diff --git a/client/rest-high-level/src/main/java/org/elasticsearch/client/MLRequestConverters.java b/client/rest-high-level/src/main/java/org/elasticsearch/client/MLRequestConverters.java index bf220d63b3c..54dd11bf6ca 100644 --- a/client/rest-high-level/src/main/java/org/elasticsearch/client/MLRequestConverters.java +++ b/client/rest-high-level/src/main/java/org/elasticsearch/client/MLRequestConverters.java @@ -40,6 +40,7 @@ import org.elasticsearch.client.ml.DeleteForecastRequest; import org.elasticsearch.client.ml.DeleteJobRequest; import org.elasticsearch.client.ml.DeleteModelSnapshotRequest; import org.elasticsearch.client.ml.DeleteTrainedModelRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryRequest; import org.elasticsearch.client.ml.EvaluateDataFrameRequest; import org.elasticsearch.client.ml.ExplainDataFrameAnalyticsRequest; import org.elasticsearch.client.ml.FindFileStructureRequest; @@ -593,6 +594,17 @@ final class MLRequestConverters { return new Request(HttpDelete.METHOD_NAME, endpoint); } + static Request estimateModelMemory(EstimateModelMemoryRequest estimateModelMemoryRequest) throws IOException { + String endpoint = new EndpointBuilder() + .addPathPartAsIs("_ml") + .addPathPartAsIs("anomaly_detectors") + .addPathPartAsIs("_estimate_model_memory") + .build(); + Request request = new Request(HttpPost.METHOD_NAME, endpoint); + request.setEntity(createEntity(estimateModelMemoryRequest, REQUEST_BODY_CONTENT_TYPE)); + return request; + } + static Request putDataFrameAnalytics(PutDataFrameAnalyticsRequest putRequest) throws IOException { String endpoint = new EndpointBuilder() .addPathPartAsIs("_ml", "data_frame", "analytics") diff --git a/client/rest-high-level/src/main/java/org/elasticsearch/client/MachineLearningClient.java b/client/rest-high-level/src/main/java/org/elasticsearch/client/MachineLearningClient.java index 504cbc541f0..61d4b52db2d 100644 --- a/client/rest-high-level/src/main/java/org/elasticsearch/client/MachineLearningClient.java +++ b/client/rest-high-level/src/main/java/org/elasticsearch/client/MachineLearningClient.java @@ -23,6 +23,8 @@ import org.elasticsearch.action.support.master.AcknowledgedResponse; import org.elasticsearch.client.ml.CloseJobRequest; import org.elasticsearch.client.ml.CloseJobResponse; import org.elasticsearch.client.ml.DeleteTrainedModelRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryResponse; import org.elasticsearch.client.ml.ExplainDataFrameAnalyticsRequest; import org.elasticsearch.client.ml.ExplainDataFrameAnalyticsResponse; import org.elasticsearch.client.ml.DeleteCalendarEventRequest; @@ -1951,6 +1953,48 @@ public final class MachineLearningClient { Collections.emptySet()); } + /** + * Estimate the model memory an analysis config is likely to need given supplied field cardinalities + *

+ * For additional info + * see Estimate Model Memory + * + * @param request The {@link EstimateModelMemoryRequest} + * @param options Additional request options (e.g. headers), use {@link RequestOptions#DEFAULT} if nothing needs to be customized + * @return {@link EstimateModelMemoryResponse} response object + */ + public EstimateModelMemoryResponse estimateModelMemory(EstimateModelMemoryRequest request, + RequestOptions options) throws IOException { + return restHighLevelClient.performRequestAndParseEntity(request, + MLRequestConverters::estimateModelMemory, + options, + EstimateModelMemoryResponse::fromXContent, + Collections.emptySet()); + } + + /** + * Estimate the model memory an analysis config is likely to need given supplied field cardinalities and notifies listener upon + * completion + *

+ * For additional info + * see Estimate Model Memory + * + * @param request The {@link EstimateModelMemoryRequest} + * @param options Additional request options (e.g. headers), use {@link RequestOptions#DEFAULT} if nothing needs to be customized + * @param listener Listener to be notified upon request completion + * @return cancellable that may be used to cancel the request + */ + public Cancellable estimateModelMemoryAsync(EstimateModelMemoryRequest request, + RequestOptions options, + ActionListener listener) { + return restHighLevelClient.performRequestAsyncAndParseEntity(request, + MLRequestConverters::estimateModelMemory, + options, + EstimateModelMemoryResponse::fromXContent, + listener, + Collections.emptySet()); + } + /** * Creates a new Data Frame Analytics config *

