updates for the latest elasticsearch client

x-pack/plugins/ml/public/application/trained_models/nodes_overview/nodes_list.tsx

@@ -30,6 +30,7 @@ import {
 import { MemoryPreviewChart } from './memory_preview_chart';
 import { useFieldFormatter } from '../../contexts/kibana/use_field_formatter';
 import { ListingPageUrlState } from '../../../../common/types/common';
+import { useToastNotificationService } from '../../services/toast_notification_service';
 
 export type NodeItem = NodeDeploymentStatsResponse;
 
@@ -46,6 +47,7 @@ export const getDefaultNodesListState = (): ListingPageUrlState => ({
 
 export const NodesList: FC = () => {
   const trainedModelsApiService = useTrainedModelsApiService();
+  const { displayErrorToast } = useToastNotificationService();
   const bytesFormatter = useFieldFormatter('bytes');
   const [items, setItems] = useState<NodeItem[]>([]);
   const [isLoading, setIsLoading] = useState(false);
@@ -60,10 +62,19 @@ export const NodesList: FC = () => {
   const searchQueryText = pageState.queryText ?? '';
 
   const fetchNodesData = useCallback(async () => {
-    const nodesResponse = await trainedModelsApiService.getTrainedModelsNodesOverview();
-    setItems(nodesResponse.nodes);
-    setIsLoading(false);
-    refreshAnalyticsList$.next(REFRESH_ANALYTICS_LIST_STATE.IDLE);
+    try {
+      const nodesResponse = await trainedModelsApiService.getTrainedModelsNodesOverview();
+      setItems(nodesResponse.nodes);
+      setIsLoading(false);
+      refreshAnalyticsList$.next(REFRESH_ANALYTICS_LIST_STATE.IDLE);
+    } catch (e) {
+      displayErrorToast(
+        e,
+        i18n.translate('xpack.ml.trainedModels.nodesList.nodesFetchError', {
+          defaultMessage: 'Nodes fetch failed',
+        })
+      );
+    }
   }, []);
 
   const toggleDetails = (item: NodeItem) => {

x-pack/plugins/ml/server/lib/ml_client/ml_client.ts

@@ -380,26 +380,16 @@ export function getMlClient(
     async getTrainedModelsStats(...p: Parameters<MlClient['getTrainedModelsStats']>) {
       return mlClient.getTrainedModelsStats(...p);
     },
-    // TODO update when the new elasticsearch-js client is available
-    async getTrainedModelsDeploymentStats(...p: Parameters<MlClient['getTrainedModelsStats']>) {
-      return client.asCurrentUser.transport.request({
-        method: 'GET',
-        path: `/_ml/trained_models/${p[0]?.model_id ?? '*'}/deployment/_stats`,
-      });
+    async getTrainedModelDeploymentStats(
+      ...p: Parameters<MlClient['getTrainedModelDeploymentStats']>
+    ) {
+      return mlClient.getTrainedModelDeploymentStats(...p);
     },
-    // TODO update when the new elasticsearch-js client is available
-    async startTrainedModelDeployment(...p: Parameters<MlClient['deleteTrainedModel']>) {
-      return client.asCurrentUser.transport.request({
-        method: 'POST',
-        path: `/_ml/trained_models/${p[0].model_id}/deployment/_start`,
-      });
+    async startTrainedModelDeployment(...p: Parameters<MlClient['startTrainedModelDeployment']>) {
+      return mlClient.startTrainedModelDeployment(...p);
     },
-    // TODO update when the new elasticsearch-js client is available
-    async stopTrainedModelDeployment(...p: Parameters<MlClient['deleteTrainedModel']>) {
-      return client.asCurrentUser.transport.request({
-        method: 'POST',
-        path: `/_ml/trained_models/${p[0].model_id}/deployment/_stop`,
-      });
+    async stopTrainedModelDeployment(...p: Parameters<MlClient['stopTrainedModelDeployment']>) {
+      return mlClient.stopTrainedModelDeployment(...p);
     },
     async info(...p: Parameters<MlClient['info']>) {
       return mlClient.info(...p);

x-pack/plugins/ml/server/lib/ml_client/types.ts

@@ -7,24 +7,11 @@
 
 import { ElasticsearchClient } from 'kibana/server';
 import { searchProvider } from './search';
-import { TrainedModelDeploymentStatsResponse } from '../../../common/types/trained_models';
 
