929 lines
18 KiB
TypeScript
929 lines
18 KiB
TypeScript
/*
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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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export const elasticsearchJsPlugin = (Client: any, config: any, components: any) => {
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const ca = components.clientAction.factory;
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Client.prototype.ml = components.clientAction.namespaceFactory();
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const ml = Client.prototype.ml.prototype;
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/**
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* Perform a [ml.authenticate](Retrieve details about the currently authenticated user) request
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*
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* @param {Object} params - An object with parameters used to carry out this action
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*/
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ml.jobs = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>',
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req: {
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jobId: {
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type: 'list',
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},
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},
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},
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{
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fmt: '/_ml/anomaly_detectors/',
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},
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],
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method: 'GET',
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});
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ml.jobStats = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_stats',
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req: {
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jobId: {
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type: 'list',
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},
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},
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},
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{
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fmt: '/_ml/anomaly_detectors/_stats',
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},
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],
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method: 'GET',
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});
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ml.addJob = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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],
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needBody: true,
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method: 'PUT',
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});
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ml.openJob = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_open',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.closeJob = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_close?force=<%=force%>',
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req: {
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jobId: {
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type: 'string',
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},
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force: {
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type: 'boolean',
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},
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},
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},
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_close',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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// Currently the endpoint uses a default size of 100 unless a size is supplied.
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// So until paging is supported in the UI, explicitly supply a size of 1000
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// to match the max number of docs that the endpoint can return.
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ml.getDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>',
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req: {
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analyticsId: {
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type: 'string',
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},
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},
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},
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{
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fmt: '/_ml/data_frame/analytics/_all?size=1000',
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},
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],
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method: 'GET',
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});
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ml.getDataFrameAnalyticsStats = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>/_stats',
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req: {
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analyticsId: {
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type: 'string',
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},
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},
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},
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{
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// Currently the endpoint uses a default size of 100 unless a size is supplied.
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// So until paging is supported in the UI, explicitly supply a size of 1000
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// to match the max number of docs that the endpoint can return.
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fmt: '/_ml/data_frame/analytics/_all/_stats?size=1000',
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},
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],
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method: 'GET',
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});
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ml.createDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>',
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req: {
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analyticsId: {
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type: 'string',
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},
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},
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},
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],
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needBody: true,
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method: 'PUT',
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});
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ml.evaluateDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/_evaluate',
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},
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],
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needBody: true,
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method: 'POST',
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});
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ml.explainDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/_explain',
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},
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],
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needBody: true,
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method: 'POST',
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});
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ml.deleteDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>',
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req: {
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analyticsId: {
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type: 'string',
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},
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},
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},
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],
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method: 'DELETE',
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});
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ml.startDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>/_start',
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req: {
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analyticsId: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.stopDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>/_stop?&force=<%=force%>',
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req: {
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analyticsId: {
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type: 'string',
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},
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force: {
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type: 'boolean',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.updateDataFrameAnalytics = ca({
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urls: [
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{
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fmt: '/_ml/data_frame/analytics/<%=analyticsId%>/_update',
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req: {
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analyticsId: {
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type: 'string',
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},
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},
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},
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],
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needBody: true,
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method: 'POST',
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});
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ml.deleteJob = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>?&force=<%=force%>&wait_for_completion=false',
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req: {
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jobId: {
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type: 'string',
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},
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force: {
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type: 'boolean',
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},
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},
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},
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>?&wait_for_completion=false',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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],
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method: 'DELETE',
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});