diff --git a/client/rest-high-level/src/main/java/org/elasticsearch/client/ml/EstimateModelMemoryRequest.java b/client/rest-high-level/src/main/java/org/elasticsearch/client/ml/EstimateModelMemoryRequest.java new file mode 100644 index 00000000000..b0dc8bb7c29 --- /dev/null +++ b/client/rest-high-level/src/main/java/org/elasticsearch/client/ml/EstimateModelMemoryRequest.java @@ -0,0 +1,110 @@ +/* + * Licensed to Elasticsearch under one or more contributor + * license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright + * ownership. Elasticsearch licenses this file to you under + * the Apache License, Version 2.0 (the "License"); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License 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.elasticsearch.client.ml; + +import org.elasticsearch.client.Validatable; +import org.elasticsearch.client.ValidationException; +import org.elasticsearch.client.ml.job.config.AnalysisConfig; +import org.elasticsearch.common.xcontent.ToXContentObject; +import org.elasticsearch.common.xcontent.XContentBuilder; + +import java.io.IOException; +import java.util.Collections; +import java.util.Map; +import java.util.Objects; +import java.util.Optional; + +/** + * Request to estimate the model memory an analysis config is likely to need given supplied field cardinalities. + */ +public class EstimateModelMemoryRequest implements Validatable, ToXContentObject { + + public static final String ANALYSIS_CONFIG = "analysis_config"; + public static final String OVERALL_CARDINALITY = "overall_cardinality"; + public static final String MAX_BUCKET_CARDINALITY = "max_bucket_cardinality"; + + private final AnalysisConfig analysisConfig; + private Map overallCardinality = Collections.emptyMap(); + private Map maxBucketCardinality = Collections.emptyMap(); + + @Override + public Optional validate() { + return Optional.empty(); + } + + public EstimateModelMemoryRequest(AnalysisConfig analysisConfig) { + this.analysisConfig = Objects.requireNonNull(analysisConfig); + } + + public AnalysisConfig getAnalysisConfig() { + return analysisConfig; + } + + public Map getOverallCardinality() { + return overallCardinality; + } + + public void setOverallCardinality(Map overallCardinality) { + this.overallCardinality = Collections.unmodifiableMap(overallCardinality); + } + + public Map getMaxBucketCardinality() { + return maxBucketCardinality; + } + + public void setMaxBucketCardinality(Map maxBucketCardinality) { + this.maxBucketCardinality = Collections.unmodifiableMap(maxBucketCardinality); + } + + @Override + public XContentBuilder toXContent(XContentBuilder builder, Params params) throws IOException { + builder.startObject(); + builder.field(ANALYSIS_CONFIG, analysisConfig); + if (overallCardinality.isEmpty() == false) { + builder.field(OVERALL_CARDINALITY, overallCardinality); + } + if (maxBucketCardinality.isEmpty() == false) { + builder.field(MAX_BUCKET_CARDINALITY, maxBucketCardinality); + } + builder.endObject(); + return builder; + } + + @Override + public int hashCode() { + return Objects.hash(analysisConfig, overallCardinality, maxBucketCardinality); + } + + @Override + public boolean equals(Object other) { + if (this == other) { + return true; + } + + if (other == null || getClass() != other.getClass()) { + return false; + } + + EstimateModelMemoryRequest that = (EstimateModelMemoryRequest) other; + return Objects.equals(analysisConfig, that.analysisConfig) && + Objects.equals(overallCardinality, that.overallCardinality) && + Objects.equals(maxBucketCardinality, that.maxBucketCardinality); + } +} diff --git a/client/rest-high-level/src/main/java/org/elasticsearch/client/ml/EstimateModelMemoryResponse.java b/client/rest-high-level/src/main/java/org/elasticsearch/client/ml/EstimateModelMemoryResponse.java new file mode 100644 index 00000000000..02b5c03d9b4 --- /dev/null +++ b/client/rest-high-level/src/main/java/org/elasticsearch/client/ml/EstimateModelMemoryResponse.java @@ -0,0 +1,80 @@ +/* + * Licensed to Elasticsearch under one or more contributor + * license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright + * ownership. Elasticsearch licenses this file to you under + * the Apache License, Version 2.0 (the "License"); you may + * not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, + * software distributed under the License 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.elasticsearch.client.ml; + +import org.elasticsearch.common.ParseField; +import org.elasticsearch.common.unit.ByteSizeValue; +import org.elasticsearch.common.xcontent.ConstructingObjectParser; +import org.elasticsearch.common.xcontent.XContentParser; + +import java.util.Objects; + +import static org.elasticsearch.common.xcontent.ConstructingObjectParser.constructorArg; + +public class EstimateModelMemoryResponse { + + public static final ParseField MODEL_MEMORY_ESTIMATE = new ParseField("model_memory_estimate"); + + static final ConstructingObjectParser PARSER = + new ConstructingObjectParser<>( + "estimate_model_memory", + true, + args -> new EstimateModelMemoryResponse((String) args[0])); + + static { + PARSER.declareString(constructorArg(), MODEL_MEMORY_ESTIMATE); + } + + public static EstimateModelMemoryResponse fromXContent(final XContentParser parser) { + return PARSER.apply(parser, null); + } + + private final ByteSizeValue modelMemoryEstimate; + + public