[ML] improve trained model stats API performance (#87978)

Previous, get trained model stats API would build every pipeline defined in cluster state. This is problematic when MANY pipelines are defined. Especially if those pipelines take some time to parse (consider GROK). This improvement is part of fixing: #87931
elastic · Jun 28, 2022 · 6847c0b · 6847c0b
1 parent 87bc952
commit 6847c0b
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Showing 9 changed files with 387 additions and 300 deletions.
diff --git a/docs/changelog/87978.yaml b/docs/changelog/87978.yaml
@@ -0,0 +1,5 @@
+pr: 87978
+summary: Improve trained model stats API performance
+area: Machine Learning
+type: bug
+issues: []
diff --git a/...ests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/TestFeatureResetIT.java b/...ests/src/javaRestTest/java/org/elasticsearch/xpack/ml/integration/TestFeatureResetIT.java
@@ -47,7 +47,6 @@
 import java.util.concurrent.TimeUnit;
 import java.util.stream.Collectors;
 
-import static org.elasticsearch.xpack.ml.inference.ingest.InferenceProcessor.Factory.countNumberInferenceProcessors;
 import static org.elasticsearch.xpack.ml.integration.ClassificationIT.KEYWORD_FIELD;
 import static org.elasticsearch.xpack.ml.integration.MlNativeDataFrameAnalyticsIntegTestCase.buildAnalytics;
 import static org.elasticsearch.xpack.ml.integration.PyTorchModelIT.BASE_64_ENCODED_MODEL;
@@ -56,6 +55,7 @@
 import static org.elasticsearch.xpack.ml.support.BaseMlIntegTestCase.createScheduledJob;
 import static org.elasticsearch.xpack.ml.support.BaseMlIntegTestCase.getDataCounts;
 import static org.elasticsearch.xpack.ml.support.BaseMlIntegTestCase.indexDocs;
+import static org.elasticsearch.xpack.ml.utils.InferenceProcessorInfoExtractor.countInferenceProcessors;
 import static org.hamcrest.Matchers.containsString;
 import static org.hamcrest.Matchers.empty;
 import static org.hamcrest.Matchers.equalTo;
@@ -132,9 +132,7 @@ public void testMLFeatureReset() throws Exception {
         client().execute(DeletePipelineAction.INSTANCE, new DeletePipelineRequest("feature_reset_inference_pipeline")).actionGet();
         createdPipelines.remove("feature_reset_inference_pipeline");
 
-        assertBusy(
-            () -> assertThat(countNumberInferenceProcessors(client().admin().cluster().prepareState().get().getState()), equalTo(0))
-        );
+        assertBusy(() -> assertThat(countInferenceProcessors(client().admin().cluster().prepareState().get().getState()), equalTo(0)));
         client().execute(ResetFeatureStateAction.INSTANCE, new ResetFeatureStateRequest()).actionGet();
         assertBusy(() -> {
             List<String> indices = Arrays.asList(client().admin().indices().prepareGetIndex().addIndices(".ml*").get().indices());

diff --git a/x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearning.java b/x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/MachineLearning.java
@@ -445,7 +445,7 @@
 import static org.elasticsearch.xpack.core.ClientHelper.ML_ORIGIN;
 import static org.elasticsearch.xpack.core.ml.job.persistence.AnomalyDetectorsIndexFields.RESULTS_INDEX_PREFIX;
 import static org.elasticsearch.xpack.core.ml.job.persistence.AnomalyDetectorsIndexFields.STATE_INDEX_PREFIX;
-import static org.elasticsearch.xpack.ml.inference.ingest.InferenceProcessor.Factory.countNumberInferenceProcessors;
+import static org.elasticsearch.xpack.ml.utils.InferenceProcessorInfoExtractor.countInferenceProcessors;
 
 public class MachineLearning extends Plugin
     implements
@@ -1910,7 +1910,7 @@ public void cleanUpFeature(
 
