[ML] fix x-pack usage regression caused by index migration (#36936)

Changes the feature usage retrieval to use the job manager rather than
directly talking to the cluster state, because jobs can now be either in
cluster state or stored in an index

This is a follow-up of #36702 / #36698

x-pack/plugin/ml/qa/native-multi-node-tests/src/test/java/org/elasticsearch/xpack/ml/integration/MlJobIT.java

@@ -13,6 +13,7 @@
 import org.elasticsearch.common.util.concurrent.ConcurrentCollections;
 import org.elasticsearch.common.util.concurrent.ConcurrentMapLong;
 import org.elasticsearch.common.util.concurrent.ThreadContext;
+import org.elasticsearch.common.xcontent.support.XContentMapValues;
 import org.elasticsearch.test.SecuritySettingsSourceField;
 import org.elasticsearch.test.rest.ESRestTestCase;
 import org.elasticsearch.xpack.core.ml.integration.MlRestTestStateCleaner;
@@ -22,7 +23,9 @@
 import org.junit.After;
 
 import java.io.IOException;
+import java.util.Collections;
 import java.util.Locale;
+import java.util.Map;
 import java.util.concurrent.atomic.AtomicInteger;
 import java.util.concurrent.atomic.AtomicReference;
 import java.util.regex.Matcher;
@@ -111,6 +114,21 @@ public void testGetJobs_GivenMultipleJobs() throws Exception {
         assertThat(implicitAll, containsString("\"job_id\":\"given-multiple-jobs-job-3\""));
     }
 
+    // tests the _xpack/usage endpoint
+    public void testUsage() throws IOException {
+        createFarequoteJob("job-1");
+        createFarequoteJob("job-2");
+        Map<String, Object> usage = entityAsMap(client().performRequest(new Request("GET", "_xpack/usage")));
+        assertEquals(2, XContentMapValues.extractValue("ml.jobs._all.count", usage));
+        assertEquals(2, XContentMapValues.extractValue("ml.jobs.closed.count", usage));
+        Response openResponse = client().performRequest(new Request("POST", MachineLearning.BASE_PATH + "anomaly_detectors/job-1/_open"));
+        assertEquals(Collections.singletonMap("opened", true), entityAsMap(openResponse));
+        usage = entityAsMap(client().performRequest(new Request("GET", "_xpack/usage")));
+        assertEquals(2, XContentMapValues.extractValue("ml.jobs._all.count", usage));
+        assertEquals(1, XContentMapValues.extractValue("ml.jobs.closed.count", usage));
+        assertEquals(1, XContentMapValues.extractValue("ml.jobs.opened.count", usage));
+    }
+
     private Response createFarequoteJob(String jobId) throws IOException {
         Request request = new Request("PUT", MachineLearning.BASE_PATH + "anomaly_detectors/" + jobId);
         request.setJsonEntity(

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearning.java

@@ -165,6 +165,7 @@
 import org.elasticsearch.xpack.ml.datafeed.DatafeedManager;
 import org.elasticsearch.xpack.ml.datafeed.persistence.DatafeedConfigProvider;
 import org.elasticsearch.xpack.ml.job.JobManager;
+import org.elasticsearch.xpack.ml.job.JobManagerHolder;
 import org.elasticsearch.xpack.ml.job.UpdateJobProcessNotifier;
 import org.elasticsearch.xpack.ml.job.categorization.MlClassicTokenizer;
 import org.elasticsearch.xpack.ml.job.categorization.MlClassicTokenizerFactory;
@@ -372,7 +373,8 @@ public Collection<Object> createComponents(Client client, ClusterService cluster
                                                NamedXContentRegistry xContentRegistry, Environment environment,
                                                NodeEnvironment nodeEnvironment, NamedWriteableRegistry namedWriteableRegistry) {
         if (enabled == false || transportClientMode || tribeNode || tribeNodeClient) {
-            return emptyList();
+            // special holder for @link(MachineLearningFeatureSetUsage) which needs access to job manager, empty if ML is disabled
+            return Collections.singletonList(new JobManagerHolder());
         }
 
         Auditor auditor = new Auditor(client, clusterService.getNodeName());
@@ -382,6 +384,9 @@ public Collection<Object> createComponents(Client client, ClusterService cluster
         UpdateJobProcessNotifier notifier = new UpdateJobProcessNotifier(client, clusterService, threadPool);
         JobManager jobManager = new JobManager(env, settings, jobResultsProvider, clusterService, auditor, threadPool, client, notifier);
 