EstimateModelMemoryResponse(String modelMemoryEstimate) { + this.modelMemoryEstimate = ByteSizeValue.parseBytesSizeValue(modelMemoryEstimate, MODEL_MEMORY_ESTIMATE.getPreferredName()); + } + + /** + * @return An estimate of the model memory the supplied analysis config is likely to need given the supplied field cardinalities. + */ + public ByteSizeValue getModelMemoryEstimate() { + return modelMemoryEstimate; + } + + @Override + public boolean equals(Object o) { + + if (this == o) { + return true; + } + if (o == null || getClass() != o.getClass()) { + return false; + } + + EstimateModelMemoryResponse other = (EstimateModelMemoryResponse) o; + return Objects.equals(this.modelMemoryEstimate, other.modelMemoryEstimate); + } + + @Override + public int hashCode() { + return Objects.hash(modelMemoryEstimate); + } +} diff --git a/client/rest-high-level/src/test/java/org/elasticsearch/client/MLRequestConvertersTests.java b/client/rest-high-level/src/test/java/org/elasticsearch/client/MLRequestConvertersTests.java index a9a42c979ab..ac4196b4cc1 100644 --- a/client/rest-high-level/src/test/java/org/elasticsearch/client/MLRequestConvertersTests.java +++ b/client/rest-high-level/src/test/java/org/elasticsearch/client/MLRequestConvertersTests.java @@ -36,6 +36,7 @@ import org.elasticsearch.client.ml.DeleteForecastRequest; import org.elasticsearch.client.ml.DeleteJobRequest; import org.elasticsearch.client.ml.DeleteModelSnapshotRequest; import org.elasticsearch.client.ml.DeleteTrainedModelRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryRequest; import org.elasticsearch.client.ml.EvaluateDataFrameRequest; import org.elasticsearch.client.ml.EvaluateDataFrameRequestTests; import org.elasticsearch.client.ml.ExplainDataFrameAnalyticsRequest; @@ -107,6 +108,7 @@ import org.elasticsearch.common.Strings; import org.elasticsearch.common.settings.Settings; import org.elasticsearch.common.unit.TimeValue; import org.elasticsearch.common.xcontent.NamedXContentRegistry; +import org.elasticsearch.common.xcontent.ToXContent; import org.elasticsearch.common.xcontent.XContentBuilder; import org.elasticsearch.common.xcontent.XContentParser; import org.elasticsearch.common.xcontent.XContentType; @@ -695,6 +697,25 @@ public class MLRequestConvertersTests extends ESTestCase { assertEquals("/_ml/calendars/" + calendarId + "/events/" + eventId, request.getEndpoint()); } + public void testEstimateModelMemory() throws Exception { + String byFieldName = randomAlphaOfLength(10); + String influencerFieldName = randomAlphaOfLength(10); + AnalysisConfig analysisConfig = AnalysisConfig.builder( + Collections.singletonList( + Detector.builder().setFunction("count").setByFieldName(byFieldName).build() + )).setInfluencers(Collections.singletonList(influencerFieldName)).build(); + EstimateModelMemoryRequest estimateModelMemoryRequest = new EstimateModelMemoryRequest(analysisConfig); + estimateModelMemoryRequest.setOverallCardinality(Collections.singletonMap(byFieldName, randomNonNegativeLong())); + estimateModelMemoryRequest.setMaxBucketCardinality(Collections.singletonMap(influencerFieldName, randomNonNegativeLong())); + Request request = MLRequestConverters.estimateModelMemory(estimateModelMemoryRequest); + assertEquals(HttpPost.METHOD_NAME, request.getMethod()); + assertEquals("/_ml/anomaly_detectors/_estimate_model_memory", request.getEndpoint()); + + XContentBuilder builder = JsonXContent.contentBuilder(); + builder = estimateModelMemoryRequest.toXContent(builder, ToXContent.EMPTY_PARAMS); + assertEquals(Strings.toString(builder), requestEntityToString(request)); + } + public void testPutDataFrameAnalytics() throws IOException { PutDataFrameAnalyticsRequest putRequest = new PutDataFrameAnalyticsRequest(randomDataFrameAnalyticsConfig()); Request request = MLRequestConverters.putDataFrameAnalytics(putRequest); diff --git a/client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java b/client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java index e98751f5ba5..a3fe5c69ee0 100644 --- a/client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java +++ b/client/rest-high-level/src/test/java/org/elasticsearch/client/MachineLearningIT.java @@ -46,6 +46,8 @@ import org.elasticsearch.client.ml.DeleteJobRequest; import org.elasticsearch.client.ml.DeleteJobResponse; import org.elasticsearch.client.ml.DeleteModelSnapshotRequest; import org.elasticsearch.client.ml.DeleteTrainedModelRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryResponse; import org.elasticsearch.client.ml.EvaluateDataFrameRequest; import org.elasticsearch.client.ml.EvaluateDataFrameResponse; import org.elasticsearch.client.ml.ExplainDataFrameAnalyticsRequest; @@ -1274,6 +1276,27 @@ public class MachineLearningIT extends ESRestHighLevelClientTestCase { assertThat(remainingIds, not(hasItem(deletedEvent))); } + public void testEstimateModelMemory() throws Exception { + MachineLearningClient