         // validate no pipelines are using machine learning models
         ActionListener<AcknowledgedResponse> afterResetModeSet = ActionListener.wrap(acknowledgedResponse -> {
-            int numberInferenceProcessors = countNumberInferenceProcessors(clusterService.state());
+            int numberInferenceProcessors = countInferenceProcessors(clusterService.state());
             if (numberInferenceProcessors > 0) {
                 unsetResetModeListener.onFailure(
                     new RuntimeException(

diff --git a/...src/main/java/org/elasticsearch/xpack/ml/action/TransportGetTrainedModelsStatsAction.java b/...src/main/java/org/elasticsearch/xpack/ml/action/TransportGetTrainedModelsStatsAction.java
@@ -6,7 +6,6 @@
  */
 package org.elasticsearch.xpack.ml.action;
 
-import org.elasticsearch.ElasticsearchException;
 import org.elasticsearch.action.ActionListener;
 import org.elasticsearch.action.admin.cluster.node.stats.NodeStats;
 import org.elasticsearch.action.admin.cluster.node.stats.NodesStatsAction;
@@ -27,10 +26,7 @@
 import org.elasticsearch.core.Tuple;
 import org.elasticsearch.index.query.QueryBuilder;
 import org.elasticsearch.index.query.QueryBuilders;
-import org.elasticsearch.ingest.IngestMetadata;
-import org.elasticsearch.ingest.IngestService;
 import org.elasticsearch.ingest.IngestStats;
-import org.elasticsearch.ingest.Pipeline;
 import org.elasticsearch.search.SearchHit;
 import org.elasticsearch.search.sort.SortOrder;
 import org.elasticsearch.tasks.Task;
@@ -47,15 +43,13 @@
 import org.elasticsearch.xpack.core.ml.inference.trainedmodel.InferenceStats;
 import org.elasticsearch.xpack.core.ml.inference.trainedmodel.TrainedModelSizeStats;
 import org.elasticsearch.xpack.ml.inference.ModelAliasMetadata;
-import org.elasticsearch.xpack.ml.inference.ingest.InferenceProcessor;
 import org.elasticsearch.xpack.ml.inference.persistence.TrainedModelDefinitionDoc;
 import org.elasticsearch.xpack.ml.inference.persistence.TrainedModelProvider;
 
 import java.util.ArrayList;
 import java.util.Collections;
 import java.util.HashMap;
 import java.util.LinkedHashMap;
-import java.util.LinkedHashSet;
 import java.util.List;
 import java.util.Map;
 import java.util.Set;
@@ -65,29 +59,27 @@
 
 import static org.elasticsearch.xpack.core.ClientHelper.ML_ORIGIN;
 import static org.elasticsearch.xpack.core.ClientHelper.executeAsyncWithOrigin;
+import static org.elasticsearch.xpack.ml.utils.InferenceProcessorInfoExtractor.pipelineIdsByModelIdsOrAliases;
 
 public class TransportGetTrainedModelsStatsAction extends HandledTransportAction<
     GetTrainedModelsStatsAction.Request,
     GetTrainedModelsStatsAction.Response> {
 
     private final Client client;
     private final ClusterService clusterService;
-    private final IngestService ingestService;
     private final TrainedModelProvider trainedModelProvider;
 
     @Inject
     public TransportGetTrainedModelsStatsAction(
         TransportService transportService,
         ActionFilters actionFilters,
         ClusterService clusterService,
-        IngestService ingestService,
         TrainedModelProvider trainedModelProvider,
         Client client
     ) {
         super(GetTrainedModelsStatsAction.NAME, transportService, actionFilters, GetTrainedModelsStatsAction.Request::new);
         this.client = client;
         this.clusterService = clusterService;
-        this.ingestService = ingestService;
         this.trainedModelProvider = trainedModelProvider;
     }
 
@@ -136,7 +128,6 @@ protected void doExecute(
                 .collect(Collectors.toSet());
             Map<String, Set<String>> pipelineIdsByModelIdsOrAliases = pipelineIdsByModelIdsOrAliases(
                 clusterService.state(),
-                ingestService,
                 allPossiblePipelineReferences
             );
             Map<String, IngestStats> modelIdIngestStats = inferenceIngestStatsByModelId(
@@ -270,37 +261,6 @@ static String[] ingestNodes(final ClusterState clusterState) {
         return clusterState.nodes().getIngestNodes().keySet().toArray(String[]::new);
     }
 
