diff --git a/build.gradle b/build.gradle index 9733e3626..05f4b5da4 100644 --- a/build.gradle +++ b/build.gradle @@ -560,11 +560,12 @@ dependencies { // implementation scope let the dependency in both compiling and running classpath, but // not leaked through to clients (Opensearch). Here we force the jackson version to whatever // opensearch uses. - compile 'software.amazon.randomcutforest:randomcutforest-core:2.0.1' implementation 'software.amazon.randomcutforest:randomcutforest-serialization:2.0.1' implementation "com.fasterxml.jackson.core:jackson-core:${versions.jackson}" implementation "com.fasterxml.jackson.core:jackson-databind:${versions.jackson}" implementation "com.fasterxml.jackson.core:jackson-annotations:${versions.jackson}" + compile files('lib/randomcutforest-parkservices-2.0.1.jar') + compile files('lib/randomcutforest-core-2.0.1.jar') // used for serializing/deserializing rcf models. compile group: 'io.protostuff', name: 'protostuff-core', version: '1.7.4' diff --git a/lib/randomcutforest-core-2.0.1.jar b/lib/randomcutforest-core-2.0.1.jar new file mode 100644 index 000000000..e6661862e Binary files /dev/null and b/lib/randomcutforest-core-2.0.1.jar differ diff --git a/lib/randomcutforest-parkservices-2.0.1.jar b/lib/randomcutforest-parkservices-2.0.1.jar new file mode 100644 index 000000000..538da0767 Binary files /dev/null and b/lib/randomcutforest-parkservices-2.0.1.jar differ diff --git a/src/main/java/org/opensearch/ad/AnomalyDetectorPlugin.java b/src/main/java/org/opensearch/ad/AnomalyDetectorPlugin.java index 6f561fd41..a9ae79299 100644 --- a/src/main/java/org/opensearch/ad/AnomalyDetectorPlugin.java +++ b/src/main/java/org/opensearch/ad/AnomalyDetectorPlugin.java @@ -69,7 +69,6 @@ import org.opensearch.ad.ml.EntityColdStarter; import org.opensearch.ad.ml.HybridThresholdingModel; import org.opensearch.ad.ml.ModelManager; -import org.opensearch.ad.ml.ModelPartitioner; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.AnomalyDetectorJob; import org.opensearch.ad.model.AnomalyResult; @@ -205,9 +204,10 @@ import org.opensearch.threadpool.ThreadPool; import org.opensearch.watcher.ResourceWatcherService; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState; import com.amazon.randomcutforest.serialize.json.v1.V1JsonToV2StateConverter; import com.amazon.randomcutforest.state.RandomCutForestMapper; -import com.amazon.randomcutforest.state.RandomCutForestState; import com.google.common.collect.ImmutableList; import com.google.common.collect.ImmutableMap; import com.google.gson.Gson; @@ -374,8 +374,6 @@ public Collection createComponents( mapper.setSaveExecutorContextEnabled(true); mapper.setSaveTreeStateEnabled(true); mapper.setPartialTreeStateEnabled(true); - Schema schema = AccessController - .doPrivileged((PrivilegedAction>) () -> RuntimeSchema.getSchema(RandomCutForestState.class)); V1JsonToV2StateConverter converter = new V1JsonToV2StateConverter(); double modelMaxSizePercent = AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE.get(settings); @@ -390,13 +388,6 @@ public Collection createComponents( adCircuitBreakerService ); - ModelPartitioner modelPartitioner = new ModelPartitioner( - AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE, - AnomalyDetectorSettings.NUM_TREES, - nodeFilter, - memoryTracker - ); - NodeStateManager stateManager = new NodeStateManager( client, xContentRegistry, @@ -404,7 +395,6 @@ public Collection createComponents( clientUtil, getClock(), AnomalyDetectorSettings.HOURLY_MAINTENANCE, - modelPartitioner, clusterService ); @@ -455,13 +445,19 @@ public PooledObject wrap(LinkedBuffer obj) { CommonName.CHECKPOINT_INDEX_NAME, gson, mapper, - schema, converter, + new ThresholdedRandomCutForestMapper(), + AccessController + .doPrivileged( + (PrivilegedAction>) () -> RuntimeSchema + .getSchema(ThresholdedRandomCutForestState.class) + ), HybridThresholdingModel.class, anomalyDetectionIndices, AnomalyDetectorSettings.MAX_CHECKPOINT_BYTES, serializeRCFBufferPool, - AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + 1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE ); Random random = new Random(42); @@ -518,11 +514,6 @@ public PooledObject wrap(LinkedBuffer obj) { interpolator, searchFeatureDao, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES, featureManager, settings, AnomalyDetectorSettings.HOURLY_MAINTENANCE, @@ -557,16 +548,10 @@ public PooledObject wrap(LinkedBuffer obj) { AnomalyDetectorSettings.TIME_DECAY, AnomalyDetectorSettings.NUM_MIN_SAMPLES, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES, AnomalyDetectorSettings.MIN_PREVIEW_SIZE, AnomalyDetectorSettings.HOURLY_MAINTENANCE, AnomalyDetectorSettings.HOURLY_MAINTENANCE, entityColdStarter, - modelPartitioner, featureManager, memoryTracker ); @@ -757,7 +742,6 @@ public PooledObject wrap(LinkedBuffer obj) { nodeFilter, multiEntityResultHandler, checkpoint, - modelPartitioner, cacheProvider, adTaskManager, adBatchTaskRunner, diff --git a/src/main/java/org/opensearch/ad/MemoryTracker.java b/src/main/java/org/opensearch/ad/MemoryTracker.java index 92a528408..acd9a07ed 100644 --- a/src/main/java/org/opensearch/ad/MemoryTracker.java +++ b/src/main/java/org/opensearch/ad/MemoryTracker.java @@ -36,12 +36,11 @@ import org.apache.logging.log4j.Logger; import org.opensearch.ad.breaker.ADCircuitBreakerService; import org.opensearch.ad.common.exception.LimitExceededException; -import org.opensearch.ad.model.AnomalyDetector; -import org.opensearch.ad.util.MathUtil; import org.opensearch.cluster.service.ClusterService; import org.opensearch.monitor.jvm.JvmService; import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** * Class to track AD memory usage. @@ -103,11 +102,11 @@ public MemoryTracker( * This function derives from the old code: https://tinyurl.com/2eaabja6 * * @param detectorId Detector Id - * @param rcf Random cut forest model + * @param trcf Thresholded random cut forest model * @return true if there is enough memory; otherwise throw LimitExceededException. */ - public synchronized boolean isHostingAllowed(String detectorId, RandomCutForest rcf) { - long requiredBytes = estimateTotalModelSize(rcf); + public synchronized boolean isHostingAllowed(String detectorId, ThresholdedRandomCutForest trcf) { + long requiredBytes = estimateTRCFModelSize(trcf); if (canAllocateReserved(requiredBytes)) { return true; } else { @@ -175,46 +174,29 @@ private void adjustOriginMemoryRelease(long memoryToConsume, Origin origin, Map< /** * Gets the estimated size of an entity's model. * - * @param forest RCF forest object + * @param trcf ThresholdedRandomCutForest object * @return estimated model size in bytes */ - public long estimateTotalModelSize(RandomCutForest forest) { - return estimateRCFModelSize(forest.getDimensions(), forest.getNumberOfTrees(), forest.getBoundingBoxCacheFraction()) - + thresholdModelBytes; - } - - /** - * Gets the estimated size of a RCF model. - * - * @param forest RCF forest object - * @return estimated model size in bytes - */ - public long estimateRCFModelSize(RandomCutForest forest) { - return estimateRCFModelSize(forest.getDimensions(), forest.getNumberOfTrees(), forest.getBoundingBoxCacheFraction()); - } - - /** - * Gets the estimated size of an entity's model according to - * the detector configuration. - * - * @param detector detector config object - * @param numberOfTrees the number of trees in a RCF forest - * @param boundingBoxCacheFraction Bounding box cache ratio in RCF - * @return estimated model size in bytes - */ - public long estimateTotalModelSize(AnomalyDetector detector, int numberOfTrees, double boundingBoxCacheFraction) { - return estimateRCFModelSize( - detector.getEnabledFeatureIds().size() * detector.getShingleSize(), - numberOfTrees, - boundingBoxCacheFraction - ) + thresholdModelBytes; + public long estimateTRCFModelSize(ThresholdedRandomCutForest trcf) { + RandomCutForest forest = trcf.getForest(); + return estimateTRCFModelSize( + forest.getDimensions(), + forest.getNumberOfTrees(), + forest.getBoundingBoxCacheFraction(), + forest.getShingleSize(), + forest.isInternalShinglingEnabled() + ); } /** * Gets the estimated size of an entity's model. * * RCF size: - * Assume the sample size is 256. A compact RCF forest consists of: + * Assume the sample size is 256. I measured the memory size of a ThresholdedRandomCutForest + * using heap dump. A ThresholdedRandomCutForest comprises a compact rcf model and + * a threshold model. + * + * A compact RCF forest consists of: * - Random number generator: 56 bytes * - PointStoreCoordinator: 24 bytes * - SequentialForestUpdateExecutor: 24 bytes @@ -224,8 +206,27 @@ public long estimateTotalModelSize(AnomalyDetector detector, int numberOfTrees, * - int array for free indexes: 256 * numberOfTrees * 4, where 4 is the size of an integer * - two int array for locationList and refCount: 256 * numberOfTrees * 4 bytes * 2 * - a float array for data store: 256 * trees * dimension * 4 bytes: due to various - * optimization like shingleSize(dimensions), we don't use all of the array. The actual - * usage percentage is + * optimization like shingleSize(dimensions), we don't use all of the array. The average + * usage percentage depends on shingle size and if internal shingling is enabled. + * I did experiments with power-of-two shingle sizes and internal shingling on/off + * by running ThresholdedRandomCutForest over a million points. + * My experiment shows that + * * if internal shingling is off, data store is filled at full + * capacity. + * * otherwise, data store usage depends on shingle size: + * + * Shingle Size usage + * 1 1 + * 2 0.53 + * 4 0.27 + * 8 0.27 + * 16 0.13 + * 32 0.07 + * 64 0.07 + * + * The formula reflects the data and fits the point store usage to the closest power-of-two case. + * For example, if shingle size is 17, we use the usage 0.13 since it is closer to 16. + * * {@code IF(dimensions>=32, 1/(LOG(dimensions+1, 2)+LOG(dimensions+1, 10)), 1/LOG(dimensions+1, 2))} * where LOG gets the logarithm of a number and the syntax of LOG is {@code LOG (number, [base])}. * We derive the formula by observing the point store usage ratio is a decreasing function of dimensions @@ -238,42 +239,67 @@ public long estimateTotalModelSize(AnomalyDetector detector, int numberOfTrees, * - SmallNodeStore (small node store since our sample size is 256, less than the max of short): 6120 * + BoxCacheFloat * - other: 104 - * - BoundingBoxFloat: (1040 + 255* (dimension * 4 * 2 + 64)) * adjusted bounding box cache usage, - * where if full we have 255 inner node and each box has 80 bytes. - * Plus metadata, we can have in total 21544 bytes. - * {@code adjusted bounding box cache usage = (bounding box cache fraction >= 0.3? 1: bounding box cache fraction)} - * {@code >= 0.3} we will still initialize bounding box cache array of the max size, - * but exclude them using the cache ratio. It is not guaranteed we will only - * use cache ratio in the array. For example, with cache ratio 0.5, we used 150 - * out of 255 elements. So can have two float array whose size is the number of - * dimensions; other constants are the metadata size. + * - BoundingBoxFloat: (1040 + 255* ((dimension * 4 + 16) * 2 + 32)) * actual bounding box cache usage, + * {@code actual bounding box cache usage = (bounding box cache fraction >= 0.3? 1: bounding box cache fraction)} + * {@code >= 0.3} we will still initialize bounding box cache array of the max size. + * 1040 is the size of BoundingBoxFloat's fields unrelated to tree size (255 nodes in our formula) * In total, RCF size is - * 56 + # trees * (2248 + 152 + 6120 + 104 + (1040 + 255* (dimension * 4 * 2 + 64)) * adjusted bounding box cache ratio) + - * (256 * # trees * 2 + 256 * # trees * dimension) * 4 bytes * 0.5 + 1064 + 24 + 24 + 16 - * = 56 + # trees * (8624 + (1040 + 255 * (dimension * 8 + 64)) * adjusted bounding box cache ratio) + 256 * # trees * - * (3 + dimension) * 4 * 0.5 + 1128 + * 56 + # trees * (2248 + 152 + 6120 + 104 + (1040 + 255* (dimension * 4 + 16) * 2 + 32)) * adjusted bounding box cache ratio) + + * (256 * # trees * 2 + 256 * # trees * dimension) * 4 bytes * point store ratio + 30744 * 2 + 15432 + 208) + 24 + 24 + 16 + * = 56 + # trees * (8624 + (1040 + 255 * (dimension * 8 + 64)) * actual bounding box cache usage) + 256 * # trees * + * dimension * 4 * point store ratio + 77192 + * + * Thresholder size + * + Preprocessor: + * - lastShingledInput and lastShingledPoint: 2*(dimension*8 + 16) (2 due to 2 double arrays, 16 are array object size) + * - previousTimeStamps: shingle*8 + * - other: 248 + * - BasicThrehsolder: 256 + * + lastAnomalyAttribution: + * - high and low: 2*(dimension*8 + 16)(2 due to 2 double arrays, 16 are array object) + * - other 24 + * - lastAnomalyPoint and lastExpectedPoint: 2*(dimension*8 + 16) + * - other like ThresholdedRandomCutForest object size: 96 + * In total, thresholder size is: + * 6*(dimension*8 + 16) + shingle*8 + 248 + 256 + 24 + 96 + * = 6*(dimension*8 + 16) + shingle*8 + 624 * * @param dimension The number of feature dimensions in RCF * @param numberOfTrees The number of trees in RCF - * @param boundingBoxCacheFraction Bounding box cache ratio in RCF - * @return estimated RCF model size + * @param boundingBoxCacheFraction Bounding box cache usage in RCF + * @param shingleSize shingle size + * @param internalShingling whether internal shingling is enabled or not + * @return estimated TRCF model size + * + * @throws IllegalArgumentException when the input shingle size is out of range [1, 64] */ - public long estimateRCFModelSize(int dimension, int numberOfTrees, double boundingBoxCacheFraction) { + public long estimateTRCFModelSize( + int dimension, + int numberOfTrees, + double boundingBoxCacheFraction, + int shingleSize, + boolean internalShingling + ) { double averagePointStoreUsage = 0; - int logNumber = dimension + 1; - if (dimension >= 32) { - averagePointStoreUsage = 1.0d / (MathUtil.log2(logNumber) + Math.log10(logNumber)); + if (!internalShingling || shingleSize == 1) { + averagePointStoreUsage = 1; + } else if (shingleSize <= 3) { + averagePointStoreUsage = 0.53; + } else if (shingleSize <= 12) { + averagePointStoreUsage = 0.27; + } else if (shingleSize <= 24) { + averagePointStoreUsage = 0.13; + } else if (shingleSize <= 64) { + averagePointStoreUsage = 0.07; } else { - averagePointStoreUsage = 1.0d / MathUtil.log2(logNumber); + throw new IllegalArgumentException("out of range shingle size " + shingleSize); } + double actualBoundingBoxUsage = boundingBoxCacheFraction >= 0.3 ? 1d : boundingBoxCacheFraction; long compactRcfSize = (long) (56 + numberOfTrees * (8624 + (1040 + 255 * (dimension * 8 + 64)) * actualBoundingBoxUsage) + 256 - * numberOfTrees * (3 + dimension) * 4 * averagePointStoreUsage + 1128); - return compactRcfSize; - } - - public long estimateTotalModelSize(int dimension, int numberOfTrees, double boundingBoxCacheFraction) { - return estimateRCFModelSize(dimension, numberOfTrees, boundingBoxCacheFraction) + thresholdModelBytes; + * numberOfTrees * dimension * 4 * averagePointStoreUsage + 77192); + long thresholdSize = 6 * (dimension * 8 + 16) + shingleSize * 8 + 624; + return compactRcfSize + thresholdSize; } /** diff --git a/src/main/java/org/opensearch/ad/NodeStateManager.java b/src/main/java/org/opensearch/ad/NodeStateManager.java index 167be0855..67f297c39 100644 --- a/src/main/java/org/opensearch/ad/NodeStateManager.java +++ b/src/main/java/org/opensearch/ad/NodeStateManager.java @@ -45,10 +45,9 @@ import org.opensearch.action.get.GetRequest; import org.opensearch.action.get.GetResponse; import org.opensearch.ad.common.exception.EndRunException; -import org.opensearch.ad.common.exception.LimitExceededException; import org.opensearch.ad.constant.CommonErrorMessages; import org.opensearch.ad.constant.CommonName; -import org.opensearch.ad.ml.ModelPartitioner; +import org.opensearch.ad.ml.SingleStreamModelIdMapper; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.transport.BackPressureRouting; import org.opensearch.ad.util.ClientUtil; @@ -73,7 +72,6 @@ public class NodeStateManager implements MaintenanceState, CleanState { public static final String NO_ERROR = "no_error"; private ConcurrentHashMap states; private Client client; - private ModelPartitioner modelPartitioner; private NamedXContentRegistry xContentRegistry; private ClientUtil clientUtil; // map from detector id to the map of ES node id to the node's backpressureMuter @@ -92,7 +90,6 @@ public class NodeStateManager implements MaintenanceState, CleanState { * @param clientUtil AD Client utility * @param clock A UTC clock * @param stateTtl Max time to keep state in memory - * @param modelPartitioner Used to partiton a RCF forest * @param clusterService Cluster service accessor */ public NodeStateManager( @@ -102,12 +99,10 @@ public NodeStateManager( ClientUtil clientUtil, Clock clock, Duration stateTtl, - ModelPartitioner modelPartitioner, ClusterService clusterService ) { this.states = new ConcurrentHashMap<>(); this.client = client; - this.modelPartitioner = modelPartitioner; this.xContentRegistry = xContentRegistry; this.clientUtil = clientUtil; this.backpressureMuter = new ConcurrentHashMap<>(); @@ -133,26 +128,6 @@ public NodeStateManager( }); } - /** - * Get the number of RCF model's partition number for detector adID - * @param adID detector id - * @param detector object - * @return the number of RCF model's partition number for adID - * @throws LimitExceededException when there is no sufficient resource available - */ - public int getPartitionNumber(String adID, AnomalyDetector detector) { - NodeState state = states.get(adID); - if (state != null && state.getPartitonNumber() > 0) { - return state.getPartitonNumber(); - } - - int partitionNum = modelPartitioner.getPartitionedForestSizes(detector).getKey(); - state = states.computeIfAbsent(adID, id -> new NodeState(id, clock)); - state.setPartitonNumber(partitionNum); - - return partitionNum; - } - /** * Get Detector config object if present * @param adID detector Id @@ -220,7 +195,7 @@ public void getDetectorCheckpoint(String adID, ActionListener listener) return; } - GetRequest request = new GetRequest(CommonName.CHECKPOINT_INDEX_NAME, modelPartitioner.getRcfModelId(adID, 0)); + GetRequest request = new GetRequest(CommonName.CHECKPOINT_INDEX_NAME, SingleStreamModelIdMapper.getRcfModelId(adID, 0)); clientUtil.asyncRequest(request, client::get, onGetCheckpointResponse(adID, listener)); } diff --git a/src/main/java/org/opensearch/ad/caching/CacheBuffer.java b/src/main/java/org/opensearch/ad/caching/CacheBuffer.java index 9561d8c9a..636616a43 100644 --- a/src/main/java/org/opensearch/ad/caching/CacheBuffer.java +++ b/src/main/java/org/opensearch/ad/caching/CacheBuffer.java @@ -253,7 +253,7 @@ public ModelState remove(String keyToRemove) { // null model has only samples. For null model we save a checkpoint // regardless of last checkpoint time. whether If we don't save, // we throw the new samples and might never be able to initialize the model - boolean isNullModel = modelRemoved.getRcf() == null || modelRemoved.getThreshold() == null; + boolean isNullModel = !modelRemoved.getTrcf().isPresent(); checkpointWriteQueue.write(valueRemoved, isNullModel, RequestPriority.MEDIUM); modelRemoved.clear(); @@ -326,7 +326,6 @@ public List> maintenance() { try { ModelState modelState = entry.getValue(); Instant now = clock.instant(); - if (modelState.getLastUsedTime().plus(modelTtl).isBefore(now)) { // race conditions can happen between the put and one of the following operations: // remove: not a problem as all of the data structures are concurrent. diff --git a/src/main/java/org/opensearch/ad/caching/PriorityCache.java b/src/main/java/org/opensearch/ad/caching/PriorityCache.java index c50eb0c53..65d9d5348 100644 --- a/src/main/java/org/opensearch/ad/caching/PriorityCache.java +++ b/src/main/java/org/opensearch/ad/caching/PriorityCache.java @@ -446,15 +446,15 @@ public Pair, List> selectUpdateCandidate( private CacheBuffer computeBufferIfAbsent(AnomalyDetector detector, String detectorId) { CacheBuffer buffer = activeEnities.get(detectorId); if (buffer == null) { - long requiredBytes = getReservedDetectorMemory(detector); + long requiredBytes = getRequiredMemory(detector, dedicatedCacheSize); if (memoryTracker.canAllocateReserved(requiredBytes)) { memoryTracker.consumeMemory(requiredBytes, true, Origin.HC_DETECTOR); long intervalSecs = detector.getDetectorIntervalInSeconds(); + buffer = new CacheBuffer( dedicatedCacheSize, intervalSecs, - memoryTracker - .estimateTotalModelSize(detector, numberOfTrees, AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO), + getRequiredMemory(detector, 1), memoryTracker, clock, modelTtl, @@ -475,9 +475,22 @@ private CacheBuffer computeBufferIfAbsent(AnomalyDetector detector, String detec return buffer; } - private long getReservedDetectorMemory(AnomalyDetector detector) { - return dedicatedCacheSize * memoryTracker - .estimateTotalModelSize(detector, numberOfTrees, AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO); + /** + * + * @param detector Detector config accessor + * @param numberOfEntity number of entities + * @return Memory in bytes required for hosting numberOfEntity entities + */ + private long getRequiredMemory(AnomalyDetector detector, int numberOfEntity) { + int dimension = detector.getEnabledFeatureIds().size() * detector.getShingleSize(); + return numberOfEntity * memoryTracker + .estimateTRCFModelSize( + dimension, + numberOfTrees, + AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO, + detector.getShingleSize().intValue(), + true + ); } /** @@ -707,7 +720,8 @@ public long getTotalUpdates(String detectorId, String entityModelId) { Optional modelOptional = cacheBuffer.getModel(entityModelId); // TODO: make it work for shingles. samples.size() is not the real shingle long accumulatedShingles = modelOptional - .map(model -> model.getRcf()) + .flatMap(model -> model.getTrcf()) + .map(trcf -> trcf.getForest()) .map(rcf -> rcf.getTotalUpdates()) .orElseGet( () -> modelOptional.map(model -> model.getSamples()).map(samples -> samples.size()).map(Long::valueOf).orElse(0L) @@ -807,15 +821,8 @@ private void maintainInactiveCache() { // // make sure no model has been stored due to bugs for (ModelState state : inActiveEntities.asMap().values()) { EntityModel model = state.getModel(); - if (model != null && (model.getRcf() != null || model.getThreshold() != null)) { - LOG - .warn( - new ParameterizedMessage( - "Inactive entity's model is null: [{}], [{}]. Maybe there are bugs.", - model.getRcf(), - model.getThreshold() - ) - ); + if (model != null && model.getTrcf().isPresent()) { + LOG.warn(new ParameterizedMessage("Inactive entity's model is null: [{}]. Maybe there are bugs.", state.getModelId())); state.setModel(null); } } diff --git a/src/main/java/org/opensearch/ad/cluster/HashRing.java b/src/main/java/org/opensearch/ad/cluster/HashRing.java index 559c6bb56..c21e0bd26 100644 --- a/src/main/java/org/opensearch/ad/cluster/HashRing.java +++ b/src/main/java/org/opensearch/ad/cluster/HashRing.java @@ -56,6 +56,7 @@ import org.opensearch.action.admin.cluster.node.info.PluginsAndModules; import org.opensearch.ad.common.exception.AnomalyDetectionException; import org.opensearch.ad.ml.ModelManager; +import org.opensearch.ad.ml.SingleStreamModelIdMapper; import org.opensearch.ad.util.DiscoveryNodeFilterer; import org.opensearch.client.AdminClient; import org.opensearch.client.Client; @@ -371,7 +372,7 @@ private void rebuildCirclesForRealtimeAD() { modelManager .stopModel( // stopModel will clear model cache - modelManager.getDetectorIdForModelId(modelId), + SingleStreamModelIdMapper.getDetectorIdForModelId(modelId), modelId, ActionListener .wrap( diff --git a/src/main/java/org/opensearch/ad/ml/CheckpointDao.java b/src/main/java/org/opensearch/ad/ml/CheckpointDao.java index f6a3feb99..219ad897c 100644 --- a/src/main/java/org/opensearch/ad/ml/CheckpointDao.java +++ b/src/main/java/org/opensearch/ad/ml/CheckpointDao.java @@ -27,7 +27,6 @@ package org.opensearch.ad.ml; import java.io.IOException; -import java.io.StringReader; import java.security.AccessController; import java.security.PrivilegedAction; import java.time.Instant; @@ -35,9 +34,11 @@ import java.time.ZonedDateTime; import java.util.AbstractMap.SimpleImmutableEntry; import java.util.ArrayDeque; +import java.util.ArrayList; import java.util.Arrays; import java.util.Base64; import java.util.HashMap; +import java.util.List; import java.util.Locale; import java.util.Map; import java.util.Map.Entry; @@ -74,7 +75,6 @@ import org.opensearch.ad.model.Entity; import org.opensearch.ad.util.ClientUtil; import org.opensearch.client.Client; -import org.opensearch.common.Strings; import org.opensearch.index.IndexNotFoundException; import org.opensearch.index.query.MatchQueryBuilder; import org.opensearch.index.reindex.BulkByScrollResponse; @@ -84,14 +84,15 @@ import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState; import com.amazon.randomcutforest.serialize.json.v1.V1JsonToV2StateConverter; import com.amazon.randomcutforest.state.RandomCutForestMapper; import com.amazon.randomcutforest.state.RandomCutForestState; import com.google.gson.Gson; import com.google.gson.JsonObject; import com.google.gson.JsonParser; -import com.google.gson.TypeAdapter; -import com.google.gson.stream.JsonReader; import io.protostuff.LinkedBuffer; import io.protostuff.ProtostuffIOUtil; @@ -116,6 +117,7 @@ public class CheckpointDao { public static final String ENTITY_SAMPLE = "sp"; public static final String ENTITY_RCF = "rcf"; public static final String ENTITY_THRESHOLD = "th"; + public static final String ENTITY_TRCF = "trcf"; public static final String FIELD_MODEL = "model"; public static final String FIELD_MODELV2 = "modelV2"; public static final String TIMESTAMP = "timestamp"; @@ -130,8 +132,9 @@ public class CheckpointDao { private Gson gson; private RandomCutForestMapper mapper; - private Schema schema; private V1JsonToV2StateConverter converter; + private ThresholdedRandomCutForestMapper trcfMapper; + private Schema trcfSchema; private final Class thresholdingModelClass; @@ -140,9 +143,10 @@ public class CheckpointDao { // we won't read/write a checkpoint larger than a threshold private final int maxCheckpointBytes; - private final TypeAdapter strictGsonObjectAdapter; private final GenericObjectPool serializeRCFBufferPool; private final int serializeRCFBufferSize; + // anomaly rate + private double anomalyRate; /** * Constructor with dependencies and configuration. @@ -152,13 +156,15 @@ public class CheckpointDao { * @param indexName name of the index for model checkpoints * @param gson accessor to Gson functionality * @param mapper RCF model serialization utility - * @param schema RandomCutForestState schema used by ProtoStuff * @param converter converter from rcf v1 serde to protostuff based format + * @param trcfMapper TRCF serialization mapper + * @param trcfSchema TRCF serialization schema * @param thresholdingModelClass thresholding model's class * @param indexUtil Index utility methods * @param maxCheckpointBytes max checkpoint size in bytes * @param serializeRCFBufferPool object pool for serializing rcf models * @param serializeRCFBufferSize the size of the buffer for RCF serialization + * @param anomalyRate anomaly rate */ public CheckpointDao( Client client, @@ -166,27 +172,30 @@ public CheckpointDao( String indexName, Gson gson, RandomCutForestMapper mapper, - Schema schema, V1JsonToV2StateConverter converter, + ThresholdedRandomCutForestMapper trcfMapper, + Schema trcfSchema, Class thresholdingModelClass, AnomalyDetectionIndices indexUtil, int maxCheckpointBytes, GenericObjectPool serializeRCFBufferPool, - int serializeRCFBufferSize + int serializeRCFBufferSize, + double anomalyRate ) { this.client = client; this.clientUtil = clientUtil; this.indexName = indexName; this.gson = gson; this.mapper = mapper; - this.schema = schema; this.converter = converter; + this.trcfMapper = trcfMapper; + this.trcfSchema = trcfSchema; this.thresholdingModelClass = thresholdingModelClass; this.indexUtil = indexUtil; this.maxCheckpointBytes = maxCheckpointBytes; - this.strictGsonObjectAdapter = new Gson().getAdapter(JsonObject.class); this.serializeRCFBufferPool = serializeRCFBufferPool; this.serializeRCFBufferSize = serializeRCFBufferSize; + this.anomalyRate = anomalyRate; } private void saveModelCheckpointSync(Map source, String modelId) { @@ -208,9 +217,9 @@ private void putModelCheckpoint(String modelId, Map source, Acti * @param forest the rcf model * @param listener onResponse is called with null when the operation is completed */ - public void putRCFCheckpoint(String modelId, RandomCutForest forest, ActionListener listener) { + public void putTRCFCheckpoint(String modelId, ThresholdedRandomCutForest forest, ActionListener listener) { Map source = new HashMap<>(); - String modelCheckpoint = rcfModelToCheckpoint(forest); + String modelCheckpoint = toCheckpoint(forest); if (modelCheckpoint != null) { source.put(FIELD_MODELV2, modelCheckpoint); source.put(TIMESTAMP, ZonedDateTime.now(ZoneOffset.UTC)); @@ -293,21 +302,22 @@ private void saveModelCheckpointAsync(Map source, String modelId public Map toIndexSource(ModelState modelState) throws IOException { Map source = new HashMap<>(); EntityModel model = modelState.getModel(); - String serializedModel = toCheckpoint(model); - if (serializedModel == null || serializedModel.length() > maxCheckpointBytes) { + Optional serializedModel = toCheckpoint(model, modelState.getModelId()); + if (!serializedModel.isPresent() || serializedModel.get().length() > maxCheckpointBytes) { logger .warn( new ParameterizedMessage( - "[{}]'s model empty or too large: [{}] bytes", + "[{}]'s model is empty or too large: [{}] bytes", modelState.getModelId(), - serializedModel == null ? 0 : serializedModel.length() + serializedModel.isPresent() ? serializedModel.get().length() : 0 ) ); return source; } String detectorId = modelState.getDetectorId(); source.put(DETECTOR_ID, detectorId); - source.put(FIELD_MODELV2, serializedModel); + // we cannot pass Optional as OpenSearch does not know how to serialize an Optional value + source.put(FIELD_MODELV2, serializedModel.get()); source.put(TIMESTAMP, ZonedDateTime.now(ZoneOffset.UTC)); source.put(CommonName.SCHEMA_VERSION_FIELD, indexUtil.getSchemaVersion(ADIndex.CHECKPOINT)); Optional entity = model.getEntity(); @@ -318,64 +328,93 @@ public Map toIndexSource(ModelState modelState) thr return source; } - public String toCheckpoint(EntityModel model) { - return AccessController.doPrivileged((PrivilegedAction) () -> { + /** + * Serialized an EntityModel + * @param model input model + * @param modelId model id + * @return serialized string + */ + public Optional toCheckpoint(EntityModel model, String modelId) { + return AccessController.doPrivileged((PrivilegedAction>) () -> { if (model == null) { logger.warn("Empty model"); - return null; + return Optional.empty(); } - JsonObject json = new JsonObject(); - if (model.getSamples() != null && !(model.getSamples().isEmpty())) { - json.add(ENTITY_SAMPLE, gson.toJsonTree(model.getSamples())); - } - if (model.getRcf() != null) { - String serializedRCF = rcfModelToCheckpoint(model.getRcf()); - if (!Strings.isEmpty(serializedRCF)) { - json.addProperty(ENTITY_RCF, serializedRCF); + try { + JsonObject json = new JsonObject(); + if (model.getSamples() != null && !(model.getSamples().isEmpty())) { + json.add(ENTITY_SAMPLE, gson.toJsonTree(model.getSamples())); } + if (model.getTrcf().isPresent()) { + json.addProperty(ENTITY_TRCF, toCheckpoint(model.getTrcf().get())); + } + // if json is empty, it will be an empty Json string {}. No need to save it on disk. + return json.entrySet().isEmpty() ? Optional.empty() : Optional.ofNullable(gson.toJson(json)); + } catch (Exception ex) { + logger.warn(new ParameterizedMessage("fail to generate checkpoint for [{}]", modelId), ex); } - if (model.getThreshold() != null) { - json.addProperty(ENTITY_THRESHOLD, gson.toJson(model.getThreshold())); - } - return gson.toJson(json); + return Optional.empty(); }); } - private String rcfModelToCheckpoint(RandomCutForest model) { - LinkedBuffer borrowedBuffer = null; + private String toCheckpoint(ThresholdedRandomCutForest trcf) { + String checkpoint = null; + Map.Entry result = checkoutOrNewBuffer(); + LinkedBuffer buffer = result.getKey(); + boolean needCheckin = result.getValue(); try { - borrowedBuffer = serializeRCFBufferPool.borrowObject(); - try { - return rcfModelToCheckpoint(model, borrowedBuffer); - } catch (Exception e) { - if (borrowedBuffer != null) { - serializeRCFBufferPool.invalidateObject(borrowedBuffer); - borrowedBuffer = null; + checkpoint = toCheckpoint(trcf, buffer); + } catch (Exception e) { + logger.error("Failed to serialize model", e); + if (needCheckin) { + try { + serializeRCFBufferPool.invalidateObject(buffer); + needCheckin = false; + } catch (Exception x) { + logger.warn("Failed to invalidate buffer", x); } - // return null when we successfully borrow an object but fail to - // create a serialized model - return null; - } finally { - if (borrowedBuffer != null) { - serializeRCFBufferPool.returnObject(borrowedBuffer); + try { + checkpoint = toCheckpoint(trcf, LinkedBuffer.allocate(serializeRCFBufferSize)); + } catch (Exception ex) { + logger.warn("Failed to generate checkpoint", ex); + } + } + } finally { + if (needCheckin) { + try { + serializeRCFBufferPool.returnObject(buffer); + } catch (Exception e) { + logger.warn("Failed to return buffer to pool", e); } } + } + return checkpoint; + } + + private Map.Entry checkoutOrNewBuffer() { + LinkedBuffer buffer = null; + boolean isCheckout = true; + try { + buffer = serializeRCFBufferPool.borrowObject(); } catch (Exception e) { - logger.error("Failed to borrow an buffer from object pool", e); - // allocate a new LinkedBuffer and create a serialized model - return rcfModelToCheckpoint(model, null); + logger.warn("Failed to borrow a buffer from pool", e); + } + if (buffer == null) { + buffer = LinkedBuffer.allocate(serializeRCFBufferSize); + isCheckout = false; } + return new SimpleImmutableEntry(buffer, isCheckout); } - String rcfModelToCheckpoint(RandomCutForest model, LinkedBuffer buffer) { - final LinkedBuffer serializationBuffer = buffer == null ? LinkedBuffer.allocate(serializeRCFBufferSize) : buffer; + private String toCheckpoint(ThresholdedRandomCutForest trcf, LinkedBuffer buffer) { try { - RandomCutForestState state = mapper.toState(model); - byte[] bytes = AccessController - .doPrivileged((PrivilegedAction) () -> ProtostuffIOUtil.toByteArray(state, schema, serializationBuffer)); + byte[] bytes = AccessController.doPrivileged((PrivilegedAction) () -> { + ThresholdedRandomCutForestState trcfState = trcfMapper.toState(trcf); + return ProtostuffIOUtil.toByteArray(trcfState, trcfSchema, buffer); + }); return Base64.getEncoder().encodeToString(bytes); } finally { - serializationBuffer.clear(); + buffer.clear(); } } @@ -466,7 +505,6 @@ public Optional> fromEntityModelCheckpoint(Map maxCheckpointBytes) { logger.warn(new ParameterizedMessage("[{}]'s model too large: [{}] bytes", modelId, model.length())); @@ -483,15 +521,29 @@ public Optional> fromEntityModelCheckpoint(Map(); } - RandomCutForest rcf = null; - if (json.has(ENTITY_RCF)) { - String serializedRCF = json.getAsJsonPrimitive(ENTITY_RCF).getAsString(); - rcf = deserializeRCFModel(serializedRCF); - } - ThresholdingModel threshold = null; - if (json.has(ENTITY_THRESHOLD)) { - // verified, don't need privileged call to get permission - threshold = this.gson.fromJson(json.getAsJsonPrimitive(ENTITY_THRESHOLD).getAsString(), thresholdingModelClass); + ThresholdedRandomCutForest trcf = null; + + if (json.has(ENTITY_TRCF)) { + trcf = toTrcf(json.getAsJsonPrimitive(ENTITY_TRCF).getAsString()); + } else { + Optional rcf = Optional.empty(); + Optional threshold = Optional.empty(); + if (json.has(ENTITY_RCF)) { + String serializedRCF = json.getAsJsonPrimitive(ENTITY_RCF).getAsString(); + rcf = deserializeRCFModel(serializedRCF, modelId); + } + if (json.has(ENTITY_THRESHOLD)) { + // verified, don't need privileged call to get permission + threshold = Optional + .ofNullable( + this.gson.fromJson(json.getAsJsonPrimitive(ENTITY_THRESHOLD).getAsString(), thresholdingModelClass) + ); + } + + Optional convertedTRCF = convertToTRCF(rcf, threshold); + if (convertedTRCF.isPresent()) { + trcf = convertedTRCF.get(); + } } String lastCheckpointTimeString = (String) (checkpoint.get(TIMESTAMP)); @@ -505,66 +557,86 @@ public Optional> fromEntityModelCheckpoint(Map(new EntityModel(entity, samples, rcf, threshold), timestamp)); + EntityModel entityModel = new EntityModel(entity, samples, trcf); + return Optional.of(new SimpleImmutableEntry<>(entityModel, timestamp)); }); } catch (Exception e) { - logger.warn("Exception while deserializing checkpoint", e); - throw e; + logger.warn("Exception while deserializing checkpoint " + modelId, e); + // checkpoint corrupted (e.g., a checkpoint not recognized by current code + // due to bugs). Better redo training. + return Optional.empty(); } } - private RandomCutForest deserializeRCFModel(String rcfCheckpoint) { - if (Strings.isEmpty(rcfCheckpoint)) { - return null; - } - - String checkpoint = rcfCheckpoint.trim(); - RandomCutForest forest = null; - try { - if (isVersionEqualsOne(checkpoint)) { - RandomCutForestState state = converter.convert(checkpoint, Precision.FLOAT_32); - forest = mapper.toModel(state); - } else { + private ThresholdedRandomCutForest toTrcf(String checkpoint) { + ThresholdedRandomCutForest trcf = null; + if (checkpoint != null && !checkpoint.isEmpty()) { + try { byte[] bytes = Base64.getDecoder().decode(checkpoint); - RandomCutForestState state = schema.newMessage(); + ThresholdedRandomCutForestState state = trcfSchema.newMessage(); AccessController.doPrivileged((PrivilegedAction) () -> { - ProtostuffIOUtil.mergeFrom(bytes, state, schema); + ProtostuffIOUtil.mergeFrom(bytes, state, trcfSchema); return null; }); - forest = mapper.toModel(state); + trcf = trcfMapper.toModel(state); + } catch (RuntimeException e) { + logger.error("Failed to deserialize TRCF model", e); } - } catch (Exception e) { - logger.error("Unexpected deserialization error", e); } - return forest; + return trcf; } - /** - * Whether the input checkpoint has version 1.0 - * - * @param checkpoint the checkpoint to check - * @return whether the input checkpoint has version 1.0, where we serialize - * rcf model to Json, and when needed deserializing a forest from JSON. - */ - private boolean isVersionEqualsOne(String checkpoint) { - // Pre-checking that the first character is '{' and the last is '}'. If it's not the case, - // it is the new serialization format - // To make sure, a more expensive way that parses the JSON and check exception is done after - // the pre-checks. - return checkpoint.charAt(0) == '{' && checkpoint.charAt(checkpoint.length() - 1) == '}' && isJson(checkpoint); + private Optional deserializeRCFModel(String checkpoint, String modelId) { + if (checkpoint == null || checkpoint.isEmpty()) { + return Optional.empty(); + } + return Optional.ofNullable(AccessController.doPrivileged((PrivilegedAction) () -> { + try { + RandomCutForestState state = converter.convert(checkpoint, Precision.FLOAT_32); + return mapper.toModel(state); + } catch (Exception e) { + logger.error("Unexpected error when deserializing " + modelId, e); + return null; + } + })); } - /** - * @param testString test string - * @return if the given string is json or not - */ - private boolean isJson(String testString) { - try (JsonReader reader = new JsonReader(new StringReader(testString))) { - strictGsonObjectAdapter.read(reader); - reader.hasNext(); // throws on multiple top level values - return true; - } catch (IOException e) { - return false; + private void deserializeTRCFModel( + GetResponse response, + String rcfModelId, + ActionListener> listener + ) { + Object model = null; + if (response.isExists()) { + try { + model = response.getSource().get(FIELD_MODELV2); + if (model != null) { + listener.onResponse(Optional.ofNullable(toTrcf((String) model))); + } else { + Object modelV1 = response.getSource().get(FIELD_MODEL); + Optional forest = deserializeRCFModel((String) modelV1, rcfModelId); + if (!forest.isPresent()) { + logger.error("Unexpected error when deserializing [{}]", rcfModelId); + listener.onResponse(Optional.empty()); + return; + } + String thresholdingModelId = SingleStreamModelIdMapper.getThresholdModelIdFromRCFModelId(rcfModelId); + // query for threshold model and combinne rcf and threshold model into a ThresholdedRandomCutForest + getThresholdModel( + thresholdingModelId, + ActionListener + .wrap( + thresholdingModel -> { listener.onResponse(convertToTRCF(forest, thresholdingModel)); }, + listener::onFailure + ) + ); + } + } catch (Exception e) { + logger.error(new ParameterizedMessage("Unexpected error when deserializing [{}]", rcfModelId), e); + listener.onResponse(Optional.empty()); + } + } else { + listener.onResponse(Optional.empty()); } } @@ -603,22 +675,24 @@ public Optional> processGetResponse(GetResponse resp * @param modelId id of the model * @param listener onResponse is called with the model checkpoint, or empty for no such model */ - public void getRCFModel(String modelId, ActionListener> listener) { - clientUtil.asyncRequest(new GetRequest(indexName, modelId), client::get, ActionListener.wrap(response -> { - Optional rcfCheckpoint = processRCFModelCheckpoint(response); - if (!rcfCheckpoint.isPresent()) { - listener.onFailure(new ResourceNotFoundException("", "Fail to find model " + modelId)); - return; - } - listener - .onResponse( - rcfCheckpoint - .map( - checkpoint -> AccessController - .doPrivileged((PrivilegedAction) () -> deserializeRCFModel((String) checkpoint)) - ) - ); - }, listener::onFailure)); + public void getTRCFModel(String modelId, ActionListener> listener) { + clientUtil + .asyncRequest( + new GetRequest(indexName, modelId), + client::get, + ActionListener + .wrap( + response -> deserializeTRCFModel(response, modelId, listener), + exception -> { + // expected exception, don't print stack trace + if (exception instanceof IndexNotFoundException) { + listener.onResponse(Optional.empty()); + } else { + listener.onFailure(exception); + } + } + ) + ); } /** @@ -642,18 +716,16 @@ public void getThresholdModel(String modelId, ActionListener processRCFModelCheckpoint(GetResponse response) { - Object model = null; - if (response.isExists()) { - model = response.getSource().get(FIELD_MODELV2); - if (model == null) { - model = response.getSource().get(FIELD_MODEL); + }, + exception -> { + // expected exception, don't print stack trace + if (exception instanceof IndexNotFoundException) { + listener.onResponse(Optional.empty()); + } else { + listener.onFailure(exception); + } } - } - return Optional.ofNullable(model); + )); } private Optional processThresholdModelCheckpoint(GetResponse response) { @@ -694,4 +766,17 @@ public void batchWrite(BulkRequest request, ActionListener listene })); } } + + private Optional convertToTRCF(Optional rcf, Optional kllThreshold) { + if (!rcf.isPresent()) { + return Optional.empty(); + } + // if there is no threshold model (e.g., threshold model is deleted by HourlyCron), we are gonna + // start with empty list of rcf scores + List scores = new ArrayList<>(); + if (kllThreshold.isPresent()) { + scores = kllThreshold.get().extractScores(); + } + return Optional.of(new ThresholdedRandomCutForest(rcf.get(), anomalyRate, scores)); + } } diff --git a/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java b/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java index 5e49db1d2..141247921 100644 --- a/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java +++ b/src/main/java/org/opensearch/ad/ml/EntityColdStarter.java @@ -34,6 +34,7 @@ import java.util.AbstractMap.SimpleImmutableEntry; import java.util.ArrayDeque; import java.util.ArrayList; +import java.util.Arrays; import java.util.Collections; import java.util.List; import java.util.Map; @@ -42,7 +43,6 @@ import java.util.Queue; import java.util.concurrent.ConcurrentHashMap; import java.util.stream.Collectors; -import java.util.stream.DoubleStream; import java.util.stream.Stream; import org.apache.logging.log4j.LogManager; @@ -70,8 +70,8 @@ import org.opensearch.common.settings.Settings; import org.opensearch.threadpool.ThreadPool; -import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** * Training models for HCAD detectors @@ -87,11 +87,6 @@ public class EntityColdStarter implements MaintenanceState { private final double rcfTimeDecay; private final int numMinSamples; private final double thresholdMinPvalue; - private final double thresholdMaxRankError; - private final double thresholdMaxScore; - private final int thresholdNumLogNormalQuantiles; - private final int thresholdDownsamples; - private final long thresholdMaxSamples; private final int maxSampleStride; private final int maxTrainSamples; private final Interpolator interpolator; @@ -126,11 +121,6 @@ public class EntityColdStarter implements MaintenanceState { * @param interpolator Used to generate data points between samples. * @param searchFeatureDao Used to issue ES queries. * @param thresholdMinPvalue min P-value for thresholding - * @param thresholdMaxRankError max rank error for thresholding - * @param thresholdMaxScore max RCF score to thresholding - * @param thresholdNumLogNormalQuantiles num of lognormal quantiles for thresholding - * @param thresholdDownsamples the number of samples to keep during downsampling - * @param thresholdMaxSamples the max number of samples before downsampling * @param featureManager Used to create features for models. * @param settings ES settings accessor * @param modelTtl time-to-live before last access time of the cold start cache. @@ -152,11 +142,6 @@ public EntityColdStarter( Interpolator interpolator, SearchFeatureDao searchFeatureDao, double thresholdMinPvalue, - double thresholdMaxRankError, - double thresholdMaxScore, - int thresholdNumLogNormalQuantiles, - int thresholdDownsamples, - long thresholdMaxSamples, FeatureManager featureManager, Settings settings, Duration modelTtl, @@ -176,11 +161,6 @@ public EntityColdStarter( this.interpolator = interpolator; this.searchFeatureDao = searchFeatureDao; this.thresholdMinPvalue = thresholdMinPvalue; - this.thresholdMaxRankError = thresholdMaxRankError; - this.thresholdMaxScore = thresholdMaxScore; - this.thresholdNumLogNormalQuantiles = thresholdNumLogNormalQuantiles; - this.thresholdDownsamples = thresholdDownsamples; - this.thresholdMaxSamples = thresholdMaxSamples; this.featureManager = featureManager; this.coolDownMinutes = (int) (COOLDOWN_MINUTES.get(settings).getMinutes()); this.doorKeepers = new ConcurrentHashMap<>(); @@ -202,11 +182,6 @@ public EntityColdStarter( Interpolator interpolator, SearchFeatureDao searchFeatureDao, double thresholdMinPvalue, - double thresholdMaxRankError, - double thresholdMaxScore, - int thresholdNumLogNormalQuantiles, - int thresholdDownsamples, - long thresholdMaxSamples, FeatureManager featureManager, Settings settings, Duration modelTtl, @@ -225,11 +200,6 @@ public EntityColdStarter( interpolator, searchFeatureDao, thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples, featureManager, settings, modelTtl, @@ -363,7 +333,7 @@ private void trainModelFromDataSegments( } int dimensions = dataPoints.get(0)[0].length * shingleSize; - RandomCutForest.Builder rcfBuilder = RandomCutForest + ThresholdedRandomCutForest.Builder rcfBuilder = ThresholdedRandomCutForest .builder() .dimensions(dimensions) .sampleSize(rcfSampleSize) @@ -380,45 +350,21 @@ private void trainModelFromDataSegments( // vector is x1, x2, x3, x4, now we add x3, x4, x5, x6. RCF will recognize // overlapping x3, x4, and only store x5, x6. .shingleSize(shingleSize) - .internalShinglingEnabled(true); + .internalShinglingEnabled(true) + .anomalyRate(1 - this.thresholdMinPvalue); if (rcfSeed > 0) { rcfBuilder.randomSeed(rcfSeed); } - RandomCutForest rcf = rcfBuilder.build(); - List allScores = new ArrayList<>(); - int totalLength = 0; - // get continuous data points and send for training - for (double[][] continuousDataPoints : dataPoints) { - double[] scores = trainRCFModel(continuousDataPoints, rcf); - allScores.add(scores); - totalLength += scores.length; - } + ThresholdedRandomCutForest trcf = new ThresholdedRandomCutForest(rcfBuilder); + + dataPoints.stream().flatMap(d -> Arrays.stream(d)).forEach(s -> trcf.process(s, 0)); EntityModel model = entityState.getModel(); if (model == null) { - model = new EntityModel(entity, new ArrayDeque<>(), null, null); + model = new EntityModel(entity, new ArrayDeque<>(), null); } - model.setRcf(rcf); - double[] joinedScores = new double[totalLength]; - - int destStart = 0; - for (double[] scores : allScores) { - System.arraycopy(scores, 0, joinedScores, destStart, scores.length); - destStart += scores.length; - } - - // Train thresholding model - ThresholdingModel threshold = new HybridThresholdingModel( - thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples - ); - threshold.train(joinedScores); - model.setThreshold(threshold); + model.setTrcf(trcf); entityState.setLastUsedTime(clock.instant()); @@ -426,27 +372,6 @@ private void trainModelFromDataSegments( checkpointWriteQueue.write(entityState, true, RequestPriority.MEDIUM); } - /** - * Train the RCF model using given data points - * @param dataPoints Data points - * @param rcf RCF model to be trained - * @return scores returned by RCF models - */ - private double[] trainRCFModel(double[][] dataPoints, RandomCutForest rcf) { - if (dataPoints.length == 0 || dataPoints[0].length == 0) { - throw new IllegalArgumentException("Data points must not be empty."); - } - - double[] scores = new double[dataPoints.length]; - - for (int j = 0; j < dataPoints.length; j++) { - scores[j] = rcf.getAnomalyScore(dataPoints[j]); - rcf.update(dataPoints[j]); - } - - return DoubleStream.of(scores).filter(score -> score > 0).toArray(); - } - /** * Get training data for an entity. * @@ -699,7 +624,7 @@ private boolean hasEnoughSample(List dataPoints, ModelState coldstartDatapoints, String modelId, ModelState entityState) { EntityModel model = entityState.getModel(); if (model == null) { - model = new EntityModel(null, new ArrayDeque<>(), null, null); + model = new EntityModel(null, new ArrayDeque<>(), null); } for (double[][] consecutivePoints : coldstartDatapoints) { for (int i = 0; i < consecutivePoints.length; i++) { diff --git a/src/main/java/org/opensearch/ad/ml/EntityModel.java b/src/main/java/org/opensearch/ad/ml/EntityModel.java index 83bcb7de6..7a6f265a0 100644 --- a/src/main/java/org/opensearch/ad/ml/EntityModel.java +++ b/src/main/java/org/opensearch/ad/ml/EntityModel.java @@ -32,20 +32,25 @@ import org.opensearch.ad.model.Entity; -import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; public class EntityModel { private Entity entity; // TODO: sample should record timestamp private Queue samples; - private RandomCutForest rcf; - private ThresholdingModel threshold; + private ThresholdedRandomCutForest trcf; - public EntityModel(Entity entity, Queue samples, RandomCutForest rcf, ThresholdingModel threshold) { + /** + * Constructor with TRCF. + * + * @param entity entity if any + * @param samples samples with the model + * @param trcf thresholded rcf model + */ + public EntityModel(Entity entity, Queue samples, ThresholdedRandomCutForest trcf) { this.entity = entity; this.samples = samples; - this.rcf = rcf; - this.threshold = threshold; + this.trcf = trcf; } /** @@ -70,27 +75,28 @@ public void addSample(double[] sample) { } } - public RandomCutForest getRcf() { - return this.rcf; - } - - public ThresholdingModel getThreshold() { - return this.threshold; - } - - public void setRcf(RandomCutForest rcf) { - this.rcf = rcf; + /** + * Sets an trcf model. + * + * @param trcf an trcf model + */ + public void setTrcf(ThresholdedRandomCutForest trcf) { + this.trcf = trcf; } - public void setThreshold(ThresholdingModel threshold) { - this.threshold = threshold; + /** + * Returns optional trcf model. + * + * @return the trcf model or empty + */ + public Optional getTrcf() { + return Optional.ofNullable(this.trcf); } public void clear() { if (samples != null) { samples.clear(); } - rcf = null; - threshold = null; + trcf = null; } } diff --git a/src/main/java/org/opensearch/ad/ml/HybridThresholdingModel.java b/src/main/java/org/opensearch/ad/ml/HybridThresholdingModel.java index 8c8142ccd..ca487381c 100644 --- a/src/main/java/org/opensearch/ad/ml/HybridThresholdingModel.java +++ b/src/main/java/org/opensearch/ad/ml/HybridThresholdingModel.java @@ -26,12 +26,16 @@ package org.opensearch.ad.ml; +import java.util.ArrayList; +import java.util.List; + import org.apache.commons.math3.special.Erf; import org.apache.commons.math3.stat.descriptive.SummaryStatistics; import com.google.gson.annotations.Expose; import com.google.gson.annotations.JsonAdapter; import com.yahoo.sketches.kll.KllFloatsSketch; +import com.yahoo.sketches.kll.KllFloatsSketchIterator; /** * A model for converting raw anomaly scores into anomaly grades. @@ -255,7 +259,7 @@ public void train(double[] anomalyScores) { raw anomaly scores are positive and non-zero. */ final double maxScorePvalue = computeLogNormalCdf(maxScore, mu, sigma); - final double pvalueStep = maxScorePvalue / ((double) numLogNormalQuantiles + 1.0); + final double pvalueStep = maxScorePvalue / (numLogNormalQuantiles + 1.0); for (double pvalue = pvalueStep; pvalue < maxScorePvalue; pvalue += pvalueStep) { double currentScore = computeLogNormalQuantile(pvalue, mu, sigma); update(currentScore); @@ -363,7 +367,7 @@ public double confidence() { */ private void downsample() { KllFloatsSketch downsampledQuantileSketch = new KllFloatsSketch(quantileSketch.getK()); - double pvalueStep = 1.0 / ((double) downsampleNumSamples - 1.0); + double pvalueStep = 1.0 / (downsampleNumSamples - 1.0); for (double pvalue = 0.0; pvalue < 1.0; pvalue += pvalueStep) { float score = quantileSketch.getQuantile(pvalue); downsampledQuantileSketch.update(score); @@ -371,4 +375,14 @@ private void downsample() { downsampledQuantileSketch.update((float) maxScore); this.quantileSketch = downsampledQuantileSketch; } + + @Override + public List extractScores() { + KllFloatsSketchIterator iter = quantileSketch.iterator(); + List scores = new ArrayList<>(); + while (iter.next()) { + scores.add((double) iter.getValue()); + } + return scores; + } } diff --git a/src/main/java/org/opensearch/ad/ml/ModelManager.java b/src/main/java/org/opensearch/ad/ml/ModelManager.java index 7dc1510ec..061ca5216 100644 --- a/src/main/java/org/opensearch/ad/ml/ModelManager.java +++ b/src/main/java/org/opensearch/ad/ml/ModelManager.java @@ -41,10 +41,7 @@ import java.util.Queue; import java.util.Set; import java.util.concurrent.ConcurrentHashMap; -import java.util.regex.Matcher; -import java.util.regex.Pattern; import java.util.stream.Collectors; -import java.util.stream.DoubleStream; import java.util.stream.Stream; import org.apache.logging.log4j.LogManager; @@ -52,25 +49,22 @@ import org.opensearch.action.ActionListener; import org.opensearch.ad.DetectorModelSize; import org.opensearch.ad.MemoryTracker; -import org.opensearch.ad.common.exception.LimitExceededException; import org.opensearch.ad.common.exception.ResourceNotFoundException; import org.opensearch.ad.constant.CommonErrorMessages; import org.opensearch.ad.feature.FeatureManager; -import org.opensearch.ad.ml.rcf.CombinedRcfResult; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.Entity; import org.opensearch.ad.settings.AnomalyDetectorSettings; -import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.amazon.randomcutforest.returntypes.DiVector; /** * A facade managing ML operations and models. */ public class ModelManager implements DetectorModelSize { - protected static final String DETECTOR_ID_PATTERN = "(.*)_model_.+"; - protected static final String ENTITY_SAMPLE = "sp"; protected static final String ENTITY_RCF = "rcf"; protected static final String ENTITY_THRESHOLD = "th"; @@ -91,12 +85,10 @@ public String getName() { } } - private static final double FULL_CONFIDENCE_EXPONENT = 18.43; // exponent over which confidence is 1 - private static final Logger logger = LogManager.getLogger(ModelManager.class); // states - private RCFMemoryAwareConcurrentHashmap forests; + private TRCFMemoryAwareConcurrentHashmap forests; private Map> thresholds; // configuration @@ -105,11 +97,6 @@ public String getName() { private final double rcfTimeDecay; private final int rcfNumMinSamples; private final double thresholdMinPvalue; - private final double thresholdMaxRankError; - private final double thresholdMaxScore; - private final int thresholdNumLogNormalQuantiles; - private final int thresholdDownsamples; - private final long thresholdMaxSamples; private final int minPreviewSize; private final Duration modelTtl; private final Duration checkpointInterval; @@ -120,7 +107,6 @@ public String getName() { public FeatureManager featureManager; private EntityColdStarter entityColdStarter; - private ModelPartitioner modelPartitioner; private MemoryTracker memoryTracker; /** @@ -133,16 +119,10 @@ public String getName() { * @param rcfTimeDecay time decay for RCF * @param rcfNumMinSamples minimum samples for RCF to score * @param thresholdMinPvalue min P-value for thresholding - * @param thresholdMaxRankError max rank error for thresholding - * @param thresholdMaxScore max RCF score to thresholding - * @param thresholdNumLogNormalQuantiles num of lognormal quantiles for thresholding - * @param thresholdDownsamples the number of samples to keep during downsampling - * @param thresholdMaxSamples the max number of samples before downsampling * @param minPreviewSize minimum number of data points for preview * @param modelTtl time to live for hosted models * @param checkpointInterval interval between checkpoints * @param entityColdStarter HCAD cold start utility - * @param modelPartitioner Used to partition RCF models * @param featureManager Used to create features for models * @param memoryTracker AD memory usage tracker */ @@ -154,16 +134,10 @@ public ModelManager( double rcfTimeDecay, int rcfNumMinSamples, double thresholdMinPvalue, - double thresholdMaxRankError, - double thresholdMaxScore, - int thresholdNumLogNormalQuantiles, - int thresholdDownsamples, - long thresholdMaxSamples, int minPreviewSize, Duration modelTtl, Duration checkpointInterval, EntityColdStarter entityColdStarter, - ModelPartitioner modelPartitioner, FeatureManager featureManager, MemoryTracker memoryTracker ) { @@ -174,87 +148,18 @@ public ModelManager( this.rcfTimeDecay = rcfTimeDecay; this.rcfNumMinSamples = rcfNumMinSamples; this.thresholdMinPvalue = thresholdMinPvalue; - this.thresholdMaxRankError = thresholdMaxRankError; - this.thresholdMaxScore = thresholdMaxScore; - this.thresholdNumLogNormalQuantiles = thresholdNumLogNormalQuantiles; - this.thresholdDownsamples = thresholdDownsamples; - this.thresholdMaxSamples = thresholdMaxSamples; this.minPreviewSize = minPreviewSize; this.modelTtl = modelTtl; this.checkpointInterval = checkpointInterval; - this.forests = new RCFMemoryAwareConcurrentHashmap<>(memoryTracker); + this.forests = new TRCFMemoryAwareConcurrentHashmap<>(memoryTracker); this.thresholds = new ConcurrentHashMap<>(); this.entityColdStarter = entityColdStarter; - this.modelPartitioner = modelPartitioner; this.featureManager = featureManager; this.memoryTracker = memoryTracker; } - /** - * Combines RCF results into a single result. - * - * Final RCF score is calculated by averaging scores weighted by model size (number of trees). - * Confidence is the weighted average of confidence with confidence for missing models being 0. - * Attribution is normalized weighted average for the most recent feature dimensions. - * - * @param rcfResults RCF results from partitioned models - * @param numFeatures number of features for attribution - * @return combined RCF result - */ - public CombinedRcfResult combineRcfResults(List rcfResults, int numFeatures) { - CombinedRcfResult combinedResult = null; - if (rcfResults.isEmpty()) { - combinedResult = new CombinedRcfResult(0, 0, new double[0]); - } else { - int totalForestSize = rcfResults.stream().mapToInt(RcfResult::getForestSize).sum(); - if (totalForestSize == 0) { - combinedResult = new CombinedRcfResult(0, 0, new double[0]); - } else { - double score = rcfResults.stream().mapToDouble(r -> r.getScore() * r.getForestSize()).sum() / totalForestSize; - double confidence = rcfResults.stream().mapToDouble(r -> r.getConfidence() * r.getForestSize()).sum() / Math - .max(rcfNumTrees, totalForestSize); - double[] attribution = combineAttribution(rcfResults, numFeatures, totalForestSize); - combinedResult = new CombinedRcfResult(score, confidence, attribution); - } - } - return combinedResult; - } - - private double[] combineAttribution(List rcfResults, int numFeatures, int totalForestSize) { - double[] combined = new double[numFeatures]; - double sum = 0; - for (RcfResult result : rcfResults) { - double[] attribution = result.getAttribution(); - for (int i = 0; i < numFeatures; i++) { - double attr = attribution[attribution.length - numFeatures + i] * result.getForestSize() / totalForestSize; - combined[i] += attr; - sum += attr; - } - } - for (int i = 0; i < numFeatures; i++) { - combined[i] /= sum; - } - return combined; - } - - /** - * Gets the detector id from the model id. - * - * @param modelId id of a model - * @return id of the detector the model is for - * @throws IllegalArgumentException if model id is invalid - */ - public String getDetectorIdForModelId(String modelId) { - Matcher matcher = Pattern.compile(DETECTOR_ID_PATTERN).matcher(modelId); - if (matcher.matches()) { - return matcher.group(1); - } else { - throw new IllegalArgumentException("Invalid model id " + modelId); - } - } - /** * Returns to listener the RCF anomaly result using the specified model. * @@ -270,28 +175,39 @@ public void getRcfResult(String detectorId, String modelId, double[] point, Acti getRcfResult(forests.get(modelId), point, listener); } else { checkpointDao - .getRCFModel( + .getTRCFModel( modelId, ActionListener - .wrap(checkpoint -> processRcfCheckpoint(checkpoint, modelId, detectorId, point, listener), listener::onFailure) + .wrap( + restoredModel -> processRestoredTRcf(restoredModel, modelId, detectorId, point, listener), + listener::onFailure + ) ); } } - private void getRcfResult(ModelState modelState, double[] point, ActionListener listener) { + private void getRcfResult(ModelState modelState, double[] point, ActionListener listener) { modelState.setLastUsedTime(clock.instant()); - RandomCutForest rcf = modelState.getModel(); - double score = rcf.getAnomalyScore(point); - double confidence = computeRcfConfidence(rcf); - int forestSize = rcf.getNumberOfTrees(); - double[] attribution = getAnomalyAttribution(rcf, point); - rcf.update(point); - listener.onResponse(new RcfResult(score, confidence, forestSize, attribution, rcf.getTotalUpdates())); + ThresholdedRandomCutForest rcf = modelState.getModel(); + AnomalyDescriptor result = rcf.process(point, 0); + double score = result.getRcfScore(); + double confidence = result.getDataConfidence(); + int forestSize = result.getForestSize(); + double grade = result.getAnomalyGrade(); + + // result.getAttribution() is null when anomaly grade is less than or equals to 0 + // need to create an empty array for bwc because the old node expects an non-empty array + int dimension = rcf.getForest().getDimensions(); + double[] attribution = new double[dimension]; + if (result.getAttribution() != null) { + attribution = getAnomalyAttribution(result.getAttribution()); + } + + listener.onResponse(new RcfResult(score, confidence, forestSize, attribution, result.getTotalUpdates(), grade)); } - private double[] getAnomalyAttribution(RandomCutForest rcf, double[] point) { - DiVector vec = rcf.getAnomalyAttribution(point); + private double[] getAnomalyAttribution(DiVector vec) { vec.renormalize(1d); double[] attribution = new double[vec.getDimensions()]; for (int i = 0; i < attribution.length; i++) { @@ -300,28 +216,27 @@ private double[] getAnomalyAttribution(RandomCutForest rcf, double[] point) { return attribution; } - private Optional> restoreCheckpoint( - Optional rcfCheckpoint, + private Optional> restoreModelState( + Optional rcfModel, String modelId, String detectorId ) { - if (!rcfCheckpoint.isPresent()) { + if (!rcfModel.isPresent()) { return Optional.empty(); } - - return rcfCheckpoint + return rcfModel .filter(rcf -> memoryTracker.isHostingAllowed(detectorId, rcf)) .map(rcf -> ModelState.createSingleEntityModelState(rcf, modelId, detectorId, ModelType.RCF.getName(), clock)); } - private void processRcfCheckpoint( - Optional rcfCheckpoint, + private void processRestoredTRcf( + Optional rcfModel, String modelId, String detectorId, double[] point, ActionListener listener ) { - Optional> model = restoreCheckpoint(rcfCheckpoint, modelId, detectorId); + Optional> model = restoreModelState(rcfModel, modelId, detectorId); if (model.isPresent()) { forests.put(modelId, model.get()); getRcfResult(model.get(), point, listener); @@ -337,17 +252,18 @@ private void processRcfCheckpoint( * @param detectorId detector Id * @param listener listener to return total updates of rcf */ - private void processRcfCheckpoint( - Optional checkpointModel, + private void processRestoredCheckpoint( + Optional checkpointModel, String modelId, String detectorId, ActionListener listener ) { logger.info("Restoring checkpoint for {}", modelId); - Optional> model = restoreCheckpoint(checkpointModel, modelId, detectorId); + Optional> model = restoreModelState(checkpointModel, modelId, detectorId); if (model.isPresent()) { forests.put(modelId, model.get()); - listener.onResponse(model.get().getModel().getTotalUpdates()); + if (model.get().getModel() != null && model.get().getModel().getForest() != null) + listener.onResponse(model.get().getModel().getForest().getTotalUpdates()); } else { listener.onFailure(new ResourceNotFoundException(detectorId, CommonErrorMessages.NO_CHECKPOINT_ERR_MSG + modelId)); } @@ -450,11 +366,11 @@ private void stopModel(Map> models, String modelId, Ac .filter(model -> model.getLastCheckpointTime().plus(checkpointInterval).isBefore(now)); if (modelState.isPresent()) { T model = modelState.get().getModel(); - if (model instanceof RandomCutForest) { + if (model instanceof ThresholdedRandomCutForest) { checkpointDao - .putRCFCheckpoint( + .putTRCFCheckpoint( modelId, - (RandomCutForest) model, + (ThresholdedRandomCutForest) model, ActionListener.wrap(r -> listener.onResponse(null), listener::onFailure) ); } else if (model instanceof ThresholdingModel) { @@ -492,7 +408,7 @@ private void clearModels(String detectorId, Map models, ActionListene private void clearModelForIterator(String detectorId, Map models, Iterator idIter, ActionListener listener) { if (idIter.hasNext()) { String modelId = idIter.next(); - if (getDetectorIdForModelId(modelId).equals(detectorId)) { + if (SingleStreamModelIdMapper.getDetectorIdForModelId(modelId).equals(detectorId)) { models.remove(modelId); checkpointDao .deleteModelCheckpoint( @@ -527,31 +443,9 @@ public void trainModel(AnomalyDetector anomalyDetector, double[][] dataPoints, A listener.onFailure(new IllegalArgumentException("Data points must not be empty.")); } else { int rcfNumFeatures = dataPoints[0].length; - // creates partitioned RCF models try { - Entry partitionResults = modelPartitioner - .getPartitionedForestSizes( - RandomCutForest - .builder() - .dimensions(rcfNumFeatures) - .sampleSize(rcfNumSamplesInTree) - .numberOfTrees(rcfNumTrees) - .outputAfter(rcfNumSamplesInTree) - .parallelExecutionEnabled(false) - .compact(true) - .precision(Precision.FLOAT_32) - .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) - // same with dimension for opportunistic memory saving - .shingleSize(anomalyDetector.getShingleSize()) - .build(), - anomalyDetector.getDetectorId() - ); - int numForests = partitionResults.getKey(); - int forestSize = partitionResults.getValue(); - double[] scores = new double[dataPoints.length]; - Arrays.fill(scores, 0.); - trainModelForStep(anomalyDetector, dataPoints, rcfNumFeatures, numForests, forestSize, scores, 0, listener); - } catch (LimitExceededException e) { + trainModelForStep(anomalyDetector, dataPoints, rcfNumFeatures, 0, listener); + } catch (Exception e) { listener.onFailure(e); } } @@ -561,70 +455,27 @@ private void trainModelForStep( AnomalyDetector detector, double[][] dataPoints, int rcfNumFeatures, - int numForests, - int forestSize, - final double[] scores, int step, ActionListener listener ) { - if (step < numForests) { - RandomCutForest rcf = RandomCutForest - .builder() - .dimensions(rcfNumFeatures) - .sampleSize(rcfNumSamplesInTree) - .numberOfTrees(forestSize) - .timeDecay(rcfTimeDecay) - .outputAfter(rcfNumMinSamples) - .parallelExecutionEnabled(false) - .compact(true) - .precision(Precision.FLOAT_32) - .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) - // same with dimension for opportunistic memory saving - .shingleSize(detector.getShingleSize()) - .build(); - for (int j = 0; j < dataPoints.length; j++) { - scores[j] += rcf.getAnomalyScore(dataPoints[j]); - rcf.update(dataPoints[j]); - } - String modelId = modelPartitioner.getRcfModelId(detector.getDetectorId(), step); - checkpointDao - .putRCFCheckpoint( - modelId, - rcf, - ActionListener - .wrap( - r -> trainModelForStep( - detector, - dataPoints, - rcfNumFeatures, - numForests, - forestSize, - scores, - step + 1, - listener - ), - listener::onFailure - ) - ); - } else { - double[] rcfScores = DoubleStream.of(scores).filter(score -> score > 0).map(score -> score / numForests).toArray(); - - // Train thresholding model - ThresholdingModel threshold = new HybridThresholdingModel( - thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples - ); - threshold.train(rcfScores); + ThresholdedRandomCutForest trcf = ThresholdedRandomCutForest + .builder() + .dimensions(rcfNumFeatures) + .sampleSize(rcfNumSamplesInTree) + .numberOfTrees(rcfNumTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(rcfNumMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .shingleSize(detector.getShingleSize()) + .anomalyRate(1 - thresholdMinPvalue) + .build(); + Arrays.stream(dataPoints).forEach(s -> trcf.process(s, 0)); - // Persist thresholding model - String modelId = modelPartitioner.getThresholdModelId(detector.getDetectorId()); - checkpointDao - .putThresholdCheckpoint(modelId, threshold, ActionListener.wrap(r -> listener.onResponse(null), listener::onFailure)); - } + String modelId = SingleStreamModelIdMapper.getRcfModelId(detector.getDetectorId(), step); + checkpointDao.putTRCFCheckpoint(modelId, trcf, ActionListener.wrap(r -> listener.onResponse(null), listener::onFailure)); } /** @@ -664,8 +515,8 @@ private void maintenanceForIterator( maintenanceForIterator(models, iter, listener); }); T model = modelState.getModel(); - if (model instanceof RandomCutForest) { - checkpointDao.putRCFCheckpoint(modelId, (RandomCutForest) model, checkpointListener); + if (model instanceof ThresholdedRandomCutForest) { + checkpointDao.putTRCFCheckpoint(modelId, (ThresholdedRandomCutForest) model, checkpointListener); } else if (model instanceof ThresholdingModel) { checkpointDao.putThresholdCheckpoint(modelId, (ThresholdingModel) model, checkpointListener); } else { @@ -695,7 +546,7 @@ public List getPreviewResults(double[][] dataPoints, int shi int rcfNumFeatures = dataPoints[0].length; // speed is important in preview. We don't want cx to wait too long. // thus use the default value of boundingBoxCacheFraction = 1 - RandomCutForest forest = RandomCutForest + ThresholdedRandomCutForest forest = ThresholdedRandomCutForest .builder() .randomSeed(0L) .dimensions(rcfNumFeatures) @@ -707,51 +558,15 @@ public List getPreviewResults(double[][] dataPoints, int shi .compact(true) .precision(Precision.FLOAT_32) .boundingBoxCacheFraction(AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO) - // same with dimension for opportunistic memory saving .shingleSize(shingleSize) + .anomalyRate(1 - this.thresholdMinPvalue) .build(); - double[] rcfScores = Arrays.stream(dataPoints).mapToDouble(point -> { - double score = forest.getAnomalyScore(point); - forest.update(point); - return score; - }).filter(score -> score > 0.).toArray(); - // Train thresholding model - ThresholdingModel threshold = new HybridThresholdingModel( - thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples - ); - threshold.train(rcfScores); - - // Get results from trained models return Arrays.stream(dataPoints).map(point -> { - double rcfScore = forest.getAnomalyScore(point); - forest.update(point); - ThresholdingResult result = new ThresholdingResult(threshold.grade(rcfScore), threshold.confidence(), rcfScore); - threshold.update(rcfScore); - return result; + AnomalyDescriptor descriptor = forest.process(point, 0); + return new ThresholdingResult(descriptor.getAnomalyGrade(), descriptor.getDataConfidence(), descriptor.getRcfScore()); }).collect(Collectors.toList()); } - /** - * Computes the probabilities of non-coldstart points in the current forest. - */ - double computeRcfConfidence(RandomCutForest forest) { - long total = forest.getTotalUpdates(); - double lambda = forest.getTimeDecay(); - double totalExponent = total * lambda; - if (totalExponent >= FULL_CONFIDENCE_EXPONENT) { - return 1.; - } else { - double eTotal = Math.exp(totalExponent); - double confidence = (eTotal - Math.exp(lambda * Math.min(total, forest.getSampleSize()))) / (eTotal - 1); - return Math.max(0, confidence); // Replaces -0 wth 0 for cosmetic purpose. - } - } - /** * Get all RCF partition's size corresponding to a detector. Thresholding models' size is a constant since they are small in size (KB). * @param detectorId detector id @@ -763,12 +578,12 @@ public Map getModelSize(String detectorId) { forests .entrySet() .stream() - .filter(entry -> getDetectorIdForModelId(entry.getKey()).equals(detectorId)) - .forEach(entry -> { res.put(entry.getKey(), memoryTracker.estimateRCFModelSize(entry.getValue().getModel())); }); + .filter(entry -> SingleStreamModelIdMapper.getDetectorIdForModelId(entry.getKey()).equals(detectorId)) + .forEach(entry -> { res.put(entry.getKey(), memoryTracker.estimateTRCFModelSize(entry.getValue().getModel())); }); thresholds .entrySet() .stream() - .filter(entry -> getDetectorIdForModelId(entry.getKey()).equals(detectorId)) + .filter(entry -> SingleStreamModelIdMapper.getDetectorIdForModelId(entry.getKey()).equals(detectorId)) .forEach(entry -> { res.put(entry.getKey(), (long) memoryTracker.getThresholdModelBytes()); }); return res; } @@ -780,14 +595,19 @@ public Map getModelSize(String detectorId) { * @param listener listener to return the result */ public void getTotalUpdates(String modelId, String detectorId, ActionListener listener) { - ModelState model = forests.get(modelId); + ModelState model = forests.get(modelId); if (model != null) { - listener.onResponse(model.getModel().getTotalUpdates()); + if (model.getModel() != null && model.getModel().getForest() != null) { + listener.onResponse(model.getModel().getForest().getTotalUpdates()); + } else { + listener.onResponse(0L); + } } else { checkpointDao - .getRCFModel( + .getTRCFModel( modelId, - ActionListener.wrap(checkpoint -> processRcfCheckpoint(checkpoint, modelId, detectorId, listener), listener::onFailure) + ActionListener + .wrap(checkpoint -> processRestoredCheckpoint(checkpoint, modelId, detectorId, listener), listener::onFailure) ); } } @@ -809,59 +629,41 @@ public ThresholdingResult getAnomalyResultForEntity( Entity entity, int shingleSize ) { + ThresholdingResult result = new ThresholdingResult(0, 0, 0); if (modelState != null) { EntityModel entityModel = modelState.getModel(); if (entityModel == null) { - entityModel = new EntityModel(entity, new ArrayDeque<>(), null, null); + entityModel = new EntityModel(entity, new ArrayDeque<>(), null); modelState.setModel(entityModel); } - // trainModelFromExistingSamples may be able to make models not null - if (entityModel.getRcf() == null || entityModel.getThreshold() == null) { + + if (!entityModel.getTrcf().isPresent()) { entityColdStarter.trainModelFromExistingSamples(modelState, shingleSize); } - if (entityModel.getRcf() != null && entityModel.getThreshold() != null) { - return score(datapoint, modelId, modelState); + + if (entityModel.getTrcf().isPresent()) { + result = score(datapoint, modelId, modelState); } else { entityModel.addSample(datapoint); - return new ThresholdingResult(0, 0, 0); } - } else { - return new ThresholdingResult(0, 0, 0); } + return result; } public ThresholdingResult score(double[] feature, String modelId, ModelState modelState) { + ThresholdingResult result = null; EntityModel model = modelState.getModel(); - if (model == null) { - return new ThresholdingResult(0, 0, 0); - } - RandomCutForest rcf = model.getRcf(); - ThresholdingModel threshold = model.getThreshold(); - if (rcf == null || threshold == null) { - return new ThresholdingResult(0, 0, 0); - } - - // clear feature not scored yet - Queue samples = model.getSamples(); - while (samples != null && samples.peek() != null) { - double[] recordedFeature = samples.poll(); - double rcfScore = rcf.getAnomalyScore(recordedFeature); - rcf.update(recordedFeature); - threshold.update(rcfScore); - } - double rcfScore = rcf.getAnomalyScore(feature); - - double anomalyGrade = threshold.grade(rcfScore); - - double anomalyConfidence = computeRcfConfidence(rcf) * threshold.confidence(); - ThresholdingResult result = new ThresholdingResult(anomalyGrade, anomalyConfidence, rcfScore); - - rcf.update(feature); - if (rcfScore > 0) { - threshold.update(rcfScore); + if (model == null || !model.getTrcf().isPresent()) { + result = new ThresholdingResult(0, 0, 0); + } else { + ThresholdedRandomCutForest rcf = model.getTrcf().get(); + Optional.ofNullable(model.getSamples()).ifPresent(q -> { + q.stream().forEach(s -> rcf.process(s, 0)); + q.clear(); + }); + result = toResult(rcf.process(feature, 0)); } - modelState.setLastUsedTime(clock.instant()); return result; } @@ -887,7 +689,7 @@ public ModelState processEntityCheckpoint( ) { // entity state to instantiate ModelState modelState = new ModelState<>( - new EntityModel(entity, new ArrayDeque<>(), null, null), + new EntityModel(entity, new ArrayDeque<>(), null), modelId, detectorId, ModelType.ENTITY.getName(), @@ -904,13 +706,11 @@ public ModelState processEntityCheckpoint( } EntityModel model = modelState.getModel(); if (model == null) { - model = new EntityModel(null, new ArrayDeque<>(), null, null); + model = new EntityModel(null, new ArrayDeque<>(), null); modelState.setModel(model); } - if ((model.getRcf() == null || model.getThreshold() == null) - && model.getSamples() != null - && model.getSamples().size() >= rcfNumMinSamples) { + if (!model.getTrcf().isPresent() && model.getSamples() != null && model.getSamples().size() >= rcfNumMinSamples) { entityColdStarter.trainModelFromExistingSamples(modelState, shingleSize); } return modelState; @@ -922,4 +722,12 @@ private void combineSamples(EntityModel fromModel, EntityModel toModel) { toModel.addSample(samples.poll()); } } + + private ThresholdingResult toResult(AnomalyDescriptor anomalyDescriptor) { + return new ThresholdingResult( + anomalyDescriptor.getAnomalyGrade(), + anomalyDescriptor.getDataConfidence(), + anomalyDescriptor.getRcfScore() + ); + } } diff --git a/src/main/java/org/opensearch/ad/ml/ModelPartitioner.java b/src/main/java/org/opensearch/ad/ml/ModelPartitioner.java deleted file mode 100644 index 5a926633c..000000000 --- a/src/main/java/org/opensearch/ad/ml/ModelPartitioner.java +++ /dev/null @@ -1,167 +0,0 @@ -/* - * SPDX-License-Identifier: Apache-2.0 - * - * The OpenSearch Contributors require contributions made to - * this file be licensed under the Apache-2.0 license or a - * compatible open source license. - * - * Modifications Copyright OpenSearch Contributors. See - * GitHub history for details. - */ - -/* - * Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"). - * You may not use this file except in compliance with the License. - * A copy of the License is located at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * or in the "license" file accompanying this file. This file is distributed - * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either - * express or implied. See the License for the specific language governing - * permissions and limitations under the License. - */ - -package org.opensearch.ad.ml; - -import java.util.AbstractMap.SimpleImmutableEntry; -import java.util.Locale; -import java.util.Map.Entry; - -import org.apache.logging.log4j.LogManager; -import org.apache.logging.log4j.Logger; -import org.opensearch.ad.MemoryTracker; -import org.opensearch.ad.common.exception.LimitExceededException; -import org.opensearch.ad.constant.CommonErrorMessages; -import org.opensearch.ad.model.AnomalyDetector; -import org.opensearch.ad.settings.AnomalyDetectorSettings; -import org.opensearch.ad.util.DiscoveryNodeFilterer; - -import com.amazon.randomcutforest.RandomCutForest; -import com.amazon.randomcutforest.config.Precision; - -/** - * This class breaks the circular dependency between NodeStateManager and ModelManager - * - */ -public class ModelPartitioner { - private static final Logger LOG = LogManager.getLogger(ModelPartitioner.class); - protected static final String RCF_MODEL_ID_PATTERN = "%s_model_rcf_%d"; - protected static final String THRESHOLD_MODEL_ID_PATTERN = "%s_model_threshold"; - - private int rcfNumSamplesInTree; - private int rcfNumTrees; - private DiscoveryNodeFilterer nodeFilter; - private MemoryTracker memoryTracker; - - /** - * Constructor - * @param rcfNumSamplesInTree The sample size used by stream samplers in - * this RCF forest - * @param rcfNumTrees The number of trees in this RCF forest. - * @param nodeFilter utility class to select nodes - * @param memoryTracker AD memory usage tracker - */ - public ModelPartitioner(int rcfNumSamplesInTree, int rcfNumTrees, DiscoveryNodeFilterer nodeFilter, MemoryTracker memoryTracker) { - this.rcfNumSamplesInTree = rcfNumSamplesInTree; - this.rcfNumTrees = rcfNumTrees; - this.nodeFilter = nodeFilter; - this.memoryTracker = memoryTracker; - } - - /** - * Construct a RCF model and then partition it by forest size. - * - * A RCF model is constructed based on the number of input features. - * - * Then a RCF model is first partitioned into desired size based on heap. - * If there are more partitions than the number of nodes in the cluster, - * the model is partitioned by the number of nodes and verified to - * ensure the size of a partition does not exceed the max size limit based on heap. - * - * @param detector detector object - * @return a pair of number of partitions and size of a parition (number of trees) - * @throws LimitExceededException when there is no sufficient resource available - */ - public Entry getPartitionedForestSizes(AnomalyDetector detector) { - int shingleSize = detector.getShingleSize(); - String detectorId = detector.getDetectorId(); - int rcfNumFeatures = detector.getEnabledFeatureIds().size() * shingleSize; - return getPartitionedForestSizes( - RandomCutForest - .builder() - .dimensions(rcfNumFeatures) - .sampleSize(rcfNumSamplesInTree) - .numberOfTrees(rcfNumTrees) - .outputAfter(rcfNumSamplesInTree) - .parallelExecutionEnabled(false) - .compact(true) - .precision(Precision.FLOAT_32) - .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) - // same with dimension for opportunistic memory saving - .shingleSize(detector.getShingleSize()) - .build(), - detectorId - ); - } - - /** - * Partitions a RCF model by forest size. - * - * A RCF model is first partitioned into desired size based on heap. - * If there are more partitions than the number of nodes in the cluster, - * the model is partitioned by the number of nodes and verified to - * ensure the size of a partition does not exceed the max size limit based on heap. - * - * @param forest RCF configuration, including forest size - * @param detectorId ID of the detector with no effects on partitioning - * @return a pair of number of partitions and size of a parition (number of trees) - * @throws LimitExceededException when there is no sufficient resource available - */ - public Entry getPartitionedForestSizes(RandomCutForest forest, String detectorId) { - long totalSize = memoryTracker.estimateRCFModelSize(forest); - - // desired partitioning - long partitionSize = (Math.min(memoryTracker.getDesiredModelSize(), totalSize)); - int numPartitions = (int) Math.ceil((double) totalSize / (double) partitionSize); - int forestSize = (int) Math.ceil((double) forest.getNumberOfTrees() / (double) numPartitions); - - int numNodes = nodeFilter.getEligibleDataNodes().length; - if (numPartitions > numNodes) { - // partition by cluster size - partitionSize = (long) Math.ceil((double) totalSize / (double) numNodes); - // verify against max size limit - if (partitionSize <= memoryTracker.getHeapLimit()) { - numPartitions = numNodes; - forestSize = (int) Math.ceil((double) forest.getNumberOfTrees() / (double) numNodes); - } else { - throw new LimitExceededException(detectorId, CommonErrorMessages.MEMORY_LIMIT_EXCEEDED_ERR_MSG); - } - } - - return new SimpleImmutableEntry<>(numPartitions, forestSize); - } - - /** - * Returns the model ID for the RCF model partition. - * - * @param detectorId ID of the detector for which the RCF model is trained - * @param partitionNumber number of the partition - * @return ID for the RCF model partition - */ - public String getRcfModelId(String detectorId, int partitionNumber) { - return String.format(Locale.ROOT, RCF_MODEL_ID_PATTERN, detectorId, partitionNumber); - } - - /** - * Returns the model ID for the thresholding model. - * - * @param detectorId ID of the detector for which the thresholding model is trained - * @return ID for the thresholding model - */ - public String getThresholdModelId(String detectorId) { - return String.format(Locale.ROOT, THRESHOLD_MODEL_ID_PATTERN, detectorId); - } -} diff --git a/src/main/java/org/opensearch/ad/ml/RcfResult.java b/src/main/java/org/opensearch/ad/ml/RcfResult.java index 91dd408d2..d8cde2b2f 100644 --- a/src/main/java/org/opensearch/ad/ml/RcfResult.java +++ b/src/main/java/org/opensearch/ad/ml/RcfResult.java @@ -39,6 +39,7 @@ public class RcfResult { private final int forestSize; private final double[] attribution; private final long totalUpdates; + private final double grade; /** * Constructor with all arguments. @@ -47,17 +48,16 @@ public class RcfResult { * @param confidence RCF confidence * @param forestSize number of RCF trees used for the score * @param attribution anomaly score attribution + * @param totalUpdates total updates + * @param grade Anomaly grade */ - public RcfResult(double score, double confidence, int forestSize, double[] attribution) { - this(score, confidence, forestSize, attribution, 0); - } - - public RcfResult(double score, double confidence, int forestSize, double[] attribution, long totalUpdates) { + public RcfResult(double score, double confidence, int forestSize, double[] attribution, long totalUpdates, double grade) { this.score = score; this.confidence = confidence; this.forestSize = forestSize; this.attribution = attribution; this.totalUpdates = totalUpdates; + this.grade = grade; } /** @@ -100,6 +100,10 @@ public long getTotalUpdates() { return totalUpdates; } + public double getGrade() { + return grade; + } + @Override public boolean equals(Object o) { if (this == o) @@ -111,11 +115,12 @@ public boolean equals(Object o) { && Objects.equals(this.confidence, that.confidence) && Objects.equals(this.forestSize, that.forestSize) && Arrays.equals(this.attribution, that.attribution) - && Objects.equals(this.totalUpdates, that.totalUpdates); + && Objects.equals(this.totalUpdates, that.totalUpdates) + && Objects.equals(this.grade, that.grade); } @Override public int hashCode() { - return Objects.hash(score, confidence, forestSize, attribution, totalUpdates); + return Objects.hash(score, confidence, forestSize, attribution, totalUpdates, grade); } } diff --git a/src/main/java/org/opensearch/ad/ml/SingleStreamModelIdMapper.java b/src/main/java/org/opensearch/ad/ml/SingleStreamModelIdMapper.java new file mode 100644 index 000000000..ac3ce899d --- /dev/null +++ b/src/main/java/org/opensearch/ad/ml/SingleStreamModelIdMapper.java @@ -0,0 +1,76 @@ +/* + * SPDX-License-Identifier: Apache-2.0 + * + * The OpenSearch Contributors require contributions made to + * this file be licensed under the Apache-2.0 license or a + * compatible open source license. + * + * Modifications Copyright OpenSearch Contributors. See + * GitHub history for details. + */ + +package org.opensearch.ad.ml; + +import java.util.Locale; +import java.util.regex.Matcher; +import java.util.regex.Pattern; + +/** + * Utilities to map between single-stream models and ids. We will have circular + * dependency between ModelManager and CheckpointDao if we put these functions inside + * ModelManager. + * + */ +public class SingleStreamModelIdMapper { + protected static final String DETECTOR_ID_PATTERN = "(.*)_model_.+"; + protected static final String RCF_MODEL_ID_PATTERN = "%s_model_rcf_%d"; + protected static final String THRESHOLD_MODEL_ID_PATTERN = "%s_model_threshold"; + + /** + * Returns the model ID for the RCF model partition. + * + * @param detectorId ID of the detector for which the RCF model is trained + * @param partitionNumber number of the partition + * @return ID for the RCF model partition + */ + public static String getRcfModelId(String detectorId, int partitionNumber) { + return String.format(Locale.ROOT, RCF_MODEL_ID_PATTERN, detectorId, partitionNumber); + } + + /** + * Returns the model ID for the thresholding model. + * + * @param detectorId ID of the detector for which the thresholding model is trained + * @return ID for the thresholding model + */ + public static String getThresholdModelId(String detectorId) { + return String.format(Locale.ROOT, THRESHOLD_MODEL_ID_PATTERN, detectorId); + } + + /** + * Gets the detector id from the model id. + * + * @param modelId id of a model + * @return id of the detector the model is for + * @throws IllegalArgumentException if model id is invalid + */ + public static String getDetectorIdForModelId(String modelId) { + Matcher matcher = Pattern.compile(DETECTOR_ID_PATTERN).matcher(modelId); + if (matcher.matches()) { + return matcher.group(1); + } else { + throw new IllegalArgumentException("Invalid model id " + modelId); + } + } + + /** + * Returns the model ID for the thresholding model according to the input + * rcf model id. + * @param rcfModelId RCF model id + * @return thresholding model Id + */ + public static String getThresholdModelIdFromRCFModelId(String rcfModelId) { + String detectorId = getDetectorIdForModelId(rcfModelId); + return getThresholdModelId(detectorId); + } +} diff --git a/src/main/java/org/opensearch/ad/ml/RCFMemoryAwareConcurrentHashmap.java b/src/main/java/org/opensearch/ad/ml/TRCFMemoryAwareConcurrentHashmap.java similarity index 69% rename from src/main/java/org/opensearch/ad/ml/RCFMemoryAwareConcurrentHashmap.java rename to src/main/java/org/opensearch/ad/ml/TRCFMemoryAwareConcurrentHashmap.java index e35ed9e88..4eee26372 100644 --- a/src/main/java/org/opensearch/ad/ml/RCFMemoryAwareConcurrentHashmap.java +++ b/src/main/java/org/opensearch/ad/ml/TRCFMemoryAwareConcurrentHashmap.java @@ -31,7 +31,7 @@ import org.opensearch.ad.MemoryTracker; import org.opensearch.ad.MemoryTracker.Origin; -import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** * A customized ConcurrentHashMap that can automatically consume and release memory. @@ -40,28 +40,28 @@ * * Note: this is mainly used for single-entity detectors. */ -public class RCFMemoryAwareConcurrentHashmap extends ConcurrentHashMap> { +public class TRCFMemoryAwareConcurrentHashmap extends ConcurrentHashMap> { private final MemoryTracker memoryTracker; - public RCFMemoryAwareConcurrentHashmap(MemoryTracker memoryTracker) { + public TRCFMemoryAwareConcurrentHashmap(MemoryTracker memoryTracker) { this.memoryTracker = memoryTracker; } @Override - public ModelState remove(Object key) { - ModelState deletedModelState = super.remove(key); + public ModelState remove(Object key) { + ModelState deletedModelState = super.remove(key); if (deletedModelState != null && deletedModelState.getModel() != null) { - long memoryToRelease = memoryTracker.estimateTotalModelSize(deletedModelState.getModel()); + long memoryToRelease = memoryTracker.estimateTRCFModelSize(deletedModelState.getModel()); memoryTracker.releaseMemory(memoryToRelease, true, Origin.SINGLE_ENTITY_DETECTOR); } return deletedModelState; } @Override - public ModelState put(K key, ModelState value) { - ModelState previousAssociatedState = super.put(key, value); + public ModelState put(K key, ModelState value) { + ModelState previousAssociatedState = super.put(key, value); if (value != null && value.getModel() != null) { - long memoryToConsume = memoryTracker.estimateTotalModelSize(value.getModel()); + long memoryToConsume = memoryTracker.estimateTRCFModelSize(value.getModel()); memoryTracker.consumeMemory(memoryToConsume, true, Origin.SINGLE_ENTITY_DETECTOR); } return previousAssociatedState; diff --git a/src/main/java/org/opensearch/ad/ml/ThresholdingModel.java b/src/main/java/org/opensearch/ad/ml/ThresholdingModel.java index 1205f02aa..252f610c2 100644 --- a/src/main/java/org/opensearch/ad/ml/ThresholdingModel.java +++ b/src/main/java/org/opensearch/ad/ml/ThresholdingModel.java @@ -26,6 +26,8 @@ package org.opensearch.ad.ml; +import java.util.List; + /** * A model for converting raw anomaly scores into anomaly grades. * @@ -72,4 +74,10 @@ public interface ThresholdingModel { * @return the model confidence */ double confidence(); + + /** + * Extract scores + * @return the extract scores + */ + List extractScores(); } diff --git a/src/main/java/org/opensearch/ad/ml/rcf/CombinedRcfResult.java b/src/main/java/org/opensearch/ad/ml/rcf/CombinedRcfResult.java deleted file mode 100644 index 4eb1e157f..000000000 --- a/src/main/java/org/opensearch/ad/ml/rcf/CombinedRcfResult.java +++ /dev/null @@ -1,97 +0,0 @@ -/* - * SPDX-License-Identifier: Apache-2.0 - * - * The OpenSearch Contributors require contributions made to - * this file be licensed under the Apache-2.0 license or a - * compatible open source license. - * - * Modifications Copyright OpenSearch Contributors. See - * GitHub history for details. - */ - -/* - * Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"). - * You may not use this file except in compliance with the License. - * A copy of the License is located at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * or in the "license" file accompanying this file. This file is distributed - * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either - * express or implied. See the License for the specific language governing - * permissions and limitations under the License. - */ - -package org.opensearch.ad.ml.rcf; - -import java.util.Arrays; -import java.util.Objects; - -/** - * Data object containing combined RCF result. - */ -public class CombinedRcfResult { - - private final double score; - private final double confidence; - private final double[] attribution; - - /** - * Constructor with all arguments. - * - * @param score combined RCF score - * @param confidence confidence of the score - * @param attribution score attribution normalized to 1 - */ - public CombinedRcfResult(double score, double confidence, double[] attribution) { - this.score = score; - this.confidence = confidence; - this.attribution = attribution; - } - - /** - * Returns combined RCF score - * - * @return combined RCF score - */ - public double getScore() { - return score; - } - - /** - * Return confidence of the score. - * - * @return confidence of the score - */ - public double getConfidence() { - return confidence; - } - - /** - * Return score attribution normalized to 1. - * - * @return score attribution normalized to 1 - */ - public double[] getAttribution() { - return attribution; - } - - @Override - public boolean equals(Object o) { - if (this == o) - return true; - if (o == null || getClass() != o.getClass()) - return false; - CombinedRcfResult that = (CombinedRcfResult) o; - return Objects.equals(this.score, that.score) - && Objects.equals(this.confidence, that.confidence) - && Arrays.equals(this.attribution, that.attribution); - } - - @Override - public int hashCode() { - return Objects.hash(score, confidence, attribution); - } -} diff --git a/src/main/java/org/opensearch/ad/ratelimit/CheckpointReadWorker.java b/src/main/java/org/opensearch/ad/ratelimit/CheckpointReadWorker.java index 7a8120788..86b966dcd 100644 --- a/src/main/java/org/opensearch/ad/ratelimit/CheckpointReadWorker.java +++ b/src/main/java/org/opensearch/ad/ratelimit/CheckpointReadWorker.java @@ -231,6 +231,8 @@ protected ActionListener getResponseListener(List modelId = origRequest.getModelId(); @@ -355,7 +357,7 @@ private ActionListener> onGetDetector( EntityModel entityModel = modelState.getModel(); ThresholdingResult result = null; - if (entityModel.getRcf() != null && entityModel.getThreshold() != null) { + if (entityModel.getTrcf().isPresent()) { result = modelManager.score(origRequest.getCurrentFeature(), modelId, modelState); } else { entityModel.addSample(origRequest.getCurrentFeature()); diff --git a/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteRequest.java b/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteRequest.java index a18c05d14..9c41e55be 100644 --- a/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteRequest.java +++ b/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteRequest.java @@ -11,17 +11,17 @@ package org.opensearch.ad.ratelimit; -import org.opensearch.action.index.IndexRequest; +import org.opensearch.action.update.UpdateRequest; public class CheckpointWriteRequest extends QueuedRequest { - private final IndexRequest indexRequest; + private final UpdateRequest updateRequest; - public CheckpointWriteRequest(long expirationEpochMs, String detectorId, RequestPriority priority, IndexRequest indexRequest) { + public CheckpointWriteRequest(long expirationEpochMs, String detectorId, RequestPriority priority, UpdateRequest updateRequest) { super(expirationEpochMs, detectorId, priority); - this.indexRequest = indexRequest; + this.updateRequest = updateRequest; } - public IndexRequest getIndexRequest() { - return indexRequest; + public UpdateRequest getUpdateRequest() { + return updateRequest; } } diff --git a/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteWorker.java b/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteWorker.java index 5a98ceae7..3f20a95cd 100644 --- a/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteWorker.java +++ b/src/main/java/org/opensearch/ad/ratelimit/CheckpointWriteWorker.java @@ -30,7 +30,7 @@ import org.opensearch.action.bulk.BulkItemResponse; import org.opensearch.action.bulk.BulkRequest; import org.opensearch.action.bulk.BulkResponse; -import org.opensearch.action.index.IndexRequest; +import org.opensearch.action.update.UpdateRequest; import org.opensearch.ad.NodeStateManager; import org.opensearch.ad.breaker.ADCircuitBreakerService; import org.opensearch.ad.ml.CheckpointDao; @@ -108,7 +108,7 @@ protected void executeBatchRequest(BulkRequest request, ActionListener toProcess) { final BulkRequest bulkRequest = new BulkRequest(); for (CheckpointWriteRequest request : toProcess) { - bulkRequest.add(request.getIndexRequest()); + bulkRequest.add(request.getUpdateRequest()); } return bulkRequest; } @@ -188,11 +188,15 @@ private ActionListener> onGetDetector( return; } + modelState.setLastCheckpointTime(clock.instant()); CheckpointWriteRequest request = new CheckpointWriteRequest( System.currentTimeMillis() + detector.getDetectorIntervalInMilliseconds(), detectorId, priority, - new IndexRequest(indexName).id(modelId).source(source) + // If the document does not already exist, the contents of the upsert element + // are inserted as a new document. + // If the document exists, update fields in the map + new UpdateRequest(indexName, modelId).docAsUpsert(true).doc(source) ); put(request); @@ -235,13 +239,17 @@ public void writeAll(List> modelStates, String detectorI continue; } + state.setLastCheckpointTime(clock.instant()); allRequests .add( new CheckpointWriteRequest( System.currentTimeMillis() + detector.getDetectorIntervalInMilliseconds(), detectorId, priority, - new IndexRequest(indexName).id(modelId).source(source) + // If the document does not already exist, the contents of the upsert element + // are inserted as a new document. + // If the document exists, update fields in the map + new UpdateRequest(indexName, modelId).docAsUpsert(true).doc(source) ) ); } diff --git a/src/main/java/org/opensearch/ad/ratelimit/EntityColdStartWorker.java b/src/main/java/org/opensearch/ad/ratelimit/EntityColdStartWorker.java index 67ef06e34..c1166d4d9 100644 --- a/src/main/java/org/opensearch/ad/ratelimit/EntityColdStartWorker.java +++ b/src/main/java/org/opensearch/ad/ratelimit/EntityColdStartWorker.java @@ -106,7 +106,7 @@ protected void executeRequest(EntityRequest coldStartRequest, ActionListener modelState = new ModelState<>( - new EntityModel(coldStartRequest.getEntity(), new ArrayDeque<>(), null, null), + new EntityModel(coldStartRequest.getEntity(), new ArrayDeque<>(), null), modelId.get(), detectorId, ModelType.ENTITY.getName(), diff --git a/src/main/java/org/opensearch/ad/settings/AnomalyDetectorSettings.java b/src/main/java/org/opensearch/ad/settings/AnomalyDetectorSettings.java index 16c839434..d056b5764 100644 --- a/src/main/java/org/opensearch/ad/settings/AnomalyDetectorSettings.java +++ b/src/main/java/org/opensearch/ad/settings/AnomalyDetectorSettings.java @@ -411,8 +411,6 @@ private AnomalyDetectorSettings() {} Setting.Property.Dynamic ); - public static int THRESHOLD_MODEL_TRAINING_SIZE = 128; - // Maximum number of old AD tasks we can keep. public static int MAX_OLD_AD_TASK_DOCS = 1000; public static final Setting MAX_OLD_AD_TASK_DOCS_PER_DETECTOR = Setting diff --git a/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java b/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java index 6c2483a73..39bf81c71 100644 --- a/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java +++ b/src/main/java/org/opensearch/ad/task/ADBatchTaskCache.java @@ -29,7 +29,6 @@ import static org.opensearch.ad.settings.AnomalyDetectorSettings.NUM_MIN_SAMPLES; import static org.opensearch.ad.settings.AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE; import static org.opensearch.ad.settings.AnomalyDetectorSettings.NUM_TREES; -import static org.opensearch.ad.settings.AnomalyDetectorSettings.THRESHOLD_MODEL_TRAINING_SIZE; import static org.opensearch.ad.settings.AnomalyDetectorSettings.TIME_DECAY; import java.util.ArrayDeque; @@ -40,15 +39,13 @@ import java.util.concurrent.atomic.AtomicInteger; import java.util.concurrent.atomic.AtomicLong; -import org.opensearch.ad.ml.HybridThresholdingModel; -import org.opensearch.ad.ml.ThresholdingModel; import org.opensearch.ad.model.ADTask; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.Entity; import org.opensearch.ad.settings.AnomalyDetectorSettings; -import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** * AD batch task cache which will mainly hold these for one task: @@ -62,12 +59,10 @@ public class ADBatchTaskCache { private final String detectorId; private final String taskId; private final String detectorTaskId; - private RandomCutForest rcfModel; - private ThresholdingModel thresholdModel; + private ThresholdedRandomCutForest rcfModel; private boolean thresholdModelTrained; private Deque>> shingle; private AtomicInteger thresholdModelTrainingDataSize = new AtomicInteger(0); - private double[] thresholdModelTrainingData; private AtomicBoolean cancelled = new AtomicBoolean(false); private AtomicLong cacheMemorySize = new AtomicLong(0); private String cancelReason; @@ -86,7 +81,7 @@ protected ADBatchTaskCache(ADTask adTask) { this.shingle = new ArrayDeque<>(shingleSize); int dimensions = detector.getShingleSize() * detector.getEnabledFeatureIds().size(); - rcfModel = RandomCutForest + rcfModel = ThresholdedRandomCutForest .builder() .dimensions(dimensions) .numberOfTrees(numberOfTrees) @@ -97,23 +92,10 @@ protected ADBatchTaskCache(ADTask adTask) { .compact(true) .precision(Precision.FLOAT_32) .boundingBoxCacheFraction(AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO) - // same with dimension for opportunistic memory saving - // Usually, we use it as shingleSize(dimension). When a new point comes in, we will - // look at the point store if there is any overlapping. Say the previously-stored - // vector is x1, x2, x3, x4, now we add x3, x4, x5, x6. RCF will recognize - // overlapping x3, x4, and only store x5, x6. .shingleSize(shingleSize) + .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE) .build(); - this.thresholdModel = new HybridThresholdingModel( - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES - ); - this.thresholdModelTrainingData = new double[THRESHOLD_MODEL_TRAINING_SIZE]; this.thresholdModelTrained = false; } @@ -129,7 +111,7 @@ protected String getDetectorTaskId() { return detectorTaskId; } - protected RandomCutForest getRcfModel() { + protected ThresholdedRandomCutForest getTRcfModel() { return rcfModel; } @@ -137,10 +119,6 @@ protected Deque>> getShingle() { return shingle; } - protected ThresholdingModel getThresholdModel() { - return thresholdModel; - } - protected void setThresholdModelTrained(boolean thresholdModelTrained) { this.thresholdModelTrained = thresholdModelTrained; } @@ -149,15 +127,6 @@ protected boolean isThresholdModelTrained() { return thresholdModelTrained; } - protected double[] getThresholdModelTrainingData() { - return thresholdModelTrainingData; - } - - protected void clearTrainingData() { - this.thresholdModelTrainingData = null; - this.thresholdModelTrainingDataSize.set(0); - } - public AtomicInteger getThresholdModelTrainingDataSize() { return thresholdModelTrainingDataSize; } diff --git a/src/main/java/org/opensearch/ad/task/ADBatchTaskRunner.java b/src/main/java/org/opensearch/ad/task/ADBatchTaskRunner.java index b8e6cc289..7a35dac04 100644 --- a/src/main/java/org/opensearch/ad/task/ADBatchTaskRunner.java +++ b/src/main/java/org/opensearch/ad/task/ADBatchTaskRunner.java @@ -44,7 +44,6 @@ import static org.opensearch.ad.settings.AnomalyDetectorSettings.MAX_TOP_ENTITIES_FOR_HISTORICAL_ANALYSIS; import static org.opensearch.ad.settings.AnomalyDetectorSettings.MAX_TOP_ENTITIES_LIMIT_FOR_HISTORICAL_ANALYSIS; import static org.opensearch.ad.settings.AnomalyDetectorSettings.NUM_MIN_SAMPLES; -import static org.opensearch.ad.settings.AnomalyDetectorSettings.THRESHOLD_MODEL_TRAINING_SIZE; import static org.opensearch.ad.stats.InternalStatNames.JVM_HEAP_USAGE; import static org.opensearch.ad.stats.StatNames.AD_EXECUTING_BATCH_TASK_COUNT; import static org.opensearch.ad.util.ParseUtils.isNullOrEmpty; @@ -81,7 +80,6 @@ import org.opensearch.ad.feature.SinglePointFeatures; import org.opensearch.ad.indices.ADIndex; import org.opensearch.ad.indices.AnomalyDetectionIndices; -import org.opensearch.ad.ml.ThresholdingModel; import org.opensearch.ad.model.ADTask; import org.opensearch.ad.model.ADTaskState; import org.opensearch.ad.model.ADTaskType; @@ -125,6 +123,8 @@ import org.opensearch.transport.TransportService; import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.google.common.collect.ImmutableList; import com.google.common.collect.ImmutableMap; import com.google.common.collect.ImmutableSet; @@ -991,7 +991,7 @@ private void getDateRangeOfSourceData(ADTask adTask, BiConsumer cons dataStartTime = dataStartTime - dataStartTime % interval; dataEndTime = dataEndTime - dataEndTime % interval; logger.debug("adjusted date range: start: {}, end: {}, taskId: {}", dataStartTime, dataEndTime, taskId); - if ((dataEndTime - dataStartTime) < THRESHOLD_MODEL_TRAINING_SIZE * interval) { + if ((dataEndTime - dataStartTime) < NUM_MIN_SAMPLES * interval) { internalListener.onFailure(new AnomalyDetectionException("There is no enough data to train model").countedInStats(false)); return; } @@ -1061,8 +1061,7 @@ private void detectAnomaly( ActionListener internalListener ) { String taskId = adTask.getTaskId(); - RandomCutForest rcf = adTaskCacheManager.getRcfModel(taskId); - ThresholdingModel threshold = adTaskCacheManager.getThresholdModel(taskId); + ThresholdedRandomCutForest trcf = adTaskCacheManager.getTRcfModel(taskId); Deque>> shingle = adTaskCacheManager.getShingle(taskId); List anomalyResults = new ArrayList<>(); @@ -1101,26 +1100,14 @@ private void detectAnomaly( anomalyResults.add(anomalyResult); } else { double[] point = feature.getProcessedFeatures().get(); - double score = rcf.getAnomalyScore(point); - rcf.update(point); - double grade = 0d; - double confidence = 0d; - if (!adTaskCacheManager.isThresholdModelTrained(taskId)) { - if (adTaskCacheManager.getThresholdModelTrainingDataSize(taskId) < THRESHOLD_MODEL_TRAINING_SIZE) { - if (score > 0) { - adTaskCacheManager.addThresholdModelTrainingData(taskId, score); - } - } else { - logger.debug("training threshold model"); - threshold.train(adTaskCacheManager.getThresholdModelTrainingData(taskId)); - adTaskCacheManager.setThresholdModelTrained(taskId, true); - } - } else { - grade = threshold.grade(score); - confidence = threshold.confidence(); - if (score > 0) { - threshold.update(score); - } + // 0 is placeholder for timestamp. In the future, we will add + // data time stamp there. + AnomalyDescriptor descriptor = trcf.process(point, 0); + double score = descriptor.getRcfScore(); + double grade = descriptor.getAnomalyGrade(); + double confidence = descriptor.getDataConfidence(); + if (!adTaskCacheManager.isThresholdModelTrained(taskId) && score > 0) { + adTaskCacheManager.setThresholdModelTrained(taskId, true); } AnomalyResult anomalyResult = new AnomalyResult( adTask.getDetectorId(), @@ -1271,7 +1258,7 @@ private void updateDetectorLevelTaskState(String detectorId, String detectorTask } private float calculateInitProgress(String taskId) { - RandomCutForest rcf = adTaskCacheManager.getRcfModel(taskId); + RandomCutForest rcf = adTaskCacheManager.getTRcfModel(taskId).getForest(); if (rcf == null) { return 0.0f; } diff --git a/src/main/java/org/opensearch/ad/task/ADTaskCacheManager.java b/src/main/java/org/opensearch/ad/task/ADTaskCacheManager.java index ac4b91f0a..a63eb2ca1 100644 --- a/src/main/java/org/opensearch/ad/task/ADTaskCacheManager.java +++ b/src/main/java/org/opensearch/ad/task/ADTaskCacheManager.java @@ -32,7 +32,6 @@ import static org.opensearch.ad.settings.AnomalyDetectorSettings.MAX_BATCH_TASK_PER_NODE; import static org.opensearch.ad.settings.AnomalyDetectorSettings.MAX_CACHED_DELETED_TASKS; import static org.opensearch.ad.settings.AnomalyDetectorSettings.NUM_TREES; -import static org.opensearch.ad.settings.AnomalyDetectorSettings.THRESHOLD_MODEL_TRAINING_SIZE; import static org.opensearch.ad.util.ParseUtils.isNullOrEmpty; import java.time.Instant; @@ -47,7 +46,6 @@ import java.util.concurrent.ConcurrentHashMap; import java.util.concurrent.ConcurrentLinkedQueue; import java.util.concurrent.Semaphore; -import java.util.concurrent.atomic.AtomicInteger; import java.util.stream.Collectors; import org.apache.logging.log4j.LogManager; @@ -56,7 +54,6 @@ import org.opensearch.ad.MemoryTracker; import org.opensearch.ad.common.exception.DuplicateTaskException; import org.opensearch.ad.common.exception.LimitExceededException; -import org.opensearch.ad.ml.ThresholdingModel; import org.opensearch.ad.model.ADTask; import org.opensearch.ad.model.ADTaskState; import org.opensearch.ad.model.ADTaskType; @@ -68,6 +65,7 @@ import org.opensearch.transport.TransportService; import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.google.common.collect.ImmutableList; public class ADTaskCacheManager { @@ -257,34 +255,8 @@ public void checkRunningTaskLimit() { * @param taskId AD task id * @return RCF model */ - public RandomCutForest getRcfModel(String taskId) { - return getBatchTaskCache(taskId).getRcfModel(); - } - - public long getRcfModelTotalUpdates(String taskId) { - return getRcfModel(taskId).getTotalUpdates(); - } - - /** - * Get task threshold model. - * If task doesn't exist in cache, will throw {@link java.lang.IllegalArgumentException}. - * - * @param taskId AD task id - * @return threshold model - */ - public ThresholdingModel getThresholdModel(String taskId) { - return