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* [ML] Sample ML jobs: Updates to make anomalies clearer Minor updates to web logs data and ML job definitions to make anomalous activity more distinct. * [ML] Sample ML jobs: Further updates to jobs and data Adding missing files from previous commit and changing job naming (removing weblog_* prefix etc.) * [ML] Resolving PR review comments. * [ML] Improve anomalies in Kibana sample data Minor fixes: - Rename job group to `kibana_sample_data` throughout - Fix index pattern link in `sample_data_sets.ts` - Fix log.json.gz count test
This commit is contained in:
stevedodson
Pete Harverson
parent
b0b1c574fb
commit
5250bab872
17 changed files with 57 additions and 125 deletions
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@ -35,8 +35,8 @@ test('load log data', async () => {
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myDocsCount += docs.length;
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};
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const count = await loadData('./src/legacy/server/sample_data/data_sets/logs/logs.json.gz', bulkInsertMock);
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expect(myDocsCount).toBe(14005);
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expect(count).toBe(14005);
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expect(myDocsCount).toBe(14074);
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expect(count).toBe(14074);
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});
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test('load ecommerce data', async () => {
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@ -18,16 +18,9 @@ export function addLinksToSampleDatasets(server: any) {
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icon: 'machineLearningApp',
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});
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server.addAppLinksToSampleDataset('flights', {
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path:
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'/app/ml#/modules/check_view_or_create?id=sample_data_flights&index=d3d7af60-4c81-11e8-b3d7-01146121b73d',
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label: sampleDataLinkLabel,
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icon: 'machineLearningApp',
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});
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server.addAppLinksToSampleDataset('logs', {
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path:
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'/app/ml#/modules/check_view_or_create?id=sample_data_weblogs&index=edf84fe0-e1a0-11e7-b6d5-4dc382ef7f5b',
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'/app/ml#/modules/check_view_or_create?id=sample_data_weblogs&index=90943e30-9a47-11e8-b64d-95841ca0b247',
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label: sampleDataLinkLabel,
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icon: 'machineLearningApp',
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});
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@ -20,7 +20,6 @@ describe('ML - data recognizer', () => {
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'metricbeat_system_ecs',
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'nginx_ecs',
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'sample_data_ecommerce',
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'sample_data_flights',
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'sample_data_weblogs',
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];
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@ -1,6 +1,6 @@
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{
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"groups": ["kibana_sample_data"],
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"description": "Kibana sample eCommerce data: detect anomalies in total sales",
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"groups": ["kibana_sample_data", "kibana_sample_ecommerce"],
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"description": "Find customers spending an unusually high amount in an hour",
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"analysis_config": {
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"bucket_span": "1h",
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"detectors": [
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@ -1,3 +0,0 @@
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{
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"icon": "logoKibana"
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}
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@ -1,27 +0,0 @@
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{
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"id": "sample_data_flights",
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"title": "Kibana sample data flights",
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"description": "Find anomalies in mean flight delay",
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"type": "Sample Dataset",
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"logoFile": "logo.json",
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"defaultIndexPattern": "kibana_sample_data_flights",
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"query": {
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"bool": {
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"filter": [{ "term": { "_index": "kibana_sample_data_flights" } }]
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}
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},
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"jobs": [
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{
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"id": "mean_delay_by_carrier",
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"file": "mean_delay_by_carrier.json"
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}
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],
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"datafeeds": [
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{
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"id": "datafeed-mean_delay_by_carrier",
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"file": "datafeed_mean_delay_by_carrier.json",
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"job_id": "mean_delay_by_carrier"
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}
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],
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"kibana": {}
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}
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@ -1,9 +0,0 @@
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{
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"job_id": "JOB_ID",
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"indexes": ["INDEX_PATTERN_NAME"],
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"query": {
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"bool": {
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"filter": [{ "term": { "_index": "kibana_sample_data_flights" } }]
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}
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}
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}
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@ -1,38 +0,0 @@
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{
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"groups": ["kibana_sample_data"],
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"description": "Kibana sample flights data: detect anomalies in mean flight delay",
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"analysis_config": {
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"bucket_span": "1h",
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"detectors": [
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{
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"detector_description": "Mean flight delay time by airline carrier",
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"function": "high_mean",
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"field_name": "FlightDelayMin",
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"partition_field_name": "Carrier"
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}
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],
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"influencers": ["Carrier", "DestWeather", "OriginWeather"]
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},
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"analysis_limits": {
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"model_memory_limit": "10mb"
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},
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"data_description": {
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"time_field": "timestamp"
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},
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"model_plot_config": {
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"enabled": true
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},
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"custom_settings": {
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"created_by": "ml-module-sample",
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"custom_urls": [
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{
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"url_name": "Raw data",
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"url_value": "kibana#/discover?_g=(time:(from:\u0027$earliest$\u0027,mode:absolute,to:\u0027$latest$\u0027))&_a=(index:d3d7af60-4c81-11e8-b3d7-01146121b73d,query:(language:kuery,query:\u0027Carrier:\u0022$Carrier$\u0022\u0027),sort:!('@timestamp',desc))"
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},
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{
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"url_name": "Data dashboard",
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"url_value": "kibana#/dashboard/7adfa750-4c81-11e8-b3d7-01146121b73d?_g=(filters:!(),time:(from:\u0027$earliest$\u0027,mode:absolute,to:\u0027$latest$\u0027))&_a=(filters:!((\u0027$state\u0027:(store:appState),meta:(alias:!n,disabled:!f,index:\u0027INDEX_PATTERN_ID\u0027,key:Carrier,negate:!f,params:(query:\u0027$Carrier$\u0027),type:phrase,value:\u0027$Carrier$\u0027),query:(match:(Carrier:(query:\u0027$Carrier$\u0027,type:phrase))))),query:(language:kuery,query:\u0027\u0027))"
