[ML] Add ml jobs and datafeeds in usage API (elastic/x-pack-elasticsearch#805)
This adds basic info about jobs and datafeeds, sufficient for the first release for supporting monitoring and phone-home. In particular, usage now displays: - job count for _all and by state - detectors statistics (min, max, mean, total) for _all jobs and by job state - model size statistics (min, max, mean, total) for _all jobs and by job state - datafeed count for _all and by state Relates elastic/x-pack-elasticsearch#660 Original commit: elastic/x-pack-elasticsearch@6e0da6c3db
This commit is contained in:
Dimitris Athanasiou
GitHub
parent
ea67089cef
commit
1cfd9bd986
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@ -330,6 +330,11 @@ public class MachineLearning implements ActionPlugin {
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public Collection<Module> nodeModules() {
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List<Module> modules = new ArrayList<>();
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if (transportClientMode) {
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return modules;
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}
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modules.add(b -> {
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XPackPlugin.bindFeatureSet(b, MachineLearningFeatureSet.class);
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});
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@ -5,33 +5,56 @@
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*/
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package org.elasticsearch.xpack.ml;
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import org.apache.lucene.util.Counter;
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import org.elasticsearch.ElasticsearchException;
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import org.elasticsearch.action.ActionListener;
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import org.elasticsearch.client.Client;
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import org.elasticsearch.cluster.ClusterState;
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import org.elasticsearch.cluster.service.ClusterService;
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import org.elasticsearch.common.Nullable;
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import org.elasticsearch.common.inject.Inject;
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import org.elasticsearch.common.io.stream.StreamInput;
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import org.elasticsearch.common.io.stream.StreamOutput;
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import org.elasticsearch.common.logging.Loggers;
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import org.elasticsearch.common.settings.Settings;
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import org.elasticsearch.common.xcontent.XContentBuilder;
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import org.elasticsearch.license.XPackLicenseState;
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import org.elasticsearch.xpack.XPackFeatureSet;
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import org.elasticsearch.xpack.XPackPlugin;
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import org.elasticsearch.xpack.XPackSettings;
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import org.elasticsearch.xpack.ml.action.GetDatafeedsStatsAction;
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import org.elasticsearch.xpack.ml.action.GetJobsStatsAction;
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import org.elasticsearch.xpack.ml.datafeed.DatafeedState;
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import org.elasticsearch.xpack.ml.job.config.Job;
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import org.elasticsearch.xpack.ml.job.config.JobState;
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import org.elasticsearch.xpack.ml.job.process.NativeController;
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import org.elasticsearch.xpack.ml.job.process.NativeControllerHolder;
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import org.elasticsearch.xpack.ml.job.process.autodetect.state.ModelSizeStats;
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import org.elasticsearch.xpack.ml.utils.StatsAccumulator;
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import java.io.IOException;
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import java.util.Collections;
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import java.util.HashMap;
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import java.util.LinkedHashMap;
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import java.util.Locale;
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import java.util.Map;
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import java.util.Objects;
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import java.util.concurrent.TimeoutException;
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public class MachineLearningFeatureSet implements XPackFeatureSet {
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private final boolean enabled;
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private final XPackLicenseState licenseState;
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private final ClusterService clusterService;
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private final Client client;
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private final Map<String, Object> nativeCodeInfo;
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@Inject
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public MachineLearningFeatureSet(Settings settings, @Nullable XPackLicenseState licenseState) {
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public MachineLearningFeatureSet(Settings settings, ClusterService clusterService, Client client,
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@Nullable XPackLicenseState licenseState) {
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this.enabled = XPackSettings.MACHINE_LEARNING_ENABLED.get(settings);
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this.clusterService = Objects.requireNonNull(clusterService);
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this.client = Objects.requireNonNull(client);
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this.licenseState = licenseState;
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Map<String, Object> nativeCodeInfo = NativeController.UNKNOWN_NATIVE_CODE_INFO;
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// Don't try to get the native code version in the transport client - the controller process won't be running
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@ -79,17 +102,177 @@ public class MachineLearningFeatureSet implements XPackFeatureSet {
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@Override
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public void usage(ActionListener<XPackFeatureSet.Usage> listener) {
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listener.onResponse(new Usage(available(), enabled()));
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ClusterState state = clusterService.state();
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MlMetadata mlMetadata = state.getMetaData().custom(MlMetadata.TYPE);
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new Usage.Retriever(client, mlMetadata, available(), enabled()).execute(listener);
