Inference processors asynchronously usage write stats to the .ml-stats index after they used. In tests the write can leak into the next test causing failures depending on which test follows. This change waits for the usage stats docs to be written at the end of the test
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David Kyle
GitHub
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25e811ced7
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/*
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* Copyright Elasticsearch B.V. and/or licensed to Elasticsearch B.V. under one
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* or more contributor license agreements. Licensed under the Elastic License;
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* you may not use this file except in compliance with the Elastic License.
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*/
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package org.elasticsearch.xpack.ml.integration;
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import org.elasticsearch.client.Request;
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import org.elasticsearch.client.RequestOptions;
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import org.elasticsearch.client.Response;
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import org.elasticsearch.client.ResponseException;
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import org.elasticsearch.test.rest.ESRestTestCase;
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import org.junit.After;
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import org.junit.Before;
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import java.io.IOException;
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import java.util.Map;
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import static org.hamcrest.Matchers.equalTo;
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public class InferenceProcessorIT extends ESRestTestCase {
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private static final String MODEL_ID = "a-perfect-regression-model";
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@Before
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public void enableLogging() throws IOException {
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Request setTrace = new Request("PUT", "_cluster/settings");
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setTrace.setJsonEntity(
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"{\"transient\": {\"logger.org.elasticsearch.xpack.ml.inference\": \"TRACE\"}}"
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);
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assertThat(client().performRequest(setTrace).getStatusLine().getStatusCode(), equalTo(200));
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}
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private void putRegressionModel() throws IOException {
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Request model = new Request("PUT", "_ml/inference/" + MODEL_ID);
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model.setJsonEntity(
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" {\n" +
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" \"description\": \"empty model for tests\",\n" +
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" \"tags\": [\"regression\", \"tag1\"],\n" +
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" \"input\": {\"field_names\": [\"field1\", \"field2\"]},\n" +
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" \"inference_config\": { \"regression\": {\"results_field\": \"my_regression\"}},\n" +
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" \"definition\": {\n" +
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" \"preprocessors\": [],\n" +
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" \"trained_model\": {\n" +
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" \"tree\": {\n" +
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" \"feature_names\": [\"field1\", \"field2\"],\n" +
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" \"tree_structure\": [\n" +
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" {\"node_index\": 0, \"leaf_value\": 42}\n" +
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" ],\n" +
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" \"target_type\": \"regression\"\n" +
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" }\n" +
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" }\n" +
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" }\n" +
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" }"
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);
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assertThat(client().performRequest(model).getStatusLine().getStatusCode(), equalTo(200));
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}
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public void testCreateAndDeletePipelineWithInferenceProcessor() throws IOException {
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putRegressionModel();
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Request putPipeline = new Request("PUT", "_ingest/pipeline/regression-model-pipeline");
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putPipeline.setJsonEntity(
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" {\n" +
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" \"processors\": [\n" +
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" {\n" +
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" \"inference\" : {\n" +
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" \"model_id\" : \"a-perfect-regression-model\",\n" +
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" \"inference_config\": {\"regression\": {}},\n" +
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" \"target_field\": \"regression_field\",\n" +
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" \"field_map\": {}\n" +
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" }\n" +
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" }\n" +
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" ]\n" +
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" }"
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);
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assertThat(client().performRequest(putPipeline).getStatusLine().getStatusCode(), equalTo(200));
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// using the model will ensure it is loaded and stats will be written before it is deleted
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infer("regression-model-pipeline");
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Request deletePipeline = new Request("DELETE", "_ingest/pipeline/regression-model-pipeline");
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assertThat(client().performRequest(deletePipeline).getStatusLine().getStatusCode(), equalTo(200));
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}
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public void testCreateProcessorWithDeprecatedFields() throws IOException {
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putRegressionModel();
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Request putPipeline = new Request("PUT", "_ingest/pipeline/regression-model-deprecated-pipeline");
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putPipeline.setJsonEntity(
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"{\n" +
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" \"processors\": [\n" +
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" {\n" +
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" \"inference\" : {\n" +
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" \"model_id\" : \"a-perfect-regression-model\",\n" +
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" \"inference_config\": {\"regression\": {}},\n" +
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" \"field_mappings\": {}\n" +
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" }\n" +
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" }\n" +
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" ]\n" +
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"}"
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);
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RequestOptions ro = expectWarnings("Deprecated field [field_mappings] used, expected [field_map] instead");
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putPipeline.setOptions(ro);
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Response putResponse = client().performRequest(putPipeline);
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assertThat(putResponse.getStatusLine().getStatusCode(), equalTo(200));
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// using the model will ensure it is loaded and stats will be written before it is deleted
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infer("regression-model-deprecated-pipeline");
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Request deletePipeline = new Request("DELETE", "_ingest/pipeline/regression-model-deprecated-pipeline");
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Response deleteResponse = client().performRequest(deletePipeline);