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ml.updateJob = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_update',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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],
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needBody: true,
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method: 'POST',
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});
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ml.datafeeds = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>',
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req: {
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datafeedId: {
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type: 'list',
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},
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},
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},
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{
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fmt: '/_ml/datafeeds/',
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},
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],
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method: 'GET',
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});
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ml.datafeedStats = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_stats',
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req: {
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datafeedId: {
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type: 'list',
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},
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},
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},
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{
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fmt: '/_ml/datafeeds/_stats',
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},
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],
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method: 'GET',
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});
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ml.addDatafeed = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>',
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req: {
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datafeedId: {
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type: 'string',
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},
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},
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},
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],
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needBody: true,
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method: 'PUT',
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});
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ml.updateDatafeed = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_update',
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req: {
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datafeedId: {
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type: 'string',
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},
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},
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},
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],
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needBody: true,
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method: 'POST',
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});
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ml.deleteDatafeed = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>?force=<%=force%>',
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req: {
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datafeedId: {
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type: 'string',
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},
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force: {
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type: 'boolean',
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},
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},
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},
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>',
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req: {
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datafeedId: {
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type: 'string',
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},
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},
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},
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],
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method: 'DELETE',
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});
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ml.startDatafeed = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_start?&start=<%=start%>&end=<%=end%>',
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req: {
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datafeedId: {
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type: 'string',
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},
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start: {
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type: 'string',
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},
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end: {
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type: 'string',
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},
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},
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},
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_start?&start=<%=start%>',
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req: {
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datafeedId: {
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type: 'string',
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},
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start: {
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type: 'string',
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},
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},
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},
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_start',
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req: {
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datafeedId: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.stopDatafeed = ca({
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urls: [
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_stop?force=<%=force%>',
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req: {
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datafeedId: {
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type: 'string',
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},
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force: {
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type: 'boolean',
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},
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},
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},
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{
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_stop',
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req: {
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datafeedId: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.validateDetector = ca({
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url: {
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fmt: '/_ml/anomaly_detectors/_validate/detector',
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},
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needBody: true,
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method: 'POST',
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});
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ml.estimateModelMemory = ca({
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url: {
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fmt: '/_ml/anomaly_detectors/_estimate_model_memory',
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},
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needBody: true,
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method: 'POST',
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});
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ml.datafeedPreview = ca({
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url: {
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fmt: '/_ml/datafeeds/<%=datafeedId%>/_preview',
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req: {
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datafeedId: {
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type: 'string',
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},
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},
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},
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method: 'GET',
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});
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ml.forecast = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_forecast?&duration=<%=duration%>',
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req: {
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jobId: {
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type: 'string',
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},
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duration: {
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type: 'string',
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},
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},
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},
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/_forecast',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.records = ca({
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url: {
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/results/records',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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method: 'POST',
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});
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ml.buckets = ca({
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urls: [
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/results/buckets',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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{
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/results/buckets/<%=timestamp%>',
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req: {
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jobId: {
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type: 'string',
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},
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timestamp: {
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type: 'string',
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},
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},
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},
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],
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method: 'POST',
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});
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ml.overallBuckets = ca({
