[8.6][ML Inference] Add ML inference failure handler (elastic#142488)

* Add ML Inference failure handler
WafaaNasr · Oct 14, 2022 · a4c3115 · a4c3115
1 parent a6a04e4
commit a4c3115
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Showing 2 changed files with 71 additions and 64 deletions.
diff --git a/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts b/x-pack/plugins/enterprise_search/common/ml_inference_pipeline/index.ts
@@ -52,6 +52,20 @@ export const generateMlInferencePipelineBody = ({
           },
           model_id: model.model_id,
           target_field: `ml.inference.${destinationField}`,
+          on_failure: [
+            {
+              append: {
+                field: '_source._ingest.inference_errors',
+                value: [
+                  {
+                    pipeline: pipelineName,
+                    message: `Processor 'inference' in pipeline '${pipelineName}' failed with message '{{ _ingest.on_failure_message }}'`,
+                    timestamp: '{{{ _ingest.timestamp }}}',
+                  },
+                ],
+              },
+            },
+          ],
         },
       },
       {

diff --git a/x-pack/plugins/enterprise_search/server/lib/pipelines/create_pipeline_definitions.test.ts b/x-pack/plugins/enterprise_search/server/lib/pipelines/create_pipeline_definitions.test.ts
@@ -4,6 +4,7 @@
  * 2.0; you may not use this file except in compliance with the Elastic License
  * 2.0.
  */
+import { merge } from 'lodash';
 
 import { ElasticsearchClient } from '@kbn/core/server';
 
@@ -41,14 +42,63 @@ describe('createIndexPipelineDefinitions util function', () => {
 describe('formatMlPipelineBody util function', () => {
   const pipelineName = 'ml-inference-my-ml-proc';
   const modelId = 'my-model-id';
-  let modelInputField = 'my-model-input-field';
+  const modelInputField = 'my-model-input-field';
   const modelType = 'pytorch';
   const inferenceConfigKey = 'my-model-type';
   const modelTypes = ['pytorch', 'my-model-type'];
   const modelVersion = 3;
   const sourceField = 'my-source-field';
   const destField = 'my-dest-field';
 
+  const expectedResult = {
+    description: '',
+    processors: [
+      {
+        remove: {
+          field: `ml.inference.${destField}`,
+          ignore_missing: true,
+        },
+      },
+      {
+        inference: {
+          field_map: {
+            [sourceField]: modelInputField,
+          },
+          model_id: modelId,
+          target_field: `ml.inference.${destField}`,
+          on_failure: [
+            {
+              append: {
+                field: '_source._ingest.inference_errors',
+                value: [
+                  {
+                    pipeline: pipelineName,
+                    message: `Processor 'inference' in pipeline '${pipelineName}' failed with message '{{ _ingest.on_failure_message }}'`,
+                    timestamp: '{{{ _ingest.timestamp }}}',
+                  },
+                ],
+              },
+            },
+          ],
+        },
+      },
+      {
+        append: {
+          field: '_source._ingest.processors',
+          value: [
+            {
+              model_version: modelVersion,
+              pipeline: pipelineName,
+              processed_timestamp: '{{{ _ingest.timestamp }}}',
+              types: modelTypes,
+            },
+          ],
+        },
+      },
+    ],
+    version: 1,
+  };
+
   const mockClient = {
     ml: {
       getTrainedModels: jest.fn(),
@@ -60,41 +110,6 @@ describe('formatMlPipelineBody util function', () => {
   });
 
   it('should return the pipeline body', async () => {
-    const expectedResult = {
-      description: '',
-      processors: [
-        {
-          remove: {
-            field: `ml.inference.${destField}`,
-            ignore_missing: true,
-          },
-        },
-        {
-          inference: {
-            field_map: {
-              [sourceField]: modelInputField,
-            },
-            model_id: modelId,
-            target_field: `ml.inference.${destField}`,
-          },
-        },
-        {
-          append: {
-            field: '_source._ingest.processors',
-            value: [
-              {
-                model_version: modelVersion,
-                pipeline: pipelineName,
-                processed_timestamp: '{{{ _ingest.timestamp }}}',
-                types: modelTypes,
-              },
-            ],
-          },
-        },
-      ],
-      version: 1,
-    };
-
     const mockResponse = {
       count: 1,
       trained_model_configs: [
@@ -136,41 +151,19 @@ describe('formatMlPipelineBody util function', () => {
   });
 
   it('should insert a placeholder if model has no input fields', async () => {
-    modelInputField = 'MODEL_INPUT_FIELD';
-    const expectedResult = {
-      description: '',
+    const expectedResultWithNoInputField = merge({}, expectedResult, {
       processors: [
-        {
-          remove: {
-            field: `ml.inference.${destField}`,
-            ignore_missing: true,
-          },
-        },
+        {}, // append - we'll leave it untouched
         {
           inference: {
             field_map: {
-              [sourceField]: modelInputField,
+              [sourceField]: 'MODEL_INPUT_FIELD',
             },
-            model_id: modelId,
-            target_field: `ml.inference.${destField}`,
-          },
-        },
-        {
-          append: {
-            field: '_source._ingest.processors',
-            value: [
-              {
-                model_version: modelVersion,
-                pipeline: pipelineName,
-                processed_timestamp: '{{{ _ingest.timestamp }}}',
-                types: modelTypes,
-              },
-            ],
           },
         },
       ],
-      version: 1,
-    };
+    });
+
     const mockResponse = {
       count: 1,
       trained_model_configs: [
@@ -193,7 +186,7 @@ describe('formatMlPipelineBody util function', () => {
       destField,
       mockClient as unknown as ElasticsearchClient
     );
-    expect(actualResult).toEqual(expectedResult);
+    expect(actualResult).toEqual(expectedResultWithNoInputField);
     expect(mockClient.ml.getTrainedModels).toHaveBeenCalledTimes(1);
   });
 });