machineLearningClient = highLevelClient().machineLearning(); + + String byFieldName = randomAlphaOfLength(10); + String influencerFieldName = randomAlphaOfLength(10); + AnalysisConfig analysisConfig = AnalysisConfig.builder( + Collections.singletonList( + Detector.builder().setFunction("count").setByFieldName(byFieldName).build() + )).setInfluencers(Collections.singletonList(influencerFieldName)).build(); + EstimateModelMemoryRequest estimateModelMemoryRequest = new EstimateModelMemoryRequest(analysisConfig); + estimateModelMemoryRequest.setOverallCardinality(Collections.singletonMap(byFieldName, randomNonNegativeLong())); + estimateModelMemoryRequest.setMaxBucketCardinality(Collections.singletonMap(influencerFieldName, randomNonNegativeLong())); + + EstimateModelMemoryResponse estimateModelMemoryResponse = execute( + estimateModelMemoryRequest, + machineLearningClient::estimateModelMemory, machineLearningClient::estimateModelMemoryAsync); + + ByteSizeValue modelMemoryEstimate = estimateModelMemoryResponse.getModelMemoryEstimate(); + assertThat(modelMemoryEstimate.getBytes(), greaterThanOrEqualTo(10000000L)); + } + public void testPutDataFrameAnalyticsConfig_GivenOutlierDetectionAnalysis() throws Exception { MachineLearningClient machineLearningClient = highLevelClient().machineLearning(); String configId = "test-put-df-analytics-outlier-detection"; diff --git a/client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java b/client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java index 8393774493e..ac43ac07a3e 100644 --- a/client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java +++ b/client/rest-high-level/src/test/java/org/elasticsearch/client/documentation/MlClientDocumentationIT.java @@ -49,6 +49,8 @@ import org.elasticsearch.client.ml.DeleteJobRequest; import org.elasticsearch.client.ml.DeleteJobResponse; import org.elasticsearch.client.ml.DeleteModelSnapshotRequest; import org.elasticsearch.client.ml.DeleteTrainedModelRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryRequest; +import org.elasticsearch.client.ml.EstimateModelMemoryResponse; import org.elasticsearch.client.ml.EvaluateDataFrameRequest; import org.elasticsearch.client.ml.EvaluateDataFrameResponse; import org.elasticsearch.client.ml.ExplainDataFrameAnalyticsRequest; @@ -4133,6 +4135,65 @@ public class MlClientDocumentationIT extends ESRestHighLevelClientTestCase { } } + public void testEstimateModelMemory() throws Exception { + RestHighLevelClient client = highLevelClient(); + { + // tag::estimate-model-memory-request + Detector.Builder detectorBuilder = new Detector.Builder() + .setFunction("count") + .setPartitionFieldName("status"); + AnalysisConfig.Builder analysisConfigBuilder = + new AnalysisConfig.Builder(Collections.singletonList(detectorBuilder.build())) + .setBucketSpan(TimeValue.timeValueMinutes(10)) + .setInfluencers(Collections.singletonList("src_ip")); + EstimateModelMemoryRequest request = new EstimateModelMemoryRequest(analysisConfigBuilder.build()); // <1> + request.setOverallCardinality(Collections.singletonMap("status", 50L)); // <2> + request.setMaxBucketCardinality(Collections.singletonMap("src_ip", 30L)); // <3> + // end::estimate-model-memory-request + + // tag::estimate-model-memory-execute + EstimateModelMemoryResponse estimateModelMemoryResponse = + client.machineLearning().estimateModelMemory(request, RequestOptions.DEFAULT); + // end::estimate-model-memory-execute + + // tag::estimate-model-memory-response + ByteSizeValue modelMemoryEstimate = estimateModelMemoryResponse.getModelMemoryEstimate(); // <1> + long estimateInBytes = modelMemoryEstimate.getBytes(); + // end::estimate-model-memory-response + assertThat(estimateInBytes, greaterThan(10000000L)); + } + { + AnalysisConfig analysisConfig = + AnalysisConfig.builder(Collections.singletonList(Detector.builder().setFunction("count").build())).build(); + EstimateModelMemoryRequest request = new EstimateModelMemoryRequest(analysisConfig); + + // tag::estimate-model-memory-execute-listener + ActionListener listener = new ActionListener() { + @Override + public void onResponse(EstimateModelMemoryResponse estimateModelMemoryResponse) { + // <1> + } + + @Override + public void onFailure(Exception e) { + // <2> + } + }; + // end::estimate-model-memory-execute-listener + + // Replace the empty listener by a blocking listener in test + final CountDownLatch latch = new CountDownLatch(1); + listener = new LatchedActionListener<>(listener, latch); + + // tag::estimate-model-memory-execute-async + client.machineLearning() + .estimateModelMemoryAsync(request, RequestOptions.DEFAULT, listener); // <1> + // end::estimate-model-memory-execute-async + + assertTrue(latch.await(30L, TimeUnit.SECONDS)); + } + } + private String createFilter(RestHighLevelClient client) throws IOException { MlFilter.Builder filterBuilder = MlFilter.builder("my_safe_domains") .setDescription("A