getBatchTaskCache(taskId).getThresholdModel(); - } - - /** - * Get threshold model training data. - * If task doesn't exist in cache, will throw {@link java.lang.IllegalArgumentException}. - * - * @param taskId AD task id - * @return threshold model training data - */ - public double[] getThresholdModelTrainingData(String taskId) { - return getBatchTaskCache(taskId).getThresholdModelTrainingData(); + public ThresholdedRandomCutForest getTRcfModel(String taskId) { + return getBatchTaskCache(taskId).getTRcfModel(); } /** @@ -297,22 +269,6 @@ public int getThresholdModelTrainingDataSize(String taskId) { return getBatchTaskCache(taskId).getThresholdModelTrainingDataSize().get(); } - /** - * Add threshold model training data. - * - * @param taskId task id - * @param data training data - * @return latest threshold model training data size after adding new data - */ - public int addThresholdModelTrainingData(String taskId, double... data) { - ADBatchTaskCache taskCache = getBatchTaskCache(taskId); - double[] thresholdModelTrainingData = taskCache.getThresholdModelTrainingData(); - AtomicInteger size = taskCache.getThresholdModelTrainingDataSize(); - int dataPointsAdded = Math.min(data.length, THRESHOLD_MODEL_TRAINING_SIZE - size.get()); - System.arraycopy(data, 0, thresholdModelTrainingData, size.get(), dataPointsAdded); - return size.addAndGet(dataPointsAdded); - } - /** * Threshold model trained or not. * If task doesn't exist in cache, will throw {@link java.lang.IllegalArgumentException}. @@ -333,13 +289,6 @@ public boolean isThresholdModelTrained(String taskId) { protected void setThresholdModelTrained(String taskId, boolean trained) { ADBatchTaskCache taskCache = getBatchTaskCache(taskId); taskCache.setThresholdModelTrained(trained); - if (trained) { - int size = taskCache.getThresholdModelTrainingDataSize().get(); - long cacheSize = trainingDataMemorySize(size); - taskCache.clearTrainingData(); - taskCache.getCacheMemorySize().getAndAdd(-cacheSize); - memoryTracker.releaseMemory(cacheSize, true, HISTORICAL_SINGLE_ENTITY_DETECTOR); - } } /** @@ -416,11 +365,15 @@ private List getBatchTaskCacheByDetectorId(String detectorId) */ private long calculateADTaskCacheSize(ADTask adTask) { AnomalyDetector detector = adTask.getDetector(); - return memoryTracker.estimateTotalModelSize(detector, NUM_TREES, AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO) - + trainingDataMemorySize(THRESHOLD_MODEL_TRAINING_SIZE) + shingleMemorySize( - detector.getShingleSize(), - detector.getEnabledFeatureIds().size() - ); + int dimension = detector.getEnabledFeatureIds().size() * detector.getShingleSize(); + return memoryTracker + .estimateTRCFModelSize( + dimension, + NUM_TREES, + AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO, + detector.getShingleSize().intValue(), + false + ) + shingleMemorySize(detector.getShingleSize(), detector.getEnabledFeatureIds().size()); } /** @@ -431,9 +384,12 @@ private long calculateADTaskCacheSize(ADTask adTask) { */ public long getModelSize(String taskId) { ADBatchTaskCache batchTaskCache = getBatchTaskCache(taskId); - int dimensions = batchTaskCache.getRcfModel().getDimensions(); - int numberOfTrees = batchTaskCache.getRcfModel().getNumberOfTrees(); - return memoryTracker.estimateTotalModelSize(dimensions, numberOfTrees, AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO); + ThresholdedRandomCutForest tRCF = batchTaskCache.getTRcfModel(); + RandomCutForest rcfForest = tRCF.getForest(); + int dimensions = rcfForest.getDimensions(); + int numberOfTrees = rcfForest.getNumberOfTrees(); + return memoryTracker + .estimateTRCFModelSize(dimensions, numberOfTrees, AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO, 1, false); } /** diff --git a/src/main/java/org/opensearch/ad/task/ADTaskManager.java b/src/main/java/org/opensearch/ad/task/ADTaskManager.java index e3b94901c..6a33544fa 100644 --- a/src/main/java/org/opensearch/ad/task/ADTaskManager.java +++ b/src/main/java/org/opensearch/ad/task/ADTaskManager.java @@ -2647,7 +2647,7 @@ public ADTaskProfile getLocalADTaskProfilesByDetectorId(String detectorId) { tasksOfDetector.forEach(taskId -> { ADEntityTaskProfile entityTaskProfile = new ADEntityTaskProfile( adTaskCacheManager.getShingle(taskId).size(), - adTaskCacheManager.getRcfModelTotalUpdates(taskId), + adTaskCacheManager.getTRcfModel(taskId).getForest().getTotalUpdates(), adTaskCacheManager.isThresholdModelTrained(taskId), adTaskCacheManager.getThresholdModelTrainingDataSize(taskId), adTaskCacheManager.getModelSize(taskId), @@ -2673,7 +2673,7 @@ public ADTaskProfile getLocalADTaskProfilesByDetectorId(String detectorId) { detectorTaskProfile = new ADTaskProfile( adTaskCacheManager.getDetectorTaskId(detectorId), adTaskCacheManager.getShingle(taskId).size(), - adTaskCacheManager.getRcfModel(taskId).getTotalUpdates(), + adTaskCacheManager.getTRcfModel(taskId).getForest().getTotalUpdates(), adTaskCacheManager.isThresholdModelTrained(taskId), adTaskCacheManager.getThresholdModelTrainingDataSize(taskId), adTaskCacheManager.getModelSize(taskId), diff --git a/src/main/java/org/opensearch/ad/transport/AnomalyResultTransportAction.java b/src/main/java/org/opensearch/ad/transport/AnomalyResultTransportAction.java index 0fcfd614e..32682c4eb 100644 --- a/src/main/java/org/opensearch/ad/transport/AnomalyResultTransportAction.java +++ b/src/main/java/org/opensearch/ad/transport/AnomalyResultTransportAction.java @@ -32,7 +32,6 @@ import java.net.ConnectException; import java.util.ArrayList; -import java.util.HashMap; import java.util.HashSet; import java.util.Iterator; import java.util.List; @@ -45,7 +44,6 @@ import java.util.concurrent.atomic.AtomicReference; import java.util.stream.Collectors; -import org.apache.commons.lang3.tuple.Pair; import org.apache.logging.log4j.LogManager; import org.apache.logging.log4j.Logger; import org.apache.logging.log4j.message.ParameterizedMessage; @@ -79,9 +77,7 @@ import org.opensearch.ad.feature.FeatureManager; import org.opensearch.ad.feature.SinglePointFeatures; import org.opensearch.ad.ml.ModelManager; -import org.opensearch.ad.ml.ModelPartitioner; -import org.opensearch.ad.ml.RcfResult; -import org.opensearch.ad.ml.rcf.CombinedRcfResult; +import org.opensearch.ad.ml.SingleStreamModelIdMapper; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.Entity; import org.opensearch.ad.model.FeatureData; @@ -136,7 +132,6 @@ public class AnomalyResultTransportAction extends HandledTransportAction asThresholdNode = hashRing.getOwningNodeWithSameLocalAdVersionForRealtimeAD(thresholdModelID); - if (!asThresholdNode.isPresent()) { - listener.onFailure(new InternalFailure(adID, "Threshold model node is not available.")); + // We are going to use only 1 model partition for a single stream detector. + // That's why we use 0 here. + String rcfModelID = SingleStreamModelIdMapper.getRcfModelId(adID, 0); + Optional asRCFNode = hashRing.getOwningNodeWithSameLocalAdVersionForRealtimeAD(rcfModelID); + if (!asRCFNode.isPresent()) { + listener.onFailure(new InternalFailure(adID, "RCF model node is not available.")); return; } - DiscoveryNode thresholdNode = asThresholdNode.get(); + DiscoveryNode rcfNode = asRCFNode.get(); - if (!shouldStart(listener, adID, anomalyDetector, thresholdNode.getId(), thresholdModelID)) { + if (!shouldStart(listener, adID, anomalyDetector, rcfNode.getId(), rcfModelID)) { return; } @@ -565,15 +558,7 @@ private void executeAnomalyDetection( anomalyDetector, dataStartTime, dataEndTime, - onFeatureResponseForSingleEntityDetector( - adID, - anomalyDetector, - listener, - thresholdModelID, - thresholdNode, - dataStartTime, - dataEndTime - ) + onFeatureResponseForSingleEntityDetector(adID, anomalyDetector, listener, rcfModelID, rcfNode, dataStartTime, dataEndTime) ); } @@ -582,8 +567,8 @@ private ActionListener onFeatureResponseForSingleEntityDete String adID, AnomalyDetector detector, ActionListener listener, - String thresholdModelID, - DiscoveryNode thresholdNode, + String rcfModelId, + DiscoveryNode rcfNode, long dataStartTime, long dataEndTime ) { @@ -603,8 +588,8 @@ private ActionListener onFeatureResponseForSingleEntityDete if (!featureOptional.getUnprocessedFeatures().isPresent()) { // Feature not available is common when we have data holes. Respond empty response - // so that alerting will not print stack trace to avoid bloating our logs. - LOG.info("No data in current detection window between {} and {} for {}", dataStartTime, dataEndTime, adID); + // and don't log to avoid bloating our logs. + LOG.debug("No data in current detection window between {} and {} for {}", dataStartTime, dataEndTime, adID); listener .onResponse( new AnomalyResultResponse( @@ -618,7 +603,7 @@ private ActionListener onFeatureResponseForSingleEntityDete ) ); } else { - LOG.info("Return at least current feature value between {} and {} for {}", dataStartTime, dataEndTime, adID); + LOG.debug("Return at least current feature value between {} and {} for {}", dataStartTime, dataEndTime, adID); listener .onResponse( new AnomalyResultResponse( @@ -635,71 +620,28 @@ private ActionListener onFeatureResponseForSingleEntityDete return; } - // Can throw LimitExceededException when a single partition is more than X% of heap memory. - // Compute this number once and the value won't change unless the coordinating AD node for an - // detector changes or the cluster size changes. - int rcfPartitionNum = stateManager.getPartitionNumber(adID, detector); - - List rcfResults = new ArrayList<>(); - final AtomicReference failure = new AtomicReference(); - final AtomicInteger responseCount = new AtomicInteger(); + LOG.info("Sending RCF request to {} for model {}", rcfNode.getId(), rcfModelId); - Map> modelIdToNodeId = new HashMap<>(); - - for (int i = 0; i < rcfPartitionNum; i++) { - String rcfModelID = modelPartitioner.getRcfModelId(adID, i); - - Optional rcfNode = hashRing.getOwningNodeWithSameLocalAdVersionForRealtimeAD(rcfModelID.toString()); - if (!rcfNode.isPresent()) { - continue; - } - - DiscoveryNode node = rcfNode.get(); - String rcfNodeId = node.getId(); - if (stateManager.isMuted(rcfNodeId, adID)) { - LOG.info(String.format(Locale.ROOT, NODE_UNRESPONSIVE_ERR_MSG + " %s for model %s", rcfNodeId, rcfModelID)); - continue; - } - modelIdToNodeId.put(rcfModelID, Pair.of(rcfNodeId, node)); - } + RCFActionListener rcfListener = new RCFActionListener( + rcfModelId, + failure, + rcfNode.getId(), + detector, + listener, + featureInResponse, + adID + ); - int responsesToCollect = modelIdToNodeId.size(); - for (Map.Entry> entry : modelIdToNodeId.entrySet()) { - - String rcfModelId = entry.getKey(); - Pair rcfNodePair = entry.getValue(); - String rcfNodeId = rcfNodePair.getLeft(); - DiscoveryNode rcfNode = rcfNodePair.getRight(); - - LOG.info("Sending RCF request to {} for model {}", rcfNodeId, rcfModelId); - - RCFActionListener rcfListener = new RCFActionListener( - rcfResults, - rcfModelId.toString(), - failure, - rcfNodeId, - detector, - listener, - thresholdModelID, - thresholdNode, - featureInResponse, - responsesToCollect, - responseCount, - adID, - detector.getEnabledFeatureIds().size() + transportService + .sendRequest( + rcfNode, + RCFResultAction.NAME, + new RCFResultRequest(adID, rcfModelId, featureOptional.getProcessedFeatures().get()), + option, + new ActionListenerResponseHandler<>(rcfListener, RCFResultResponse::new) ); - - transportService - .sendRequest( - rcfNode, - RCFResultAction.NAME, - new RCFResultRequest(adID, rcfModelId, featureOptional.getProcessedFeatures().get()), - option, - new ActionListenerResponseHandler<>(rcfListener, RCFResultResponse::new) - ); - } }, exception -> { handleQueryFailure(exception, listener, adID); }); } @@ -772,9 +714,9 @@ private Exception coldStartIfNoModel(AtomicReference failure, Anomaly return null; } - // rethrow exceptions like LimitExceededException to caller + // return exceptions like LimitExceededException to caller if (!(exp instanceof ResourceNotFoundException)) { - throw exp; + return exp; } // fetch previous cold start exception @@ -841,14 +783,6 @@ private void findException(Throwable cause, String adID, AtomicReference rcfResults, int numFeatures) { - List rcfResultLib = new ArrayList<>(); - for (RCFResultResponse result : rcfResults) { - rcfResultLib.add(new RcfResult(result.getRCFScore(), result.getConfidence(), result.getForestSize(), result.getAttribution())); - } - return modelManager.combineRcfResults(rcfResultLib, numFeatures); - } - void handleExecuteException(Exception ex, ActionListener listener, String adID) { if (ex instanceof ClientException) { listener.onFailure(ex); @@ -873,48 +807,30 @@ private boolean invalidQuery(SearchPhaseExecutionException ex) { // For single entity detector class RCFActionListener implements ActionListener { - private List rcfResults; private String modelID; private AtomicReference failure; private String rcfNodeID; private AnomalyDetector detector; private ActionListener listener; - private String thresholdModelID; - private DiscoveryNode thresholdNode; private List featureInResponse; - private int nodeCount; - private final AtomicInteger responseCount; private final String adID; - private int numEnabledFeatures; RCFActionListener( - List rcfResults, String modelID, AtomicReference failure, String rcfNodeID, AnomalyDetector detector, ActionListener listener, - String thresholdModelID, - DiscoveryNode thresholdNode, List features, - int nodeCount, - AtomicInteger responseCount, - String adID, - int numEnabledFeatures + String adID ) { - this.rcfResults = rcfResults; this.modelID = modelID; + this.failure = failure; this.rcfNodeID = rcfNodeID; this.detector = detector; this.listener = listener; - this.thresholdNode = thresholdNode; - this.thresholdModelID = thresholdModelID; this.featureInResponse = features; - this.failure = failure; - this.nodeCount = nodeCount; - this.responseCount = responseCount; this.adID = adID; - this.numEnabledFeatures = numEnabledFeatures; } @Override @@ -922,176 +838,39 @@ public void onResponse(RCFResultResponse response) { try { stateManager.resetBackpressureCounter(rcfNodeID, adID); if (response != null) { - rcfResults.add(response); + listener + .onResponse( + new AnomalyResultResponse( + response.getAnomalyGrade(), + response.getConfidence(), + response.getRCFScore(), + featureInResponse, + response.getTotalUpdates(), + detector.getDetectorIntervalInMinutes() + ) + ); } else { LOG.warn(NULL_RESPONSE + " {} for {}", modelID, rcfNodeID); - } - } catch (Exception ex) { - LOG.error("Unexpected exception: {} for {}", ex, adID); - } finally { - if (nodeCount == responseCount.incrementAndGet()) { - handleRCFResults(numEnabledFeatures); - } - } - } - - @Override - public void onFailure(Exception e) { - try { - handlePredictionFailure(e, adID, rcfNodeID, failure); - } catch (Exception ex) { - LOG.error("Unexpected exception: {} for {}", ex, adID); - } finally { - if (nodeCount == responseCount.incrementAndGet()) { - handleRCFResults(numEnabledFeatures); - } - } - } - - private void handleRCFResults(int numFeatures) { - try { - Exception exception = coldStartIfNoModel(failure, detector); - if (exception != null) { - listener.onFailure(exception); - return; - } - - if (rcfResults.isEmpty()) { listener.onFailure(new InternalFailure(adID, NO_MODEL_ERR_MSG)); - return; } - - CombinedRcfResult combinedResult = getCombinedResult(rcfResults, numFeatures); - double combinedScore = combinedResult.getScore(); - - final AtomicReference anomalyResultResponse = new AtomicReference<>(); - - String thresholdNodeId = thresholdNode.getId(); - long rcfTotalUpdates = rcfResults.get(0).getTotalUpdates(); - LOG.info("Sending threshold request to {} for model {}", thresholdNodeId, thresholdModelID); - ThresholdActionListener thresholdListener = new ThresholdActionListener( - anomalyResultResponse, - featureInResponse, - thresholdNodeId, - detector, - combinedResult, - listener, - adID, - rcfTotalUpdates, - detector.getDetectorIntervalInMinutes() - ); - transportService - .sendRequest( - thresholdNode, - ThresholdResultAction.NAME, - new ThresholdResultRequest(adID, thresholdModelID, combinedScore), - option, - new ActionListenerResponseHandler<>(thresholdListener, ThresholdResultResponse::new) - ); } catch (Exception ex) { + LOG.error(new ParameterizedMessage("Unexpected exception for [{}]", adID), ex); handleExecuteException(ex, listener, adID); } } - } - - // For single entity detector - class ThresholdActionListener implements ActionListener { - private AtomicReference anomalyResultResponse; - private List features; - private AtomicReference failure; - private String thresholdNodeID; - private ActionListener listener; - private AnomalyDetector detector; - private CombinedRcfResult combinedResult; - private String adID; - private long rcfTotalUpdates; - private long detectorIntervalInMinutes; - - ThresholdActionListener( - AtomicReference anomalyResultResponse, - List features, - String thresholdNodeID, - AnomalyDetector detector, - CombinedRcfResult combinedResult, - ActionListener listener, - String adID, - long rcfTotalUpdates, - long detectorIntervalInMinutes - ) { - this.anomalyResultResponse = anomalyResultResponse; - this.features = features; - this.thresholdNodeID = thresholdNodeID; - this.detector = detector; - this.combinedResult = combinedResult; - this.failure = new AtomicReference(); - this.listener = listener; - this.adID = adID; - this.rcfTotalUpdates = rcfTotalUpdates; - this.detectorIntervalInMinutes = detectorIntervalInMinutes; - } - - @Override - public void onResponse(ThresholdResultResponse response) { - try { - anomalyResultResponse - .set( - new AnomalyResultResponse( - response.getAnomalyGrade(), - response.getConfidence(), - Double.NaN, - features, - rcfTotalUpdates, - detectorIntervalInMinutes - ) - ); - // A successful responses means the node is responsive and working. - // Thus, we can reset the backpressure router to stop muting a node. - stateManager.resetBackpressureCounter(thresholdNodeID, adID); - - } catch (Exception ex) { - LOG.error("Unexpected exception: {} for {}", ex, adID); - } finally { - handleThresholdResult(); - } - } @Override public void onFailure(Exception e) { try { - handlePredictionFailure(e, adID, thresholdNodeID, failure); - } catch (Exception ex) { - LOG.error("Unexpected exception: {} for {}", ex, adID); - } finally { - handleThresholdResult(); - } - } - - private void handleThresholdResult() { - try { + handlePredictionFailure(e, adID, rcfNodeID, failure); Exception exception = coldStartIfNoModel(failure, detector); if (exception != null) { listener.onFailure(exception); - return; - } - - if (anomalyResultResponse.get() != null) { - AnomalyResultResponse response = anomalyResultResponse.get(); - double confidence = response.getConfidence() * combinedResult.getConfidence(); - response = new AnomalyResultResponse( - response.getAnomalyGrade(), - confidence, - combinedResult.getScore(), - response.getFeatures(), - rcfTotalUpdates, - detectorIntervalInMinutes - ); - listener.onResponse(response); - } else if (failure.get() != null) { - listener.onFailure(failure.get()); } else { listener.onFailure(new InternalFailure(adID, "Node connection problem or unexpected exception")); } } catch (Exception ex) { + LOG.error(new ParameterizedMessage("Unexpected exception for [{}]", adID), ex); handleExecuteException(ex, listener, adID); } } @@ -1184,16 +963,16 @@ private boolean checkIndicesBlocked(ClusterState state, ClusterBlockLevel level, * @param listener listener to respond back to AnomalyResultRequest. * @param adID detector ID * @param detector detector instance corresponds to adID - * @param thresholdNodeId the threshold model hosting node ID for adID - * @param thresholdModelID the threshold model ID for adID + * @param rcfNodeId the rcf model hosting node ID for adID + * @param rcfModelID the rcf model ID for adID * @return if we can start anomaly prediction. */ private boolean shouldStart( ActionListener listener, String adID, AnomalyDetector detector, - String thresholdNodeId, - String thresholdModelID + String rcfNodeId, + String rcfModelID ) { ClusterState state = clusterService.state(); if (checkGlobalBlock(state)) { @@ -1201,18 +980,12 @@ private boolean shouldStart( return false; } - if (stateManager.isMuted(thresholdNodeId, adID)) { + if (stateManager.isMuted(rcfNodeId, adID)) { listener .onFailure( new InternalFailure( adID, - String - .format( - Locale.ROOT, - NODE_UNRESPONSIVE_ERR_MSG + " %s for threshold model %s", - thresholdNodeId, - thresholdModelID - ) + String.format(Locale.ROOT, NODE_UNRESPONSIVE_ERR_MSG + " %s for rcf model %s", rcfNodeId, rcfModelID) ) ); return false; @@ -1380,7 +1153,7 @@ public void onResponse(AcknowledgedResponse response) { LOG.error("Unexpected exception: {} for {}", ex, adID); handleException(ex); } finally { - if (nodeCount == responseCount.incrementAndGet() && pageIterator.hasNext()) { + if (nodeCount == responseCount.incrementAndGet() && pageIterator != null && pageIterator.hasNext()) { pageIterator.next(pageListener); } } @@ -1398,7 +1171,7 @@ public void onFailure(Exception e) { LOG.error("Unexpected exception: {} for {}", ex, adID); handleException(ex); } finally { - if (nodeCount == responseCount.incrementAndGet() && pageIterator.hasNext()) { + if (nodeCount == responseCount.incrementAndGet() && pageIterator != null && pageIterator.hasNext()) { pageIterator.next(pageListener); } } diff --git a/src/main/java/org/opensearch/ad/transport/RCFPollingTransportAction.java b/src/main/java/org/opensearch/ad/transport/RCFPollingTransportAction.java index 35973d66c..a5b668f57 100644 --- a/src/main/java/org/opensearch/ad/transport/RCFPollingTransportAction.java +++ b/src/main/java/org/opensearch/ad/transport/RCFPollingTransportAction.java @@ -38,7 +38,7 @@ import org.opensearch.ad.cluster.HashRing; import org.opensearch.ad.common.exception.AnomalyDetectionException; import org.opensearch.ad.ml.ModelManager; -import org.opensearch.ad.ml.ModelPartitioner; +import org.opensearch.ad.ml.SingleStreamModelIdMapper; import org.opensearch.ad.settings.AnomalyDetectorSettings; import org.opensearch.cluster.node.DiscoveryNode; import org.opensearch.cluster.service.ClusterService; @@ -64,7 +64,6 @@ public class RCFPollingTransportAction extends HandledTransportAction rcfNode = hashRing.getOwningNodeWithSameLocalAdVersionForRealtimeAD(rcfModelID); if (!rcfNode.isPresent()) { diff --git a/src/main/java/org/opensearch/ad/transport/RCFResultResponse.java b/src/main/java/org/opensearch/ad/transport/RCFResultResponse.java index afba1cc73..051bb7de4 100644 --- a/src/main/java/org/opensearch/ad/transport/RCFResultResponse.java +++ b/src/main/java/org/opensearch/ad/transport/RCFResultResponse.java @@ -31,6 +31,7 @@ import org.opensearch.Version; import org.opensearch.action.ActionResponse; import org.opensearch.ad.cluster.ADVersionUtil; +import org.opensearch.ad.constant.CommonName; import org.opensearch.common.io.stream.StreamInput; import org.opensearch.common.io.stream.StreamOutput; import org.opensearch.common.xcontent.ToXContentObject; @@ -48,10 +49,7 @@ public class RCFResultResponse extends ActionResponse implements ToXContentObjec private double[] attribution; private long totalUpdates = 0; private Version remoteAdVersion; - - public RCFResultResponse(double rcfScore, double confidence, int forestSize, double[] attribution) { - this(rcfScore, confidence, forestSize, attribution, 0, Version.CURRENT); - } + private double anomalyGrade; public RCFResultResponse( double rcfScore, @@ -59,6 +57,7 @@ public RCFResultResponse( int forestSize, double[] attribution, long totalUpdates, + double grade, Version remoteAdVersion ) { this.rcfScore = rcfScore; @@ -66,6 +65,7 @@ public RCFResultResponse( this.forestSize = forestSize; this.attribution = attribution; this.totalUpdates = totalUpdates; + this.anomalyGrade = grade; this.remoteAdVersion = remoteAdVersion; } @@ -77,6 +77,7 @@ public RCFResultResponse(StreamInput in) throws IOException { attribution = in.readDoubleArray(); if (in.available() > 0) { totalUpdates = in.readLong(); + anomalyGrade = in.readDouble(); } } @@ -93,7 +94,8 @@ public int getForestSize() { } /** - * Returns RCF score attribution. + * Returns RCF score attribution. Can be null when anomaly grade is less than + * or equals to 0. * * @return RCF score attribution. */ @@ -105,6 +107,10 @@ public long getTotalUpdates() { return totalUpdates; } + public double getAnomalyGrade() { + return anomalyGrade; + } + @Override public void writeTo(StreamOutput out) throws IOException { out.writeDouble(rcfScore); @@ -113,6 +119,7 @@ public void writeTo(StreamOutput out) throws IOException { out.writeDoubleArray(attribution); if (ADVersionUtil.compatibleWithVersionOnOrAfter1_1(remoteAdVersion)) { out.writeLong(totalUpdates); + out.writeDouble(anomalyGrade); } } @@ -124,6 +131,7 @@ public XContentBuilder toXContent(XContentBuilder builder, Params params) throws builder.field(FOREST_SIZE_JSON_KEY, forestSize); builder.field(ATTRIBUTION_JSON_KEY, attribution); builder.field(TOTAL_UPDATES_JSON_KEY, totalUpdates); + builder.field(CommonName.ANOMALY_GRADE_JSON_KEY, anomalyGrade); builder.endObject(); return builder; } diff --git a/src/main/java/org/opensearch/ad/transport/RCFResultTransportAction.java b/src/main/java/org/opensearch/ad/transport/RCFResultTransportAction.java index 99d44a0c8..4149cfcae 100644 --- a/src/main/java/org/opensearch/ad/transport/RCFResultTransportAction.java +++ b/src/main/java/org/opensearch/ad/transport/RCFResultTransportAction.java @@ -97,6 +97,7 @@ protected void doExecute(Task task, RCFResultRequest request, ActionListener first, Map second) { if (first.size() != second.size()) { @@ -482,4 +485,64 @@ public void testBwcInstance() { assertNotNull(bwc); } + private void setUpRCFResultResponse() { + rcfResultResponse1_1 = new RCFResultResponse(0.345, 0.123, 30, new double[] { 0.3, 0.7 }, 134, 0.4, Version.CURRENT); + rcfResultResponse1_0 = new RCFResultResponse1_0(0.345, 0.123, 30, new double[] { 0.3, 0.7 }); + } + + /** + * For RCFResultResponse, the input is a 1.1 stream. + * @throws IOException when serialization/deserialization has issues. + */ + public void testDeserializeRCFResultResponse1_1() throws IOException { + setUpRCFResultResponse(); + + rcfResultResponse1_1.writeTo(output1_1); + + StreamInput streamInput = output1_1.bytes().streamInput(); + streamInput.setVersion(V_1_1_0); + RCFResultResponse readResponse = new RCFResultResponse(streamInput); + assertArrayEquals(readResponse.getAttribution(), rcfResultResponse1_1.getAttribution(), 0.001); + assertThat(readResponse.getConfidence(), equalTo(rcfResultResponse1_1.getConfidence())); + assertThat(readResponse.getForestSize(), equalTo(rcfResultResponse1_1.getForestSize())); + assertThat(readResponse.getTotalUpdates(), equalTo(rcfResultResponse1_1.getTotalUpdates())); + assertThat(readResponse.getRCFScore(), equalTo(rcfResultResponse1_1.getRCFScore())); + } + + /** + * For RCFResultResponse, the input is a 1.0 stream. + * @throws IOException when serialization/deserialization has issues. + */ + public void testDeserializeRCFResultResponse1_0() throws IOException { + setUpRCFResultResponse(); + + rcfResultResponse1_0.writeTo(output1_0); + + StreamInput streamInput = output1_0.bytes().streamInput(); + streamInput.setVersion(Version.V_1_0_0); + RCFResultResponse readResponse = new RCFResultResponse(streamInput); + assertArrayEquals(readResponse.getAttribution(), rcfResultResponse1_0.getAttribution(), 0.001); + assertThat(readResponse.getConfidence(), equalTo(rcfResultResponse1_0.getConfidence())); + assertThat(readResponse.getForestSize(), equalTo(rcfResultResponse1_0.getForestSize())); + assertThat(readResponse.getTotalUpdates(), equalTo(0L)); + assertThat(readResponse.getRCFScore(), equalTo(rcfResultResponse1_0.getRCFScore())); + } + + /** + * For RCFResultResponse, the output is a 1.0 stream. + * @throws IOException when serialization/deserialization has issues. + */ + public void testSerializeRCFResultResponse1_0() throws IOException { + setUpRCFResultResponse(); + + rcfResultResponse1_1.writeTo(output1_0); + + StreamInput streamInput = output1_0.bytes().streamInput(); + streamInput.setVersion(Version.V_1_0_0); + RCFResultResponse1_0 readResponse = new RCFResultResponse1_0(streamInput); + assertArrayEquals(readResponse.getAttribution(), rcfResultResponse1_0.getAttribution(), 0.001); + assertThat(readResponse.getConfidence(), equalTo(rcfResultResponse1_0.getConfidence())); + assertThat(readResponse.getForestSize(), equalTo(rcfResultResponse1_0.getForestSize())); + assertThat(readResponse.getRCFScore(), equalTo(rcfResultResponse1_0.getRCFScore())); + } } diff --git a/src/test/java/org/opensearch/RCFResultResponse1_0.java b/src/test/java/org/opensearch/RCFResultResponse1_0.java new file mode 100644 index 000000000..76cdbdfb2 --- /dev/null +++ b/src/test/java/org/opensearch/RCFResultResponse1_0.java @@ -0,0 +1,102 @@ +/* + * SPDX-License-Identifier: Apache-2.0 + * + * The OpenSearch Contributors require contributions made to + * this file be licensed under the Apache-2.0 license or a + * compatible open source license. + * + * Modifications Copyright OpenSearch Contributors. See + * GitHub history for details. + */ + +/* + * Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. + * + * Licensed under the Apache License, Version 2.0 (the "License"). + * You may not use this file except in compliance with the License. + * A copy of the License is located at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * or in the "license" file accompanying this file. This file is distributed + * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either + * express or implied. See the License for the specific language governing + * permissions and limitations under the License. + */ + +package org.opensearch; + +import java.io.IOException; + +import org.opensearch.action.ActionResponse; +import org.opensearch.common.io.stream.StreamInput; +import org.opensearch.common.io.stream.StreamOutput; +import org.opensearch.common.xcontent.ToXContentObject; +import org.opensearch.common.xcontent.XContentBuilder; + +public class RCFResultResponse1_0 extends ActionResponse implements ToXContentObject { + public static final String RCF_SCORE_JSON_KEY = "rcfScore"; + public static final String CONFIDENCE_JSON_KEY = "confidence"; + public static final String FOREST_SIZE_JSON_KEY = "forestSize"; + public static final String ATTRIBUTION_JSON_KEY = "attribution"; + private double rcfScore; + private double confidence; + private int forestSize; + private double[] attribution; + + public RCFResultResponse1_0(double rcfScore, double confidence, int forestSize, double[] attribution) { + this.rcfScore = rcfScore; + this.confidence = confidence; + this.forestSize = forestSize; + this.attribution = attribution; + } + + public RCFResultResponse1_0(StreamInput in) throws IOException { + super(in); + rcfScore = in.readDouble(); + confidence = in.readDouble(); + forestSize = in.readVInt(); + attribution = in.readDoubleArray(); + } + + public double getRCFScore() { + return rcfScore; + } + + public double getConfidence() { + return confidence; + } + + public int getForestSize() { + return forestSize; + } + + /** + * Returns RCF score attribution. + * + * @return RCF score attribution. + */ + public double[] getAttribution() { + return attribution; + } + + @Override + public void writeTo(StreamOutput out) throws IOException { + out.writeDouble(rcfScore); + out.writeDouble(confidence); + out.writeVInt(forestSize); + out.writeDoubleArray(attribution); + } + + @Override + public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException { + builder.startObject(); + builder.field(RCF_SCORE_JSON_KEY, rcfScore); + builder.field(CONFIDENCE_JSON_KEY, confidence); + builder.field(FOREST_SIZE_JSON_KEY, forestSize); + builder.field(ATTRIBUTION_JSON_KEY, attribution); + builder.endObject(); + return builder; + } + +} diff --git a/src/test/java/org/opensearch/ad/MemoryTrackerTests.java b/src/test/java/org/opensearch/ad/MemoryTrackerTests.java index 9b34db3c8..593af9c00 100644 --- a/src/test/java/org/opensearch/ad/MemoryTrackerTests.java +++ b/src/test/java/org/opensearch/ad/MemoryTrackerTests.java @@ -46,23 +46,25 @@ import org.opensearch.monitor.jvm.JvmService; import org.opensearch.test.OpenSearchTestCase; -import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; public class MemoryTrackerTests extends OpenSearchTestCase { - int rcfNumFeatures; + int inputFeatures; int rcfSampleSize; int numberOfTrees; double rcfTimeDecay; int numMinSamples; + int shingleSize; + int dimension; MemoryTracker tracker; long expectedRCFModelSize; String detectorId; long largeHeapSize; long smallHeapSize; Mem mem; - RandomCutForest rcf; + ThresholdedRandomCutForest trcf; float modelMaxPercen; ClusterService clusterService; double modelMaxSizePercentage; @@ -74,11 +76,13 @@ public class MemoryTrackerTests extends OpenSearchTestCase { @Override public void setUp() throws Exception { super.setUp(); - rcfNumFeatures = 1; + inputFeatures = 1; rcfSampleSize = 256; numberOfTrees = 30; rcfTimeDecay = 0.2; numMinSamples = 128; + shingleSize = 8; + dimension = inputFeatures * shingleSize; jvmService = mock(JvmService.class); JvmInfo info = mock(JvmInfo.class); @@ -105,9 +109,9 @@ public void setUp() throws Exception { expectedRCFModelSize = 382784; detectorId = "123"; - rcf = RandomCutForest + trcf = ThresholdedRandomCutForest .builder() - .dimensions(rcfNumFeatures) + .dimensions(dimension) .sampleSize(rcfSampleSize) .numberOfTrees(numberOfTrees) .timeDecay(rcfTimeDecay) @@ -116,8 +120,8 @@ public void setUp() throws Exception { .compact(true) .precision(Precision.FLOAT_32) .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) - // same with dimension for opportunistic memory saving - .shingleSize(rcfNumFeatures) + .shingleSize(shingleSize) + .internalShinglingEnabled(true) .build(); detector = mock(AnomalyDetector.class); @@ -143,15 +147,10 @@ private void setUpSmallHeap() { public void testEstimateModelSize() { setUpBigHeap(); - assertEquals(expectedRCFModelSize + tracker.getThresholdModelBytes(), tracker.estimateTotalModelSize(rcf)); - assertTrue(tracker.isHostingAllowed(detectorId, rcf)); - - assertEquals( - expectedRCFModelSize + tracker.getThresholdModelBytes(), - tracker.estimateTotalModelSize(detector, numberOfTrees, AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) - ); + assertEquals(403491, tracker.estimateTRCFModelSize(trcf)); + assertTrue(tracker.isHostingAllowed(detectorId, trcf)); - RandomCutForest rcf2 = RandomCutForest + ThresholdedRandomCutForest rcf2 = ThresholdedRandomCutForest .builder() .dimensions(32) // 32 to trigger another calculation of point store usage .sampleSize(rcfSampleSize) @@ -162,11 +161,148 @@ public void testEstimateModelSize() { .compact(true) .precision(Precision.FLOAT_32) .