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}
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]
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}
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}
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@ -12,33 +12,33 @@
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},
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"jobs": [
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{
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"id": "low_count",
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"file": "low_count.json"
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"id": "low_request_rate",
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"file": "low_request_rate.json"
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},
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{
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"id": "count_by_response_code",
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"file": "count_by_response_code.json"
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"id": "response_code_rates",
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"file": "response_code_rates.json"
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},
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{
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"id": "distinct_count_ip",
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"file": "distinct_count_ip.json"
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"id": "url_scanning",
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"file": "url_scanning.json"
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}
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],
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"datafeeds": [
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{
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"id": "datafeed-low_count",
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"file": "datafeed_low_count.json",
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"job_id": "low_count"
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"id": "datafeed-low_request_rate",
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"file": "datafeed_low_request_rate.json",
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"job_id": "low_request_rate"
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},
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{
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"id": "datafeed-count_by_response_code",
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"file": "datafeed_count_by_response_code.json",
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"job_id": "count_by_response_code"
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"id": "datafeed-response_code_rates",
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"file": "datafeed_response_code_rates.json",
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"job_id": "response_code_rates"
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},
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{
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"id": "datafeed-distinct_count_ip",
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"file": "datafeed_distinct_count_ip.json",
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"job_id": "distinct_count_ip"
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"id": "datafeed-url_scanning",
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"file": "datafeed_url_scanning.json",
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"job_id": "url_scanning"
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}
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],
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"kibana": {}
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@ -1,9 +0,0 @@
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{
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"job_id": "JOB_ID",
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"indexes": ["INDEX_PATTERN_NAME"],
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"query": {
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"bool": {
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"filter": [{ "term": { "_index": "kibana_sample_data_logs" } }]
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}
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}
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}
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@ -0,0 +1,24 @@
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{
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"job_id": "JOB_ID",
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"indexes": ["INDEX_PATTERN_NAME"],
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"query": {
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"bool": {
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"filter": [{ "term": { "_index": "kibana_sample_data_logs" } }]
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}
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},
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"aggregations": {
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"buckets": {
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"date_histogram": {
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"field": "timestamp",
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"interval": 3600000
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},
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"aggregations": {
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"timestamp": {
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"max": {
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"field": "timestamp"
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}
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}
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}
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}
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}
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}
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@ -1,11 +1,12 @@
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{
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"groups": ["kibana_sample_logs", "kibana_sample_data"],
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"description": "Kibana sample web logs data: find anomalies in the visitor count",
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"groups": ["kibana_sample_data", "kibana_sample_web_logs"],
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"description": "Find unusually low request rates",
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"analysis_config": {
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"bucket_span": "1h",
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"summary_count_field_name": "doc_count",
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"detectors": [
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{
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"detector_description": "Low visitor count",
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"detector_description": "Low request rates",
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"function": "low_count"
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}
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],
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@ -1,11 +1,11 @@
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{
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"groups": ["kibana_sample_logs", "kibana_sample_data"],
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"description": "Kibana sample web logs data: find anomalies in the event count by HTTP response code ",
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"groups": ["kibana_sample_data", "kibana_sample_web_logs"],
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"description": "Find unusual event rates by HTTP response code (high and low)",
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"analysis_config": {
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"bucket_span": "1h",
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"detectors": [
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{
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"detector_description": "Event count by response code",
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"detector_description": "Event rate by response code",
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"function": "count",
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"partition_field_name": "response.keyword"
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}
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@ -1,13 +1,14 @@
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{
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"groups": ["kibana_sample_logs", "kibana_sample_data"],
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"description": "Kibana sample web logs data: find anomalies in the distinct count of client IPs",
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"groups": ["kibana_sample_data", "kibana_sample_web_logs"],
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"description": "Find client IPs accessing an unusually high distinct count of URLs",
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"analysis_config": {
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"bucket_span": "1h",
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"detectors": [
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{
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"detector_description": "Count of distinct client IPs",
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"function": "distinct_count",
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"field_name": "clientip"
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"detector_description": "High distinct count of URLs for a client IPs",
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"function": "high_distinct_count",
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"field_name": "url.keyword",
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"over_field_name": "clientip"
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}
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],
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"influencers": ["clientip"]
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