list of safe domains") diff --git a/docs/java-rest/high-level/ml/estimate-model-memory.asciidoc b/docs/java-rest/high-level/ml/estimate-model-memory.asciidoc new file mode 100644 index 00000000000..8e8b5f1befa --- /dev/null +++ b/docs/java-rest/high-level/ml/estimate-model-memory.asciidoc @@ -0,0 +1,42 @@ +-- +:api: estimate-model-memory +:request: EstimateModelMemoryRequest +:response: EstimateModelMemoryResponse +-- +[role="xpack"] +[id="{upid}-{api}"] +=== Estimate {anomaly-job} model memory API + +Estimate the model memory an analysis config is likely to need for +the given cardinality of the fields it references. + +[id="{upid}-{api}-request"] +==== Estimate {anomaly-job} model memory request + +A +{request}+ can be set up as follows: + +["source","java",subs="attributes,callouts,macros"] +-------------------------------------------------- +include-tagged::{doc-tests-file}[{api}-request] +-------------------------------------------------- +<1> Pass an `AnalysisConfig` to the constructor. +<2> For any `by_field_name`, `over_field_name` or + `partition_field_name` fields referenced by the + detectors, supply overall cardinality estimates + in a `Map`. +<3> For any `influencers`, supply a `Map` containing + estimates of the highest cardinality expected in + any single bucket. + +include::../execution.asciidoc[] + +[id="{upid}-{api}-response"] +==== Estimate {anomaly-job} model memory response + +The returned +{response}+ contains the model memory estimate: + +["source","java",subs="attributes,callouts,macros"] +-------------------------------------------------- +include-tagged::{doc-tests-file}[{api}-response] +-------------------------------------------------- +<1> The model memory estimate. diff --git a/docs/java-rest/high-level/supported-apis.asciidoc b/docs/java-rest/high-level/supported-apis.asciidoc index 0204ba99a5a..59678c4924f 100644 --- a/docs/java-rest/high-level/supported-apis.asciidoc +++ b/docs/java-rest/high-level/supported-apis.asciidoc @@ -297,6 +297,7 @@ The Java High Level REST Client supports the following Machine Learning APIs: * <<{upid}-put-calendar-job>> * <<{upid}-delete-calendar-job>> * <<{upid}-delete-calendar>> +* <<{upid}-estimate-model-memory>> * <<{upid}-get-data-frame-analytics>> * <<{upid}-get-data-frame-analytics-stats>> * <<{upid}-put-data-frame-analytics>> @@ -353,6 +354,7 @@ include::ml/delete-calendar-event.asciidoc[] include::ml/put-calendar-job.asciidoc[] include::ml/delete-calendar-job.asciidoc[] include::ml/delete-calendar.asciidoc[] +include::ml/estimate-model-memory.asciidoc[] include::ml/get-data-frame-analytics.asciidoc[] include::ml/get-data-frame-analytics-stats.asciidoc[] include::ml/put-data-frame-analytics.asciidoc[] diff --git a/docs/reference/ml/anomaly-detection/apis/estimate-model-memory.asciidoc b/docs/reference/ml/anomaly-detection/apis/estimate-model-memory.asciidoc new file mode 100644 index 00000000000..46a49f7cd23 --- /dev/null +++ b/docs/reference/ml/anomaly-detection/apis/estimate-model-memory.asciidoc @@ -0,0 +1,87 @@ +[role="xpack"] +[testenv="platinum"] +[[ml-estimate-model-memory]] +=== Estimate {anomaly-jobs} model memory API +++++ +Estimate model memory +++++ + +Estimates the model memory an {anomaly-job} is likely to need based on analysis +configuration details and cardinality estimates for the fields it references. + +[[ml-estimate-model-memory-request]] +==== {api-request-title} + +`POST _ml/anomaly_detectors/_estimate_model_memory` + +[[ml-estimate-model-memory-prereqs]] +==== {api-prereq-title} + +* If the {es} {security-features} are enabled, you must have `manage_ml` or +`manage` cluster privileges to use this API. See +<>. + +[[ml-estimate-model-memory-request-body]] +==== {api-request-body-title} + +`analysis_config`:: +(Required, object) For a list of the properties that you can specify in the +`analysis_config` component of the body of this API, see <>. + +`max_bucket_cardinality`:: +(Optional, object) Estimates of the highest cardinality in a single bucket +that will be observed for influencer fields over the time period that the job +analyzes data. To produce a good answer, values must be provided for +all influencer fields. It does not matter if values are provided for fields +that are not listed as `influencers`. If there are no `influencers` then +`max_bucket_cardinality` can be omitted from the request. + +`overall_cardinality`:: +(Optional, object) Estimates of the cardinality that will be observed for +fields over the whole time period that the job analyzes data. To produce +a good answer, values must be provided for fields referenced in the +`by_field_name`, `over_field_name` and `partition_field_name` of any +detectors. It does not matter if values are provided for other fields. +If no detectors have a `by_field_name`, `over_field_name` or +`partition_field_name` then `overall_cardinality` can be omitted +from the request. + +[[ml-estimate-model-memory-example]] +==== {api-examples-title} + +[source,console] +-------------------------------------------------- +POST _ml/anomaly_detectors/_estimate_model_memory +{ + "analysis_config": { + "bucket_span": "5m", + "detectors": [ + { + "function": "sum", + "field_name": "bytes", + "by_field_name": "status", + "partition_field_name": "app" + } + ], + "influencers": [ "source_ip", "dest_ip" ] + }, + "overall_cardinality": { + "status": 10, + "app": 50 + }, + "max_bucket_cardinality": { + "source_ip": 300, + "dest_ip": 30 + } +} +-------------------------------------------------- +// TEST[skip:needs-licence] + +The estimate returns the following result: + +[source,console-result] +---- +{ + "model_memory_estimate": "45mb" +} +---- diff --git a/docs/reference/ml/anomaly-detection/apis/ml-api.asciidoc b/docs/reference/ml/anomaly-detection/apis/ml-api.asciidoc index f02312cb0ac..e6514dff60a 100644 --- a/docs/reference/ml/anomaly-detection/apis/ml-api.asciidoc +++ b/docs/reference/ml/anomaly-detection/apis/ml-api.asciidoc @@ -118,6 +118,8 @@ include::delete-job.asciidoc[] include::delete-calendar-job.asciidoc[] include::delete-snapshot.asciidoc[] include::delete-expired-data.asciidoc[] +//ESTIMATE +include::estimate-model-memory.asciidoc[] //FIND include::find-file-structure.asciidoc[] //FLUSH diff --git a/x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryAction.java b/x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryAction.java index d4151564500..776c8403142 100644 --- a/x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryAction.java +++ b/x-pack/plugin/ml/src/main/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryAction.java @@ -23,6 +23,17 @@ import java.util.HashSet; import java.util.Map; import java.util.Set; +/** + * Calculates the estimated model memory requirement of an anomaly detection job + * from its analysis config and estimates of the cardinality of the various fields + * referenced in it. + * + * Answers are capped at Long.MAX_VALUE bytes, to avoid returning + * values with bigger units that cannot trivially be converted back to bytes. + * (In reality if the memory estimate is greater than Long.MAX_VALUE + * bytes then the job will be impossible to run successfully, so this is not a + * major limitation.) + */ public class TransportEstimateModelMemoryAction extends HandledTransportAction { @@ -47,23 +58,24 @@ public class TransportEstimateModelMemoryAction Map overallCardinality = request.getOverallCardinality(); Map maxBucketCardinality = request.getMaxBucketCardinality(); - long answer = BASIC_REQUIREMENT.getBytes() - + calculateDetectorsRequirementBytes(analysisConfig, overallCardinality) - + calculateInfluencerRequirementBytes(analysisConfig, maxBucketCardinality) - + calculateCategorizationRequirementBytes(analysisConfig); + long answer = BASIC_REQUIREMENT.getBytes(); + answer = addNonNegativeLongsWithMaxValueCap(answer, calculateDetectorsRequirementBytes(analysisConfig, overallCardinality)); + answer = addNonNegativeLongsWithMaxValueCap(answer, calculateInfluencerRequirementBytes(analysisConfig, maxBucketCardinality)); + answer = addNonNegativeLongsWithMaxValueCap(answer, calculateCategorizationRequirementBytes(analysisConfig)); listener.onResponse(new EstimateModelMemoryAction.Response(roundUpToNextMb(answer))); } static long calculateDetectorsRequirementBytes(AnalysisConfig analysisConfig, Map overallCardinality) { return analysisConfig.getDetectors().stream().map(detector -> calculateDetectorRequirementBytes(detector, overallCardinality)) - .reduce(0L, Long::sum); + .reduce(0L, TransportEstimateModelMemoryAction::addNonNegativeLongsWithMaxValueCap); } static long calculateDetectorRequirementBytes(Detector detector, Map overallCardinality) { long answer = 0; + // These values for detectors assume splitting is via a partition field switch (detector.getFunction()) { case COUNT: case LOW_COUNT: @@ -71,7 +83,7 @@ public class TransportEstimateModelMemoryAction case NON_ZERO_COUNT: case LOW_NON_ZERO_COUNT: case HIGH_NON_ZERO_COUNT: - answer = 1; // TODO add realistic number + answer = new ByteSizeValue(32, ByteSizeUnit.KB).getBytes(); break; case DISTINCT_COUNT: case LOW_DISTINCT_COUNT: @@ -88,7 +100,8 @@ public class TransportEstimateModelMemoryAction answer = 1; // TODO add realistic number break; case METRIC: - answer = 1; // TODO add realistic number + // metric analyses mean, min and max simultaneously, and uses about 2.5 times the memory of one of these + answer = new ByteSizeValue(160, ByteSizeUnit.KB).getBytes(); break; case MEAN: case LOW_MEAN: @@ -104,18 +117,14 @@ public class TransportEstimateModelMemoryAction case NON_NULL_SUM: case LOW_NON_NULL_SUM: case HIGH_NON_NULL_SUM: - // 64 comes from https://github.com/elastic/kibana/issues/18722 - answer = new ByteSizeValue(64, ByteSizeUnit.KB).getBytes(); - break; case MEDIAN: case LOW_MEDIAN: case HIGH_MEDIAN: - answer = 1; // TODO add realistic number - break; case VARP: case LOW_VARP: case HIGH_VARP: - answer = 1; // TODO add realistic number + // 64 comes from https://github.com/elastic/kibana/issues/18722 + answer = new ByteSizeValue(64, ByteSizeUnit.KB).getBytes(); break; case TIME_OF_DAY: case TIME_OF_WEEK: @@ -130,19 +139,26 @@ public class TransportEstimateModelMemoryAction String byFieldName = detector.getByFieldName(); if (byFieldName != null) { - answer *= cardinalityEstimate(Detector.BY_FIELD_NAME_FIELD.getPreferredName(), byFieldName, overallCardinality, true); + long cardinalityEstimate = + cardinalityEstimate(Detector.BY_FIELD_NAME_FIELD.getPreferredName(), byFieldName, overallCardinality, true); + // The memory cost of a by field is about 2/3rds that of a partition field + long multiplier = addNonNegativeLongsWithMaxValueCap(cardinalityEstimate, 2) / 3 * 2; + answer = multiplyNonNegativeLongsWithMaxValueCap(answer, multiplier); } String overFieldName = detector.getOverFieldName(); if (overFieldName != null) { - cardinalityEstimate(Detector.OVER_FIELD_NAME_FIELD.getPreferredName(), overFieldName, overallCardinality, true); - // TODO - how should "over" field cardinality affect estimate? + long cardinalityEstimate = + cardinalityEstimate(Detector.OVER_FIELD_NAME_FIELD.getPreferredName(), overFieldName, overallCardinality, true); + // Over fields don't multiply the whole estimate, just add a small amount (estimate 512 bytes) per value + answer = addNonNegativeLongsWithMaxValueCap(answer, multiplyNonNegativeLongsWithMaxValueCap(cardinalityEstimate, 512)); } String partitionFieldName = detector.getPartitionFieldName(); if (partitionFieldName != null) { - answer *= + long multiplier = cardinalityEstimate(Detector.PARTITION_FIELD_NAME_FIELD.getPreferredName(), partitionFieldName, overallCardinality, true); + answer = multiplyNonNegativeLongsWithMaxValueCap(answer, multiplier); } return answer; @@ -156,10 +172,10 @@ public class TransportEstimateModelMemoryAction pureInfluencers.removeAll(detector.extractAnalysisFields()); } - return pureInfluencers.stream() - .map(influencer -> cardinalityEstimate(AnalysisConfig.INFLUENCERS.getPreferredName(), influencer, maxBucketCardinality, false) - * BYTES_PER_INFLUENCER_VALUE) - .reduce(0L, Long::sum); + long totalInfluencerCardinality = pureInfluencers.stream() + .map(influencer -> cardinalityEstimate(AnalysisConfig.INFLUENCERS.getPreferredName(), influencer, maxBucketCardinality, false)) + .reduce(0L, TransportEstimateModelMemoryAction::addNonNegativeLongsWithMaxValueCap); + return multiplyNonNegativeLongsWithMaxValueCap(BYTES_PER_INFLUENCER_VALUE, totalInfluencerCardinality); } static long calculateCategorizationRequirementBytes(AnalysisConfig analysisConfig) { @@ -187,7 +203,25 @@ public class TransportEstimateModelMemoryAction } static ByteSizeValue roundUpToNextMb(long bytes) { - assert bytes >= 0; - return new ByteSizeValue((BYTES_IN_MB - 1 + bytes) / BYTES_IN_MB, ByteSizeUnit.MB); + assert bytes >= 0 : "negative bytes " + bytes; + return new ByteSizeValue(addNonNegativeLongsWithMaxValueCap(bytes, BYTES_IN_MB - 1) / BYTES_IN_MB, ByteSizeUnit.MB); + } + + private static long addNonNegativeLongsWithMaxValueCap(long a, long b) { + assert a >= 0; + assert b >= 0; + if (Long.MAX_VALUE - a - b < 0) { + return Long.MAX_VALUE; + } + return a + b; + } + + private static long multiplyNonNegativeLongsWithMaxValueCap(long a, long b) { + assert a >= 0; + assert b >= 0; + if (Long.MAX_VALUE / a < b) { + return Long.MAX_VALUE; + } + return a * b; } } diff --git a/x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryActionTests.java b/x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryActionTests.java index ea10c9fb5f6..30118104e65 100644 --- a/x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryActionTests.java +++ b/x-pack/plugin/ml/src/test/java/org/elasticsearch/xpack/ml/action/TransportEstimateModelMemoryActionTests.java @@ -36,7 +36,7 @@ public class TransportEstimateModelMemoryActionTests extends ESTestCase { Detector withByField = createDetector(function, "field", "buy", null, null); assertThat(TransportEstimateModelMemoryAction.calculateDetectorRequirementBytes(withByField, - overallCardinality), is(200 * 65536L)); + overallCardinality), is(134 * 65536L)); Detector withPartitionField = createDetector(function, "field", null, null, "part"); assertThat(TransportEstimateModelMemoryAction.calculateDetectorRequirementBytes(withPartitionField, @@ -44,7 +44,7 @@ public class TransportEstimateModelMemoryActionTests extends ESTestCase { Detector withByAndPartitionFields = createDetector(function, "field", "buy", null, "part"); assertThat(TransportEstimateModelMemoryAction.calculateDetectorRequirementBytes(withByAndPartitionFields, - overallCardinality), is(200 * 100 * 65536L)); + overallCardinality), is(134 * 100 * 65536L)); } public