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) // same with dimension for opportunistic memory saving - .shingleSize(rcfNumFeatures) + .shingleSize(shingleSize) .build(); - assertEquals(423733 + tracker.getThresholdModelBytes(), tracker.estimateTotalModelSize(rcf2)); + assertEquals(603708, tracker.estimateTRCFModelSize(rcf2)); assertTrue(tracker.isHostingAllowed(detectorId, rcf2)); + + ThresholdedRandomCutForest rcf3 = ThresholdedRandomCutForest + .builder() + .dimensions(9) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.BATCH_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(false) + // same with dimension for opportunistic memory saving + .shingleSize(1) + .build(); + assertEquals(1685208, tracker.estimateTRCFModelSize(rcf3)); + + ThresholdedRandomCutForest rcf4 = ThresholdedRandomCutForest + .builder() + .dimensions(6) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(1) + .build(); + assertEquals(521304, tracker.estimateTRCFModelSize(rcf4)); + + ThresholdedRandomCutForest rcf5 = ThresholdedRandomCutForest + .builder() + .dimensions(8) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(2) + .build(); + assertEquals(467340, tracker.estimateTRCFModelSize(rcf5)); + + ThresholdedRandomCutForest rcf6 = ThresholdedRandomCutForest + .builder() + .dimensions(32) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(4) + .build(); + assertEquals(603676, tracker.estimateTRCFModelSize(rcf6)); + + ThresholdedRandomCutForest rcf7 = ThresholdedRandomCutForest + .builder() + .dimensions(16) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(16) + .build(); + assertEquals(401481, tracker.estimateTRCFModelSize(rcf7)); + + ThresholdedRandomCutForest rcf8 = ThresholdedRandomCutForest + .builder() + .dimensions(320) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(32) + .build(); + assertEquals(1040432, tracker.estimateTRCFModelSize(rcf8)); + + ThresholdedRandomCutForest rcf9 = ThresholdedRandomCutForest + .builder() + .dimensions(320) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(64) + .build(); + assertEquals(1040688, tracker.estimateTRCFModelSize(rcf9)); + + ThresholdedRandomCutForest rcf10 = ThresholdedRandomCutForest + .builder() + .dimensions(325) + .sampleSize(rcfSampleSize) + .numberOfTrees(numberOfTrees) + .timeDecay(rcfTimeDecay) + .outputAfter(numMinSamples) + .parallelExecutionEnabled(false) + .compact(true) + .precision(Precision.FLOAT_32) + .boundingBoxCacheFraction(AnomalyDetectorSettings.REAL_TIME_BOUNDING_BOX_CACHE_RATIO) + .internalShinglingEnabled(true) + // same with dimension for opportunistic memory saving + .shingleSize(65) + .build(); + expectThrows(IllegalArgumentException.class, () -> tracker.estimateTRCFModelSize(rcf10)); } public void testCanAllocate() { @@ -185,7 +321,7 @@ public void testCanAllocate() { public void testCannotHost() { setUpSmallHeap(); - expectThrows(LimitExceededException.class, () -> tracker.isHostingAllowed(detectorId, rcf)); + expectThrows(LimitExceededException.class, () -> tracker.isHostingAllowed(detectorId, trcf)); } public void testMemoryToShed() { diff --git a/src/test/java/org/opensearch/ad/NodeStateManagerTests.java b/src/test/java/org/opensearch/ad/NodeStateManagerTests.java index 99fd2ecc5..e7e08d5d9 100644 --- a/src/test/java/org/opensearch/ad/NodeStateManagerTests.java +++ b/src/test/java/org/opensearch/ad/NodeStateManagerTests.java @@ -40,7 +40,6 @@ import java.time.Clock; import java.time.Duration; import java.time.Instant; -import java.util.AbstractMap.SimpleImmutableEntry; import java.util.Arrays; import java.util.Collections; import java.util.HashSet; @@ -58,7 +57,6 @@ import org.opensearch.action.get.GetRequest; import org.opensearch.action.get.GetResponse; import org.opensearch.action.search.SearchRequest; -import org.opensearch.ad.ml.ModelPartitioner; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.settings.AnomalyDetectorSettings; import org.opensearch.ad.transport.AnomalyResultTests; @@ -82,7 +80,6 @@ public class NodeStateManagerTests extends AbstractADTest { private NodeStateManager stateManager; - private ModelPartitioner modelPartitioner; private Client client; private ClientUtil clientUtil; private Clock clock; @@ -118,8 +115,6 @@ public static void tearDownAfterClass() { @Before public void setUp() throws Exception { super.setUp(); - modelPartitioner = mock(ModelPartitioner.class); - when(modelPartitioner.getPartitionedForestSizes(any(AnomalyDetector.class))).thenReturn(new SimpleImmutableEntry<>(2, 20)); client = mock(Client.class); settings = Settings .builder() @@ -146,16 +141,7 @@ public void setUp() throws Exception { ); clusterService = ClusterServiceUtils.createClusterService(threadPool, discoveryNode, clusterSettings); - stateManager = new NodeStateManager( - client, - xContentRegistry(), - settings, - clientUtil, - clock, - duration, - modelPartitioner, - clusterService - ); + stateManager = new NodeStateManager(client, xContentRegistry(), settings, clientUtil, clock, duration, clusterService); checkpointResponse = mock(GetResponse.class); } @@ -165,7 +151,6 @@ public void setUp() throws Exception { public void tearDown() throws Exception { super.tearDown(); stateManager = null; - modelPartitioner = null; client = null; clientUtil = null; detectorToCheck = null; @@ -223,19 +208,6 @@ private void setupCheckpoint(boolean responseExists) throws IOException { }).when(client).get(any(), any(ActionListener.class)); } - public void testGetPartitionNumber() throws IOException, InterruptedException { - String detectorId = setupDetector(); - AnomalyDetector detector = TestHelpers.randomAnomalyDetector(TestHelpers.randomUiMetadata(), null); - for (int i = 0; i < 2; i++) { - // call two times should return the same result - int partitionNumber = stateManager.getPartitionNumber(detectorId, detector); - assertEquals(2, partitionNumber); - } - - // the 2nd call should directly fetch cached result - verify(modelPartitioner, times(1)).getPartitionedForestSizes(any()); - } - public void testGetLastError() throws IOException, InterruptedException { String error = "blah"; assertEquals(NodeStateManager.NO_ERROR, stateManager.getLastDetectionError(adId)); @@ -276,7 +248,6 @@ public void testHasRunningQuery() throws IOException { new ClientUtil(settings, client, throttler, context), clock, duration, - modelPartitioner, clusterService ); diff --git a/src/test/java/org/opensearch/ad/caching/AbstractCacheTest.java b/src/test/java/org/opensearch/ad/caching/AbstractCacheTest.java index be76b1ff1..cb97dba68 100644 --- a/src/test/java/org/opensearch/ad/caching/AbstractCacheTest.java +++ b/src/test/java/org/opensearch/ad/caching/AbstractCacheTest.java @@ -18,6 +18,7 @@ import java.time.Duration; import java.time.Instant; import java.util.ArrayDeque; +import java.util.ArrayList; import java.util.Random; import org.junit.Before; @@ -45,6 +46,7 @@ public class AbstractCacheTest extends AbstractADTest { protected MemoryTracker memoryTracker; protected CheckpointWriteWorker checkpointWriteQueue; protected Random random; + protected int shingleSize; @Override @Before @@ -56,6 +58,15 @@ public void setUp() throws Exception { detectorDuration = Duration.ofMinutes(5); when(detector.getDetectionIntervalDuration()).thenReturn(detectorDuration); when(detector.getDetectorIntervalInSeconds()).thenReturn(detectorDuration.getSeconds()); + when(detector.getEnabledFeatureIds()).thenReturn(new ArrayList() { + { + add("a"); + add("b"); + add("c"); + } + }); + shingleSize = 4; + when(detector.getShingleSize()).thenReturn(shingleSize); entity1 = Entity.createSingleAttributeEntity("attributeName1", "attributeVal1"); entity2 = Entity.createSingleAttributeEntity("attributeName1", "attributeVal2"); @@ -89,7 +100,7 @@ public void setUp() throws Exception { initialPriority = cacheBuffer.getPriorityTracker().getUpdatedPriority(0); modelState1 = new ModelState<>( - new EntityModel(entity1, new ArrayDeque<>(), null, null), + new EntityModel(entity1, new ArrayDeque<>(), null), modelId1, detectorId, ModelType.ENTITY.getName(), @@ -98,7 +109,7 @@ public void setUp() throws Exception { ); modelState2 = new ModelState<>( - new EntityModel(entity2, new ArrayDeque<>(), null, null), + new EntityModel(entity2, new ArrayDeque<>(), null), modelId2, detectorId, ModelType.ENTITY.getName(), @@ -107,7 +118,7 @@ public void setUp() throws Exception { ); modelState3 = new ModelState<>( - new EntityModel(entity3, new ArrayDeque<>(), null, null), + new EntityModel(entity3, new ArrayDeque<>(), null), modelId3, detectorId, ModelType.ENTITY.getName(), @@ -116,7 +127,7 @@ public void setUp() throws Exception { ); modelState4 = new ModelState<>( - new EntityModel(entity4, new ArrayDeque<>(), null, null), + new EntityModel(entity4, new ArrayDeque<>(), null), modelId4, detectorId, ModelType.ENTITY.getName(), diff --git a/src/test/java/org/opensearch/ad/caching/PriorityCacheTests.java b/src/test/java/org/opensearch/ad/caching/PriorityCacheTests.java index a6be916c5..f2339d622 100644 --- a/src/test/java/org/opensearch/ad/caching/PriorityCacheTests.java +++ b/src/test/java/org/opensearch/ad/caching/PriorityCacheTests.java @@ -34,6 +34,7 @@ import static org.mockito.Mockito.doAnswer; import static org.mockito.Mockito.doThrow; import static org.mockito.Mockito.mock; +import static org.mockito.Mockito.spy; import static org.mockito.Mockito.times; import static org.mockito.Mockito.verify; import static org.mockito.Mockito.when; @@ -55,8 +56,8 @@ import org.apache.logging.log4j.Logger; import org.junit.Before; import org.mockito.ArgumentCaptor; -import org.opensearch.OpenSearchException; import org.opensearch.ad.MemoryTracker; +import org.opensearch.ad.breaker.ADCircuitBreakerService; import org.opensearch.ad.common.exception.AnomalyDetectionException; import org.opensearch.ad.common.exception.LimitExceededException; import org.opensearch.ad.ml.CheckpointDao; @@ -70,6 +71,10 @@ import org.opensearch.cluster.service.ClusterService; import org.opensearch.common.settings.ClusterSettings; import org.opensearch.common.settings.Settings; +import org.opensearch.common.unit.ByteSizeValue; +import org.opensearch.monitor.jvm.JvmInfo; +import org.opensearch.monitor.jvm.JvmInfo.Mem; +import org.opensearch.monitor.jvm.JvmService; import org.opensearch.threadpool.Scheduler.ScheduledCancellable; import org.opensearch.threadpool.ThreadPool; @@ -103,7 +108,12 @@ public void setUp() throws Exception { Collections .unmodifiableSet( new HashSet<>( - Arrays.asList(AnomalyDetectorSettings.DEDICATED_CACHE_SIZE, AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE) + Arrays + .asList( + AnomalyDetectorSettings.DEDICATED_CACHE_SIZE, + AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE, + AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE + ) ) ) ); @@ -131,7 +141,7 @@ public void setUp() throws Exception { cacheProvider = new CacheProvider(cache).get(); - when(memoryTracker.estimateTotalModelSize(any(AnomalyDetector.class), anyInt(), anyDouble())).thenReturn(memoryPerEntity); + when(memoryTracker.estimateTRCFModelSize(anyInt(), anyInt(), anyDouble(), anyInt(), anyBoolean())).thenReturn(memoryPerEntity); when(memoryTracker.canAllocateReserved(anyLong())).thenReturn(true); detector2 = mock(AnomalyDetector.class); @@ -144,6 +154,43 @@ public void setUp() throws Exception { } public void testCacheHit() { + // 800 MB is the limit + long largeHeapSize = 800_000_000; + JvmInfo info = mock(JvmInfo.class); + Mem mem = mock(Mem.class); + when(info.getMem()).thenReturn(mem); + when(mem.getHeapMax()).thenReturn(new ByteSizeValue(largeHeapSize)); + JvmService jvmService = mock(JvmService.class); + when(jvmService.info()).thenReturn(info); + + // ClusterService clusterService = mock(ClusterService.class); + float modelMaxPercen = 0.1f; + // Settings settings = Settings.builder().put(AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE.getKey(), modelMaxPercen).build(); + // ClusterSettings clusterSettings = new ClusterSettings( + // settings, + // Collections.unmodifiableSet(new HashSet<>(Arrays.asList(AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE))) + // ); + // when(clusterService.getClusterSettings()).thenReturn(clusterSettings); + + memoryTracker = spy(new MemoryTracker(jvmService, modelMaxPercen, 0.002, clusterService, mock(ADCircuitBreakerService.class))); + + EntityCache cache = new PriorityCache( + checkpoint, + dedicatedCacheSize, + AnomalyDetectorSettings.CHECKPOINT_TTL, + AnomalyDetectorSettings.MAX_INACTIVE_ENTITIES, + memoryTracker, + AnomalyDetectorSettings.NUM_TREES, + clock, + clusterService, + AnomalyDetectorSettings.HOURLY_MAINTENANCE, + threadPool, + checkpointWriteQueue, + AnomalyDetectorSettings.MAINTENANCE_FREQ_CONSTANT + ); + + cacheProvider = new CacheProvider(cache).get(); + // cache miss due to door keeper assertEquals(null, cacheProvider.get(modelState1.getModelId(), detector)); // cache miss due to empty cache @@ -154,8 +201,7 @@ public void testCacheHit() { ModelState hitState = cacheProvider.get(modelState1.getModelId(), detector); assertEquals(detectorId, hitState.getDetectorId()); EntityModel model = hitState.getModel(); - assertEquals(null, model.getRcf()); - assertEquals(null, model.getThreshold()); + assertEquals(false, model.getTrcf().isPresent()); assertTrue(model.getSamples().isEmpty()); modelState1.getModel().addSample(point); assertTrue(Arrays.equals(point, model.getSamples().peek())); @@ -164,14 +210,16 @@ public void testCacheHit() { ArgumentCaptor reserved = ArgumentCaptor.forClass(Boolean.class); ArgumentCaptor origin = ArgumentCaptor.forClass(MemoryTracker.Origin.class); + // input dimension: 3, shingle: 4 + long expectedMemoryPerEntity = 436828L; verify(memoryTracker, times(1)).consumeMemory(memoryConsumed.capture(), reserved.capture(), origin.capture()); - assertEquals(dedicatedCacheSize * memoryPerEntity, memoryConsumed.getValue().intValue()); + assertEquals(dedicatedCacheSize * expectedMemoryPerEntity, memoryConsumed.getValue().intValue()); assertEquals(true, reserved.getValue().booleanValue()); assertEquals(MemoryTracker.Origin.HC_DETECTOR, origin.getValue()); - for (int i = 0; i < 2; i++) { - cacheProvider.get(modelId2, detector); - } + // for (int i = 0; i < 2; i++) { + // cacheProvider.get(modelId2, detector); + // } } public void testInActiveCache() { @@ -210,7 +258,7 @@ public void testSharedCache() { cacheProvider.get(modelId3, detector2); } modelState3 = new ModelState<>( - new EntityModel(entity3, new ArrayDeque<>(), null, null), + new EntityModel(entity3, new ArrayDeque<>(), null), modelId3, detectorId2, ModelType.ENTITY.getName(), @@ -226,7 +274,7 @@ public void testSharedCache() { cacheProvider.get(modelId4, detector2); } modelState4 = new ModelState<>( - new EntityModel(entity4, new ArrayDeque<>(), null, null), + new EntityModel(entity4, new ArrayDeque<>(), null), modelId4, detectorId2, ModelType.ENTITY.getName(), @@ -388,7 +436,8 @@ class FailedCleanRunnable implements Runnable { public void run() { try { cacheProvider.maintenance(); - } catch (OpenSearchException e) { + } catch (Exception e) { + // maintenance can throw AnomalyDetectionException, catch it here singalThreadToStart.countDown(); } } @@ -488,7 +537,7 @@ private void replaceInOtherCacheSetUp() { Entity entity5 = Entity.createSingleAttributeEntity("attributeName1", "attributeVal5"); Entity entity6 = Entity.createSingleAttributeEntity("attributeName1", "attributeVal6"); ModelState modelState5 = new ModelState<>( - new EntityModel(entity5, new ArrayDeque<>(), null, null), + new EntityModel(entity5, new ArrayDeque<>(), null), entity5.getModelId(detectorId2).get(), detectorId2, ModelType.ENTITY.getName(), @@ -496,7 +545,7 @@ private void replaceInOtherCacheSetUp() { 0 ); ModelState modelState6 = new ModelState<>( - new EntityModel(entity6, new ArrayDeque<>(), null, null), + new EntityModel(entity6, new ArrayDeque<>(), null), entity6.getModelId(detectorId2).get(), detectorId2, ModelType.ENTITY.getName(), diff --git a/src/test/java/org/opensearch/ad/cluster/HashRingTests.java b/src/test/java/org/opensearch/ad/cluster/HashRingTests.java index 9e2a01eed..93fd3043d 100644 --- a/src/test/java/org/opensearch/ad/cluster/HashRingTests.java +++ b/src/test/java/org/opensearch/ad/cluster/HashRingTests.java @@ -120,7 +120,7 @@ public void setUp() throws Exception { clusterAdminClient = mock(ClusterAdminClient.class); when(adminClient.cluster()).thenReturn(clusterAdminClient); - String modelId = "123-threshold"; + String modelId = "123_model_threshold"; modelManager = mock(ModelManager.class); doAnswer(invocation -> { Set res = new HashSet<>(); diff --git a/src/test/java/org/opensearch/ad/e2e/DetectionResultEvalutationIT.java b/src/test/java/org/opensearch/ad/e2e/DetectionResultEvalutationIT.java index 6c7369119..a38573529 100644 --- a/src/test/java/org/opensearch/ad/e2e/DetectionResultEvalutationIT.java +++ b/src/test/java/org/opensearch/ad/e2e/DetectionResultEvalutationIT.java @@ -71,7 +71,7 @@ public void testDataset() throws Exception { // TODO: this test case will run for a much longer time and timeout with security enabled if (!isHttps()) { disableResourceNotFoundFaultTolerence(); - verifyAnomaly("synthetic", 1, 1500, 8, .9, .9, 10); + verifyAnomaly("synthetic", 1, 1500, 8, .5, .9, 10); } } diff --git a/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java b/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java index b6b505b96..1bf5987d6 100644 --- a/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java +++ b/src/test/java/org/opensearch/ad/ml/CheckpointDaoTests.java @@ -29,14 +29,25 @@ import static org.mockito.ArgumentMatchers.eq; import static org.mockito.Matchers.any; import static org.mockito.Mockito.doAnswer; +import static org.mockito.Mockito.doReturn; +import static org.mockito.Mockito.doThrow; import static org.mockito.Mockito.mock; import static org.mockito.Mockito.never; +import static org.mockito.Mockito.spy; import static org.mockito.Mockito.times; import static org.mockito.Mockito.verify; import static org.mockito.Mockito.when; +import static org.opensearch.action.DocWriteResponse.Result.UPDATED; +import static org.opensearch.ad.ml.CheckpointDao.FIELD_MODEL; import static org.opensearch.ad.ml.CheckpointDao.FIELD_MODELV2; +import static org.opensearch.ad.ml.CheckpointDao.TIMESTAMP; +import java.io.BufferedReader; +import java.io.File; +import java.io.FileNotFoundException; +import java.io.FileReader; import java.io.IOException; +import java.net.URISyntaxException; import java.security.AccessController; import java.security.PrivilegedAction; import java.time.Clock; @@ -56,8 +67,10 @@ import java.util.Set; import java.util.concurrent.CountDownLatch; import java.util.concurrent.TimeUnit; +import java.util.concurrent.atomic.AtomicReference; import java.util.function.BiConsumer; +import org.apache.commons.lang3.tuple.Pair; import org.apache.commons.pool2.BasePooledObjectFactory; import org.apache.commons.pool2.PooledObject; import org.apache.commons.pool2.impl.DefaultPooledObject; @@ -71,6 +84,7 @@ import org.mockito.MockitoAnnotations; import org.opensearch.ResourceAlreadyExistsException; import org.opensearch.action.ActionListener; +import org.opensearch.action.ActionRequest; import org.opensearch.action.DocWriteRequest; import org.opensearch.action.DocWriteResponse; import org.opensearch.action.admin.indices.create.CreateIndexResponse; @@ -87,9 +101,9 @@ import org.opensearch.action.get.MultiGetRequest; import org.opensearch.action.get.MultiGetResponse; import org.opensearch.action.index.IndexRequest; +import org.opensearch.action.support.replication.ReplicationResponse; import org.opensearch.action.update.UpdateRequest; import org.opensearch.action.update.UpdateResponse; -import org.opensearch.ad.common.exception.ResourceNotFoundException; import org.opensearch.ad.constant.CommonName; import org.opensearch.ad.indices.AnomalyDetectionIndices; import org.opensearch.ad.settings.AnomalyDetectorSettings; @@ -98,7 +112,6 @@ import org.opensearch.index.IndexNotFoundException; import org.opensearch.index.engine.VersionConflictEngineException; import org.opensearch.index.shard.ShardId; -import org.opensearch.monitor.jvm.JvmInfo; import org.opensearch.test.OpenSearchTestCase; import test.org.opensearch.ad.util.JsonDeserializer; @@ -107,9 +120,11 @@ import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState; import com.amazon.randomcutforest.serialize.json.v1.V1JsonToV2StateConverter; import com.amazon.randomcutforest.state.RandomCutForestMapper; -import com.amazon.randomcutforest.state.RandomCutForestState; import com.google.gson.Gson; import com.google.gson.GsonBuilder; @@ -138,13 +153,13 @@ public class CheckpointDaoTests extends OpenSearchTestCase { @Mock private AnomalyDetectionIndices indexUtil; + private Schema trcfSchema; + // configuration private String indexName; // test data private String modelId; - private String model; - private Map docSource; private Gson gson; private Class thresholdingModelClass; @@ -152,10 +167,10 @@ public class CheckpointDaoTests extends OpenSearchTestCase { private int maxCheckpointBytes = 1_000_000; private GenericObjectPool serializeRCFBufferPool; private RandomCutForestMapper mapper; - private Schema schema; + private ThresholdedRandomCutForestMapper trcfMapper; private V1JsonToV2StateConverter converter; + double anomalyRate; - @SuppressWarnings("unchecked") @Before public void setup() { MockitoAnnotations.initMocks(this); @@ -171,13 +186,17 @@ public void setup() { mapper = new RandomCutForestMapper(); mapper.setSaveExecutorContextEnabled(true); - schema = AccessController - .doPrivileged((PrivilegedAction>) () -> RuntimeSchema.getSchema(RandomCutForestState.class)); - converter = new V1JsonToV2StateConverter(); - long heapSizeBytes = JvmInfo.jvmInfo().getMem().getHeapMax().getBytes(); + trcfMapper = new ThresholdedRandomCutForestMapper(); + trcfSchema = AccessController + .doPrivileged( + (PrivilegedAction>) () -> RuntimeSchema + .getSchema(ThresholdedRandomCutForestState.class) + ); - serializeRCFBufferPool = AccessController.doPrivileged(new PrivilegedAction>() { + converter = new V1JsonToV2StateConverter(); + + serializeRCFBufferPool = spy(AccessController.doPrivileged(new PrivilegedAction>() { @Override public GenericObjectPool run() { return new GenericObjectPool<>(new BasePooledObjectFactory() { @@ -192,32 +211,34 @@ public PooledObject wrap(LinkedBuffer obj) { } }); } - }); + })); serializeRCFBufferPool.setMaxTotal(AnomalyDetectorSettings.MAX_TOTAL_RCF_SERIALIZATION_BUFFERS); serializeRCFBufferPool.setMaxIdle(AnomalyDetectorSettings.MAX_TOTAL_RCF_SERIALIZATION_BUFFERS); serializeRCFBufferPool.setMinIdle(0); serializeRCFBufferPool.setBlockWhenExhausted(false); serializeRCFBufferPool.setTimeBetweenEvictionRuns(AnomalyDetectorSettings.HOURLY_MAINTENANCE); + anomalyRate = 0.005; checkpointDao = new CheckpointDao( client, clientUtil, indexName, gson, mapper, - schema, converter, + trcfMapper, + trcfSchema, thresholdingModelClass, indexUtil, maxCheckpointBytes, serializeRCFBufferPool, - AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + anomalyRate ); when(indexUtil.doesCheckpointIndexExist()).thenReturn(true); modelId = "testModelId"; - model = "testModel"; } @Override @@ -226,7 +247,7 @@ public void tearDown() throws Exception { serializeRCFBufferPool.close(); } - private RandomCutForest createRCF() { + private ThresholdedRandomCutForest createTRCF() { int dimensions = 4; int numberOfTrees = 1; int sampleSize = 256; @@ -234,7 +255,7 @@ private RandomCutForest createRCF() { Random random = new Random(); long seed = random.nextLong(); double[][] data = MLUtil.generateShingledData(dataSize, dimensions, 2); - RandomCutForest forest = RandomCutForest + ThresholdedRandomCutForest forest = ThresholdedRandomCutForest .builder() .compact(true) .dimensions(dimensions) @@ -245,7 +266,7 @@ private RandomCutForest createRCF() { .boundingBoxCacheFraction(0) .build(); for (double[] point : data) { - forest.update(point); + forest.process(point, 0); } return forest; } @@ -261,7 +282,7 @@ private void verifyPutModelCheckpointAsync() { ActionListener listener = mock(ActionListener.class); - checkpointDao.putRCFCheckpoint(modelId, createRCF(), listener); + checkpointDao.putTRCFCheckpoint(modelId, createTRCF(), listener); UpdateRequest updateRequest = requestCaptor.getValue(); assertEquals(indexName, updateRequest.index()); @@ -317,41 +338,119 @@ public void test_putModelCheckpoint_callListener_unexpected_exception() { }).when(indexUtil).initCheckpointIndex(any()); ActionListener listener = mock(ActionListener.class); - checkpointDao.putRCFCheckpoint(modelId, createRCF(), listener); + checkpointDao.putTRCFCheckpoint(modelId, createTRCF(), listener); verify(clientUtil, never()).asyncRequest(any(), any(), any()); } @SuppressWarnings("unchecked") public void test_getModelCheckpoint_returnExpectedToListener() { - ArgumentCaptor requestCaptor = ArgumentCaptor.forClass(GetRequest.class); + // ArgumentCaptor requestCaptor = ArgumentCaptor.forClass(GetRequest.class); + UpdateResponse updateResponse = new UpdateResponse( + new ReplicationResponse.ShardInfo(3, 2), + new ShardId(CommonName.CHECKPOINT_INDEX_NAME, "uuid", 2), + CommonName.CHECKPOINT_INDEX_NAME, + "1", + 7, + 17, + 2, + UPDATED + ); + AtomicReference getRequest = new AtomicReference<>(); doAnswer(invocation -> { - ActionListener listener = invocation.getArgument(2); - listener.onResponse(getResponse); + ActionRequest request = invocation.getArgument(0); + if (request instanceof GetRequest) { + getRequest.set((GetRequest) request); + ActionListener listener = invocation.getArgument(2); + listener.onResponse(getResponse); + } else { + UpdateRequest updateRequest = (UpdateRequest) request; + when(getResponse.getSource()).thenReturn(updateRequest.doc().sourceAsMap()); + ActionListener listener = invocation.getArgument(2); + listener.onResponse(updateResponse); + } return null; - }).when(clientUtil).asyncRequest(requestCaptor.capture(), any(BiConsumer.class), any(ActionListener.class)); + }).when(clientUtil).asyncRequest(any(), any(BiConsumer.class), any(ActionListener.class)); when(getResponse.isExists()).thenReturn(true); - RandomCutForest rcf = createRCF(); - docSource = new HashMap<>(); - docSource.put(FIELD_MODELV2, checkpointDao.rcfModelToCheckpoint(rcf, null)); + ThresholdedRandomCutForest trcf = createTRCF(); - when(getResponse.getSource()).thenReturn(docSource); + final CountDownLatch inProgressLatch = new CountDownLatch(1); + checkpointDao.putTRCFCheckpoint(modelId, trcf, ActionListener.wrap(response -> { inProgressLatch.countDown(); }, exception -> { + assertTrue("Should not reach here ", false); + inProgressLatch.countDown(); + })); - ActionListener> listener = mock(ActionListener.class); - checkpointDao.getRCFModel(modelId, listener); + ActionListener> listener = mock(ActionListener.class); + checkpointDao.getTRCFModel(modelId, listener); - GetRequest getRequest = requestCaptor.getValue(); - assertEquals(indexName, getRequest.index()); - assertEquals(modelId, getRequest.id()); - ArgumentCaptor> responseCaptor = ArgumentCaptor.forClass(Optional.class); + GetRequest capturedGetRequest = getRequest.get(); + assertEquals(indexName, capturedGetRequest.index()); + assertEquals(modelId, capturedGetRequest.id()); + ArgumentCaptor> responseCaptor = ArgumentCaptor.forClass(Optional.class); + verify(listener).onResponse(responseCaptor.capture()); + Optional result = responseCaptor.getValue(); + assertTrue(result.isPresent()); + RandomCutForest deserializedForest = result.get().getForest(); + RandomCutForest serializedForest = trcf.getForest(); + assertEquals(deserializedForest.getDimensions(), serializedForest.getDimensions()); + assertEquals(deserializedForest.getNumberOfTrees(), serializedForest.getNumberOfTrees()); + assertEquals(deserializedForest.getSampleSize(), serializedForest.getSampleSize()); + } + + @SuppressWarnings("unchecked") + public void test_getModelCheckpoint_Bwc() { + // ArgumentCaptor requestCaptor = ArgumentCaptor.forClass(GetRequest.class); + UpdateResponse updateResponse = new UpdateResponse( + new ReplicationResponse.ShardInfo(3, 2), + new ShardId(CommonName.CHECKPOINT_INDEX_NAME, "uuid", 2), + CommonName.CHECKPOINT_INDEX_NAME, + "1", + 7, + 17, + 2, + UPDATED + ); + AtomicReference getRequest = new AtomicReference<>(); + doAnswer(invocation -> { + ActionRequest request = invocation.getArgument(0); + if (request instanceof GetRequest) { + getRequest.set((GetRequest) request); + ActionListener listener = invocation.getArgument(2); + listener.onResponse(getResponse); + } else { + UpdateRequest updateRequest = (UpdateRequest) request; + when(getResponse.getSource()).thenReturn(updateRequest.doc().sourceAsMap()); + ActionListener listener = invocation.getArgument(2); + listener.onResponse(updateResponse); + } + return null; + }).when(clientUtil).asyncRequest(any(), any(BiConsumer.class), any(ActionListener.class)); + when(getResponse.isExists()).thenReturn(true); + + ThresholdedRandomCutForest trcf = createTRCF(); + + final CountDownLatch inProgressLatch = new CountDownLatch(1); + checkpointDao.putTRCFCheckpoint(modelId, trcf, ActionListener.wrap(response -> { inProgressLatch.countDown(); }, exception -> { + assertTrue("Should not reach here ", false); + inProgressLatch.countDown(); + })); + + ActionListener> listener = mock(ActionListener.class); + checkpointDao.getTRCFModel(modelId, listener); + + GetRequest capturedGetRequest = getRequest.get(); + assertEquals(indexName, capturedGetRequest.index()); + assertEquals(modelId, capturedGetRequest.id()); + ArgumentCaptor> responseCaptor = ArgumentCaptor.forClass(Optional.class); verify(listener).onResponse(responseCaptor.capture()); - Optional result = responseCaptor.getValue(); + Optional result = responseCaptor.getValue(); assertTrue(result.isPresent()); - RandomCutForest forest = result.get(); - assertEquals(forest.getDimensions(), rcf.getDimensions()); - assertEquals(forest.getNumberOfTrees(), rcf.getNumberOfTrees()); - assertEquals(forest.getSampleSize(), rcf.getSampleSize()); + RandomCutForest deserializedForest = result.get().getForest(); + RandomCutForest serializedForest = trcf.getForest(); + assertEquals(deserializedForest.getDimensions(), serializedForest.getDimensions()); + assertEquals(deserializedForest.getNumberOfTrees(), serializedForest.getNumberOfTrees()); + assertEquals(deserializedForest.getSampleSize(), serializedForest.getSampleSize()); } @SuppressWarnings("unchecked") @@ -364,16 +463,19 @@ public void test_getModelCheckpoint_returnEmptyToListener_whenModelNotFound() { }).when(clientUtil).asyncRequest(requestCaptor.capture(), any(BiConsumer.class), any(ActionListener.class)); when(getResponse.isExists()).thenReturn(false); - ActionListener> listener = mock(ActionListener.class); - checkpointDao.getRCFModel(modelId, listener); + ActionListener> listener = mock(ActionListener.class); + checkpointDao.getTRCFModel(modelId, listener); GetRequest getRequest = requestCaptor.getValue(); assertEquals(indexName, getRequest.index()); assertEquals(modelId, getRequest.id()); - ArgumentCaptor responseCaptor = ArgumentCaptor.forClass(Exception.class); - verify(listener).onFailure(responseCaptor.capture()); - Exception exception = responseCaptor.getValue(); - assertTrue(exception instanceof ResourceNotFoundException); + // ArgumentCaptor responseCaptor = ArgumentCaptor.forClass(Exception.class); + // verify(listener).onFailure(responseCaptor.capture()); + // Exception exception = responseCaptor.getValue(); + // assertTrue(exception instanceof ResourceNotFoundException); + ArgumentCaptor> responseCaptor = ArgumentCaptor.forClass(Optional.class); + verify(listener).onResponse(responseCaptor.capture()); + assertTrue(!responseCaptor.getValue().isPresent()); } @SuppressWarnings("unchecked") @@ -407,7 +509,7 @@ public void test_restore() throws IOException { when(getResponse.isExists()).thenReturn(true); Map source = new HashMap<>(); source.put(CheckpointDao.DETECTOR_ID, state.getDetectorId()); - source.put(CheckpointDao.FIELD_MODEL, checkpointDao.toCheckpoint(modelToSave)); + source.put(CheckpointDao.FIELD_MODELV2, checkpointDao.toCheckpoint(modelToSave, modelId).get()); source.put(CheckpointDao.TIMESTAMP, "2020-10-11T22:58:23.610392Z"); when(getResponse.getSource()).thenReturn(source); @@ -439,10 +541,9 @@ public void test_restore() throws IOException { Queue samplesToSave = modelToSave.getSamples(); assertEquals(samplesToSave.size(), queue.size()); assertTrue(Arrays.equals(samplesToSave.peek(), queue.peek())); - logger.info(modelToSave.getRcf()); - logger.info(model.getRcf()); - assertEquals(modelToSave.getRcf().getTotalUpdates(), model.getRcf().getTotalUpdates()); - assertTrue(model.getThreshold() != null); + logger.info(modelToSave.getTrcf()); + logger.info(model.getTrcf()); + assertEquals(modelToSave.getTrcf().get().getForest().getTotalUpdates(), model.getTrcf().get().getForest().getTotalUpdates()); } public void test_batch_write_no_index() { @@ -592,13 +693,15 @@ public void test_too_large_checkpoint() throws IOException { indexName, gson, mapper, - schema, converter, + trcfMapper, + trcfSchema, thresholdingModelClass, indexUtil, 1, // make the max checkpoint size 1 byte only serializeRCFBufferPool, - AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + anomalyRate ); ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).build()); @@ -609,7 +712,12 @@ public void test_too_large_checkpoint() throws IOException { public void test_to_index_source() throws IOException { ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).build()); - assertTrue(!checkpointDao.toIndexSource(state).isEmpty()); + Map source = checkpointDao.toIndexSource(state); + assertTrue(!source.isEmpty()); + for (Object obj : source.values()) { + // Opensearch cannot recognize Optional + assertTrue(!(obj instanceof Optional)); + } } @SuppressWarnings("unchecked") @@ -622,21 +730,23 @@ public void testBorrowFromPoolFailure() throws Exception { indexName, gson, mapper, - schema, converter, + trcfMapper, + trcfSchema, thresholdingModelClass, indexUtil, 1, // make the max checkpoint size 1 byte only mockSerializeRCFBufferPool, - AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + anomalyRate ); ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).build()); - assertTrue(!checkpointDao.toCheckpoint(state.getModel()).isEmpty()); + assertTrue(!checkpointDao.toCheckpoint(state.getModel(), modelId).get().isEmpty()); } public void testMapperFailure() throws IOException { - RandomCutForestMapper mockMapper = mock(RandomCutForestMapper.class); + ThresholdedRandomCutForestMapper mockMapper = mock(ThresholdedRandomCutForestMapper.class); when(mockMapper.toState(any())).thenThrow(RuntimeException.class); checkpointDao = new CheckpointDao( @@ -644,28 +754,264 @@ public void testMapperFailure() throws IOException { clientUtil, indexName, gson, - mockMapper, - schema, + mapper, converter, + mockMapper, + trcfSchema, thresholdingModelClass, indexUtil, 1, // make the max checkpoint size 1 byte only serializeRCFBufferPool, - AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + anomalyRate ); ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).build()); - String json = checkpointDao.toCheckpoint(state.getModel()); - assertEquals(null, JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_RCF)); + String json = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + assertEquals(null, JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_SAMPLE)); - assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_THRESHOLD)); + // assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_THRESHOLD)); + // assertNotNull(JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); } public void testEmptySample() throws IOException { ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).sampleSize(0).build()); - String json = checkpointDao.toCheckpoint(state.getModel()); - assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_RCF)); + String json = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + // assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); assertEquals(null, JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_SAMPLE)); - assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_THRESHOLD)); + // assertTrue(null != JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_THRESHOLD)); + assertNotNull(JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); + } + + public void testToCheckpointErcfCheckoutFail() throws Exception { + when(serializeRCFBufferPool.borrowObject()).thenThrow(RuntimeException.class); + + ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).sampleSize(0).build()); + String json = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + + assertNotNull(JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); + } + + @SuppressWarnings("unchecked") + private void setUpMockTrcf() { + trcfMapper = mock(ThresholdedRandomCutForestMapper.class); + trcfSchema = mock(Schema.class); + checkpointDao = new CheckpointDao( + client, + clientUtil, + indexName, + gson, + mapper, + converter, + trcfMapper, + trcfSchema, + thresholdingModelClass, + indexUtil, + maxCheckpointBytes, + serializeRCFBufferPool, + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + anomalyRate + ); + } + + public void testToCheckpointTrcfCheckoutBufferFail() throws Exception { + setUpMockTrcf(); + when(trcfMapper.toState(any())).thenThrow(RuntimeException.class).thenReturn(null); + + ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).sampleSize(0).build()); + String json = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + + assertNotNull(JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); + } + + public void testToCheckpointTrcfFailNewBuffer() throws Exception { + setUpMockTrcf(); + doReturn(null).when(serializeRCFBufferPool).borrowObject(); + when(trcfMapper.toState(any())).thenThrow(RuntimeException.class); + + ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).sampleSize(0).build()); + String json = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + + assertNull(JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); + } + + public void testToCheckpointTrcfCheckoutBufferInvalidateFail() throws Exception { + setUpMockTrcf(); + when(trcfMapper.toState(any())).thenThrow(RuntimeException.class).thenReturn(null); + doThrow(RuntimeException.class).when(serializeRCFBufferPool).invalidateObject(any()); + + ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).sampleSize(0).build()); + String json = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + + assertNotNull(JsonDeserializer.getChildNode(json, CheckpointDao.ENTITY_TRCF)); + } + + public void testFromEntityModelCheckpointWithTrcf() throws Exception { + ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).build()); + String model = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + + Map entity = new HashMap<>(); + entity.put(FIELD_MODELV2, model); + entity.put(TIMESTAMP, Instant.now().toString()); + Optional> result = checkpointDao.fromEntityModelCheckpoint(entity, this.modelId); + + assertTrue(result.isPresent()); + Entry pair = result.get(); + EntityModel entityModel = pair.getKey(); + assertTrue(entityModel.getTrcf().isPresent()); + } + + public void testFromEntityModelCheckpointTrcfMapperFail() throws Exception { + setUpMockTrcf(); + when(trcfMapper.toModel(any())).thenThrow(RuntimeException.class); + ModelState state = MLUtil.randomModelState(new RandomModelStateConfig.Builder().fullModel(true).build()); + String model = checkpointDao.toCheckpoint(state.getModel(), modelId).get(); + + Map entity = new HashMap<>(); + entity.put(FIELD_MODELV2, model); + entity.put(TIMESTAMP, Instant.now().toString()); + Optional> result = checkpointDao.fromEntityModelCheckpoint(entity, this.modelId); + + assertTrue(result.isPresent()); + Entry pair = result.get(); + EntityModel entityModel = pair.getKey(); + assertFalse(entityModel.getTrcf().isPresent()); + } + + private Pair, Instant> setUp1_0Model(String checkpointFileName) throws FileNotFoundException, + IOException, + URISyntaxException { + String model = null; + try ( + FileReader v1CheckpointFile = new FileReader(new File(getClass().getResource(checkpointFileName).toURI())); + BufferedReader rr = new BufferedReader(v1CheckpointFile) + ) { + model = rr.readLine(); + } + + Instant now = Instant.now(); + Map entity = new HashMap<>(); + entity.put(FIELD_MODEL, model); + entity.put(TIMESTAMP, now.toString()); + return