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}
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public static class Usage extends XPackFeatureSet.Usage {
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public Usage(StreamInput input) throws IOException {
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super(input);
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private static final String ALL = "_all";
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private static final String JOBS_FIELD = "jobs";
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private static final String DATAFEEDS_FIELD = "datafeeds";
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private static final String COUNT = "count";
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private static final String DETECTORS = "detectors";
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private static final String MODEL_SIZE = "model_size";
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private final Map<String, Object> jobsUsage;
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private final Map<String, Object> datafeedsUsage;
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public Usage(boolean available, boolean enabled, Map<String, Object> jobsUsage,
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Map<String, Object> datafeedsUsage) {
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super(XPackPlugin.MACHINE_LEARNING, available, enabled);
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this.jobsUsage = Objects.requireNonNull(jobsUsage);
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this.datafeedsUsage = Objects.requireNonNull(datafeedsUsage);
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}
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public Usage(boolean available, boolean enabled) {
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super(XPackPlugin.MACHINE_LEARNING, available, enabled);
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public Usage(StreamInput in) throws IOException {
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super(in);
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this.jobsUsage = in.readMap();
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this.datafeedsUsage = in.readMap();
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}
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@Override
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public void writeTo(StreamOutput out) throws IOException {
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super.writeTo(out);
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out.writeMap(jobsUsage);
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out.writeMap(datafeedsUsage);
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}
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@Override
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protected void innerXContent(XContentBuilder builder, Params params) throws IOException {
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super.innerXContent(builder, params);
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if (jobsUsage != null) {
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builder.field(JOBS_FIELD, jobsUsage);
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}
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if (datafeedsUsage != null) {
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builder.field(DATAFEEDS_FIELD, datafeedsUsage);
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}
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}
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public static class Retriever {
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private final Client client;
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private final MlMetadata mlMetadata;
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private final boolean available;
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private final boolean enabled;
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private Map<String, Object> jobsUsage;
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private Map<String, Object> datafeedsUsage;
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public Retriever(Client client, MlMetadata mlMetadata, boolean available, boolean enabled) {
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this.client = Objects.requireNonNull(client);
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this.mlMetadata = mlMetadata;
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this.available = available;
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this.enabled = enabled;
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this.jobsUsage = new LinkedHashMap<>();
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this.datafeedsUsage = new LinkedHashMap<>();
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}
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public void execute(ActionListener<XPackFeatureSet.Usage> listener) {
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if (enabled == false) {
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listener.onResponse(new Usage(available, enabled, Collections.emptyMap(), Collections.emptyMap()));
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return;
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}
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// Step 2. Extract usage from datafeeds stats and return usage response
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ActionListener<GetDatafeedsStatsAction.Response> datafeedStatsListener =
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ActionListener.wrap(response -> {
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addDatafeedsUsage(response);
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listener.onResponse(new Usage(
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available, enabled, jobsUsage, datafeedsUsage));
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},
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error -> {
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listener.onFailure(error);
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}
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);
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// Step 1. Extract usage from jobs stats and then request stats for all datafeeds
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GetJobsStatsAction.Request jobStatsRequest = new GetJobsStatsAction.Request(Job.ALL);
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ActionListener<GetJobsStatsAction.Response> jobStatsListener = ActionListener.wrap(
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response -> {
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addJobsUsage(response);
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GetDatafeedsStatsAction.Request datafeedStatsRequest =
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new GetDatafeedsStatsAction.Request(GetDatafeedsStatsAction.ALL);
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client.execute(GetDatafeedsStatsAction.INSTANCE, datafeedStatsRequest,
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datafeedStatsListener);
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},
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error -> {