-    static Map<String, Set<String>> pipelineIdsByModelIdsOrAliases(ClusterState state, IngestService ingestService, Set<String> modelIds) {
-        IngestMetadata ingestMetadata = state.metadata().custom(IngestMetadata.TYPE);
-        Map<String, Set<String>> pipelineIdsByModelIds = new HashMap<>();
-        if (ingestMetadata == null) {
-            return pipelineIdsByModelIds;
-        }
-
-        ingestMetadata.getPipelines().forEach((pipelineId, pipelineConfiguration) -> {
-            try {
-                Pipeline pipeline = Pipeline.create(
-                    pipelineId,
-                    pipelineConfiguration.getConfigAsMap(),
-                    ingestService.getProcessorFactories(),
-                    ingestService.getScriptService()
-                );
-                pipeline.getProcessors().forEach(processor -> {
-                    if (processor instanceof InferenceProcessor inferenceProcessor) {
-                        if (modelIds.contains(inferenceProcessor.getModelId())) {
-                            pipelineIdsByModelIds.computeIfAbsent(inferenceProcessor.getModelId(), m -> new LinkedHashSet<>())
-                                .add(pipelineId);
-                        }
-                    }
-                });
-            } catch (Exception ex) {
-                throw new ElasticsearchException("unexpected failure gathering pipeline information", ex);
-            }
-        });
-
-        return pipelineIdsByModelIds;
-    }
-
     static IngestStats ingestStatsForPipelineIds(NodeStats nodeStats, Set<String> pipelineIds) {
         IngestStats fullNodeStats = nodeStats.getIngestStats();
         Map<String, List<IngestStats.ProcessorStat>> filteredProcessorStats = new HashMap<>(fullNodeStats.getProcessorStats());

diff --git a/...ugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/ingest/InferenceProcessor.java b/...ugin/ml/src/main/java/org/elasticsearch/xpack/ml/inference/ingest/InferenceProcessor.java
@@ -14,17 +14,13 @@
 import org.elasticsearch.action.ActionListener;
 import org.elasticsearch.client.internal.Client;
 import org.elasticsearch.cluster.ClusterState;
-import org.elasticsearch.cluster.metadata.Metadata;
 import org.elasticsearch.cluster.service.ClusterService;
 import org.elasticsearch.common.settings.Setting;
 import org.elasticsearch.common.settings.Settings;
 import org.elasticsearch.common.xcontent.LoggingDeprecationHandler;
 import org.elasticsearch.ingest.AbstractProcessor;
 import org.elasticsearch.ingest.ConfigurationUtils;
 import org.elasticsearch.ingest.IngestDocument;
-import org.elasticsearch.ingest.IngestMetadata;
-import org.elasticsearch.ingest.Pipeline;
-import org.elasticsearch.ingest.PipelineConfiguration;
 import org.elasticsearch.ingest.Processor;
 import org.elasticsearch.rest.RestStatus;
 import org.elasticsearch.xpack.core.ml.action.InferModelAction;
@@ -55,6 +51,7 @@
 import org.elasticsearch.xpack.core.ml.utils.ExceptionsHelper;
 import org.elasticsearch.xpack.ml.inference.loadingservice.LocalModel;
 import org.elasticsearch.xpack.ml.notifications.InferenceAuditor;
+import org.elasticsearch.xpack.ml.utils.InferenceProcessorInfoExtractor;
 
 import java.util.Collections;
 import java.util.HashMap;
@@ -65,7 +62,6 @@
 import java.util.function.Consumer;
 
 import static org.elasticsearch.ingest.IngestDocument.INGEST_KEY;
-import static org.elasticsearch.ingest.Pipeline.PROCESSORS_KEY;
 import static org.elasticsearch.xpack.core.ClientHelper.ML_ORIGIN;
 import static org.elasticsearch.xpack.core.ClientHelper.executeAsyncWithOrigin;
 import static org.elasticsearch.xpack.core.ml.inference.results.InferenceResults.MODEL_ID_RESULTS_FIELD;
@@ -192,9 +188,6 @@ public String getType() {
 
     public static final class Factory implements Processor.Factory, Consumer<ClusterState> {
 