Pair.of(entity, now); + } + + public void testFromEntityModelCheckpointBWC() throws FileNotFoundException, IOException, URISyntaxException { + Pair, Instant> modelPair = setUp1_0Model("checkpoint_2.json"); + Instant now = modelPair.getRight(); + + Optional> result = checkpointDao.fromEntityModelCheckpoint(modelPair.getLeft(), this.modelId); + assertTrue(result.isPresent()); + Entry pair = result.get(); + assertEquals(now, pair.getValue()); + + EntityModel entityModel = pair.getKey(); + + Queue samples = entityModel.getSamples(); + assertEquals(6, samples.size()); + double[] firstSample = samples.peek(); + assertEquals(1, firstSample.length); + assertEquals(0.6832234717598454, firstSample[0], 1e-10); + + ThresholdedRandomCutForest trcf = entityModel.getTrcf().get(); + RandomCutForest forest = trcf.getForest(); + assertEquals(1, forest.getDimensions()); + assertEquals(10, forest.getNumberOfTrees()); + assertEquals(256, forest.getSampleSize()); + // there are at least 10 scores in the checkpoint + assertTrue(trcf.getThresholder().getCount() > 10); + + Random random = new Random(0); + for (int i = 0; i < 100; i++) { + double[] point = getPoint(forest.getDimensions(), random); + double score = trcf.process(point, 0).getRcfScore(); + assertTrue(score > 0); + forest.update(point); + } + } + + public void testFromEntityModelCheckpointModelTooLarge() throws FileNotFoundException, IOException, URISyntaxException { + Pair, Instant> modelPair = setUp1_0Model("checkpoint_2.json"); + checkpointDao = new CheckpointDao( + client, + clientUtil, + indexName, + gson, + mapper, + converter, + trcfMapper, + trcfSchema, + thresholdingModelClass, + indexUtil, + 100_000, // checkpoint_2.json is of 224603 bytes. + serializeRCFBufferPool, + AnomalyDetectorSettings.SERIALIZATION_BUFFER_BYTES, + anomalyRate + ); + Optional> result = checkpointDao.fromEntityModelCheckpoint(modelPair.getLeft(), this.modelId); + // checkpoint is only configured to take in 1 MB checkpoint at most. But the checkpoint here is of 1408047 bytes. + assertTrue(!result.isPresent()); + } + + // test no model is present in checkpoint + public void testFromEntityModelCheckpointEmptyModel() throws FileNotFoundException, IOException, URISyntaxException { + Map entity = new HashMap<>(); + entity.put(TIMESTAMP, Instant.now().toString()); + + Optional> result = checkpointDao.fromEntityModelCheckpoint(entity, this.modelId); + assertTrue(!result.isPresent()); + } + + public void testFromEntityModelCheckpointEmptySamples() throws FileNotFoundException, IOException, URISyntaxException { + Pair, Instant> modelPair = setUp1_0Model("checkpoint_1.json"); + Optional> result = checkpointDao.fromEntityModelCheckpoint(modelPair.getLeft(), this.modelId); + assertTrue(result.isPresent()); + Queue samples = result.get().getKey().getSamples(); + assertEquals(0, samples.size()); + } + + public void testFromEntityModelCheckpointNoRCF() throws FileNotFoundException, IOException, URISyntaxException { + Pair, Instant> modelPair = setUp1_0Model("checkpoint_3.json"); + Optional> result = checkpointDao.fromEntityModelCheckpoint(modelPair.getLeft(), this.modelId); + assertTrue(result.isPresent()); + assertTrue(!result.get().getKey().getTrcf().isPresent()); + } + + public void testFromEntityModelCheckpointNoThreshold() throws FileNotFoundException, IOException, URISyntaxException { + Pair, Instant> modelPair = setUp1_0Model("checkpoint_4.json"); + Optional> result = checkpointDao.fromEntityModelCheckpoint(modelPair.getLeft(), this.modelId); + assertTrue(result.isPresent()); + + ThresholdedRandomCutForest trcf = result.get().getKey().getTrcf().get(); + RandomCutForest forest = trcf.getForest(); + assertEquals(1, forest.getDimensions()); + assertEquals(10, forest.getNumberOfTrees()); + assertEquals(256, forest.getSampleSize()); + // there are no scores in the checkpoint + assertEquals(0, trcf.getThresholder().getCount()); + } + + public void testFromEntityModelCheckpointWithEntity() throws Exception { + ModelState state = MLUtil + .randomModelState(new RandomModelStateConfig.Builder().fullModel(true).entityAttributes(true).build()); + Map content = checkpointDao.toIndexSource(state); + // Opensearch will convert from java.time.ZonedDateTime to String. Here I am converting to simulate that + content.put(TIMESTAMP, "2021-09-23T05:00:37.93195Z"); + + Optional> result = checkpointDao.fromEntityModelCheckpoint(content, this.modelId); + + assertTrue(result.isPresent()); + Entry pair = result.get(); + EntityModel entityModel = pair.getKey(); + assertTrue(entityModel.getEntity().isPresent()); + assertEquals(state.getModel().getEntity().get(), entityModel.getEntity().get()); + } + + private double[] getPoint(int dimensions, Random random) { + double[] point = new double[dimensions]; + for (int i = 0; i < point.length; i++) { + point[i] = random.nextDouble(); + } + return point; } } diff --git a/src/test/java/org/opensearch/ad/ml/CheckpointDeleteTests.java b/src/test/java/org/opensearch/ad/ml/CheckpointDeleteTests.java index 36aa89901..dfbf6fc46 100644 --- a/src/test/java/org/opensearch/ad/ml/CheckpointDeleteTests.java +++ b/src/test/java/org/opensearch/ad/ml/CheckpointDeleteTests.java @@ -38,6 +38,7 @@ import org.apache.commons.pool2.impl.GenericObjectPool; import org.junit.After; import org.junit.Before; +import org.mockito.Mock; import org.opensearch.OpenSearchException; import org.opensearch.action.ActionListener; import org.opensearch.ad.AbstractADTest; @@ -50,9 +51,10 @@ import org.opensearch.index.reindex.DeleteByQueryAction; import org.opensearch.index.reindex.ScrollableHitSource; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestMapper; +import com.amazon.randomcutforest.parkservices.state.ThresholdedRandomCutForestState; import com.amazon.randomcutforest.serialize.json.v1.V1JsonToV2StateConverter; import com.amazon.randomcutforest.state.RandomCutForestMapper; -import com.amazon.randomcutforest.state.RandomCutForestState; import com.google.gson.Gson; import io.protostuff.LinkedBuffer; @@ -81,6 +83,14 @@ private enum DeleteExecutionMode { private int maxCheckpointBytes; private GenericObjectPool objectPool; + @Mock + private ThresholdedRandomCutForestMapper ercfMapper; + + @Mock + private Schema ercfSchema; + + double anomalyRate; + @SuppressWarnings("unchecked") @Override @Before @@ -96,24 +106,26 @@ public void setUp() throws Exception { maxCheckpointBytes = 1_000_000; RandomCutForestMapper mapper = mock(RandomCutForestMapper.class); - Schema schema = mock(Schema.class); V1JsonToV2StateConverter converter = mock(V1JsonToV2StateConverter.class); objectPool = mock(GenericObjectPool.class); int deserializeRCFBufferSize = 512; + anomalyRate = 0.005; checkpointDao = new CheckpointDao( client, clientUtil, CommonName.CHECKPOINT_INDEX_NAME, gson, mapper, - schema, converter, + ercfMapper, + ercfSchema, HybridThresholdingModel.class, indexUtil, maxCheckpointBytes, objectPool, - deserializeRCFBufferSize + deserializeRCFBufferSize, + anomalyRate ); } diff --git a/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java b/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java index be69e1f4f..37902ef8f 100644 --- a/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java +++ b/src/test/java/org/opensearch/ad/ml/EntityColdStarterTests.java @@ -56,7 +56,6 @@ import java.util.concurrent.TimeUnit; import java.util.concurrent.atomic.AtomicBoolean; -import org.apache.commons.lang3.tuple.Triple; import org.opensearch.Version; import org.opensearch.action.ActionListener; import org.opensearch.action.get.GetRequest; @@ -84,6 +83,7 @@ import org.opensearch.cluster.node.DiscoveryNode; import org.opensearch.cluster.node.DiscoveryNodeRole; import org.opensearch.cluster.service.ClusterService; +import org.opensearch.common.collect.Tuple; import org.opensearch.common.settings.ClusterSettings; import org.opensearch.common.settings.Setting; import org.opensearch.common.settings.Settings; @@ -94,8 +94,9 @@ import test.org.opensearch.ad.util.MLUtil; -import com.amazon.randomcutforest.RandomCutForest; import com.amazon.randomcutforest.config.Precision; +import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; public class EntityColdStarterTests extends AbstractADTest { int numMinSamples; @@ -146,8 +147,6 @@ public void setUp() throws Exception { return null; }).when(clientUtil).asyncRequest(any(GetRequest.class), any(), any(ActionListener.class)); - ModelPartitioner modelPartitioner = mock(ModelPartitioner.class); - Set> nodestateSetting = new HashSet<>(ClusterSettings.BUILT_IN_CLUSTER_SETTINGS); nodestateSetting.add(MAX_RETRY_FOR_UNRESPONSIVE_NODE); nodestateSetting.add(BACKOFF_MINUTES); @@ -170,7 +169,6 @@ public void setUp() throws Exception { clientUtil, clock, AnomalyDetectorSettings.HOURLY_MAINTENANCE, - modelPartitioner, clusterService ); @@ -214,11 +212,6 @@ public void setUp() throws Exception { interpolator, searchFeatureDao, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES, featureManager, settings, AnomalyDetectorSettings.HOURLY_MAINTENANCE, @@ -250,20 +243,19 @@ private void checkSemaphoreRelease() throws InterruptedException { // train using samples directly public void testTrainUsingSamples() throws InterruptedException { Queue samples = MLUtil.createQueueSamples(numMinSamples); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); entityColdStarter.trainModel(entity, detectorId, modelState, listener); - RandomCutForest forest = model.getRcf(); - assertTrue(forest != null); - assertEquals(numMinSamples, forest.getTotalUpdates()); - assertTrue(model.getThreshold() != null); + assertTrue(model.getTrcf().isPresent()); + ThresholdedRandomCutForest ercf = model.getTrcf().get(); + assertEquals(numMinSamples, ercf.getForest().getTotalUpdates()); checkSemaphoreRelease(); } public void testColdStart() throws InterruptedException, IOException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -285,11 +277,10 @@ public void testColdStart() throws InterruptedException, IOException { entityColdStarter.trainModel(entity, detectorId, modelState, listener); waitForColdStartFinish(); - RandomCutForest forest = model.getRcf(); - assertTrue(forest != null); + assertTrue(model.getTrcf().isPresent()); + ThresholdedRandomCutForest ercf = model.getTrcf().get(); // maxSampleStride * (continuousSampledArray.length - 1) + 1 = 64 * 2 + 1 = 129 - assertEquals(129, forest.getTotalUpdates()); - assertTrue(model.getThreshold() != null); + assertEquals(129, ercf.getForest().getTotalUpdates()); // sleep 1 secs to give time for the last timestamp record to expire when superShortLastColdStartTimeState = true Thread.sleep(1000L); @@ -298,14 +289,11 @@ public void testColdStart() throws InterruptedException, IOException { released.set(false); // too frequent cold start of the same detector will fail samples = MLUtil.createQueueSamples(1); - model = new EntityModel(entity, samples, null, null); + model = new EntityModel(entity, samples, null); entityColdStarter.trainModel(entity, detectorId, modelState, listener); waitForColdStartFinish(); - forest = model.getRcf(); - - assertTrue(forest == null); - assertTrue(model.getThreshold() == null); + assertFalse(model.getTrcf().isPresent()); checkSemaphoreRelease(); } @@ -322,7 +310,7 @@ private void waitForColdStartFinish() throws InterruptedException { // min max: miss one public void testMissMin() throws IOException, InterruptedException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -335,16 +323,14 @@ public void testMissMin() throws IOException, InterruptedException { verify(searchFeatureDao, never()).getColdStartSamplesForPeriods(any(), any(), any(), anyBoolean(), any()); - RandomCutForest forest = model.getRcf(); - assertTrue(forest == null); - assertTrue(model.getThreshold() == null); + assertTrue(!model.getTrcf().isPresent()); checkSemaphoreRelease(); } // two segments of samples, one segment has 3 samples, while another one has only 1 public void testTwoSegmentsWithSingleSample() throws InterruptedException, IOException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -374,19 +360,19 @@ public void testTwoSegmentsWithSingleSample() throws InterruptedException, IOExc Thread.sleep(500L); i++; } - RandomCutForest forest = model.getRcf(); - assertTrue(forest != null); + assertTrue(model.getTrcf().isPresent()); + ThresholdedRandomCutForest ercf = model.getTrcf().get(); + // 1st segment: maxSampleStride * (continuousSampledArray.length - 1) + 1 = 64 * 2 + 1 = 129 // 2nd segment: 1 - assertEquals(130, forest.getTotalUpdates()); - assertTrue(model.getThreshold() != null); + assertEquals(130, ercf.getForest().getTotalUpdates()); checkSemaphoreRelease(); } // two segments of samples, one segment has 3 samples, while another one 2 samples public void testTwoSegments() throws InterruptedException, IOException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -417,18 +403,17 @@ public void testTwoSegments() throws InterruptedException, IOException { Thread.sleep(500L); i++; } - RandomCutForest forest = model.getRcf(); - assertTrue(forest != null); + assertTrue(model.getTrcf().isPresent()); + ThresholdedRandomCutForest ercf = model.getTrcf().get(); // 1st segment: maxSampleStride * (continuousSampledArray.length - 1) + 1 = 64 * 2 + 1 = 129 // 2nd segment: maxSampleStride * (continuousSampledArray.length - 1) + 1 = 64 * 1 + 1 = 65 - assertEquals(194, forest.getTotalUpdates()); - assertTrue(model.getThreshold() != null); + assertEquals(194, ercf.getForest().getTotalUpdates()); checkSemaphoreRelease(); } public void testThrottledColdStart() throws InterruptedException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -448,7 +433,7 @@ public void testThrottledColdStart() throws InterruptedException { public void testColdStartException() throws InterruptedException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -465,7 +450,7 @@ public void testColdStartException() throws InterruptedException { public void testNotEnoughSamples() throws InterruptedException, IOException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -492,8 +477,7 @@ public void testNotEnoughSamples() throws InterruptedException, IOException { Thread.sleep(500L); i++; } - assertTrue(model.getRcf() == null); - assertTrue(model.getThreshold() == null); + assertTrue(!model.getTrcf().isPresent()); // 1st segment: maxSampleStride * (continuousSampledArray.length - 1) + 1 = 64 * 1 + 1 = 65 // 65 + origin 1 data points assertEquals("real sample size is " + model.getSamples().size(), 66, model.getSamples().size()); @@ -501,7 +485,7 @@ public void testNotEnoughSamples() throws InterruptedException, IOException { public void testEmptyDataRange() throws InterruptedException { Queue samples = MLUtil.createQueueSamples(1); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); doAnswer(invocation -> { @@ -513,8 +497,7 @@ public void testEmptyDataRange() throws InterruptedException { entityColdStarter.trainModel(entity, detectorId, modelState, listener); waitForColdStartFinish(); - assertTrue(model.getRcf() == null); - assertTrue(model.getThreshold() == null); + assertTrue(!model.getTrcf().isPresent()); // the min-max range is too small and thus no data range can be found assertEquals("real sample size is " + model.getSamples().size(), 1, model.getSamples().size()); } @@ -523,7 +506,7 @@ public void testTrainModelFromExistingSamplesEnoughSamples() { int inputDimension = 2; int dimensions = inputDimension * detector.getShingleSize(); - RandomCutForest.Builder rcfConfig = RandomCutForest + ThresholdedRandomCutForest.Builder rcfConfig = ThresholdedRandomCutForest .builder() .compact(true) .dimensions(dimensions) @@ -536,13 +519,13 @@ public void testTrainModelFromExistingSamplesEnoughSamples() { .outputAfter(numMinSamples) .parallelExecutionEnabled(false) .sampleSize(AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE) - .internalShinglingEnabled(true); - Triple, RandomCutForest, ThresholdingModel> models = MLUtil.prepareModel(inputDimension, rcfConfig); - Queue samples = models.getLeft(); - RandomCutForest rcf = models.getMiddle(); - ThresholdingModel threshold = models.getRight(); + .internalShinglingEnabled(true) + .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE); + Tuple, ThresholdedRandomCutForest> models = MLUtil.prepareModel(inputDimension, rcfConfig); + Queue samples = models.v1(); + ThresholdedRandomCutForest rcf = models.v2(); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); ModelManager modelManager = new ModelManager( @@ -553,16 +536,10 @@ public void testTrainModelFromExistingSamplesEnoughSamples() { AnomalyDetectorSettings.TIME_DECAY, AnomalyDetectorSettings.NUM_MIN_SAMPLES, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES, AnomalyDetectorSettings.MIN_PREVIEW_SIZE, AnomalyDetectorSettings.HOURLY_MAINTENANCE, AnomalyDetectorSettings.HOURLY_MAINTENANCE, entityColdStarter, - mock(ModelPartitioner.class), mock(FeatureManager.class), mock(MemoryTracker.class) ); @@ -572,25 +549,19 @@ public void testTrainModelFromExistingSamplesEnoughSamples() { // make sure we trained the expected models for (int i = 0; i < 100; i++) { double[] point = r.ints(inputDimension, 0, 50).asDoubleStream().toArray(); - double score = rcf.getAnomalyScore(point); + AnomalyDescriptor descriptor = rcf.process(point, 0); ThresholdingResult result = modelManager .getAnomalyResultForEntity(point, modelState, modelId, entity, detector.getShingleSize()); - assertEquals(score, result.getRcfScore(), 1e-10); - assertEquals(threshold.grade(score), result.getGrade(), 1e-10); - assertEquals(modelManager.computeRcfConfidence(rcf) * threshold.confidence(), result.getConfidence(), 1e-10); - rcf.update(point); - threshold.update(score); + assertEquals(descriptor.getRcfScore(), result.getRcfScore(), 1e-10); + assertEquals(descriptor.getAnomalyGrade(), result.getGrade(), 1e-10); } } public void testTrainModelFromExistingSamplesNotEnoughSamples() { Queue samples = new ArrayDeque<>(); - EntityModel model = new EntityModel(entity, samples, null, null); + EntityModel model = new EntityModel(entity, samples, null); modelState = new ModelState<>(model, modelId, detectorId, ModelType.ENTITY.getName(), clock, priority); entityColdStarter.trainModelFromExistingSamples(modelState, detector.getShingleSize()); - RandomCutForest adRCF = modelState.getModel().getRcf(); - ThresholdingModel adThreshold = modelState.getModel().getThreshold(); - assertTrue(adRCF == null); - assertTrue(adThreshold == null); + assertTrue(!modelState.getModel().getTrcf().isPresent()); } } diff --git a/src/test/java/org/opensearch/ad/ml/EntityModelTests.java b/src/test/java/org/opensearch/ad/ml/EntityModelTests.java index ac959e4f7..1f4afe829 100644 --- a/src/test/java/org/opensearch/ad/ml/EntityModelTests.java +++ b/src/test/java/org/opensearch/ad/ml/EntityModelTests.java @@ -11,24 +11,63 @@ package org.opensearch.ad.ml; +import java.util.ArrayDeque; + +import org.junit.Before; +import org.junit.Test; import org.opensearch.test.OpenSearchTestCase; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; + public class EntityModelTests extends OpenSearchTestCase { + + private ThresholdedRandomCutForest trcf; + + @Before + public void setup() { + this.trcf = new ThresholdedRandomCutForest(ThresholdedRandomCutForest.builder().dimensions(2).internalShinglingEnabled(true)); + } + public void testNullInternalSampleQueue() { - EntityModel model = new EntityModel(null, null, null, null); + EntityModel model = new EntityModel(null, null, null); model.addSample(new double[] { 0.8 }); assertEquals(1, model.getSamples().size()); } public void testNullInputSample() { - EntityModel model = new EntityModel(null, null, null, null); + EntityModel model = new EntityModel(null, null, null); model.addSample(null); assertEquals(0, model.getSamples().size()); } public void testEmptyInputSample() { - EntityModel model = new EntityModel(null, null, null, null); + EntityModel model = new EntityModel(null, null, null); model.addSample(new double[] {}); assertEquals(0, model.getSamples().size()); } + + @Test + public void trcf_constructor() { + EntityModel em = new EntityModel(null, new ArrayDeque<>(), trcf); + assertEquals(trcf, em.getTrcf().get()); + } + + @Test + public void clear() { + EntityModel em = new EntityModel(null, new ArrayDeque<>(), trcf); + + em.clear(); + + assertTrue(em.getSamples().isEmpty()); + assertFalse(em.getTrcf().isPresent()); + } + + @Test + public void setTrcf() { + EntityModel em = new EntityModel(null, null, null); + assertFalse(em.getTrcf().isPresent()); + + em.setTrcf(this.trcf); + assertTrue(em.getTrcf().isPresent()); + } } diff --git a/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java b/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java index 7d545092d..625d740de 100644 --- a/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java +++ b/src/test/java/org/opensearch/ad/ml/ModelManagerTests.java @@ -26,15 +26,14 @@ package org.opensearch.ad.ml; -import static java.util.Arrays.asList; import static org.junit.Assert.assertEquals; import static org.junit.Assert.assertFalse; import static org.junit.Assert.assertTrue; import static org.mockito.ArgumentMatchers.any; -import static org.mockito.Matchers.anyObject; +import static org.mockito.ArgumentMatchers.anyLong; +import static org.mockito.ArgumentMatchers.anyString; import static org.mockito.Matchers.eq; import static org.mockito.Mockito.doAnswer; -import static org.mockito.Mockito.doReturn; import static org.mockito.Mockito.doThrow; import static org.mockito.Mockito.mock; import static org.mockito.Mockito.never; @@ -46,8 +45,9 @@ import java.time.Clock; import java.time.Duration; import java.time.Instant; -import java.util.AbstractMap.SimpleEntry; import java.util.AbstractMap.SimpleImmutableEntry; +import java.util.ArrayDeque; +import java.util.Arrays; import java.util.Collections; import java.util.List; import java.util.Map.Entry; @@ -82,7 +82,6 @@ import org.opensearch.ad.feature.FeatureManager; import org.opensearch.ad.feature.SearchFeatureDao; import org.opensearch.ad.ml.ModelManager.ModelType; -import org.opensearch.ad.ml.rcf.CombinedRcfResult; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.Entity; import org.opensearch.ad.ratelimit.CheckpointWriteWorker; @@ -103,6 +102,8 @@ import test.org.opensearch.ad.util.RandomModelStateConfig; import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.AnomalyDescriptor; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.amazon.randomcutforest.returntypes.DiVector; import com.google.common.collect.Sets; @@ -137,6 +138,15 @@ public class ModelManagerTests { @Mock private EntityCache cache; + @Mock + private ModelState modelState; + + @Mock + private EntityModel entityModel; + + @Mock + private ThresholdedRandomCutForest trcf; + private double modelDesiredSizePercentage; private double modelMaxSizePercentage; private int numTrees; @@ -145,16 +155,10 @@ public class ModelManagerTests { private double rcfTimeDecay; private int numMinSamples; private double thresholdMinPvalue; - private double thresholdMaxRankError; - private double thresholdMaxScore; - private int thresholdNumLogNormalQuantiles; - private int thresholdDownsamples; - private long thresholdMaxSamples; private int minPreviewSize; private Duration modelTtl; private Duration checkpointInterval; - private RandomCutForest rcf; - private ModelPartitioner modelPartitioner; + private ThresholdedRandomCutForest rcf; @Mock private HybridThresholdingModel hybridThresholdingModel; @@ -197,11 +201,6 @@ public void setup() { rcfTimeDecay = 1.0 / 1024; numMinSamples = 1; thresholdMinPvalue = 0.95; - thresholdMaxRankError = 1e-4; - thresholdMaxScore = 8.0; - thresholdNumLogNormalQuantiles = 10000; - thresholdDownsamples = 1_000_000; - thresholdMaxSamples = 2_000_000; minPreviewSize = 500; modelTtl = Duration.ofHours(1); checkpointInterval = Duration.ofHours(1); @@ -214,29 +213,19 @@ public void setup() { } point = new double[] { 2 }; - rcf = spy(RandomCutForest.builder().dimensions(numFeatures).sampleSize(numSamples).numberOfTrees(numTrees).build()); - when(rcf.getAnomalyAttribution(point)).thenReturn(attributionVec); - - when(jvmService.info().getMem().getHeapMax().getBytes()).thenReturn(10_000_000_000L); - - when(anomalyDetector.getShingleSize()).thenReturn(shingleSize); + rcf = spy(ThresholdedRandomCutForest.builder().dimensions(numFeatures).sampleSize(numSamples).numberOfTrees(numTrees).build()); + double score = 11.; - settings = Settings.builder().put("plugins.anomaly_detection.model_max_size_percent", modelMaxSizePercentage).build(); - final Set> settingsSet = Stream - .concat( - ClusterSettings.BUILT_IN_CLUSTER_SETTINGS.stream(), - Sets.newHashSet(AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE).stream() - ) - .collect(Collectors.toSet()); - ClusterSettings clusterSettings = new ClusterSettings(settings, settingsSet); - clusterService = new ClusterService(settings, clusterSettings, null); - MemoryTracker memoryTracker = new MemoryTracker( - jvmService, - modelMaxSizePercentage, - modelDesiredSizePercentage, - clusterService, - adCircuitBreakerService - ); + double confidence = 0.091353632; + double grade = 0.1; + AnomalyDescriptor descriptor = new AnomalyDescriptor(); + descriptor.setRcfScore(score); + descriptor.setForestSize(numTrees); + descriptor.setDataConfidence(confidence); + descriptor.setAnomalyGrade(grade); + descriptor.setAttribution(attributionVec); + descriptor.setTotalUpdates(numSamples); + when(rcf.process(any(), anyLong())).thenReturn(descriptor); ExecutorService executorService = mock(ExecutorService.class); when(threadPool.executor(AnomalyDetectorPlugin.AD_THREAD_POOL_NAME)).thenReturn(executorService); @@ -246,11 +235,12 @@ public void setup() { return null; }).when(executorService).execute(any(Runnable.class)); - modelPartitioner = spy(new ModelPartitioner(numSamples, numTrees, nodeFilter, memoryTracker)); - now = Instant.now(); when(clock.instant()).thenReturn(now); + memoryTracker = mock(MemoryTracker.class); + when(memoryTracker.isHostingAllowed(anyString(), any())).thenReturn(true); + modelManager = spy( new ModelManager( checkpointDao, @@ -260,16 +250,10 @@ public void setup() { rcfTimeDecay, numMinSamples, thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples, minPreviewSize, modelTtl, checkpointInterval, entityColdStarter, - modelPartitioner, featureManager, memoryTracker ) @@ -278,6 +262,12 @@ public void setup() { detectorId = "detectorId"; rcfModelId = "detectorId_model_rcf_1"; thresholdModelId = "detectorId_model_threshold"; + + when(this.modelState.getModel()).thenReturn(this.entityModel); + when(this.entityModel.getTrcf()).thenReturn(Optional.of(this.trcf)); + settings = Settings.builder().put("plugins.anomaly_detection.model_max_size_percent", modelMaxSizePercentage).build(); + + when(anomalyDetector.getShingleSize()).thenReturn(shingleSize); } private Object[] getDetectorIdForModelIdData() { @@ -293,7 +283,7 @@ private Object[] getDetectorIdForModelIdData() { @Test @Parameters(method = "getDetectorIdForModelIdData") public void getDetectorIdForModelId_returnExpectedId(String modelId, String expectedDetectorId) { - assertEquals(expectedDetectorId, modelManager.getDetectorIdForModelId(modelId)); + assertEquals(expectedDetectorId, SingleStreamModelIdMapper.getDetectorIdForModelId(modelId)); } private Object[] getDetectorIdForModelIdIllegalArgument() { @@ -303,52 +293,7 @@ private Object[] getDetectorIdForModelIdIllegalArgument() { @Test(expected = IllegalArgumentException.class) @Parameters(method = "getDetectorIdForModelIdIllegalArgument") public void getDetectorIdForModelId_throwIllegalArgument_forInvalidId(String modelId) { - modelManager.getDetectorIdForModelId(modelId); - } - - private Object[] combineRcfResultsData() { - double[] attribution = new double[] { 1 }; - return new Object[] { - new Object[] { asList(), 1, new CombinedRcfResult(0, 0, new double[0]) }, - new Object[] { asList(new RcfResult(0, 0, 0, new double[0])), 1, new CombinedRcfResult(0, 0, new double[0]) }, - new Object[] { asList(new RcfResult(1, 0, 50, attribution)), 1, new CombinedRcfResult(1, 0, attribution) }, - new Object[] { - asList(new RcfResult(1, 0, 50, attribution), new RcfResult(2, 0, 50, attribution)), - 1, - new CombinedRcfResult(1.5, 0, attribution) }, - new Object[] { - asList(new RcfResult(1, 0, 40, attribution), new RcfResult(2, 0, 60, attribution), new RcfResult(3, 0, 100, attribution)), - 1, - new CombinedRcfResult(2.3, 0, attribution) }, - new Object[] { asList(new RcfResult(0, 1, 100, attribution)), 1, new CombinedRcfResult(0, 1, attribution) }, - new Object[] { asList(new RcfResult(0, 1, 50, attribution)), 1, new CombinedRcfResult(0, 0.5, attribution) }, - new Object[] { asList(new RcfResult(0, 0.5, 1000, attribution)), 1, new CombinedRcfResult(0, 0.5, attribution) }, - new Object[] { - asList(new RcfResult(0, 1, 50, attribution), new RcfResult(0, 0, 50, attribution)), - 1, - new CombinedRcfResult(0, 0.5, attribution) }, - new Object[] { - asList(new RcfResult(0, 0.5, 50, attribution), new RcfResult(0, 0.5, 50, attribution)), - 1, - new CombinedRcfResult(0, 0.5, attribution) }, - new Object[] { - asList(new RcfResult(0, 1, 20, attribution), new RcfResult(0, 1, 30, attribution), new RcfResult(0, 0.5, 50, attribution)), - 1, - new CombinedRcfResult(0, 0.75, attribution) }, - new Object[] { - asList(new RcfResult(1, 0, 20, new double[] { 0, 0, .5, .5 }), new RcfResult(1, 0, 80, new double[] { 0, .5, .25, .25 })), - 2, - new CombinedRcfResult(1, 0, new double[] { .5, .5 }) }, - new Object[] { - asList(new RcfResult(1, 0, 25, new double[] { 0, 0, 1, .0 }), new RcfResult(1, 0, 75, new double[] { 0, 0, 0, 1 })), - 2, - new CombinedRcfResult(1, 0, new double[] { .25, .75 }) }, }; - } - - @Test - @Parameters(method = "combineRcfResultsData") - public void combineRcfResults_returnExpected(List results, int numFeatures, CombinedRcfResult expected) { - assertEquals(expected, modelManager.combineRcfResults(results, numFeatures)); + SingleStreamModelIdMapper.getDetectorIdForModelId(modelId); } private ImmutableOpenMap createDataNodes(int numDataNodes) { @@ -360,7 +305,7 @@ private ImmutableOpenMap createDataNodes(int numDataNodes } private Object[] getPartitionedForestSizesData() { - RandomCutForest rcf = RandomCutForest.builder().dimensions(1).sampleSize(10).numberOfTrees(100).build(); + ThresholdedRandomCutForest rcf = ThresholdedRandomCutForest.builder().dimensions(1).sampleSize(10).numberOfTrees(100).build(); return new Object[] { // one partition given sufficient large nodes new Object[] { rcf, 100L, 100_000L, createDataNodes(10), pair(1, 100) }, @@ -374,93 +319,50 @@ private Object[] getPartitionedForestSizesData() { new Object[] { rcf, 100L, 1_000L, createDataNodes(1), pair(1, 100) } }; } - @Test - @Parameters(method = "getPartitionedForestSizesData") - public void getPartitionedForestSizes_returnExpected( - RandomCutForest rcf, - long totalModelSize, - long heapSize, - ImmutableOpenMap dataNodes, - Entry expected - ) { - when(jvmService.info().getMem().getHeapMax().getBytes()).thenReturn(heapSize); - MemoryTracker memoryTracker = spy( - new MemoryTracker(jvmService, modelMaxSizePercentage, modelDesiredSizePercentage, clusterService, adCircuitBreakerService) - ); - - when(memoryTracker.estimateRCFModelSize(rcf)).thenReturn(totalModelSize); - - modelPartitioner = spy(new ModelPartitioner(numSamples, numTrees, nodeFilter, memoryTracker)); - - when(nodeFilter.getEligibleDataNodes()).thenReturn(dataNodes.values().toArray(DiscoveryNode.class)); - - assertEquals(expected, modelPartitioner.getPartitionedForestSizes(rcf, "id")); - } - - private Object[] getPartitionedForestSizesLimitExceededData() { - RandomCutForest rcf = RandomCutForest.builder().dimensions(1).sampleSize(10).numberOfTrees(100).build(); - return new Object[] { - new Object[] { rcf, 101L, 1_000L, createDataNodes(1) }, - new Object[] { rcf, 201L, 1_000L, createDataNodes(2) }, - new Object[] { rcf, 3001L, 10_000L, createDataNodes(3) } }; - } - - @Test(expected = LimitExceededException.class) - @Parameters(method = "getPartitionedForestSizesLimitExceededData") - public void getPartitionedForestSizes_throwLimitExceeded( - RandomCutForest rcf, - long totalModelSize, - long heapSize, - ImmutableOpenMap dataNodes - ) { - when(jvmService.info().getMem().getHeapMax().getBytes()).thenReturn(heapSize); - MemoryTracker memoryTracker = spy( - new MemoryTracker(jvmService, modelMaxSizePercentage, modelDesiredSizePercentage, clusterService, adCircuitBreakerService) - ); - when(memoryTracker.estimateRCFModelSize(rcf)).thenReturn(totalModelSize); - modelPartitioner = spy(new ModelPartitioner(numSamples, numTrees, nodeFilter, memoryTracker)); - - when(nodeFilter.getEligibleDataNodes()).thenReturn(dataNodes.values().toArray(DiscoveryNode.class)); - - modelPartitioner.getPartitionedForestSizes(rcf, "id"); - } - private Object[] estimateModelSizeData() { return new Object[] { - new Object[] { RandomCutForest.builder().dimensions(1).sampleSize(256).numberOfTrees(100).build(), 819200L }, - new Object[] { RandomCutForest.builder().dimensions(5).sampleSize(256).numberOfTrees(100).build(), 4096000L } }; + new Object[] { ThresholdedRandomCutForest.builder().dimensions(1).sampleSize(256).numberOfTrees(100).build(), 819200L }, + new Object[] { ThresholdedRandomCutForest.builder().dimensions(5).sampleSize(256).numberOfTrees(100).build(), 4096000L } }; } @Parameters(method = "estimateModelSizeData") - public void estimateModelSize_returnExpected(RandomCutForest rcf, long expectedSize) { - assertEquals(expectedSize, memoryTracker.estimateRCFModelSize(rcf)); + public void estimateModelSize_returnExpected(ThresholdedRandomCutForest rcf, long expectedSize) { + assertEquals(expectedSize, memoryTracker.estimateTRCFModelSize(rcf)); } @Test public void getRcfResult_returnExpectedToListener() { double[] point = new double[0]; - RandomCutForest forest = mock(RandomCutForest.class); + ThresholdedRandomCutForest rForest = mock(ThresholdedRandomCutForest.class); + RandomCutForest rcf = mock(RandomCutForest.class); + when(rForest.getForest()).thenReturn(rcf); + when(rcf.getDimensions()).thenReturn(8); double score = 11.; doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); - listener.onResponse(Optional.of(forest)); + ActionListener> listener = invocation.getArgument(1); + listener.onResponse(Optional.of(rForest)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); - when(forest.getAnomalyScore(point)).thenReturn(score); - when(forest.getNumberOfTrees()).thenReturn(numTrees); - when(forest.getTimeDecay()).thenReturn(rcfTimeDecay); - when(forest.getSampleSize()).thenReturn(numSamples); - when(forest.getTotalUpdates()).thenReturn((long) numSamples); - when(forest.getAnomalyAttribution(point)).thenReturn(attributionVec); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); + + double confidence = 0.091353632; + double grade = 0.1; + AnomalyDescriptor descriptor = new AnomalyDescriptor(); + descriptor.setRcfScore(score); + descriptor.setForestSize(numTrees); + descriptor.setDataConfidence(confidence); + descriptor.setAnomalyGrade(grade); + descriptor.setAttribution(attributionVec); + descriptor.setTotalUpdates(numSamples); + when(rForest.process(any(), anyLong())).thenReturn(descriptor); ActionListener listener = mock(ActionListener.class); modelManager.getRcfResult(detectorId, rcfModelId, point, listener); - RcfResult expected = new RcfResult(score, 0, numTrees, new double[] { 0.5, 0.5 }, 10); + RcfResult expected = new RcfResult(score, confidence, numTrees, new double[] { 0.5, 0.5 }, numSamples, grade); verify(listener).onResponse(eq(expected)); - when(forest.getTotalUpdates()).thenReturn(numSamples + 1L); + descriptor.setTotalUpdates(numSamples + 1L); listener = mock(ActionListener.class); modelManager.getRcfResult(detectorId, rcfModelId, point, listener); @@ -475,7 +377,7 @@ public void getRcfResult_throwToListener_whenNoCheckpoint() { ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.empty()); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); ActionListener listener = mock(ActionListener.class); modelManager.getRcfResult(detectorId, rcfModelId, new double[0], listener); @@ -486,12 +388,21 @@ public void getRcfResult_throwToListener_whenNoCheckpoint() { @Test public void getRcfResult_throwToListener_whenHeapLimitExceed() { doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); + ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(rcf)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); when(jvmService.info().getMem().getHeapMax().getBytes()).thenReturn(1_000L); + final Set> settingsSet = Stream + .concat( + ClusterSettings.BUILT_IN_CLUSTER_SETTINGS.stream(), + Sets.newHashSet(AnomalyDetectorSettings.MODEL_MAX_SIZE_PERCENTAGE).stream() + ) + .collect(Collectors.toSet()); + ClusterSettings clusterSettings = new ClusterSettings(settings, settingsSet); + clusterService = new ClusterService(settings, clusterSettings, null); + MemoryTracker memoryTracker = new MemoryTracker( jvmService, modelMaxSizePercentage, @@ -512,16 +423,10 @@ public void getRcfResult_throwToListener_whenHeapLimitExceed() { rcfTimeDecay, numMinSamples, thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples, minPreviewSize, modelTtl, checkpointInterval, entityColdStarter, - modelPartitioner, featureManager, memoryTracker ) @@ -606,13 +511,13 @@ public void getAllModelIds_returnEmpty_forNoModels() { @Test public void stopModel_returnExpectedToListener_whenRcfStop() { - RandomCutForest forest = mock(RandomCutForest.class); + ThresholdedRandomCutForest forest = mock(ThresholdedRandomCutForest.class); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); + ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(forest)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); modelManager.getRcfResult(detectorId, rcfModelId, new double[0], rcfResultListener); when(clock.instant()).thenReturn(Instant.EPOCH); @@ -620,7 +525,7 @@ public void stopModel_returnExpectedToListener_whenRcfStop() { ActionListener listener = invocation.getArgument(2); listener.onResponse(null); return null; - }).when(checkpointDao).putRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); + }).when(checkpointDao).putTRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); ActionListener listener = mock(ActionListener.class); modelManager.stopModel(detectorId, rcfModelId, listener); @@ -651,19 +556,19 @@ public void stopModel_returnExpectedToListener_whenThresholdStop() { @Test public void stopModel_throwToListener_whenCheckpointFail() { - RandomCutForest forest = mock(RandomCutForest.class); + ThresholdedRandomCutForest forest = mock(ThresholdedRandomCutForest.class); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); + ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(forest)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); modelManager.getRcfResult(detectorId, rcfModelId, new double[0], rcfResultListener); when(clock.instant()).thenReturn(Instant.EPOCH); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(2); listener.onFailure(new RuntimeException()); return null; - }).when(checkpointDao).putRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); + }).when(checkpointDao).putTRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); ActionListener listener = mock(ActionListener.class); modelManager.stopModel(detectorId, rcfModelId, listener); @@ -679,12 +584,12 @@ public void clear_callListener_whenRcfDeleted() { ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(forest)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(forest)); return null; - }).when(checkpointDao).getRCFModel(eq(otherModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(otherModelId), any(ActionListener.class)); modelManager.getRcfResult(detectorId, rcfModelId, new double[0], rcfResultListener); modelManager.getRcfResult(otherModelId, otherModelId, new double[0], rcfResultListener); doAnswer(invocation -> { @@ -722,12 +627,11 @@ public void clear_callListener_whenThresholdDeleted() { @Test public void clear_throwToListener_whenDeleteFail() { - RandomCutForest forest = mock(RandomCutForest.class); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); - listener.onResponse(Optional.of(forest)); + ActionListener> listener = invocation.getArgument(1); + listener.onResponse(Optional.of(rcf)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); modelManager.getRcfResult(detectorId, rcfModelId, new double[0], rcfResultListener); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(1); @@ -744,24 +648,17 @@ public void clear_throwToListener_whenDeleteFail() { @Test public void trainModel_returnExpectedToListener_putCheckpoints() { double[][] trainData = new Random().doubles().limit(100).mapToObj(d -> new double[] { d }).toArray(double[][]::new); - doReturn(new SimpleEntry<>(2, 10)).when(modelPartitioner).getPartitionedForestSizes(anyObject(), anyObject()); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(2); listener.onResponse(null); return null; - }).when(checkpointDao).putThresholdCheckpoint(any(), any(), any(ActionListener.class)); - doAnswer(invocation -> { - ActionListener listener = invocation.getArgument(2); - listener.onResponse(null); - return null; - }).when(checkpointDao).putRCFCheckpoint(any(), any(), any(ActionListener.class)); + }).when(checkpointDao).putTRCFCheckpoint(any(), any(), any(ActionListener.class)); ActionListener listener = mock(ActionListener.class); modelManager.trainModel(anomalyDetector, trainData, listener); verify(listener).onResponse(eq(null)); - verify(checkpointDao, times(1)).putThresholdCheckpoint(any(), any(), any()); - verify(checkpointDao, times(2)).putRCFCheckpoint(any(), any(), any()); + verify(checkpointDao, times(1)).putTRCFCheckpoint(any(), any(), any()); } private Object[] trainModelIllegalArgumentData() { @@ -779,7 +676,7 @@ public void trainModel_throwIllegalArgumentToListener_forInvalidTrainData(double @Test public void trainModel_throwLimitExceededToListener_whenLimitExceed() { - doThrow(new LimitExceededException(null, null)).when(modelPartitioner).getPartitionedForestSizes(anyObject(), anyObject()); + doThrow(new LimitExceededException(null, null)).when(checkpointDao).putTRCFCheckpoint(any(), any(), any()); ActionListener listener = mock(ActionListener.class); modelManager.trainModel(anomalyDetector, new double[][] { { 0 } }, listener); @@ -789,14 +686,14 @@ public void trainModel_throwLimitExceededToListener_whenLimitExceed() { @Test public void getRcfModelId_returnNonEmptyString() { - String rcfModelId = modelPartitioner.getRcfModelId(anomalyDetector.getDetectorId(), 0); + String rcfModelId = SingleStreamModelIdMapper.getRcfModelId(anomalyDetector.getDetectorId(), 0); assertFalse(rcfModelId.isEmpty()); } @Test public void getThresholdModelId_returnNonEmptyString() { - String thresholdModelId = modelPartitioner.getThresholdModelId(anomalyDetector.getDetectorId()); + String thresholdModelId = SingleStreamModelIdMapper.getThresholdModelId(anomalyDetector.getDetectorId()); assertFalse(thresholdModelId.isEmpty()); } @@ -810,29 +707,29 @@ public void maintenance_returnExpectedToListener_forRcfModel() { String successModelId = "testSuccessModelId"; String failModelId = "testFailModelId"; double[] point = new double[0]; - RandomCutForest forest = mock(RandomCutForest.class); - RandomCutForest failForest = mock(RandomCutForest.class); + ThresholdedRandomCutForest forest = mock(ThresholdedRandomCutForest.class); + ThresholdedRandomCutForest failForest = mock(ThresholdedRandomCutForest.class); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); + ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(forest)); return null; - }).when(checkpointDao).getRCFModel(eq(successModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(successModelId), any(ActionListener.class)); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); + ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(failForest)); return null; - }).when(checkpointDao).getRCFModel(eq(failModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(failModelId), any(ActionListener.class)); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(2); listener.onResponse(null); return null; - }).when(checkpointDao).putRCFCheckpoint(eq(successModelId), eq(forest), any(ActionListener.class)); + }).when(checkpointDao).putTRCFCheckpoint(eq(successModelId), eq(forest), any(ActionListener.class)); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(2); listener.onFailure(new RuntimeException()); return null; - }).when(checkpointDao).putRCFCheckpoint(eq(failModelId), eq(failForest), any(ActionListener.class)); + }).when(checkpointDao).putTRCFCheckpoint(eq(failModelId), eq(failForest), any(ActionListener.class)); when(clock.instant()).thenReturn(Instant.EPOCH); ActionListener scoreListener = mock(ActionListener.class); modelManager.getRcfResult(detectorId, successModelId, point, scoreListener); @@ -842,8 +739,8 @@ public void maintenance_returnExpectedToListener_forRcfModel() { modelManager.maintenance(listener); verify(listener).onResponse(eq(null)); - verify(checkpointDao, times(1)).putRCFCheckpoint(eq(successModelId), eq(forest), any(ActionListener.class)); - verify(checkpointDao, times(1)).putRCFCheckpoint(eq(failModelId), eq(failForest), any(ActionListener.class)); + verify(checkpointDao, times(1)).putTRCFCheckpoint(eq(successModelId), eq(forest), any(ActionListener.class)); + verify(checkpointDao, times(1)).putTRCFCheckpoint(eq(failModelId), eq(failForest), any(ActionListener.class)); } @Test @@ -887,18 +784,18 @@ public void maintenance_returnExpectedToListener_forThresholdModel() { @Test public void maintenance_returnExpectedToListener_stopModel() { double[] point = new double[0]; - RandomCutForest forest = mock(RandomCutForest.class); + ThresholdedRandomCutForest forest = mock(ThresholdedRandomCutForest.class); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); + ActionListener> listener = invocation.getArgument(1); listener.onResponse(Optional.of(forest)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(2); listener.onResponse(null); return null; - }).when(checkpointDao).putRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); + }).when(checkpointDao).putTRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); when(clock.instant()).thenReturn(Instant.EPOCH, Instant.EPOCH, Instant.EPOCH.plus(modelTtl.plusSeconds(1))); ActionListener scoreListener = mock(ActionListener.class); modelManager.getRcfResult(detectorId, rcfModelId, point, scoreListener); @@ -908,25 +805,23 @@ public void maintenance_returnExpectedToListener_stopModel() { verify(listener).onResponse(eq(null)); modelManager.getRcfResult(detectorId, rcfModelId, point, scoreListener); - verify(checkpointDao, times(2)).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + verify(checkpointDao, times(2)).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); } @Test public void maintenance_returnExpectedToListener_doNothing() { double[] point = new double[0]; - RandomCutForest forest = mock(RandomCutForest.class); doAnswer(invocation -> { - ActionListener> listener = invocation.getArgument(1); - listener.onResponse(Optional.of(forest)); + ActionListener> listener = invocation.getArgument(1); + listener.onResponse(Optional.of(rcf)); return null; - }).when(checkpointDao).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + }).when(checkpointDao).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(2); listener.onResponse(null); return null; - }).when(checkpointDao).putRCFCheckpoint(eq(rcfModelId), eq(forest), any(ActionListener.class)); - when(forest.getAnomalyAttribution(point)).thenReturn(attributionVec); + }).when(checkpointDao).putTRCFCheckpoint(eq(rcfModelId), eq(rcf), any(ActionListener.class)); when(clock.instant()).thenReturn(Instant.MIN); ActionListener scoreListener = mock(ActionListener.class); modelManager.getRcfResult(detectorId, rcfModelId, point, scoreListener); @@ -939,7 +834,7 @@ public void maintenance_returnExpectedToListener_doNothing() { verify(listener).onResponse(eq(null)); modelManager.getRcfResult(detectorId, rcfModelId, point, scoreListener); - verify(checkpointDao, times(1)).getRCFModel(eq(rcfModelId), any(ActionListener.class)); + verify(checkpointDao, times(1)).getTRCFModel(eq(rcfModelId), any(ActionListener.class)); } @Test @@ -1042,11 +937,6 @@ public void getEmptyStateFullSamples() { interpolator, searchFeatureDao, AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES, featureManager, settings, AnomalyDetectorSettings.HOURLY_MAINTENANCE, @@ -1062,16 +952,10 @@ public void getEmptyStateFullSamples() { rcfTimeDecay, numMinSamples, thresholdMinPvalue, - thresholdMaxRankError, - thresholdMaxScore, - thresholdNumLogNormalQuantiles, - thresholdDownsamples, - thresholdMaxSamples, minPreviewSize, modelTtl, checkpointInterval, entityColdStarter, - modelPartitioner, featureManager, memoryTracker ) @@ -1080,7 +964,7 @@ public void getEmptyStateFullSamples() { ModelState state = MLUtil .randomModelState(new RandomModelStateConfig.Builder().fullModel(false).sampleSize(numMinSamples).build()); EntityModel model = state.getModel(); - assertTrue(model.getRcf() == null || model.getThreshold() == null); + assertTrue(!model.getTrcf().isPresent()); ThresholdingResult result = modelManager.getAnomalyResultForEntity(new double[] { -1 }, state, "", null, shingleSize); // model outputs scores assertTrue(result.getRcfScore() != 0); @@ -1123,4 +1007,42 @@ public void scoreSamples() { assertEquals(0, state.getModel().getSamples().size()); assertEquals(now, state.getLastUsedTime()); } + + public void getAnomalyResultForEntity_withTrcf() { + AnomalyDescriptor anomalyDescriptor = new AnomalyDescriptor(); + anomalyDescriptor.setRcfScore(2); + anomalyDescriptor.setDataConfidence(1); + anomalyDescriptor.setAnomalyGrade(1); + when(this.trcf.process(this.point, 0)).thenReturn(anomalyDescriptor); + + ThresholdingResult result = modelManager + .getAnomalyResultForEntity(this.point, this.modelState, this.detectorId, null, this.shingleSize); + assertEquals( + new ThresholdingResult( + anomalyDescriptor.getAnomalyGrade(), + anomalyDescriptor.getDataConfidence(), + anomalyDescriptor.getRcfScore() + ), + result + ); + } + + @Test + public void score_with_trcf() { + AnomalyDescriptor anomalyDescriptor = new AnomalyDescriptor(); + anomalyDescriptor.setRcfScore(2); + anomalyDescriptor.setAnomalyGrade(1); + when(this.trcf.process(this.point, 0)).thenReturn(anomalyDescriptor); + when(this.entityModel.getSamples()).thenReturn(new ArrayDeque<>(Arrays.asList(this.point))); + + ThresholdingResult result = modelManager.score(this.point, this.detectorId, this.modelState); + assertEquals( + new ThresholdingResult( + anomalyDescriptor.getAnomalyGrade(), + anomalyDescriptor.getDataConfidence(), + anomalyDescriptor.getRcfScore() + ), + result + ); + } } diff --git a/src/test/java/org/opensearch/ad/ml/ModelPartitionerTests.java b/src/test/java/org/opensearch/ad/ml/ModelPartitionerTests.java deleted file mode 100644 index 0a9e98ffd..000000000 --- a/src/test/java/org/opensearch/ad/ml/ModelPartitionerTests.java +++ /dev/null @@ -1,95 +0,0 @@ -/* - * SPDX-License-Identifier: Apache-2.0 - * - * The OpenSearch Contributors require contributions made to - * this file be licensed under the Apache-2.0 license or a - * compatible open source license. - * - * Modifications Copyright OpenSearch Contributors. See - * GitHub history for details. - */ - -package org.opensearch.ad.ml; - -import static org.mockito.Matchers.any; -import static org.mockito.Mockito.mock; -import static org.mockito.Mockito.when; - -import java.io.IOException; -import java.time.temporal.ChronoUnit; -import java.util.Map.Entry; - -import org.opensearch.ad.MemoryTracker; -import org.opensearch.ad.TestHelpers; -import org.opensearch.ad.model.AnomalyDetector; -import org.opensearch.ad.model.Feature; -import org.opensearch.ad.model.IntervalTimeConfiguration; -import org.opensearch.ad.settings.AnomalyDetectorSettings; -import org.opensearch.ad.util.DiscoveryNodeFilterer; -import org.opensearch.cluster.node.DiscoveryNode; -import org.opensearch.test.OpenSearchTestCase; - -import com.google.common.collect.ImmutableList; - -public class ModelPartitionerTests extends OpenSearchTestCase { - private ModelPartitioner partitioner; - private DiscoveryNodeFilterer nodeFilter; - private MemoryTracker memoryTracker; - - @Override - public void setUp() throws Exception { - super.setUp(); - nodeFilter = mock(DiscoveryNodeFilterer.class); - memoryTracker = mock(MemoryTracker.class); - when(memoryTracker.getDesiredModelSize()).thenReturn(200_000L); - when(memoryTracker.estimateRCFModelSize(any())).thenReturn(352_064L); - partitioner = new ModelPartitioner( - AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE, - AnomalyDetectorSettings.NUM_TREES, - nodeFilter, - memoryTracker - ); - } - - /** - * The detector has a rcf model of size 352064 bytes. Since the desired model - * size is 200_000, we can have two models; - * - * @throws IOException when failing to generate random query for detector config - */ - public void testGetPartitionedForestSizes() throws IOException { - DiscoveryNode[] nodes = new DiscoveryNode[2]; - nodes[0] = mock(DiscoveryNode.class); - nodes[1] = mock(DiscoveryNode.class); - when(nodeFilter.getEligibleDataNodes()).thenReturn(nodes); - Feature feature1 = TestHelpers.randomFeature(true); - Feature feature2 = TestHelpers.randomFeature(false); - - AnomalyDetector detector = new AnomalyDetector( - randomAlphaOfLength(10), - randomLong(), - randomAlphaOfLength(20), - randomAlphaOfLength(30), - randomAlphaOfLength(5), - ImmutableList.of(randomAlphaOfLength(20)), - // 1 feature enabled - ImmutableList.of(feature1, feature2), - TestHelpers.randomQuery(), - new IntervalTimeConfiguration(5, ChronoUnit.MINUTES), - TestHelpers.randomIntervalTimeConfiguration(), - 4, // shingle size 4 - null, - randomInt(), - null, - null, - null - ); - - Entry partition = partitioner.getPartitionedForestSizes(detector); - - // 2 partitions - assertEquals(2, partition.getKey().intValue()); - // each partition has AnomalyDetectorSettings.NUM_TREES / 2 trees - assertEquals(AnomalyDetectorSettings.NUM_TREES / 2, partition.getValue().intValue()); - } -} diff --git a/src/test/java/org/opensearch/ad/ml/RcfResultTests.java b/src/test/java/org/opensearch/ad/ml/RcfResultTests.java index 5ab194cd2..23b531b98 100644 --- a/src/test/java/org/opensearch/ad/ml/RcfResultTests.java +++ b/src/test/java/org/opensearch/ad/ml/RcfResultTests.java @@ -43,8 +43,10 @@ public class RcfResultTests { private double score = 1.; private double confidence = 0; private int forestSize = 10; + private int totalUpdate = 32; + private double grade = 0.1; private double[] attribution = new double[] { 1. }; - private RcfResult rcfResult = new RcfResult(score, confidence, forestSize, attribution); + private RcfResult rcfResult = new RcfResult(score, confidence, forestSize, attribution, totalUpdate, grade); @Test public void getters_returnExcepted() { @@ -58,12 +60,12 @@ private Object[] equalsData() { new Object[] { rcfResult, null, false }, new Object[] { rcfResult, rcfResult, true }, new Object[] { rcfResult, 1, false }, - new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, attribution), true }, - new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize, attribution), false }, - new Object[] { rcfResult, new RcfResult(score, confidence, forestSize + 1, attribution), false }, - new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize + 1, attribution), false }, - new Object[] { rcfResult, new RcfResult(score, confidence + 1, forestSize, attribution), false }, - new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, new double[] { 2. }), false }, }; + new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, attribution, totalUpdate, grade), true }, + new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score, confidence, forestSize + 1, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize + 1, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score, confidence + 1, forestSize, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, new double[] { 2. }, totalUpdate, grade), false }, }; } @Test @@ -74,11 +76,11 @@ public void equals_returnExpected(RcfResult result, Object other, boolean expect private Object[] hashCodeData() { return new Object[] { - new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, attribution), true }, - new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize, attribution), false }, - new Object[] { rcfResult, new RcfResult(score, confidence, forestSize + 1, attribution), false }, - new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize + 1, attribution), false }, - new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, new double[] { 2. }), false }, }; + new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, attribution, totalUpdate, grade), true }, + new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score, confidence, forestSize + 1, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score + 1, confidence, forestSize + 1, attribution, totalUpdate, grade), false }, + new Object[] { rcfResult, new RcfResult(score, confidence, forestSize, new double[] { 2. }, totalUpdate, grade), false }, }; } @Test diff --git a/src/test/java/org/opensearch/ad/ml/SingleStreamModelIdMapperTests.java b/src/test/java/org/opensearch/ad/ml/SingleStreamModelIdMapperTests.java new file mode 100644 index 000000000..59a0d02da --- /dev/null +++ b/src/test/java/org/opensearch/ad/ml/SingleStreamModelIdMapperTests.java @@ -0,0 +1,24 @@ +/* + * SPDX-License-Identifier: Apache-2.0 + * + * The OpenSearch Contributors require contributions made to + * this file be licensed under the Apache-2.0 license or a + * compatible open source license. + * + * Modifications Copyright OpenSearch Contributors. See + * GitHub history for details. + */ + +package org.opensearch.ad.ml; + +import org.opensearch.test.OpenSearchTestCase; + +public class SingleStreamModelIdMapperTests extends OpenSearchTestCase { + public void testGetThresholdModelIdFromRCFModelId() { + assertEquals( + "Y62IGnwBFHAk-4HQQeoo_model_threshold", + SingleStreamModelIdMapper.getThresholdModelIdFromRCFModelId("Y62IGnwBFHAk-4HQQeoo_model_rcf_1") + ); + } + +} diff --git a/src/test/java/org/opensearch/ad/ml/rcf/CombinedRcfResultTests.java b/src/test/java/org/opensearch/ad/ml/rcf/CombinedRcfResultTests.java deleted file mode 100644 index 94d86392a..000000000 --- a/src/test/java/org/opensearch/ad/ml/rcf/CombinedRcfResultTests.java +++ /dev/null @@ -1,87 +0,0 @@ -/* - * SPDX-License-Identifier: Apache-2.0 - * - * The OpenSearch Contributors require contributions made to - * this file be licensed under the Apache-2.0 license or a - * compatible open source license. - * - * Modifications Copyright OpenSearch Contributors. See - * GitHub history for details. - */ - -/* - * Copyright 2020 Amazon.com, Inc. or its affiliates. All Rights Reserved. - * - * Licensed under the Apache License, Version 2.0 (the "License"). - * You may not use this file except in compliance with the License. - * A copy of the License is located at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * or in the "license" file accompanying this file. This file is distributed - * on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either - * express or implied. See the License for the specific language governing - * permissions and limitations under the License. - */ - -package org.opensearch.ad.ml.rcf; - -import static org.junit.Assert.assertEquals; -import static org.junit.Assert.assertTrue; - -import java.util.Arrays; - -import junitparams.JUnitParamsRunner; -import junitparams.Parameters; - -import org.junit.Test; -import org.junit.runner.RunWith; - -@RunWith(JUnitParamsRunner.class) -public class CombinedRcfResultTests { - - private double score = 1.; - private double confidence = .5; - private double[] attribution = new double[] { 1. }; - private CombinedRcfResult rcfResult = new CombinedRcfResult(score, confidence, attribution); - - @Test - public void getters_returnExcepted() { - assertEquals(score, rcfResult.getScore(), 1e-8); - assertEquals(confidence, rcfResult.getConfidence(), 1e-8); - assertTrue(Arrays.equals(attribution, rcfResult.getAttribution())); - } - - private Object[] equalsData() { - return new Object[] { - new Object[] { rcfResult, null, false }, - new Object[] { rcfResult, rcfResult, true }, - new Object[] { rcfResult, 1, false }, - new Object[] { rcfResult, new CombinedRcfResult(score, confidence, attribution), true }, - new Object[] { rcfResult, new CombinedRcfResult(score + 1, confidence, attribution), false }, - new Object[] { rcfResult, new CombinedRcfResult(score, confidence + 1, attribution), false }, - new Object[] { rcfResult, new CombinedRcfResult(score + 1, confidence + 1, attribution), false }, - new Object[] { rcfResult, new CombinedRcfResult(score, confidence, new double[] { 0. }), false }, }; - } - - @Test - @Parameters(method = "equalsData") - public void equals_returnExpected(CombinedRcfResult result, Object other, boolean expected) { - assertEquals(expected, result.equals(other)); - } - - private Object[] hashCodeData() { - return new Object[] { - new Object[] { rcfResult, new CombinedRcfResult(score, confidence, attribution), true }, - new Object[] { rcfResult, new CombinedRcfResult(score + 1, confidence, attribution), false }, - new Object[] { rcfResult, new CombinedRcfResult(score, confidence + 1, attribution), false }, - new Object[] { rcfResult, new CombinedRcfResult(score + 1, confidence + 1, attribution), false }, - new Object[] { rcfResult, new CombinedRcfResult(score, confidence, new double[] { 0. }), false }, }; - } - - @Test - @Parameters(method = "hashCodeData") - public void hashCode_returnExpected(CombinedRcfResult result, CombinedRcfResult other, boolean expected) { - assertEquals(expected, result.hashCode() == other.hashCode()); - } -} diff --git a/src/test/java/org/opensearch/ad/model/EntityTests.java b/src/test/java/org/opensearch/ad/model/EntityTests.java index f15d9610f..f3affd6c1 100644 --- a/src/test/java/org/opensearch/ad/model/EntityTests.java +++ b/src/test/java/org/opensearch/ad/model/EntityTests.java @@ -11,6 +11,8 @@ package org.opensearch.ad.model; +import java.util.Collections; +import java.util.Optional; import java.util.TreeMap; import org.opensearch.ad.AbstractADTest; @@ -27,8 +29,16 @@ public void testToString() { String val2 = "app_4"; attributes.put(name1, val1); attributes.put(name2, val2); - String detectorId = "detectorId"; Entity entity = Entity.createEntityFromOrderedMap(attributes); assertEquals("host=server_2,service=app_4", entity.toString()); } + + public void test_getModelId_returnId_withNoAttributes() { + String detectorId = "id"; + Entity entity = Entity.createEntityByReordering(Collections.emptyMap()); + + Optional modelId = entity.getModelId(detectorId); + + assertTrue(!modelId.isPresent()); + } } diff --git a/src/test/java/org/opensearch/ad/task/ADTaskCacheManagerTests.java b/src/test/java/org/opensearch/ad/task/ADTaskCacheManagerTests.java index e158ccb48..6ccce7178 100644 --- a/src/test/java/org/opensearch/ad/task/ADTaskCacheManagerTests.java +++ b/src/test/java/org/opensearch/ad/task/ADTaskCacheManagerTests.java @@ -110,10 +110,8 @@ public void testPutTask() throws IOException { assertEquals(1, adTaskCacheManager.size()); assertTrue(adTaskCacheManager.contains(adTask.getTaskId())); assertTrue(adTaskCacheManager.containsTaskOfDetector(adTask.getDetectorId())); - assertNotNull(adTaskCacheManager.getRcfModel(adTask.getTaskId())); + assertNotNull(adTaskCacheManager.getTRcfModel(adTask.getTaskId())); assertNotNull(adTaskCacheManager.getShingle(adTask.getTaskId())); - assertNotNull(adTaskCacheManager.getThresholdModel(adTask.getTaskId())); - assertNotNull(adTaskCacheManager.getThresholdModelTrainingData(adTask.getTaskId())); assertFalse(adTaskCacheManager.isThresholdModelTrained(adTask.getTaskId())); adTaskCacheManager.remove(adTask.getTaskId(), randomAlphaOfLength(5), randomAlphaOfLength(5)); assertEquals(0, adTaskCacheManager.size()); @@ -199,18 +197,16 @@ public void testThresholdModelTrained() throws IOException { ADTask adTask = TestHelpers.randomAdTask(); adTaskCacheManager.add(adTask); assertEquals(1, adTaskCacheManager.size()); - int size = adTaskCacheManager.addThresholdModelTrainingData(adTask.getTaskId(), randomDouble(), randomDouble()); - long cacheSize = adTaskCacheManager.trainingDataMemorySize(size); adTaskCacheManager.setThresholdModelTrained(adTask.getTaskId(), false); verify(memoryTracker, never()).releaseMemory(anyLong(), anyBoolean(), eq(HISTORICAL_SINGLE_ENTITY_DETECTOR)); adTaskCacheManager.setThresholdModelTrained(adTask.getTaskId(), true); - verify(memoryTracker, times(1)).releaseMemory(eq(cacheSize), eq(true), eq(HISTORICAL_SINGLE_ENTITY_DETECTOR)); + verify(memoryTracker, times(0)).releaseMemory(anyLong(), eq(true), eq(HISTORICAL_SINGLE_ENTITY_DETECTOR)); } public void testTaskNotExist() { IllegalArgumentException e = expectThrows( IllegalArgumentException.class, - () -> adTaskCacheManager.getRcfModel(randomAlphaOfLength(5)) + () -> adTaskCacheManager.getTRcfModel(randomAlphaOfLength(5)) ); assertEquals("AD task not in cache", e.getMessage()); } diff --git a/src/test/java/org/opensearch/ad/task/ADTaskManagerTests.java b/src/test/java/org/opensearch/ad/task/ADTaskManagerTests.java index 15019d400..d25ba8b8c 100644 --- a/src/test/java/org/opensearch/ad/task/ADTaskManagerTests.java +++ b/src/test/java/org/opensearch/ad/task/ADTaskManagerTests.java @@ -148,6 +148,8 @@ import org.opensearch.transport.TransportResponseHandler; import org.opensearch.transport.TransportService; +import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; import com.google.common.collect.ImmutableList; import com.google.common.collect.ImmutableMap; @@ -764,7 +766,11 @@ public void testGetLocalADTaskProfilesByDetectorId() { when(adTaskCacheManager.getTasksOfDetector(anyString())).thenReturn(tasksOfDetector); Deque>> shingle = new LinkedBlockingDeque<>(); when(adTaskCacheManager.getShingle(anyString())).thenReturn(shingle); - when(adTaskCacheManager.getRcfModelTotalUpdates(anyString())).thenReturn(randomLongBetween(100, 1000)); + ThresholdedRandomCutForest trcf = mock(ThresholdedRandomCutForest.class); + when(adTaskCacheManager.getTRcfModel(anyString())).thenReturn(trcf); + RandomCutForest rcf = mock(RandomCutForest.class); + when(trcf.getForest()).thenReturn(rcf); + when(rcf.getTotalUpdates()).thenReturn(randomLongBetween(100, 1000)); when(adTaskCacheManager.isThresholdModelTrained(anyString())).thenReturn(true); when(adTaskCacheManager.getThresholdModelTrainingDataSize(anyString())).thenReturn(randomIntBetween(100, 1000)); when(adTaskCacheManager.getModelSize(anyString())).thenReturn(randomLongBetween(100, 1000)); diff --git a/src/test/java/org/opensearch/ad/transport/ADStatsTests.java b/src/test/java/org/opensearch/ad/transport/ADStatsTests.java index b387ad430..fa7a220b8 100644 --- a/src/test/java/org/opensearch/ad/transport/ADStatsTests.java +++ b/src/test/java/org/opensearch/ad/transport/ADStatsTests.java @@ -155,7 +155,7 @@ public void testADStatsNodeResponseWithEntity() throws IOException, JsonPathNotF attributes.put(name2, val2); String detectorId = "detectorId"; Entity entity = Entity.createEntityFromOrderedMap(attributes); - EntityModel entityModel = new EntityModel(entity, null, null, null); + EntityModel entityModel = new EntityModel(entity, null, null); Clock clock = mock(Clock.class); when(clock.instant()).thenReturn(Instant.now()); ModelState state = new ModelState( diff --git a/src/test/java/org/opensearch/ad/transport/AnomalyResultTests.java b/src/test/java/org/opensearch/ad/transport/AnomalyResultTests.java index 7b0465aeb..5bc79e9cc 100644 --- a/src/test/java/org/opensearch/ad/transport/AnomalyResultTests.java +++ b/src/test/java/org/opensearch/ad/transport/AnomalyResultTests.java @@ -34,8 +34,8 @@ import static org.hamcrest.Matchers.is; import static org.hamcrest.Matchers.nullValue; import static org.mockito.ArgumentMatchers.any; +import static org.mockito.ArgumentMatchers.anyString; import static org.mockito.Mockito.anyDouble; -import static org.mockito.Mockito.anyInt; import static org.mockito.Mockito.anyLong; import static org.mockito.Mockito.anyString; import static org.mockito.Mockito.doAnswer; @@ -60,8 +60,8 @@ import java.util.HashMap; import java.util.List; import java.util.Map; +import java.util.NoSuchElementException; import java.util.Optional; -import java.util.concurrent.atomic.AtomicInteger; import java.util.function.Function; import org.junit.After; @@ -69,8 +69,7 @@ import org.junit.Assert; import org.junit.Before; import org.junit.BeforeClass; -import org.mockito.invocation.InvocationOnMock; -import org.mockito.stubbing.Answer; +import org.mockito.ArgumentCaptor; import org.opensearch.OpenSearchTimeoutException; import org.opensearch.Version; import org.opensearch.action.ActionListener; @@ -97,10 +96,9 @@ import org.opensearch.ad.feature.FeatureManager; import org.opensearch.ad.feature.SinglePointFeatures; import org.opensearch.ad.ml.ModelManager; -import org.opensearch.ad.ml.ModelPartitioner; import org.opensearch.ad.ml.RcfResult; +import org.opensearch.ad.ml.SingleStreamModelIdMapper; import org.opensearch.ad.ml.ThresholdingResult; -import org.opensearch.ad.ml.rcf.CombinedRcfResult; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.DetectorInternalState; import org.opensearch.ad.model.FeatureData; @@ -154,19 +152,18 @@ public class AnomalyResultTests extends AbstractADTest { private NodeStateManager stateManager; private FeatureManager featureQuery; private ModelManager normalModelManager; - private ModelPartitioner normalModelPartitioner; private Client client; private AnomalyDetector detector; private HashRing hashRing; private IndexNameExpressionResolver indexNameResolver; - private String rcfModelIDPrefix; private String thresholdModelID; private String adID; private String featureId; private String featureName; private ADCircuitBreakerService adCircuitBreakerService; private ADStats adStats; - private int partitionNum; + private double confidence; + private double anomalyGrade; private ADTaskManager adTaskManager; @BeforeClass @@ -193,9 +190,6 @@ public void setUp() throws Exception { settings = clusterService.getSettings(); stateManager = mock(NodeStateManager.class); - // return 2 RCF partitions - partitionNum = 2; - when(stateManager.getPartitionNumber(any(String.class), any(AnomalyDetector.class))).thenReturn(partitionNum); when(stateManager.isMuted(any(String.class), any(String.class))).thenReturn(false); when(stateManager.markColdStartRunning(anyString())).thenReturn(() -> {}); @@ -216,6 +210,7 @@ public void setUp() throws Exception { listener.onResponse(Optional.of(detector)); return null; }).when(stateManager).getAnomalyDetector(any(String.class), any(ActionListener.class)); + when(detector.getDetectorIntervalInMinutes()).thenReturn(1L); hashRing = mock(HashRing.class); Optional localNode = Optional.of(clusterService.state().nodes().getLocalNode()); @@ -230,13 +225,14 @@ public void setUp() throws Exception { }).when(featureQuery).getCurrentFeatures(any(AnomalyDetector.class), anyLong(), anyLong(), any(ActionListener.class)); double rcfScore = 0.2; + confidence = 0.91; + anomalyGrade = 0.5; normalModelManager = mock(ModelManager.class); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(3); - listener.onResponse(new RcfResult(0.2, 0, 100, new double[] { 1 })); + listener.onResponse(new RcfResult(rcfScore, confidence, 100, new double[] { 1 }, 1L, anomalyGrade)); return null; }).when(normalModelManager).getRcfResult(any(String.class), any(String.class), any(double[].class), any(ActionListener.class)); - when(normalModelManager.combineRcfResults(any(), anyInt())).thenReturn(new CombinedRcfResult(0, 1.0d, new double[] { 1 })); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(3); @@ -244,19 +240,8 @@ public void setUp() throws Exception { return null; }).when(normalModelManager).getThresholdingResult(any(String.class), any(String.class), anyDouble(), any(ActionListener.class)); - normalModelPartitioner = mock(ModelPartitioner.class); - rcfModelIDPrefix = "123-rcf-"; - when(normalModelPartitioner.getRcfModelId(any(String.class), anyInt())).thenAnswer(new Answer() { - - @Override - public String answer(InvocationOnMock invocation) throws Throwable { - Integer partition = (Integer) invocation.getArgument(1); - return rcfModelIDPrefix + partition.toString(); - } - - }); - thresholdModelID = "123-threshold"; - when(normalModelPartitioner.getThresholdModelId(any(String.class))).thenReturn(thresholdModelID); + thresholdModelID = SingleStreamModelIdMapper.getThresholdModelId(adID); // "123-threshold"; + // when(normalModelPartitioner.getThresholdModelId(any(String.class))).thenReturn(thresholdModelID); adCircuitBreakerService = mock(ADCircuitBreakerService.class); when(adCircuitBreakerService.isOpen()).thenReturn(false); @@ -363,7 +348,6 @@ public void testNormal() throws IOException { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -379,7 +363,7 @@ public void testNormal() throws IOException { action.doExecute(null, request, listener); AnomalyResultResponse response = listener.actionGet(10000L); - assertAnomalyResultResponse(response, 0, 1, 0d); + assertAnomalyResultResponse(response, anomalyGrade, confidence, 0d); } private void assertAnomalyResultResponse(AnomalyResultResponse response, double anomalyGrade, double confidence, double featureData) { @@ -487,7 +471,6 @@ public void sendRequest( stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, realClusterService, indexNameResolver, @@ -510,35 +493,6 @@ public void noModelExceptionTemplate(Exception exception, String adID, String er noModelExceptionTemplate(exception, adID, exception.getClass(), error); } - public void testNormalColdStart() { - noModelExceptionTemplate( - new ResourceNotFoundException(adID, ""), - adID, - InternalFailure.class, - AnomalyResultTransportAction.NO_MODEL_ERR_MSG - ); - } - - public void testNormalColdStartRemoteException() { - noModelExceptionTemplate( - new NotSerializableExceptionWrapper(new ResourceNotFoundException(adID, "")), - adID, - AnomalyDetectionException.class, - AnomalyResultTransportAction.NO_MODEL_ERR_MSG - ); - } - - public void testNullPointerExceptionWhenRCF() { - noModelExceptionTemplate(new NullPointerException(), adID, EndRunException.class, CommonErrorMessages.BUG_RESPONSE); - } - - public void testADExceptionWhenColdStart() { - String error = "blah"; - when(stateManager.fetchExceptionAndClear(any(String.class))).thenReturn(Optional.of(new AnomalyDetectionException(adID, error))); - - noModelExceptionTemplate(new ResourceNotFoundException(adID, ""), adID, AnomalyDetectionException.class, error); - } - @SuppressWarnings("unchecked") public void testInsufficientCapacityExceptionDuringColdStart() { @@ -568,7 +522,6 @@ public void testInsufficientCapacityExceptionDuringColdStart() { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -612,7 +565,6 @@ public void testInsufficientCapacityExceptionDuringRestoringModel() { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -640,7 +592,7 @@ public T read(StreamInput in) throws IOException { @Override @SuppressWarnings("unchecked") public