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listener.onFailure(error);
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}
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);
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// Step 0. Kick off the chain of callbacks by requesting jobs stats
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client.execute(GetJobsStatsAction.INSTANCE, jobStatsRequest, jobStatsListener);
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}
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private void addJobsUsage(GetJobsStatsAction.Response response) {
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StatsAccumulator allJobsDetectorsStats = new StatsAccumulator();
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StatsAccumulator allJobsModelSizeStats = new StatsAccumulator();
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Map<JobState, Counter> jobCountByState = new HashMap<>();
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Map<JobState, StatsAccumulator> detectorStatsByState = new HashMap<>();
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Map<JobState, StatsAccumulator> modelSizeStatsByState = new HashMap<>();
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Map<String, Job> jobs = mlMetadata.getJobs();
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for (GetJobsStatsAction.Response.JobStats jobStats
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: response.getResponse().results()) {
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ModelSizeStats modelSizeStats = jobStats.getModelSizeStats();
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int detectorsCount = jobs.get(jobStats.getJobId()).getAnalysisConfig()
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.getDetectors().size();
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double modelSize = modelSizeStats == null ? 0.0
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: jobStats.getModelSizeStats().getModelBytes();
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allJobsDetectorsStats.add(detectorsCount);
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allJobsModelSizeStats.add(modelSize);
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JobState jobState = jobStats.getState();
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jobCountByState.computeIfAbsent(jobState, js -> Counter.newCounter()).addAndGet(1);
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detectorStatsByState.computeIfAbsent(jobState,
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js -> new StatsAccumulator()).add(detectorsCount);
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modelSizeStatsByState.computeIfAbsent(jobState,
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js -> new StatsAccumulator()).add(modelSize);
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}
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jobsUsage.put(ALL, createJobUsageEntry(jobs.size(), allJobsDetectorsStats,
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allJobsModelSizeStats));
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for (JobState jobState : jobCountByState.keySet()) {
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jobsUsage.put(jobState.name().toLowerCase(Locale.ROOT), createJobUsageEntry(
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jobCountByState.get(jobState).get(),
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detectorStatsByState.get(jobState),
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modelSizeStatsByState.get(jobState)));
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}
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}
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private Map<String, Object> createJobUsageEntry(long count, StatsAccumulator detectorStats,
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StatsAccumulator modelSizeStats) {
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Map<String, Object> usage = new HashMap<>();
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usage.put(COUNT, count);
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usage.put(DETECTORS, detectorStats.asMap());
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usage.put(MODEL_SIZE, modelSizeStats.asMap());
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return usage;
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}
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private void addDatafeedsUsage(GetDatafeedsStatsAction.Response response) {
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Map<DatafeedState, Counter> datafeedCountByState = new HashMap<>();
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for (GetDatafeedsStatsAction.Response.DatafeedStats datafeedStats
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: response.getResponse().results()) {
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datafeedCountByState.computeIfAbsent(datafeedStats.getDatafeedState(),
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ds -> Counter.newCounter()).addAndGet(1);
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}
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datafeedsUsage.put(ALL, createDatafeedUsageEntry(response.getResponse().count()));
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for (DatafeedState datafeedState : datafeedCountByState.keySet()) {
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datafeedsUsage.put(datafeedState.name().toLowerCase(Locale.ROOT),
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createDatafeedUsageEntry(datafeedCountByState.get(datafeedState).get()));
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}
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}
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private Map<String, Object> createDatafeedUsageEntry(long count) {
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Map<String, Object> usage = new HashMap<>();
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usage.put(COUNT, count);
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return usage;
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}
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}
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}
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}
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@ -16,7 +16,6 @@ import org.elasticsearch.common.logging.Loggers;
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import org.elasticsearch.common.unit.TimeValue;
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import org.elasticsearch.script.Script;
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import org.elasticsearch.search.SearchHit;
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import org.elasticsearch.search.builder.SearchSourceBuilder;
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import org.elasticsearch.search.fetch.StoredFieldsContext;
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import org.elasticsearch.search.sort.SortOrder;
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import org.elasticsearch.xpack.ml.datafeed.extractor.DataExtractor;
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@ -0,0 +1,57 @@
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/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the Elastic License;
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* you may not use this file except in compliance with the Elastic License.