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url: {
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fmt: '/_ml/anomaly_detectors/<%=jobId%>/results/overall_buckets',
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req: {
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jobId: {
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type: 'string',
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},
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},
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},
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method: 'POST',
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});
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ml.privilegeCheck = ca({
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|
url: {
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fmt: '/_security/user/_has_privileges',
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},
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needBody: true,
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method: 'POST',
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});
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// Currently the endpoint uses a default size of 100 unless a size is supplied. So until paging is supported in the UI, explicitly supply a size of 1000
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ml.calendars = ca({
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urls: [
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{
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fmt: '/_ml/calendars/<%=calendarId%>',
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req: {
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calendarId: {
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type: 'string',
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},
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|
},
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|
},
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|
{
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fmt: '/_ml/calendars?size=1000',
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},
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],
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method: 'GET',
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|
});
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|
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|
ml.deleteCalendar = ca({
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|
url: {
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fmt: '/_ml/calendars/<%=calendarId%>',
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req: {
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calendarId: {
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type: 'string',
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},
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},
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|
},
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method: 'DELETE',
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});
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|
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|
ml.addCalendar = ca({
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|
url: {
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fmt: '/_ml/calendars/<%=calendarId%>',
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|
req: {
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calendarId: {
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|
type: 'string',
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|
},
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|
},
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},
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|
needBody: true,
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method: 'PUT',
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|
});
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|
|
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ml.addJobToCalendar = ca({
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|
url: {
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fmt: '/_ml/calendars/<%=calendarId%>/jobs/<%=jobId%>',
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|
req: {
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|
calendarId: {
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|
type: 'string',
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|
},
|
|
jobId: {
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|
type: 'string',
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|
},
|
|
},
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|
},
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|
method: 'PUT',
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|
});
|
|
|
|
ml.removeJobFromCalendar = ca({
|
|
url: {
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fmt: '/_ml/calendars/<%=calendarId%>/jobs/<%=jobId%>',
|
|
req: {
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|
calendarId: {
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|
type: 'string',
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|
},
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|
jobId: {
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|
type: 'string',
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},
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},
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|
},
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|
method: 'DELETE',
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|
});
|
|
|
|
ml.events = ca({
|
|
urls: [
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|
{
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|
fmt: '/_ml/calendars/<%=calendarId%>/events',
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|
req: {
|
|
calendarId: {
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|
type: 'string',
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|
},
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|
},
|
|
},
|
|
{
|
|
fmt: '/_ml/calendars/<%=calendarId%>/events?&job_id=<%=jobId%>',
|
|
req: {
|
|
calendarId: {
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|
type: 'string',
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|
},
|
|
jobId: {
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|
type: 'string',
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|
},
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|
},
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|
},
|
|
{
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|
fmt: '/_ml/calendars/<%=calendarId%>/events?&after=<%=start%>&before=<%=end%>',
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|
req: {
|
|
calendarId: {
|
|
type: 'string',
|
|
},
|
|
start: {
|
|
type: 'string',
|
|
},
|
|
end: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
{
|
|
fmt:
|
|
'/_ml/calendars/<%=calendarId%>/events?&after=<%=start%>&before=<%=end%>&job_id=<%=jobId%>',
|
|
req: {
|
|
calendarId: {
|
|
type: 'string',
|
|
},
|
|
start: {
|
|
type: 'string',
|
|
},
|
|
end: {
|
|
type: 'string',
|
|
},
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'GET',
|
|
});
|
|
|
|
ml.addEvent = ca({
|
|
url: {
|
|
fmt: '/_ml/calendars/<%=calendarId%>/events',
|
|
req: {
|
|
calendarId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
needBody: true,
|
|
method: 'POST',
|
|
});
|
|
|
|
ml.deleteEvent = ca({
|
|
url: {
|
|
fmt: '/_ml/calendars/<%=calendarId%>/events/<%=eventId%>',
|
|
req: {
|
|
calendarId: {
|
|
type: 'string',
|
|
},
|
|
eventId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
method: 'DELETE',
|
|
});
|
|
// Currently the endpoint uses a default size of 100 unless a size is supplied. So until paging is supported in the UI, explicitly supply a size of 1000
|
|
ml.filters = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/filters/<%=filterId%>',
|
|
req: {
|
|
filterId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
{
|
|
fmt: '/_ml/filters?size=1000',
|
|
},
|
|
],
|
|
method: 'GET',
|
|
});
|
|
|
|
ml.addFilter = ca({
|
|
url: {
|
|
fmt: '/_ml/filters/<%=filterId%>',
|
|
req: {
|
|
filterId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
needBody: true,
|
|
method: 'PUT',
|
|
});
|
|
|
|
ml.updateFilter = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/filters/<%=filterId%>/_update',
|
|
req: {
|
|
filterId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
needBody: true,
|
|
method: 'POST',
|
|
});
|
|
|
|
ml.deleteFilter = ca({
|
|
url: {
|
|
fmt: '/_ml/filters/<%=filterId%>',
|
|
req: {
|
|
filterId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
method: 'DELETE',
|
|
});
|
|
|
|
ml.info = ca({
|
|
url: {
|
|
fmt: '/_ml/info',
|
|
},
|
|
method: 'GET',
|
|
});
|
|
|
|
ml.fileStructure = ca({
|
|
urls: [
|
|
{
|
|
fmt:
|
|
'/_ml/find_file_structure?&explain=true&charset=<%=charset%>&format=<%=format%>&has_header_row=<%=has_header_row%>&column_names=<%=column_names%>&delimiter=<%=delimiter%>"e=<%=quote%>&should_trim_fields=<%=should_trim_fields%>&grok_pattern=<%=grok_pattern%>×tamp_field=<%=timestamp_field%>×tamp_format=<%=timestamp_format%>&lines_to_sample=<%=lines_to_sample%>',
|
|
req: {
|
|
charset: {
|
|
type: 'string',
|
|
},
|
|
format: {
|
|
type: 'string',
|
|
},
|
|
has_header_row: {
|
|
type: 'string',
|
|
},
|
|
column_names: {
|
|
type: 'string',
|
|
},
|
|
delimiter: {
|
|
type: 'string',
|
|
},
|
|
quote: {
|
|
type: 'string',
|
|
},
|
|
should_trim_fields: {
|
|
type: 'string',
|
|
},
|
|
grok_pattern: {
|
|
type: 'string',
|
|
},
|
|
timestamp_field: {
|
|
type: 'string',
|
|
},
|
|
timestamp_format: {
|
|
type: 'string',
|
|
},
|
|
lines_to_sample: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
{
|
|
fmt: '/_ml/find_file_structure?&explain=true',
|
|
},
|
|
],
|
|
needBody: true,
|
|
method: 'POST',
|
|
});
|
|
|
|
ml.rollupIndexCapabilities = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/<%=indexPattern%>/_rollup/data',
|
|
req: {
|
|
indexPattern: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'GET',
|
|
});
|
|
|
|
ml.categories = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/results/categories/<%=categoryId%>',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
categoryId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/results/categories',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'GET',
|
|
});
|
|
|
|
ml.modelSnapshots = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/model_snapshots/<%=snapshotId%>',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
snapshotId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/model_snapshots',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'GET',
|
|
});
|
|
|
|
ml.updateModelSnapshot = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/model_snapshots/<%=snapshotId%>/_update',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
snapshotId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'POST',
|
|
needBody: true,
|
|
});
|
|
|
|
ml.deleteModelSnapshot = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/model_snapshots/<%=snapshotId%>',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
snapshotId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'DELETE',
|
|
});
|
|
|
|
ml.revertModelSnapshot = ca({
|
|
urls: [
|
|
{
|
|
fmt: '/_ml/anomaly_detectors/<%=jobId%>/model_snapshots/<%=snapshotId%>/_revert',
|
|
req: {
|
|
jobId: {
|
|
type: 'string',
|
|
},
|
|
snapshotId: {
|
|
type: 'string',
|
|
},
|
|
},
|
|
},
|
|
],
|
|
method: 'POST',
|
|
});
|
|
};
|