 type OrigMlClient = ElasticsearchClient['ml'];
 
 export interface MlClient extends OrigMlClient {
   anomalySearch: ReturnType<typeof searchProvider>['anomalySearch'];
-  // TODO remove when the new elasticsearch-js client is available
-  getTrainedModelsDeploymentStats: (options?: { model_id?: string }) => Promise<{
-    body: { count: number; deployment_stats: TrainedModelDeploymentStatsResponse[] };
-  }>;
-  // TODO remove when the new elasticsearch-js client is available
-  startTrainedModelDeployment: (options: { model_id: string }) => Promise<{
-    body: { acknowledge: boolean };
-  }>;
-  // TODO remove when the new elasticsearch-js client is available
-  stopTrainedModelDeployment: (options: { model_id: string }) => Promise<{
-    body: { acknowledge: boolean };
-  }>;
 }
 
 export type MlClientParams =
@@ -61,6 +48,9 @@ export type MlClientParams =
   | Parameters<MlClient['getRecords']>
   | Parameters<MlClient['getTrainedModels']>
   | Parameters<MlClient['getTrainedModelsStats']>
+  | Parameters<MlClient['getTrainedModelDeploymentStats']>
+  | Parameters<MlClient['startTrainedModelDeployment']>
+  | Parameters<MlClient['stopTrainedModelDeployment']>
   | Parameters<MlClient['info']>
   | Parameters<MlClient['openJob']>
   | Parameters<MlClient['postCalendarEvents']>

x-pack/plugins/ml/server/models/data_frame_analytics/models_provider.ts

@@ -7,7 +7,7 @@
 
 import type { IScopedClusterClient } from 'kibana/server';
 import { sumBy, pick } from 'lodash';
-import { NodesInfoNodeInfo } from '@elastic/elasticsearch/api/types';
+import { NodesInfoNodeInfo } from '@elastic/elasticsearch/lib/api/typesWithBodyKey';
 import type {
   NodeDeploymentStatsResponse,
   PipelineDefinition,
@@ -18,6 +18,7 @@ import {
   MemoryOverviewService,
   NATIVE_EXECUTABLE_CODE_OVERHEAD,
 } from '../memory_overview/memory_overview_service';
+import { TrainedModelDeploymentStatsResponse } from '../../../common/types/trained_models';
 
 export type ModelService = ReturnType<typeof modelsProvider>;
 
@@ -77,15 +78,15 @@ export function modelsProvider(
         throw new Error('Memory overview service is not provided');
       }
 
-      const { body: deploymentStats } = await mlClient.getTrainedModelsDeploymentStats();
+      const { body: deploymentStats } = await mlClient.getTrainedModelDeploymentStats({
+        model_id: '*',
+      });
 
       const {
         body: { nodes: clusterNodes },
       } = await client.asCurrentUser.nodes.stats();
 
-      const mlNodes = Object.entries(clusterNodes).filter(([id, node]) =>
-        node.roles.includes('ml')
-      );
+      const mlNodes = Object.entries(clusterNodes).filter(([, node]) => node.roles.includes('ml'));
 
       const adMemoryReport = await memoryOverviewService.getAnomalyDetectionMemoryOverview();
       const dfaMemoryReport = await memoryOverviewService.getDFAMemoryOverview();
@@ -94,7 +95,9 @@ export function modelsProvider(
         ([nodeId, node]) => {
           const nodeFields = pick(node, NODE_FIELDS) as RequiredNodeFields;
 
-          const allocatedModels = deploymentStats.deployment_stats
+          const allocatedModels = (
+            deploymentStats.deployment_stats as TrainedModelDeploymentStatsResponse[]
+          )
             .filter((v) => v.nodes.some((n) => Object.keys(n.node)[0] === nodeId))
             .map(({ nodes, ...rest }) => {
               const { node: tempNode, ...nodeRest } = nodes.find(

x-pack/plugins/ml/server/routes/trained_models.ts

@@ -302,7 +302,7 @@ export function trainedModelsRoutes({ router, routeGuard }: RouteInitialization)
     routeGuard.fullLicenseAPIGuard(async ({ mlClient, request, response }) => {
       try {
         const { modelId } = request.params;
-        const { body } = await mlClient.getTrainedModelsDeploymentStats({
+        const { body } = await mlClient.getTrainedModelDeploymentStats({
           model_id: modelId,
         });
         return response.ok({