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*/
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package org.elasticsearch.xpack.ml.utils;
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import java.util.HashMap;
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import java.util.Map;
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/**
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* Helper class to collect min, max, avg and total statistics for a quantity
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*/
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public class StatsAccumulator {
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private static final String MIN = "min";
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private static final String MAX = "max";
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private static final String AVG = "avg";
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private static final String TOTAL = "total";
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private long count;
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private double total;
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private Double min;
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private Double max;
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public void add(double value) {
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count++;
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total += value;
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min = min == null ? value : (value < min ? value : min);
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max = max == null ? value : (value > max ? value : max);
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}
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public double getMin() {
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return min == null ? 0.0 : min;
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}
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public double getMax() {
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return max == null ? 0.0 : max;
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}
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public double getAvg() {
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return count == 0.0 ? 0.0 : total/count;
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}
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public double getTotal() {
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return total;
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}
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public Map<String, Double> asMap() {
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Map<String, Double> map = new HashMap<>();
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map.put(MIN, getMin());
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map.put(MAX, getMax());
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map.put(AVG, getAvg());
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map.put(TOTAL, getTotal());
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return map;
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}
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}
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@ -5,30 +5,67 @@
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*/
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package org.elasticsearch.xpack.ml;
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import org.elasticsearch.action.ActionListener;
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import org.elasticsearch.action.support.PlainActionFuture;
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import org.elasticsearch.client.Client;
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import org.elasticsearch.cluster.ClusterState;
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import org.elasticsearch.cluster.metadata.MetaData;
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import org.elasticsearch.cluster.service.ClusterService;
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import org.elasticsearch.common.io.stream.BytesStreamOutput;
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import org.elasticsearch.common.settings.Settings;
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import org.elasticsearch.common.xcontent.ToXContent;
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import org.elasticsearch.common.xcontent.XContentBuilder;
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import org.elasticsearch.common.xcontent.XContentFactory;
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import org.elasticsearch.license.XPackLicenseState;
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import org.elasticsearch.test.ESTestCase;
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import org.elasticsearch.xpack.XPackFeatureSet;
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import org.elasticsearch.xpack.XPackFeatureSet.Usage;
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import org.elasticsearch.xpack.XPackPlugin;
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import org.elasticsearch.xpack.ml.action.GetDatafeedsStatsAction;
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import org.elasticsearch.xpack.ml.action.GetJobsStatsAction;