-        private static final String FOREACH_PROCESSOR_NAME = "foreach";
-        // Any more than 10 nestings of processors, we stop searching for inference processor definitions
-        private static final int MAX_INFERENCE_PROCESSOR_SEARCH_RECURSIONS = 10;
         private static final Logger logger = LogManager.getLogger(Factory.class);
 
         private final Client client;
@@ -213,86 +206,12 @@ public Factory(Client client, ClusterService clusterService, Settings settings)
         @Override
         public void accept(ClusterState state) {
             minNodeVersion = state.nodes().getMinNodeVersion();
-            currentInferenceProcessors = countNumberInferenceProcessors(state);
-        }
-
-        public static int countNumberInferenceProcessors(ClusterState state) {
-            Metadata metadata = state.getMetadata();
-            if (metadata == null) {
-                return 0;
-            }
-            IngestMetadata ingestMetadata = metadata.custom(IngestMetadata.TYPE);
-            if (ingestMetadata == null) {
-                return 0;
-            }
-
-            int count = 0;
-            for (PipelineConfiguration configuration : ingestMetadata.getPipelines().values()) {
-                Map<String, Object> configMap = configuration.getConfigAsMap();
-                try {
-                    List<Map<String, Object>> processorConfigs = ConfigurationUtils.readList(null, null, configMap, PROCESSORS_KEY);
-                    for (Map<String, Object> processorConfigWithKey : processorConfigs) {
-                        for (Map.Entry<String, Object> entry : processorConfigWithKey.entrySet()) {
-                            count += numInferenceProcessors(entry.getKey(), entry.getValue());
-                        }
-                    }
-                    // We cannot throw any exception here. It might break other pipelines.
-                } catch (Exception ex) {
-                    logger.debug(() -> "failed gathering processors for pipeline [" + configuration.getId() + "]", ex);
-                }
+            try {
+                currentInferenceProcessors = InferenceProcessorInfoExtractor.countInferenceProcessors(state);
+            } catch (Exception ex) {
+                // We cannot throw any exception here. It might break other pipelines.
+                logger.debug("failed gathering processors for pipelines", ex);
             }
-            return count;
-        }
-
-        @SuppressWarnings("unchecked")
-        static int numInferenceProcessors(String processorType, Object processorDefinition) {
-            return numInferenceProcessors(processorType, (Map<String, Object>) processorDefinition, 0);
-        }
-
-        @SuppressWarnings("unchecked")
-        static int numInferenceProcessors(String processorType, Map<String, Object> processorDefinition, int level) {
-            int count = 0;
-            // arbitrary, but we must limit this somehow
-            if (level > MAX_INFERENCE_PROCESSOR_SEARCH_RECURSIONS) {
-                return count;
-            }
-            if (processorType == null || processorDefinition == null) {
-                return count;
-            }
-            if (TYPE.equals(processorType)) {
-                count++;
-            }
-            if (FOREACH_PROCESSOR_NAME.equals(processorType)) {
-                Map<String, Object> innerProcessor = (Map<String, Object>) processorDefinition.get("processor");
-                if (innerProcessor != null) {
-                    // a foreach processor should only have a SINGLE nested processor. Iteration is for simplicity's sake.
-                    for (Map.Entry<String, Object> innerProcessorWithName : innerProcessor.entrySet()) {
-                        count += numInferenceProcessors(
-                            innerProcessorWithName.getKey(),
-                            (Map<String, Object>) innerProcessorWithName.getValue(),
-                            level + 1
-                        );
-                    }
-                }
-            }
-            if (processorDefinition.containsKey(Pipeline.ON_FAILURE_KEY)) {
-                List<Map<String, Object>> onFailureConfigs = ConfigurationUtils.readList(
-                    null,
-                    null,
-                    processorDefinition,
-                    Pipeline.ON_FAILURE_KEY
-                );
-                count += onFailureConfigs.stream()
-                    .flatMap(map -> map.entrySet().stream())
-                    .mapToInt(entry -> numInferenceProcessors(entry.getKey(), (Map<String, Object>) entry.getValue(), level + 1))
-                    .sum();
-            }
-            return count;
-        }
-
-        // Used for testing
-        int numInferenceProcessors() {
-            return currentInferenceProcessors;
         }
 
         @Override