+        // special holder for @link(MachineLearningFeatureSetUsage) which needs access to job manager if ML is enabled
+        JobManagerHolder jobManagerHolder = new JobManagerHolder(jobManager);
+
         JobDataCountsPersister jobDataCountsPersister = new JobDataCountsPersister(client);
         JobResultsPersister jobResultsPersister = new JobResultsPersister(client);
 
@@ -440,6 +445,7 @@ public Collection<Object> createComponents(Client client, ClusterService cluster
                 jobConfigProvider,
                 datafeedConfigProvider,
                 jobManager,
+                jobManagerHolder,
                 autodetectProcessManager,
                 new MlInitializationService(settings, threadPool, clusterService, client),
                 jobDataCountsPersister,

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearningFeatureSet.java

@@ -26,12 +26,12 @@
 import org.elasticsearch.xpack.core.XPackSettings;
 import org.elasticsearch.xpack.core.XPackField;
 import org.elasticsearch.xpack.core.ml.MachineLearningFeatureSetUsage;
-import org.elasticsearch.xpack.core.ml.MlMetadata;
 import org.elasticsearch.xpack.core.ml.action.GetDatafeedsStatsAction;
 import org.elasticsearch.xpack.core.ml.action.GetJobsStatsAction;
 import org.elasticsearch.xpack.core.ml.datafeed.DatafeedState;
 import org.elasticsearch.xpack.core.ml.job.config.Job;
 import org.elasticsearch.xpack.core.ml.job.config.JobState;
+import org.elasticsearch.xpack.ml.job.JobManagerHolder;
 import org.elasticsearch.xpack.ml.process.NativeController;
 import org.elasticsearch.xpack.ml.process.NativeControllerHolder;
 import org.elasticsearch.xpack.core.ml.job.process.autodetect.state.ModelSizeStats;
@@ -48,6 +48,7 @@
 import java.util.Map;
 import java.util.Objects;
 import java.util.concurrent.TimeoutException;
+import java.util.stream.Collectors;
 
 public class MachineLearningFeatureSet implements XPackFeatureSet {
 
@@ -61,15 +62,17 @@ public class MachineLearningFeatureSet implements XPackFeatureSet {
     private final XPackLicenseState licenseState;
     private final ClusterService clusterService;
     private final Client client;
+    private final JobManagerHolder jobManagerHolder;
     private final Map<String, Object> nativeCodeInfo;
 
     @Inject
     public MachineLearningFeatureSet(Environment environment, ClusterService clusterService, Client client,
-                                     @Nullable XPackLicenseState licenseState) {
+                                     @Nullable XPackLicenseState licenseState, JobManagerHolder jobManagerHolder) {
         this.enabled = XPackSettings.MACHINE_LEARNING_ENABLED.get(environment.settings());
         this.clusterService = Objects.requireNonNull(clusterService);
         this.client = Objects.requireNonNull(client);
         this.licenseState = licenseState;
+        this.jobManagerHolder = jobManagerHolder;
         Map<String, Object> nativeCodeInfo = NativeController.UNKNOWN_NATIVE_CODE_INFO;
         // Don't try to get the native code version if ML is disabled - it causes too much controversy
         // if ML has been disabled because of some OS incompatibility.  Also don't try to get the native
@@ -135,7 +138,7 @@ public Map<String, Object> nativeCodeInfo() {
     @Override
     public void usage(ActionListener<XPackFeatureSet.Usage> listener) {
         ClusterState state = clusterService.state();
-        new Retriever(client, MlMetadata.getMlMetadata(state), available(), enabled(), mlNodeCount(state)).execute(listener);
+        new Retriever(client, jobManagerHolder, available(), enabled(), mlNodeCount(state)).execute(listener);
     }
 
     private int mlNodeCount(final ClusterState clusterState) {
@@ -156,16 +159,16 @@ private int mlNodeCount(final ClusterState clusterState) {
     public static class Retriever {
 
         private final Client client;
-        private final MlMetadata mlMetadata;
+        private final JobManagerHolder jobManagerHolder;
         private final boolean available;
         private final boolean enabled;
         private Map<String, Object> jobsUsage;
         private Map<String, Object> datafeedsUsage;
         private int nodeCount;
 