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assertThat(deleteResponse.getStatusLine().getStatusCode(), equalTo(200));
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}
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public void infer(String pipelineId) throws IOException {
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Request putDoc = new Request("POST", "any_index/_doc?pipeline=" + pipelineId);
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putDoc.setJsonEntity("{\"field1\": 1, \"field2\": 2}");
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Response response = client().performRequest(putDoc);
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assertThat(response.getStatusLine().getStatusCode(), equalTo(201));
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}
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@After
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@SuppressWarnings("unchecked")
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public void waitForStatsDoc() throws Exception {
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assertBusy( () -> {
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Request searchForStats = new Request("GET", ".ml-stats-*/_search?rest_total_hits_as_int");
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searchForStats.setJsonEntity(
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"{\n" +
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" \"query\": {\n" +
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" \"bool\": {\n" +
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" \"filter\": [\n" +
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" {\n" +
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" \"term\": {\n" +
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" \"type\": \"inference_stats\"\n" +
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" }\n" +
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" },\n" +
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" {\n" +
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" \"term\": {\n" +
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" \"model_id\": \"" + MODEL_ID + "\"\n" +
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" }\n" +
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" }\n" +
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" ]\n" +
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" }\n" +
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" }\n" +
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"}"
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);
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try {
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Response searchResponse = client().performRequest(searchForStats);
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Map<String, Object> responseAsMap = entityAsMap(searchResponse);
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Map<String, Object> hits = (Map<String, Object>)responseAsMap.get("hits");
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assertThat(responseAsMap.toString(), hits.get("total"), equalTo(1));
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} catch (ResponseException e) {
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// the search may fail because the index is not ready yet in which case retry
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if (e.getMessage().contains("search_phase_execution_exception") == false) {
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throw e;
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}
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}
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});
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}
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}
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@ -431,6 +431,10 @@ public class ModelLoadingService implements ClusterStateListener {
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INFERENCE_MODEL_CACHE_TTL.getKey());
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auditIfNecessary(notification.getKey(), msg);
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}
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logger.trace(() -> new ParameterizedMessage("Persisting stats for evicted model [{}]",
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notification.getValue().model.getModelId()));
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// If the model is no longer referenced, flush the stats to persist as soon as possible
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notification.getValue().model.persistStats(referencedModels.contains(notification.getKey()) == false);
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} finally {
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@ -29,28 +29,6 @@ setup:
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}
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---
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"Test create and delete pipeline with inference processor":
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- do:
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ingest.put_pipeline:
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id: "regression-model-pipeline"
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body: >
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{
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"processors": [
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{
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"inference" : {
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"model_id" : "a-perfect-regression-model",
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"inference_config": {"regression": {}},
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"target_field": "regression_field",
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"field_map": {}
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}
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}
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]
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}
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- match: { acknowledged: true }
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- do:
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ingest.delete_pipeline:
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id: "regression-model-pipeline"
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---
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"Test create processor with missing mandatory fields":
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- do:
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catch: /\[model_id\] required property is missing/
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@ -68,35 +46,7 @@ setup:
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}
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]
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}
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---
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"Test create processor with deprecated fields":
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- skip:
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features:
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- "warnings"
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- "allowed_warnings"
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- do:
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warnings:
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- 'Deprecated field [field_mappings] used, expected [field_map] instead'
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ingest.put_pipeline:
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id: "regression-model-pipeline"
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body: >
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{
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"processors": [
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{
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"inference" : {
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"model_id" : "a-perfect-regression-model",
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"inference_config": {"regression": {}},
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"field_mappings": {}
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}
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}
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]
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}
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- do:
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allowed_warnings:
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- 'Deprecated field [field_mappings] used, expected [field_map] instead'
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ingest.delete_pipeline:
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id: "regression-model-pipeline"
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---
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"Test simulate":
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- do:
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