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import org.elasticsearch.xpack.ml.action.util.QueryPage;
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import org.elasticsearch.xpack.ml.datafeed.DatafeedConfig;
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import org.elasticsearch.xpack.ml.datafeed.DatafeedState;
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import org.elasticsearch.xpack.ml.job.config.AnalysisConfig;
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import org.elasticsearch.xpack.ml.job.config.Detector;
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import org.elasticsearch.xpack.ml.job.config.Job;
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import org.elasticsearch.xpack.ml.job.config.JobState;
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import org.elasticsearch.xpack.ml.job.process.autodetect.state.ModelSizeStats;
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import org.elasticsearch.xpack.watcher.support.xcontent.XContentSource;
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import org.junit.Before;
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import java.util.Arrays;
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import java.util.Collections;
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import java.util.Date;
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import java.util.List;
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import static org.hamcrest.Matchers.equalTo;
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import static org.hamcrest.Matchers.notNullValue;
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import static org.hamcrest.Matchers.nullValue;
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import static org.hamcrest.core.Is.is;
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import static org.mockito.Matchers.any;
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import static org.mockito.Matchers.same;
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import static org.mockito.Mockito.doAnswer;
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import static org.mockito.Mockito.mock;
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import static org.mockito.Mockito.when;
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public class MachineLearningFeatureSetTests extends ESTestCase {
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private ClusterService clusterService;
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private Client client;
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private XPackLicenseState licenseState;
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@Before
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public void init() throws Exception {
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clusterService = mock(ClusterService.class);
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client = mock(Client.class);
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licenseState = mock(XPackLicenseState.class);
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givenJobs(Collections.emptyList(), Collections.emptyList());
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givenDatafeeds(Collections.emptyList());
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}
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public void testAvailable() throws Exception {
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MachineLearningFeatureSet featureSet = new MachineLearningFeatureSet(Settings.EMPTY, licenseState);
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MachineLearningFeatureSet featureSet = new MachineLearningFeatureSet(Settings.EMPTY, clusterService, client, licenseState);
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boolean available = randomBoolean();
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when(licenseState.isMachineLearningAllowed()).thenReturn(available);
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assertThat(featureSet.available(), is(available));
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@ -52,7 +89,7 @@ public class MachineLearningFeatureSetTests extends ESTestCase {
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settings.put("xpack.ml.enabled", enabled);
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}
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boolean expected = enabled || useDefault;
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MachineLearningFeatureSet featureSet = new MachineLearningFeatureSet(settings.build(), licenseState);
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MachineLearningFeatureSet featureSet = new MachineLearningFeatureSet(settings.build(), clusterService, client, licenseState);
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assertThat(featureSet.enabled(), is(expected));
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PlainActionFuture<Usage> future = new PlainActionFuture<>();
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featureSet.usage(future);
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@ -65,4 +102,144 @@ public class MachineLearningFeatureSetTests extends ESTestCase {
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assertThat(serializedUsage.enabled(), is(expected));
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}
