = client.machineLearning().openJob(openJobRequest, RequestOptions.DEFAULT);
boolean isOpened = openJobResponse.isOpened(); //<1>
//end::x-pack-ml-open-job-execute

}

{
//tag::x-pack-ml-open-job-listener
ActionListener<OpenJobResponse> listener = new ActionListener<OpenJobResponse>() {
@Override
public void onResponse(OpenJobResponse openJobResponse) {
//<1>
}

@Override
public void onFailure(Exception e) {
//<2>
}
};
//end::x-pack-ml-open-job-listener
OpenJobRequest openJobRequest = new OpenJobRequest("opening-my-second-machine-learning-job");
// Replace the empty listener by a blocking listener in test
final CountDownLatch latch = new CountDownLatch(1);
listener = new LatchedActionListener<>(listener, latch);

// tag::x-pack-ml-open-job-execute-async
client.machineLearning().openJobAsync(openJobRequest, RequestOptions.DEFAULT, listener); //<1>
// end::x-pack-ml-open-job-execute-async

assertTrue(latch.await(30L, TimeUnit.SECONDS));
}

}
}
55 changes: 55 additions & 0 deletions docs/java-rest/high-level/ml/open-job.asciidoc
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[[java-rest-high-x-pack-ml-open-job]]
=== Open Job API

The Open Job API provides the ability to open {ml} jobs in the cluster.
It accepts a `OpenJobRequest` object and responds
with a `OpenJobResponse` object.

[[java-rest-high-x-pack-ml-open-job-request]]
==== Open Job Request

An `OpenJobRequest` object gets created with an existing non-null `jobId`.

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-open-job-request]
--------------------------------------------------
<1> Constructing a new request referencing an existing `jobId`
<2> Optionally setting the `timeout` value for how long the
execution should wait for the job to be opened.

[[java-rest-high-x-pack-ml-open-job-execution]]
==== Execution

The request can be executed through the `MachineLearningClient` contained
in the `RestHighLevelClient` object, accessed via the `machineLearningClient()` method.

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-open-job-execute]
--------------------------------------------------
<1> `isOpened()` from the `OpenJobResponse` indicates if the job was successfully
opened or not.

[[java-rest-high-x-pack-ml-open-job-execution-async]]
==== Asynchronous Execution

The request can also be executed asynchronously:

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-open-job-execute-async]
--------------------------------------------------
<1> The `OpenJobRequest` to execute and the `ActionListener` to use when
the execution completes

The method does not block and returns immediately. The passed `ActionListener` is used
to notify the caller of completion. A typical `ActionListner` for `OpenJobResponse` may
look like

["source","java",subs="attributes,callouts,macros"]
--------------------------------------------------
include-tagged::{doc-tests}/MlClientDocumentationIT.java[x-pack-ml-open-job-listener]
--------------------------------------------------
<1> `onResponse` is called back when the action is completed successfully
<2> `onFailure` is called back when some unexpected error occurs
4 changes: 3 additions & 1 deletion docs/java-rest/high-level/supported-apis.asciidoc
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Expand Up @@ -205,8 +205,10 @@ include::licensing/delete-license.asciidoc[]
The Java High Level REST Client supports the following Machine Learning APIs:

* <<java-rest-high-x-pack-ml-put-job>>
* <<java-rest-high-x-pack-ml-open-job>>

include::ml/put_job.asciidoc[]
include::ml/put-job.asciidoc[]
include::ml/open-job.asciidoc[]

== Migration APIs

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