void handleResponse(T response) { - handler.handleResponse((T) new RCFResultResponse(1, 1, 100, new double[0], randomInt(), Version.CURRENT)); + handler.handleResponse((T) new RCFResultResponse(1, 1, 100, new double[0], randomInt(), randomDouble(), Version.CURRENT)); } @Override @@ -716,7 +668,6 @@ public void sendRequest( stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, realClusterService, indexNameResolver, @@ -735,10 +686,6 @@ public void sendRequest( assertTrue("actual message: " + exception.getMessage(), exception.getMessage().contains(error)); } - public void testThresholdException() { - thresholdExceptionTestTemplate(new NullPointerException(), adID, EndRunException.class, CommonErrorMessages.BUG_RESPONSE); - } - public void testCircuitBreaker() { ADCircuitBreakerService breakerService = mock(ADCircuitBreakerService.class); @@ -762,7 +709,6 @@ public void testCircuitBreaker() { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -822,7 +768,6 @@ private void nodeNotConnectedExceptionTemplate(boolean isRCF, boolean temporary, adCircuitBreakerService, hashRing ); - new ThresholdResultTransportAction(new ActionFilters(Collections.emptySet()), exceptionTransportService, normalModelManager); AnomalyResultTransportAction action = new AnomalyResultTransportAction( new ActionFilters(Collections.emptySet()), @@ -832,7 +777,6 @@ private void nodeNotConnectedExceptionTemplate(boolean isRCF, boolean temporary, stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, hackedClusterService, indexNameResolver, @@ -854,34 +798,18 @@ private void nodeNotConnectedExceptionTemplate(boolean isRCF, boolean temporary, verify(stateManager, never()).addPressure(any(String.class), any(String.class)); } else { verify(hashRing, never()).buildCirclesForRealtimeAD(); - // expect 2 times since we have 2 RCF model partitions verify(stateManager, times(numberOfBuildCall)).addPressure(any(String.class), any(String.class)); } } public void testRCFNodeNotConnectedException() { - // we expect two hashRing.build calls since we have two RCF model partitions and - // both of them returns node not connected exception - nodeNotConnectedExceptionTemplate(true, false, 2); + // we expect one hashRing.build calls since we have one RCF model partitions + nodeNotConnectedExceptionTemplate(true, false, 1); } public void testTemporaryRCFNodeNotConnectedException() { - // we expect two backpressure incrementBackpressureCounter calls since we have - // two RCF model partitions and both of them returns node not connected - // exception - nodeNotConnectedExceptionTemplate(true, true, 2); - } - - public void testThresholdNodeNotConnectedException() { - // we expect one hashRing.build calls since we have one threshold model - // partition - nodeNotConnectedExceptionTemplate(false, false, 1); - } - - public void testTemporaryThresholdNodeNotConnectedException() { - // we expect one backpressure incrementBackpressureCounter call since we have - // one threshold model partition - nodeNotConnectedExceptionTemplate(false, true, 1); + // we expect one hashRing.build calls since we have one RCF model partitions + nodeNotConnectedExceptionTemplate(true, true, 1); } @SuppressWarnings("unchecked") @@ -901,7 +829,6 @@ public void testMute() { muteStateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -942,7 +869,6 @@ public void alertingRequestTemplate(boolean anomalyResultIndexExists) throws IOE stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -990,14 +916,6 @@ public String executor() { ); } - public void testAlertingRequestWithoutResultIndex() throws IOException { - alertingRequestTemplate(false); - } - - public void testAlertingRequestWithResultIndex() throws IOException { - alertingRequestTemplate(true); - } - public void testSerialzationResponse() throws IOException { AnomalyResultResponse response = new AnomalyResultResponse( 4, @@ -1089,6 +1007,7 @@ public void testNegativeTime() { } // no exception should be thrown + @SuppressWarnings("unchecked") public void testOnFailureNull() throws IOException { AnomalyResultTransportAction action = new AnomalyResultTransportAction( new ActionFilters(Collections.emptySet()), @@ -1098,7 +1017,6 @@ public void testOnFailureNull() throws IOException { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1109,13 +1027,42 @@ public void testOnFailureNull() throws IOException { adTaskManager ); AnomalyResultTransportAction.RCFActionListener listener = action.new RCFActionListener( - null, null, null, null, null, null, null, null, null, 0, new AtomicInteger(), null, 1 + null, null, null, null, mock(ActionListener.class), null, null ); listener.onFailure(null); } + static class ColdStartConfig { + boolean coldStartRunning = false; + Exception getCheckpointException = null; + + ColdStartConfig(Builder builder) { + this.coldStartRunning = builder.coldStartRunning; + this.getCheckpointException = builder.getCheckpointException; + } + + static class Builder { + boolean coldStartRunning = false; + Exception getCheckpointException = null; + + Builder coldStartRunning(boolean coldStartRunning) { + this.coldStartRunning = coldStartRunning; + return this; + } + + Builder getCheckpointException(Exception exception) { + this.getCheckpointException = exception; + return this; + } + + public ColdStartConfig build() { + return new ColdStartConfig(this); + } + } + } + @SuppressWarnings("unchecked") - private void setUpColdStart(ThreadPool mockThreadPool, boolean coldStartRunning) { + private void setUpColdStart(ThreadPool mockThreadPool, ColdStartConfig config) { SinglePointFeatures mockSinglePoint = mock(SinglePointFeatures.class); when(mockSinglePoint.getProcessedFeatures()).thenReturn(Optional.empty()); @@ -1128,11 +1075,16 @@ private void setUpColdStart(ThreadPool mockThreadPool, boolean coldStartRunning) doAnswer(invocation -> { ActionListener listener = invocation.getArgument(1); - listener.onResponse(Boolean.FALSE); + if (config.getCheckpointException == null) { + listener.onResponse(Boolean.FALSE); + } else { + listener.onFailure(config.getCheckpointException); + } + return null; }).when(stateManager).getDetectorCheckpoint(any(String.class), any(ActionListener.class)); - when(stateManager.isColdStartRunning(any(String.class))).thenReturn(coldStartRunning); + when(stateManager.isColdStartRunning(any(String.class))).thenReturn(config.coldStartRunning); setUpADThreadPool(mockThreadPool); } @@ -1140,7 +1092,7 @@ private void setUpColdStart(ThreadPool mockThreadPool, boolean coldStartRunning) @SuppressWarnings("unchecked") public void testColdStartNoTrainingData() throws Exception { ThreadPool mockThreadPool = mock(ThreadPool.class); - setUpColdStart(mockThreadPool, false); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(false).build()); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(1); @@ -1156,7 +1108,6 @@ public void testColdStartNoTrainingData() throws Exception { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1178,7 +1129,7 @@ public void testColdStartNoTrainingData() throws Exception { @SuppressWarnings("unchecked") public void testConcurrentColdStart() throws Exception { ThreadPool mockThreadPool = mock(ThreadPool.class); - setUpColdStart(mockThreadPool, true); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(true).build()); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(1); @@ -1194,7 +1145,6 @@ public void testConcurrentColdStart() throws Exception { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1216,7 +1166,7 @@ public void testConcurrentColdStart() throws Exception { @SuppressWarnings("unchecked") public void testColdStartTimeoutPutCheckpoint() throws Exception { ThreadPool mockThreadPool = mock(ThreadPool.class); - setUpColdStart(mockThreadPool, false); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(false).build()); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(1); @@ -1238,7 +1188,6 @@ public void testColdStartTimeoutPutCheckpoint() throws Exception { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1260,7 +1209,7 @@ public void testColdStartTimeoutPutCheckpoint() throws Exception { @SuppressWarnings("unchecked") public void testColdStartIllegalArgumentException() throws Exception { ThreadPool mockThreadPool = mock(ThreadPool.class); - setUpColdStart(mockThreadPool, false); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(false).build()); doAnswer(invocation -> { ActionListener> listener = invocation.getArgument(1); @@ -1282,7 +1231,6 @@ public void testColdStartIllegalArgumentException() throws Exception { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1333,7 +1281,6 @@ public void featureTestTemplate(FeatureTestMode mode) throws IOException { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1421,7 +1368,6 @@ private void globalBlockTemplate(BlockType type, String errLogMsg, Settings inde stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, hackedClusterService, indexNameResolver, @@ -1460,6 +1406,7 @@ public void testIndexReadBlock() { ); } + @SuppressWarnings("unchecked") public void testNullRCFResult() { AnomalyResultTransportAction action = new AnomalyResultTransportAction( new ActionFilters(Collections.emptySet()), @@ -1469,7 +1416,6 @@ public void testNullRCFResult() { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1480,12 +1426,78 @@ public void testNullRCFResult() { adTaskManager ); AnomalyResultTransportAction.RCFActionListener listener = action.new RCFActionListener( - null, "123-rcf-0", null, "123", null, null, null, null, null, 0, new AtomicInteger(), null, 1 + "123-rcf-0", null, "123", null, mock(ActionListener.class), null, null ); listener.onResponse(null); assertTrue(testAppender.containsMessage(AnomalyResultTransportAction.NULL_RESPONSE)); } + @SuppressWarnings("unchecked") + public void testNormalRCFResult() { + AnomalyResultTransportAction action = new AnomalyResultTransportAction( + new ActionFilters(Collections.emptySet()), + transportService, + settings, + client, + stateManager, + featureQuery, + normalModelManager, + hashRing, + clusterService, + indexNameResolver, + adCircuitBreakerService, + adStats, + threadPool, + NamedXContentRegistry.EMPTY, + adTaskManager + ); + ActionListener listener = mock(ActionListener.class); + AnomalyResultTransportAction.RCFActionListener rcfListener = action.new RCFActionListener( + "123-rcf-0", null, "nodeID", detector, listener, null, adID + ); + double[] attribution = new double[] { 1. }; + long totalUpdates = 32; + double grade = 0.5; + ArgumentCaptor responseCaptor = ArgumentCaptor.forClass(AnomalyResultResponse.class); + rcfListener.onResponse(new RCFResultResponse(0.3, 0, 26, attribution, totalUpdates, grade, Version.CURRENT)); + verify(listener, times(1)).onResponse(responseCaptor.capture()); + assertEquals(grade, responseCaptor.getValue().getAnomalyGrade(), 1e-10); + } + + @SuppressWarnings("unchecked") + public void testNullPointerRCFResult() { + AnomalyResultTransportAction action = new AnomalyResultTransportAction( + new ActionFilters(Collections.emptySet()), + transportService, + settings, + client, + stateManager, + featureQuery, + normalModelManager, + hashRing, + clusterService, + indexNameResolver, + adCircuitBreakerService, + adStats, + threadPool, + NamedXContentRegistry.EMPTY, + adTaskManager + ); + ActionListener listener = mock(ActionListener.class); + // detector being null causes NullPointerException + AnomalyResultTransportAction.RCFActionListener rcfListener = action.new RCFActionListener( + "123-rcf-0", null, "nodeID", null, listener, null, adID + ); + double[] attribution = new double[] { 1. }; + long totalUpdates = 32; + double grade = 0.5; + ArgumentCaptor failureCaptor = ArgumentCaptor.forClass(Exception.class); + rcfListener.onResponse(new RCFResultResponse(0.3, 0, 26, attribution, totalUpdates, grade, Version.CURRENT)); + verify(listener, times(1)).onFailure(failureCaptor.capture()); + Exception failure = failureCaptor.getValue(); + assertTrue(failure instanceof InternalFailure); + } + @SuppressWarnings("unchecked") public void testAllFeaturesDisabled() throws IOException { doAnswer(invocation -> { @@ -1502,7 +1514,6 @@ public void testAllFeaturesDisabled() throws IOException { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1523,7 +1534,7 @@ public void testAllFeaturesDisabled() throws IOException { @SuppressWarnings("unchecked") public void testEndRunDueToNoTrainingData() { ThreadPool mockThreadPool = mock(ThreadPool.class); - setUpColdStart(mockThreadPool, false); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(false).build()); ModelManager rcfManager = mock(ModelManager.class); doAnswer(invocation -> { @@ -1566,7 +1577,6 @@ public void testEndRunDueToNoTrainingData() { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -1585,19 +1595,74 @@ public void testEndRunDueToNoTrainingData() { verify(stateManager, times(1)).markColdStartRunning(eq(adID)); } - public void testRCFNodeCircuitBreakerBroken() { - ADCircuitBreakerService brokenCircuitBreaker = mock(ADCircuitBreakerService.class); - when(brokenCircuitBreaker.isOpen()).thenReturn(true); + @SuppressWarnings({ "unchecked" }) + public void testColdStartEndRunException() { + ThreadPool mockThreadPool = mock(ThreadPool.class); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(false).build()); - // These constructors register handler in transport service - new RCFResultTransportAction( + doAnswer(invocation -> { + ActionListener> listener = invocation.getArgument(1); + listener.onResponse(Optional.empty()); + return null; + }).when(featureQuery).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); + + when(stateManager.fetchExceptionAndClear(anyString())) + .thenReturn( + Optional + .of( + new EndRunException( + adID, + CommonErrorMessages.INVALID_SEARCH_QUERY_MSG, + new NoSuchElementException("No value present"), + false + ) + ) + ); + AnomalyResultRequest request = new AnomalyResultRequest(adID, 100, 200); + PlainActionFuture listener = new PlainActionFuture<>(); + + AnomalyResultTransportAction action = new AnomalyResultTransportAction( new ActionFilters(Collections.emptySet()), transportService, + settings, + client, + stateManager, + featureQuery, normalModelManager, - brokenCircuitBreaker, - hashRing + hashRing, + clusterService, + indexNameResolver, + adCircuitBreakerService, + adStats, + mockThreadPool, + NamedXContentRegistry.EMPTY, + adTaskManager ); - new ThresholdResultTransportAction(new ActionFilters(Collections.emptySet()), transportService, normalModelManager); + + action.doExecute(null, request, listener); + assertException(listener, EndRunException.class, CommonErrorMessages.INVALID_SEARCH_QUERY_MSG); + verify(featureQuery, times(1)).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); + } + + @SuppressWarnings({ "unchecked" }) + public void testColdStartEndRunExceptionNow() { + ThreadPool mockThreadPool = mock(ThreadPool.class); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().coldStartRunning(false).build()); + + when(stateManager.fetchExceptionAndClear(anyString())) + .thenReturn( + Optional + .of( + new EndRunException( + adID, + CommonErrorMessages.INVALID_SEARCH_QUERY_MSG, + new NoSuchElementException("No value present"), + true + ) + ) + ); + AnomalyResultRequest request = new AnomalyResultRequest(adID, 100, 200); + PlainActionFuture listener = new PlainActionFuture<>(); AnomalyResultTransportAction action = new AnomalyResultTransportAction( new ActionFilters(Collections.emptySet()), @@ -1607,22 +1672,97 @@ public void testRCFNodeCircuitBreakerBroken() { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, adCircuitBreakerService, adStats, - threadPool, + mockThreadPool, NamedXContentRegistry.EMPTY, adTaskManager ); + action.doExecute(null, request, listener); + assertException(listener, EndRunException.class, CommonErrorMessages.INVALID_SEARCH_QUERY_MSG); + verify(featureQuery, never()).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); + } + + @SuppressWarnings({ "unchecked" }) + public void testColdStartBecauseFailtoGetCheckpoint() { + ThreadPool mockThreadPool = mock(ThreadPool.class); + setUpColdStart( + mockThreadPool, + new ColdStartConfig.Builder().getCheckpointException(new IndexNotFoundException(CommonName.CHECKPOINT_INDEX_NAME)).build() + ); + + doAnswer(invocation -> { + ActionListener> listener = invocation.getArgument(1); + listener.onResponse(Optional.empty()); + return null; + }).when(featureQuery).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); + AnomalyResultRequest request = new AnomalyResultRequest(adID, 100, 200); PlainActionFuture listener = new PlainActionFuture<>(); - action.doExecute(null, request, listener); - assertException(listener, LimitExceededException.class, CommonErrorMessages.MEMORY_CIRCUIT_BROKEN_ERR_MSG); + AnomalyResultTransportAction action = new AnomalyResultTransportAction( + new ActionFilters(Collections.emptySet()), + transportService, + settings, + client, + stateManager, + featureQuery, + normalModelManager, + hashRing, + clusterService, + indexNameResolver, + adCircuitBreakerService, + adStats, + mockThreadPool, + NamedXContentRegistry.EMPTY, + adTaskManager + ); + + action.doExecute(null, request, listener); + AnomalyResultResponse response = listener.actionGet(10000L); + assertEquals(Double.NaN, response.getAnomalyGrade(), 0.001); + verify(featureQuery, times(1)).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); } + @SuppressWarnings({ "unchecked" }) + public void testNoColdStartDueToUnknownException() { + ThreadPool mockThreadPool = mock(ThreadPool.class); + setUpColdStart(mockThreadPool, new ColdStartConfig.Builder().getCheckpointException(new RuntimeException()).build()); + + doAnswer(invocation -> { + ActionListener> listener = invocation.getArgument(1); + listener.onResponse(Optional.empty()); + return null; + }).when(featureQuery).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); + + AnomalyResultRequest request = new AnomalyResultRequest(adID, 100, 200); + PlainActionFuture listener = new PlainActionFuture<>(); + + AnomalyResultTransportAction action = new AnomalyResultTransportAction( + new ActionFilters(Collections.emptySet()), + transportService, + settings, + client, + stateManager, + featureQuery, + normalModelManager, + hashRing, + clusterService, + indexNameResolver, + adCircuitBreakerService, + adStats, + mockThreadPool, + NamedXContentRegistry.EMPTY, + adTaskManager + ); + + action.doExecute(null, request, listener); + AnomalyResultResponse response = listener.actionGet(10000L); + assertEquals(Double.NaN, response.getAnomalyGrade(), 0.001); + verify(featureQuery, never()).getColdStartData(any(AnomalyDetector.class), any(ActionListener.class)); + } } diff --git a/src/test/java/org/opensearch/ad/transport/EntityResultTransportActionTests.java b/src/test/java/org/opensearch/ad/transport/EntityResultTransportActionTests.java index 12cf3bd45..145235bf6 100644 --- a/src/test/java/org/opensearch/ad/transport/EntityResultTransportActionTests.java +++ b/src/test/java/org/opensearch/ad/transport/EntityResultTransportActionTests.java @@ -167,27 +167,7 @@ public void setUp() throws Exception { now = Instant.now(); when(clock.instant()).thenReturn(now); - manager = new ModelManager( - null, - clock, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - 0, - null, - null, - mock(EntityColdStarter.class), - null, - null, - null - ); + manager = new ModelManager(null, clock, 0, 0, 0, 0, 0, 0, null, null, mock(EntityColdStarter.class), null, null); provider = mock(CacheProvider.class); entityCache = mock(EntityCache.class); diff --git a/src/test/java/org/opensearch/ad/transport/MultiEntityResultTests.java b/src/test/java/org/opensearch/ad/transport/MultiEntityResultTests.java index e34212a8a..a81d3bf09 100644 --- a/src/test/java/org/opensearch/ad/transport/MultiEntityResultTests.java +++ b/src/test/java/org/opensearch/ad/transport/MultiEntityResultTests.java @@ -99,7 +99,6 @@ import org.opensearch.ad.feature.FeatureManager; import org.opensearch.ad.indices.AnomalyDetectionIndices; import org.opensearch.ad.ml.ModelManager; -import org.opensearch.ad.ml.ModelPartitioner; import org.opensearch.ad.ml.ThresholdingResult; import org.opensearch.ad.model.AnomalyDetector; import org.opensearch.ad.model.Entity; @@ -161,7 +160,6 @@ public class MultiEntityResultTests extends AbstractADTest { private Client client; private FeatureManager featureQuery; private ModelManager normalModelManager; - private ModelPartitioner normalModelPartitioner; private HashRing hashRing; private ClusterService clusterService; private IndexNameExpressionResolver indexNameResolver; @@ -235,8 +233,6 @@ public void setUp() throws Exception { normalModelManager = mock(ModelManager.class); - normalModelPartitioner = mock(ModelPartitioner.class); - hashRing = mock(HashRing.class); Set> anomalyResultSetting = new HashSet<>(ClusterSettings.BUILT_IN_CLUSTER_SETTINGS); @@ -293,7 +289,6 @@ public void setUp() throws Exception { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -445,7 +440,6 @@ public void setUpNormlaStateManager() throws IOException { return null; }).when(clientUtil).asyncRequest(any(GetRequest.class), any(), any(ActionListener.class)); - ModelPartitioner modelPartitioner = mock(ModelPartitioner.class); stateManager = new NodeStateManager( client, xContentRegistry(), @@ -453,7 +447,6 @@ public void setUpNormlaStateManager() throws IOException { clientUtil, clock, AnomalyDetectorSettings.HOURLY_MAINTENANCE, - modelPartitioner, clusterService ); @@ -465,7 +458,6 @@ public void setUpNormlaStateManager() throws IOException { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, @@ -705,7 +697,6 @@ public void sendRequest( stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, realClusterService, indexNameResolver, @@ -747,7 +738,6 @@ public void testCircuitBreakerOpen() throws InterruptedException, IOException { return null; }).when(clientUtil).asyncRequest(any(GetRequest.class), any(), any(ActionListener.class)); - ModelPartitioner modelPartitioner = mock(ModelPartitioner.class); stateManager = new NodeStateManager( client, xContentRegistry(), @@ -755,7 +745,6 @@ public void testCircuitBreakerOpen() throws InterruptedException, IOException { clientUtil, clock, AnomalyDetectorSettings.HOURLY_MAINTENANCE, - modelPartitioner, clusterService ); @@ -767,7 +756,6 @@ public void testCircuitBreakerOpen() throws InterruptedException, IOException { stateManager, featureQuery, normalModelManager, - normalModelPartitioner, hashRing, clusterService, indexNameResolver, diff --git a/src/test/java/org/opensearch/ad/transport/RCFPollingTests.java b/src/test/java/org/opensearch/ad/transport/RCFPollingTests.java index 51b011bb5..2f2969cc2 100644 --- a/src/test/java/org/opensearch/ad/transport/RCFPollingTests.java +++ b/src/test/java/org/opensearch/ad/transport/RCFPollingTests.java @@ -27,7 +27,6 @@ package org.opensearch.ad.transport; import static org.mockito.ArgumentMatchers.any; -import static org.mockito.Mockito.anyInt; import static org.mockito.Mockito.doAnswer; import static org.mockito.Mockito.mock; import static org.mockito.Mockito.when; @@ -52,7 +51,7 @@ import org.opensearch.ad.common.exception.JsonPathNotFoundException; import org.opensearch.ad.constant.CommonName; import org.opensearch.ad.ml.ModelManager; -import org.opensearch.ad.ml.ModelPartitioner; +import org.opensearch.ad.ml.SingleStreamModelIdMapper; import org.opensearch.cluster.node.DiscoveryNode; import org.opensearch.cluster.service.ClusterService; import org.opensearch.common.io.stream.StreamInput; @@ -79,14 +78,13 @@ public class RCFPollingTests extends AbstractADTest { Gson gson = new GsonBuilder().create(); private String detectorId = "jqIG6XIBEyaF3zCMZfcB"; - private String model0Id = detectorId + "_rcf_0"; + private String model0Id; private long totalUpdates = 3L; private String nodeId = "abc"; private ClusterService clusterService; private HashRing hashRing; private TransportAddress transportAddress1; private ModelManager manager; - private ModelPartitioner modelPartitioner; private TransportService transportService; private PlainActionFuture future; private RCFPollingTransportAction action; @@ -109,7 +107,6 @@ private void registerHandler(FakeNode node) { node.transportService, Settings.EMPTY, manager, - modelPartitioner, hashRing, node.clusterService ); @@ -135,8 +132,7 @@ public void setUp() throws Exception { future = new PlainActionFuture<>(); request = new RCFPollingRequest(detectorId); - modelPartitioner = mock(ModelPartitioner.class); - when(modelPartitioner.getRcfModelId(any(String.class), anyInt())).thenReturn(model0Id); + model0Id = SingleStreamModelIdMapper.getRcfModelId(detectorId, 0); doAnswer(invocation -> { Object[] args = invocation.getArguments(); @@ -217,7 +213,6 @@ public void testNormal() { transportService, Settings.EMPTY, manager, - modelPartitioner, hashRing, clusterService ); @@ -228,14 +223,12 @@ public void testNormal() { } public void testNoNodeFoundForModel() { - when(modelPartitioner.getRcfModelId(any(String.class), anyInt())).thenReturn(model0Id); when(hashRing.getOwningNodeWithSameLocalAdVersionForRealtimeAD(any(String.class))).thenReturn(Optional.empty()); action = new RCFPollingTransportAction( mock(ActionFilters.class), transportService, Settings.EMPTY, manager, - modelPartitioner, hashRing, clusterService ); @@ -323,7 +316,6 @@ public void testGetRemoteNormalResponse() { realTransportService, Settings.EMPTY, manager, - modelPartitioner, hashRing, clusterService ); @@ -352,7 +344,6 @@ public void testGetRemoteFailureResponse() { realTransportService, Settings.EMPTY, manager, - modelPartitioner, hashRing, clusterService ); diff --git a/src/test/java/org/opensearch/ad/transport/RCFResultTests.java b/src/test/java/org/opensearch/ad/transport/RCFResultTests.java index 13da8b598..879af870e 100644 --- a/src/test/java/org/opensearch/ad/transport/RCFResultTests.java +++ b/src/test/java/org/opensearch/ad/transport/RCFResultTests.java @@ -43,6 +43,7 @@ import org.hamcrest.Matchers; import org.junit.Before; +import org.opensearch.Version; import org.opensearch.action.ActionListener; import org.opensearch.action.ActionRequestValidationException; import org.opensearch.action.support.ActionFilters; @@ -78,7 +79,10 @@ public class RCFResultTests extends OpenSearchTestCase { private double[] attribution = new double[] { 1. }; private HashRing hashRing; private DiscoveryNode node; + private long totalUpdates = 32; + private double grade = 0.5; + @Override @Before public void setUp() throws Exception { super.setUp(); @@ -110,7 +114,7 @@ public void testNormal() { ); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(3); - listener.onResponse(new RcfResult(0, 0, 25, attribution)); + listener.onResponse(new RcfResult(0, 0, 25, attribution, totalUpdates, grade)); return null; }).when(manager).getRcfResult(any(String.class), any(String.class), any(double[].class), any(ActionListener.class)); @@ -160,7 +164,7 @@ public void testExecutionException() { } public void testSerialzationResponse() throws IOException { - RCFResultResponse response = new RCFResultResponse(0.3, 0, 26, attribution); + RCFResultResponse response = new RCFResultResponse(0.3, 0, 26, attribution, totalUpdates, grade, Version.CURRENT); BytesStreamOutput output = new BytesStreamOutput(); response.writeTo(output); @@ -172,7 +176,7 @@ public void testSerialzationResponse() throws IOException { } public void testJsonResponse() throws IOException, JsonPathNotFoundException { - RCFResultResponse response = new RCFResultResponse(0.3, 0, 26, attribution); + RCFResultResponse response = new RCFResultResponse(0.3, 0, 26, attribution, totalUpdates, grade, Version.CURRENT); XContentBuilder builder = jsonBuilder(); response.toXContent(builder, ToXContent.EMPTY_PARAMS); @@ -238,7 +242,7 @@ public void testCircuitBreaker() { ); doAnswer(invocation -> { ActionListener listener = invocation.getArgument(3); - listener.onResponse(new RcfResult(0, 0, 25, attribution)); + listener.onResponse(new RcfResult(0, 0, 25, attribution, totalUpdates, grade)); return null; }).when(manager).getRcfResult(any(String.class), any(String.class), any(double[].class), any(ActionListener.class)); when(breakerService.isOpen()).thenReturn(true); diff --git a/src/test/java/org/opensearch/ad/transport/ThresholdResultTests.java b/src/test/java/org/opensearch/ad/transport/ThresholdResultTests.java index 440ab0ecd..862691c9c 100644 --- a/src/test/java/org/opensearch/ad/transport/ThresholdResultTests.java +++ b/src/test/java/org/opensearch/ad/transport/ThresholdResultTests.java @@ -137,11 +137,16 @@ public void testJsonResponse() throws IOException, JsonPathNotFoundException { assertEquals(JsonDeserializer.getDoubleValue(json, CommonName.CONFIDENCE_JSON_KEY), response.getConfidence(), 0.001); } - public void testEmptyID() { + public void testEmptyDetectorID() { ActionRequestValidationException e = new ThresholdResultRequest(null, "123-threshold", 2).validate(); assertThat(e.validationErrors(), Matchers.hasItem(CommonErrorMessages.AD_ID_MISSING_MSG)); } + public void testEmptyModelID() { + ActionRequestValidationException e = new ThresholdResultRequest("123", "", 2).validate(); + assertThat(e.validationErrors(), Matchers.hasItem(CommonErrorMessages.MODEL_ID_MISSING_MSG)); + } + public void testSerialzationRequest() throws IOException { ThresholdResultRequest response = new ThresholdResultRequest("123", "123-threshold", 2); BytesStreamOutput output = new BytesStreamOutput(); diff --git a/src/test/java/test/org/opensearch/ad/util/MLUtil.java b/src/test/java/test/org/opensearch/ad/util/MLUtil.java index de17e44d9..8b8a31f44 100644 --- a/src/test/java/test/org/opensearch/ad/util/MLUtil.java +++ b/src/test/java/test/org/opensearch/ad/util/MLUtil.java @@ -30,21 +30,20 @@ import java.time.Clock; import java.util.ArrayDeque; +import java.util.HashMap; +import java.util.Map; import java.util.Queue; import java.util.Random; -import java.util.stream.DoubleStream; import java.util.stream.IntStream; -import org.apache.commons.lang3.tuple.Triple; import org.opensearch.ad.ml.EntityModel; -import org.opensearch.ad.ml.HybridThresholdingModel; import org.opensearch.ad.ml.ModelManager.ModelType; import org.opensearch.ad.ml.ModelState; -import org.opensearch.ad.ml.ThresholdingModel; import org.opensearch.ad.model.Entity; import org.opensearch.ad.settings.AnomalyDetectorSettings; +import org.opensearch.common.collect.Tuple; -import com.amazon.randomcutforest.RandomCutForest; +import com.amazon.randomcutforest.parkservices.ThresholdedRandomCutForest; /** * Cannot use TestUtil inside ML tests since it uses com.carrotsearch.randomizedtesting.RandomizedRunner @@ -81,11 +80,20 @@ public static ModelState randomModelState(RandomModelStateConfig co int sampleSize = config.getSampleSize() != null ? config.getSampleSize() : random.nextInt(minSampleSize); Clock clock = config.getClock() != null ? config.getClock() : Clock.systemUTC(); + Entity entity = null; + if (config.hasEntityAttributes()) { + Map attributes = new HashMap<>(); + attributes.put("a", "a1"); + attributes.put("b", "b1"); + entity = Entity.createEntityByReordering(attributes); + } else { + entity = Entity.createSingleAttributeEntity("", ""); + } EntityModel model = null; if (fullModel) { - model = createNonEmptyModel(detectorId, sampleSize); + model = createNonEmptyModel(detectorId, sampleSize, entity); } else { - model = createEmptyModel(Entity.createSingleAttributeEntity("", ""), sampleSize); + model = createEmptyModel(entity, sampleSize); } return new ModelState<>(model, detectorId, detectorId, ModelType.ENTITY.getName(), clock, priority); @@ -93,47 +101,37 @@ public static ModelState randomModelState(RandomModelStateConfig co public static EntityModel createEmptyModel(Entity entity, int sampleSize) { Queue samples = createQueueSamples(sampleSize); - return new EntityModel(entity, samples, null, null); + return new EntityModel(entity, samples, null); } public static EntityModel createEmptyModel(Entity entity) { return createEmptyModel(entity, random.nextInt(minSampleSize)); } - public static EntityModel createNonEmptyModel(String detectorId, int sampleSize) { + public static EntityModel createNonEmptyModel(String detectorId, int sampleSize, Entity entity) { Queue samples = createQueueSamples(sampleSize); - RandomCutForest rcf = RandomCutForest - .builder() - .dimensions(1) - .sampleSize(AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE) - .numberOfTrees(AnomalyDetectorSettings.NUM_TREES) - .timeDecay(AnomalyDetectorSettings.TIME_DECAY) - .outputAfter(AnomalyDetectorSettings.NUM_MIN_SAMPLES) - .parallelExecutionEnabled(false) - .build(); int numDataPoints = random.nextInt(1000) + AnomalyDetectorSettings.NUM_MIN_SAMPLES; - double[] scores = new double[numDataPoints]; - for (int j = 0; j < numDataPoints; j++) { - double[] dataPoint = new double[] { random.nextDouble() }; - scores[j] = rcf.getAnomalyScore(dataPoint); - rcf.update(dataPoint); - } - - double[] nonZeroScores = DoubleStream.of(scores).filter(score -> score > 0).toArray(); - ThresholdingModel threshold = new HybridThresholdingModel( - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES + ThresholdedRandomCutForest trcf = new ThresholdedRandomCutForest( + ThresholdedRandomCutForest + .builder() + .dimensions(1) + .sampleSize(AnomalyDetectorSettings.NUM_SAMPLES_PER_TREE) + .numberOfTrees(AnomalyDetectorSettings.NUM_TREES) + .timeDecay(AnomalyDetectorSettings.TIME_DECAY) + .outputAfter(AnomalyDetectorSettings.NUM_MIN_SAMPLES) + .parallelExecutionEnabled(false) + .internalShinglingEnabled(true) + .anomalyRate(1 - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE) ); - threshold.train(nonZeroScores); - return new EntityModel(Entity.createSingleAttributeEntity("", ""), samples, rcf, threshold); + for (int i = 0; i < numDataPoints; i++) { + trcf.process(new double[] { random.nextDouble() }, i); + } + EntityModel entityModel = new EntityModel(entity, samples, trcf); + return entityModel; } public static EntityModel createNonEmptyModel(String detectorId) { - return createNonEmptyModel(detectorId, random.nextInt(minSampleSize)); + return createNonEmptyModel(detectorId, random.nextInt(minSampleSize), Entity.createSingleAttributeEntity("", "")); } /** @@ -191,35 +189,23 @@ static double[] getDataD(int num, double amplitude, double noise, long seed) { * @param rcfConfig RCF config * @return models and return training samples */ - public static Triple, RandomCutForest, ThresholdingModel> prepareModel( + public static Tuple, ThresholdedRandomCutForest> prepareModel( int inputDimension, - RandomCutForest.Builder rcfConfig + ThresholdedRandomCutForest.Builder rcfConfig ) { Queue samples = new ArrayDeque<>(); Random r = new Random(); - RandomCutForest rcf = rcfConfig.build(); - ThresholdingModel threshold = new HybridThresholdingModel( - AnomalyDetectorSettings.THRESHOLD_MIN_PVALUE, - AnomalyDetectorSettings.THRESHOLD_MAX_RANK_ERROR, - AnomalyDetectorSettings.THRESHOLD_MAX_SCORE, - AnomalyDetectorSettings.THRESHOLD_NUM_LOGNORMAL_QUANTILES, - AnomalyDetectorSettings.THRESHOLD_DOWNSAMPLES, - AnomalyDetectorSettings.THRESHOLD_MAX_SAMPLES - ); + ThresholdedRandomCutForest rcf = new ThresholdedRandomCutForest(rcfConfig); int trainDataNum = 1000; - double[] scores = new double[trainDataNum]; for (int i = 0; i < trainDataNum; i++) { double[] point = r.ints(inputDimension, 0, 50).asDoubleStream().toArray(); samples.add(point); - scores[i] = rcf.getAnomalyScore(point); - rcf.update(point); + rcf.process(point, 0); } - // train using non-zero scores - threshold.train(DoubleStream.of(scores).filter(score -> score > 0).toArray()); - return Triple.of(samples, rcf, threshold); + return Tuple.tuple(samples, rcf); } } diff --git a/src/test/java/test/org/opensearch/ad/util/RandomModelStateConfig.java b/src/test/java/test/org/opensearch/ad/util/RandomModelStateConfig.java index cefd81dc5..597b20302 100644 --- a/src/test/java/test/org/opensearch/ad/util/RandomModelStateConfig.java +++ b/src/test/java/test/org/opensearch/ad/util/RandomModelStateConfig.java @@ -34,6 +34,7 @@ public class RandomModelStateConfig { private final String detectorId; private final Integer sampleSize; private final Clock clock; + private final Boolean entityAttributes; private RandomModelStateConfig(Builder builder) { this.fullModel = builder.fullModel; @@ -41,6 +42,7 @@ private RandomModelStateConfig(Builder builder) { this.detectorId = builder.detectorId; this.sampleSize = builder.sampleSize; this.clock = builder.clock; + this.entityAttributes = builder.entityAttributes; } public Boolean getFullModel() { @@ -63,12 +65,17 @@ public Clock getClock() { return clock; } + public Boolean hasEntityAttributes() { + return entityAttributes; + } + public static class Builder { private Boolean fullModel = null; private Float priority = null; private String detectorId = null; private Integer sampleSize = null; private Clock clock = null; + private Boolean entityAttributes = false; public Builder fullModel(boolean fullModel) { this.fullModel = fullModel; @@ -95,6 +102,11 @@ public Builder clock(Clock clock) { return this; } + public Builder entityAttributes(Boolean entityAttributes) { + this.entityAttributes = entityAttributes; + return this; + } + public RandomModelStateConfig build() { RandomModelStateConfig config = new RandomModelStateConfig(this); return config; diff --git a/src/test/resources/org/opensearch/ad/ml/checkpoint_1.json b/src/test/resources/org/opensearch/ad/ml/checkpoint_1.json new file mode 100644 index 000000000..72f5ae534 --- /dev/null +++ b/src/test/resources/org/opensearch/ad/ml/checkpoint_1.json @@ -0,0 +1 @@ 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