-        public Retriever(Client client, MlMetadata mlMetadata, boolean available, boolean enabled, int nodeCount) {
+        public Retriever(Client client, JobManagerHolder jobManagerHolder, boolean available, boolean enabled, int nodeCount) {
             this.client = Objects.requireNonNull(client);
-            this.mlMetadata = mlMetadata;
+            this.jobManagerHolder = jobManagerHolder;
             this.available = available;
             this.enabled = enabled;
             this.jobsUsage = new LinkedHashMap<>();
@@ -174,7 +177,8 @@ public Retriever(Client client, MlMetadata mlMetadata, boolean available, boolea
         }
 
         public void execute(ActionListener<Usage> listener) {
-            if (enabled == false) {
+            // empty holder means either ML disabled or transport client mode
+            if (jobManagerHolder.isEmpty()) {
                 listener.onResponse(
                     new MachineLearningFeatureSetUsage(available, enabled, Collections.emptyMap(), Collections.emptyMap(), 0));
                 return;
@@ -194,20 +198,19 @@ public void execute(ActionListener<Usage> listener) {
             GetJobsStatsAction.Request jobStatsRequest = new GetJobsStatsAction.Request(MetaData.ALL);
             ActionListener<GetJobsStatsAction.Response> jobStatsListener = ActionListener.wrap(
                     response -> {
-                        addJobsUsage(response);
-                        GetDatafeedsStatsAction.Request datafeedStatsRequest =
-                                new GetDatafeedsStatsAction.Request(GetDatafeedsStatsAction.ALL);
-                        client.execute(GetDatafeedsStatsAction.INSTANCE, datafeedStatsRequest,
-                                datafeedStatsListener);
-                    },
-                    listener::onFailure
-            );
+                        jobManagerHolder.getJobManager().expandJobs(MetaData.ALL, true, ActionListener.wrap(jobs -> {
+                            addJobsUsage(response, jobs.results());
+                            GetDatafeedsStatsAction.Request datafeedStatsRequest = new GetDatafeedsStatsAction.Request(
+                                    GetDatafeedsStatsAction.ALL);
+                            client.execute(GetDatafeedsStatsAction.INSTANCE, datafeedStatsRequest, datafeedStatsListener);
+                        }, listener::onFailure));
+                    }, listener::onFailure);
 
             // Step 0. Kick off the chain of callbacks by requesting jobs stats
             client.execute(GetJobsStatsAction.INSTANCE, jobStatsRequest, jobStatsListener);
         }
 
-        private void addJobsUsage(GetJobsStatsAction.Response response) {
+        private void addJobsUsage(GetJobsStatsAction.Response response, List<Job> jobs) {
             StatsAccumulator allJobsDetectorsStats = new StatsAccumulator();
             StatsAccumulator allJobsModelSizeStats = new StatsAccumulator();
             ForecastStats allJobsForecastStats = new ForecastStats();
@@ -217,11 +220,11 @@ private void addJobsUsage(GetJobsStatsAction.Response response) {
             Map<JobState, StatsAccumulator> modelSizeStatsByState = new HashMap<>();
             Map<JobState, ForecastStats> forecastStatsByState = new HashMap<>();
 
-            Map<String, Job> jobs = mlMetadata.getJobs();
             List<GetJobsStatsAction.Response.JobStats> jobsStats = response.getResponse().results();
+            Map<String, Job> jobMap = jobs.stream().collect(Collectors.toMap(Job::getId, item -> item));
             for (GetJobsStatsAction.Response.JobStats jobStats : jobsStats) {
                 ModelSizeStats modelSizeStats = jobStats.getModelSizeStats();
-                int detectorsCount = jobs.get(jobStats.getJobId()).getAnalysisConfig()
+                int detectorsCount = jobMap.get(jobStats.getJobId()).getAnalysisConfig()
                         .getDetectors().size();
                 double modelSize = modelSizeStats == null ? 0.0
                         : jobStats.getModelSizeStats().getModelBytes();

x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/job/JobManagerHolder.java

@@ -0,0 +1,46 @@
+/*
+ * Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
+ * or more contributor license agreements. Licensed under the Elastic License;
+ * you may not use this file except in compliance with the Elastic License.
+ */
+
+package org.elasticsearch.xpack.ml.job;
+
+import org.elasticsearch.ElasticsearchException;
+
+public class JobManagerHolder {
+
+    private final JobManager instance;
+
+    /**
+     * Create an empty holder which also means that no job manager gets created.
+     */
+    public JobManagerHolder() {
+        this.instance = null;
+    }
+
+    /**
+     * Create a holder that allows lazy creation of a job manager.
+     *
+     */
+    public JobManagerHolder(JobManager jobManager) {
+        this.instance = jobManager;
+    }
+
+    public boolean isEmpty() {
+        return instance == null;
+    }
+
+    /**
+     * Get the instance of the held JobManager.
+     *
+     * @return job manager instance
+     * @throws ElasticsearchException if holder has been created with the empty constructor
+     */
+    public JobManager getJobManager() {
+        if (instance == null) {
+            throw new ElasticsearchException("Tried to get job manager although Machine Learning is disabled");
+        }
+        return instance;
+    }
+}