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public void testUsage() throws Exception {
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when(licenseState.isMachineLearningAllowed()).thenReturn(true);
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Settings.Builder settings = Settings.builder();
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settings.put("xpack.ml.enabled", true);
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Job opened1 = buildJob("opened1", Arrays.asList(buildMinDetector("foo")));
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GetJobsStatsAction.Response.JobStats opened1JobStats = buildJobStats("opened1", JobState.OPENED, 100L);
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Job opened2 = buildJob("opened2", Arrays.asList(buildMinDetector("foo"), buildMinDetector("bar")));
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GetJobsStatsAction.Response.JobStats opened2JobStats = buildJobStats("opened2", JobState.OPENED, 200L);
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Job closed1 = buildJob("closed1", Arrays.asList(buildMinDetector("foo"), buildMinDetector("bar"), buildMinDetector("foobar")));
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GetJobsStatsAction.Response.JobStats closed1JobStats = buildJobStats("closed1", JobState.CLOSED, 300L);
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givenJobs(Arrays.asList(opened1, opened2, closed1),
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Arrays.asList(opened1JobStats, opened2JobStats, closed1JobStats));
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givenDatafeeds(Arrays.asList(
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buildDatafeedStats(DatafeedState.STARTED),
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buildDatafeedStats(DatafeedState.STARTED),
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buildDatafeedStats(DatafeedState.STOPPED)
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));
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MachineLearningFeatureSet featureSet = new MachineLearningFeatureSet(settings.build(), clusterService, client, licenseState);
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PlainActionFuture<Usage> future = new PlainActionFuture<>();
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featureSet.usage(future);
|
||||
XPackFeatureSet.Usage mlUsage = future.get();
|
||||
|
||||
BytesStreamOutput out = new BytesStreamOutput();
|
||||
mlUsage.writeTo(out);
|
||||
XPackFeatureSet.Usage serializedUsage = new MachineLearningFeatureSet.Usage(out.bytes().streamInput());
|
||||
|
||||
for (XPackFeatureSet.Usage usage : Arrays.asList(mlUsage, serializedUsage)) {
|
||||
assertThat(usage, is(notNullValue()));
|
||||
assertThat(usage.name(), is(XPackPlugin.MACHINE_LEARNING));
|
||||
assertThat(usage.enabled(), is(true));
|
||||
assertThat(usage.available(), is(true));
|
||||
XContentSource source;
|
||||
try (XContentBuilder builder = XContentFactory.jsonBuilder()) {
|
||||
usage.toXContent(builder, ToXContent.EMPTY_PARAMS);
|
||||
source = new XContentSource(builder);
|
||||
}
|
||||
|
||||
assertThat(source.getValue("jobs._all.count"), equalTo(3));
|
||||
assertThat(source.getValue("jobs._all.detectors.min"), equalTo(1.0));
|
||||
assertThat(source.getValue("jobs._all.detectors.max"), equalTo(3.0));
|
||||
assertThat(source.getValue("jobs._all.detectors.total"), equalTo(6.0));
|
||||
assertThat(source.getValue("jobs._all.detectors.avg"), equalTo(2.0));
|
||||
assertThat(source.getValue("jobs._all.model_size.min"), equalTo(100.0));
|
||||
assertThat(source.getValue("jobs._all.model_size.max"), equalTo(300.0));
|
||||
assertThat(source.getValue("jobs._all.model_size.total"), equalTo(600.0));
|
||||
assertThat(source.getValue("jobs._all.model_size.avg"), equalTo(200.0));
|
||||
|
||||
assertThat(source.getValue("jobs.opened.count"), equalTo(2));
|
||||
assertThat(source.getValue("jobs.opened.detectors.min"), equalTo(1.0));
|
||||
assertThat(source.getValue("jobs.opened.detectors.max"), equalTo(2.0));
|
||||
assertThat(source.getValue("jobs.opened.detectors.total"), equalTo(3.0));
|
||||
assertThat(source.getValue("jobs.opened.detectors.avg"), equalTo(1.5));
|
||||
assertThat(source.getValue("jobs.opened.model_size.min"), equalTo(100.0));
|
||||
assertThat(source.getValue("jobs.opened.model_size.max"), equalTo(200.0));
|
||||
assertThat(source.getValue("jobs.opened.model_size.total"), equalTo(300.0));
|
||||
assertThat(source.getValue("jobs.opened.model_size.avg"), equalTo(150.0));
|
||||
|
||||
assertThat(source.getValue("jobs.closed.count"), equalTo(1));
|
||||
assertThat(source.getValue("jobs.closed.detectors.min"), equalTo(3.0));
|
||||
assertThat(source.getValue("jobs.closed.detectors.max"), equalTo(3.0));
|
||||
assertThat(source.getValue("jobs.closed.detectors.total"), equalTo(3.0));
|
||||
assertThat(source.getValue("jobs.closed.detectors.avg"), equalTo(3.0));
|
||||
assertThat(source.getValue("jobs.closed.model_size.min"), equalTo(300.0));
|
||||
assertThat(source.getValue("jobs.closed.model_size.max"), equalTo(300.0));
|
||||
assertThat(source.getValue("jobs.closed.model_size.total"), equalTo(300.0));
|
||||
assertThat(source.getValue("jobs.closed.model_size.avg"), equalTo(300.0));
|
||||
|
||||
assertThat(source.getValue("jobs.opening"), is(nullValue()));
|
||||
assertThat(source.getValue("jobs.closing"), is(nullValue()));
|
||||
assertThat(source.getValue("jobs.failed"), is(nullValue()));