void testCalculateInfluencerRequirementBytes() { @@ -98,6 +98,10 @@ public class TransportEstimateModelMemoryActionTests extends ESTestCase { equalTo(new ByteSizeValue(2, ByteSizeUnit.MB))); assertThat(TransportEstimateModelMemoryAction.roundUpToNextMb(2 * 1024 * 1024), equalTo(new ByteSizeValue(2, ByteSizeUnit.MB))); + // We don't round up at the extremes, to ensure that the resulting value can be represented as bytes in a long + // (At such extreme scale it won't be possible to actually run the analysis, so ease of use trumps precision) + assertThat(TransportEstimateModelMemoryAction.roundUpToNextMb(Long.MAX_VALUE - randomIntBetween(0, 1000000)), + equalTo(new ByteSizeValue(Long.MAX_VALUE / new ByteSizeValue(1, ByteSizeUnit.MB).getBytes() , ByteSizeUnit.MB))); } public static Detector createDetector(String function, String fieldName, String byFieldName, diff --git a/x-pack/plugin/src/test/resources/rest-api-spec/test/ml/estimate_model_memory.yml b/x-pack/plugin/src/test/resources/rest-api-spec/test/ml/estimate_model_memory.yml index 8253f12dda7..f0407860b6a 100644 --- a/x-pack/plugin/src/test/resources/rest-api-spec/test/ml/estimate_model_memory.yml +++ b/x-pack/plugin/src/test/resources/rest-api-spec/test/ml/estimate_model_memory.yml @@ -12,7 +12,7 @@ "airline": 50000 } } - - match: { model_memory_estimate: "3135mb" } + - match: { model_memory_estimate: "2094mb" } --- "Test by field also influencer": @@ -32,7 +32,7 @@ "airline": 500 } } - - match: { model_memory_estimate: "3135mb" } + - match: { model_memory_estimate: "2094mb" } --- "Test by field with independent influencer": @@ -52,7 +52,63 @@ "country": 500 } } - - match: { model_memory_estimate: "3140mb" } + - match: { model_memory_estimate: "2099mb" } + +--- +"Test over field": + - do: + ml.estimate_model_memory: + body: > + { + "analysis_config": { + "bucket_span": "1h", + "detectors": [{"function": "max", "field_name": "responsetime", "over_field_name": "airline"}] + }, + "overall_cardinality": { + "airline": 50000 + } + } + - match: { model_memory_estimate: "35mb" } + +--- +"Test over field also influencer": + - do: + ml.estimate_model_memory: + body: > + { + "analysis_config": { + "bucket_span": "1h", + "detectors": [{"function": "max", "field_name": "responsetime", "over_field_name": "airline"}], + "influencers": [ "airline" ] + }, + "overall_cardinality": { + "airline": 50000 + }, + "max_bucket_cardinality": { + "airline": 500 + } + } + - match: { model_memory_estimate: "35mb" } + +--- +"Test over field with independent influencer": + - do: + ml.estimate_model_memory: + body: > + { + "analysis_config": { + "bucket_span": "1h", + "detectors": [{"function": "max", "field_name": "responsetime", "over_field_name": "airline"}], + "influencers": [ "country" ] + }, + "overall_cardinality": { + "airline": 50000 + }, + "max_bucket_cardinality": { + "country": 500 + } + } + - match: { model_memory_estimate: "40mb" } --- "Test partition field": @@ -125,7 +181,7 @@ "country": 600 } } - - match: { model_memory_estimate: "150010mb" } + - match: { model_memory_estimate: "100060mb" } --- "Test by and partition fields also influencers": @@ -147,7 +203,7 @@ "country": 40 } } - - match: { model_memory_estimate: "150010mb" } + - match: { model_memory_estimate: "100060mb" } --- "Test by and partition fields with independent influencer": @@ -168,5 +224,65 @@ "src_ip": 500 } } - - match: { model_memory_estimate: "150015mb" } + - match: { model_memory_estimate: "100065mb" } + +--- +"Test over and partition field": + - do: + ml.estimate_model_memory: + body: > + { + "analysis_config": { + "bucket_span": "1h", + "detectors": [{"function": "max", "field_name": "responsetime", "over_field_name": "airline", "partition_field_name": "country"}] + }, + "overall_cardinality": { + "airline": 4000, + "country": 600 + } + } + - match: { model_memory_estimate: "1220mb" } + +--- +"Test over and partition fields also influencers": + - do: + ml.estimate_model_memory: + body: > + { + "analysis_config": { + "bucket_span": "1h", + "detectors": [{"function": "max", "field_name": "responsetime", "over_field_name": "airline", "partition_field_name": "country"}], + "influencers": [ "airline", "country" ] + }, + "overall_cardinality": { + "airline": 4000, + "country": 600 + }, + "max_bucket_cardinality": { + "airline": 60, + "country": 40 + } + } + - match: { model_memory_estimate: "1220mb" } + +--- +"Test over and partition fields with independent influencer": + - do: + ml.estimate_model_memory: + body: > + { + "analysis_config": { + "bucket_span": "1h", + "detectors": [{"function": "max", "field_name": "responsetime", "over_field_name": "airline", "partition_field_name": "country"}], + "influencers": [ "src_ip" ] + }, + "overall_cardinality": { + "airline": 4000, + "country": 600 + }, + "max_bucket_cardinality": { + "src_ip": 500 + } + } + - match: { model_memory_estimate: "1225mb" }