|
||||
|
||||
assertThat(source.getValue("datafeeds._all.count"), equalTo(3));
|
||||
assertThat(source.getValue("datafeeds.started.count"), equalTo(2));
|
||||
assertThat(source.getValue("datafeeds.stopped.count"), equalTo(1));
|
||||
}
|
||||
}
|
||||
|
||||
private void givenJobs(List<Job> jobs, List<GetJobsStatsAction.Response.JobStats> jobsStats) {
|
||||
MlMetadata.Builder mlMetadataBuilder = new MlMetadata.Builder();
|
||||
for (Job job : jobs) {
|
||||
mlMetadataBuilder.putJob(job, false);
|
||||
}
|
||||
ClusterState clusterState = new ClusterState.Builder(ClusterState.EMPTY_STATE)
|
||||
.metaData(new MetaData.Builder()
|
||||
.putCustom(MlMetadata.TYPE, mlMetadataBuilder.build()))
|
||||
.build();
|
||||
when(clusterService.state()).thenReturn(clusterState);
|
||||
|
||||
doAnswer(invocationOnMock -> {
|
||||
ActionListener<GetJobsStatsAction.Response> listener =
|
||||
(ActionListener<GetJobsStatsAction.Response>) invocationOnMock.getArguments()[2];
|
||||
listener.onResponse(new GetJobsStatsAction.Response(
|
||||
new QueryPage<>(jobsStats, jobsStats.size(), Job.RESULTS_FIELD)));
|
||||
return Void.TYPE;
|
||||
}).when(client).execute(same(GetJobsStatsAction.INSTANCE), any(), any());
|
||||
}
|
||||
|
||||
private void givenDatafeeds(List<GetDatafeedsStatsAction.Response.DatafeedStats> datafeedStats) {
|
||||
doAnswer(invocationOnMock -> {
|
||||
ActionListener<GetDatafeedsStatsAction.Response> listener =
|
||||
(ActionListener<GetDatafeedsStatsAction.Response>) invocationOnMock.getArguments()[2];
|
||||
listener.onResponse(new GetDatafeedsStatsAction.Response(
|
||||
new QueryPage<>(datafeedStats, datafeedStats.size(), DatafeedConfig.RESULTS_FIELD)));
|
||||
return Void.TYPE;
|
||||
}).when(client).execute(same(GetDatafeedsStatsAction.INSTANCE), any(), any());
|
||||
}
|
||||
|
||||
private static Detector buildMinDetector(String fieldName) {
|
||||
Detector.Builder detectorBuilder = new Detector.Builder();
|
||||
detectorBuilder.setFunction("min");
|
||||
detectorBuilder.setFieldName(fieldName);
|
||||
return detectorBuilder.build();
|
||||
}
|
||||
|
||||
private static Job buildJob(String jobId, List<Detector> detectors) {
|
||||
AnalysisConfig.Builder analysisConfig = new AnalysisConfig.Builder(detectors);
|
||||
return new Job.Builder(jobId)
|
||||
.setCreateTime(new Date(randomNonNegativeLong()))
|
||||
.setAnalysisConfig(analysisConfig)
|
||||
.build();
|
||||
}
|
||||
|
||||
private static GetJobsStatsAction.Response.JobStats buildJobStats(String jobId, JobState state, long modelBytes) {
|
||||
ModelSizeStats.Builder modelSizeStats = new ModelSizeStats.Builder(jobId);
|
||||
modelSizeStats.setModelBytes(modelBytes);
|
||||
GetJobsStatsAction.Response.JobStats jobStats = mock(GetJobsStatsAction.Response.JobStats.class);
|
||||
when(jobStats.getJobId()).thenReturn(jobId);
|
||||
when(jobStats.getModelSizeStats()).thenReturn(modelSizeStats.build());
|
||||
when(jobStats.getState()).thenReturn(state);
|
||||
return jobStats;
|
||||
}
|
||||
|
||||
private static GetDatafeedsStatsAction.Response.DatafeedStats buildDatafeedStats(DatafeedState state) {
|
||||
GetDatafeedsStatsAction.Response.DatafeedStats stats = mock(GetDatafeedsStatsAction.Response.DatafeedStats.class);
|
||||
when(stats.getDatafeedState()).thenReturn(state);
|
||||
return stats;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -0,0 +1,63 @@
|
|||
/*
|
||||
* 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.utils;
|
||||
|
||||
import org.elasticsearch.test.ESTestCase;
|
||||
|
||||
import java.util.HashMap;
|
||||
import java.util.Map;
|
||||
|
||||
import static org.hamcrest.Matchers.equalTo;
|
||||
|
||||
public class StatsAccumulatorTests extends ESTestCase {
|
||||
|
||||
public void testGivenNoValues() {
|
||||
StatsAccumulator accumulator = new StatsAccumulator();
|
||||
assertThat(accumulator.getMin(), equalTo(0.0));
|
||||
assertThat(accumulator.getMax(), equalTo(0.0));
|
||||
assertThat(accumulator.getTotal(), equalTo(0.0));
|
||||
assertThat(accumulator.getAvg(), equalTo(0.0));
|
||||
}
|
||||
|
||||
public void testGivenPositiveValues() {
|
||||
StatsAccumulator accumulator = new StatsAccumulator();
|
||||
|
||||
for (int i = 1; i <= 10; i++) {
|
||||
accumulator.add(i);
|
||||
}
|
||||
|
||||
assertThat(accumulator.getMin(), equalTo(1.0));
|
||||
assertThat(accumulator.getMax(), equalTo(10.0));
|
||||
assertThat(accumulator.getTotal(), equalTo(55.0));
|
||||
assertThat(accumulator.getAvg(), equalTo(5.5));
|
||||
}
|
||||
|
||||
public void testGivenNegativeValues() {
|
||||
StatsAccumulator accumulator = new StatsAccumulator();
|
||||
|
||||
for (int i = 1; i <= 10; i++) {
|
||||
accumulator.add(-1 * i);
|
||||
}
|
||||
|
||||
assertThat(accumulator.getMin(), equalTo(-10.0));
|
||||
assertThat(accumulator.getMax(), equalTo(-1.0));
|
||||
assertThat(accumulator.getTotal(), equalTo(-55.0));
|
||||
assertThat(accumulator.getAvg(), equalTo(-5.5));
|
||||
}
|
||||
|
||||
public void testAsMap() {
|
||||
StatsAccumulator accumulator = new StatsAccumulator();
|
||||
accumulator.add(5.0);
|
||||
accumulator.add(10.0);
|
||||
|
||||
Map<String, Double> expectedMap = new HashMap<>();
|
||||
expectedMap.put("min", 5.0);
|
||||
expectedMap.put("max", 10.0);
|
||||
expectedMap.put("avg", 7.5);
|
||||
expectedMap.put("total", 15.0);
|
||||
assertThat(accumulator.asMap(), equalTo(expectedMap));
|
||||
}
|
||||
}
|
||||
Loading…
Reference in New Issue