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[Response Ops][Alerting] Should framework alerts as data write out data flattened keys #166946
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Feature:Alerting/Alerts-as-Data
Issues related to Alerts-as-data and RuleRegistry
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ymao1
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Feature:Alerting/Alerts-as-Data
Issues related to Alerts-as-data and RuleRegistry
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Sep 21, 2023
Pinging @elastic/response-ops (Team:ResponseOps) |
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Currently, AAD docs are updated in a few places:
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ymao1
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Going to split this into 2 PRs:
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…tened alerts docs (#167439) Resolves #166946 ## Summary The rule registry has traditionally written out AAD docs with flattened keys, like ``` { "kibana.alert.rule.name": "test" } ``` The framework alerts client has been writing out AAD docs as objects, like ``` { "kibana": { "alert": { "rule": { "name": "test" } } } } ``` We've identified a few places where we're updating the docs where having this divergence makes things more difficult, so this is to switch the framework to writing flattened alert docs before onboarding more rule types. This PR is targeted for 8.11, which is also when we onboarded the index threshold rule type to FAAD. The only other rule type using FAAD to write docs is ES query, which landed in 8.10 so there will be a followup issue to handle the case of updating unflattened ES query AAD docs from 8.10 ## To Verify ### ES Query and Index Threshold AaD Create these rules that trigger alerts and verify that their AaD docs are written out as flattened. For the ES Query rule type, select a Metrics/Logs consumer and verify that they appear on the O11y alerts table. ### ML alerts ML alerts added in #166349 looked like: <details> <summary>Unflattened</summary> ``` { "kibana": { "alert": { "url": "/app/ml/explorer/?_g=(ml%3A(jobIds%3A!(rt-anomaly-mean-value))%2Ctime%3A(from%3A'2023-09-28T14%3A57%3A00.000Z'%2Cmode%3Aabsolute%2Cto%3A'2023-09-28T15%3A17%3A00.000Z'))&_a=(explorer%3A(mlExplorerFilter%3A(filterActive%3A!t%2CfilteredFields%3A!(key%2Cthird-key)%2CinfluencersFilterQuery%3A(bool%3A(minimum_should_match%3A1%2Cshould%3A!((match_phrase%3A(key%3Athird-key)))))%2CqueryString%3A'key%3A%22third-key%22')%2CmlExplorerSwimlane%3A()))", "reason": "Alerts are raised based on real-time scores. Remember that scores may be adjusted over time as data continues to be analyzed.", "job_id": "rt-anomaly-mean-value", "anomaly_score": 73.63508175828011, "is_interim": false, "anomaly_timestamp": 1695913620000, "top_records": [{ "job_id": "rt-anomaly-mean-value", "record_score": 73.63516446528412, "initial_record_score": 73.63516446528412, "detector_index": 0, "is_interim": false, "timestamp": 1695913620000, "partition_field_name": "key", "partition_field_value": "third-key", "function": "mean", "actual": [ 3 ], "typical": [ 4.187715468532429 ] }], "top_influencers": [{ "job_id": "rt-anomaly-mean-value", "influencer_field_name": "key", "influencer_field_value": "third-key", "influencer_score": 73.63508175828011, "initial_influencer_score": 73.63508175828011, "is_interim": false, "timestamp": 1695913620000 }], "action_group": "anomaly_score_match", "flapping": false, "flapping_history": [ true, false, false, false ], "instance": { "id": "rt-anomaly-mean-value" }, "maintenance_window_ids": [], "rule": { "category": "Anomaly detection alert", "consumer": "alerts", "execution": { "uuid": "e9e681d4-c8e4-43eb-82e5-a58bdf7ffe12" }, "name": "rt-ad-alert-influencer", "parameters": { "severity": 5, "resultType": "influencer", "includeInterim": false, "jobSelection": { "jobIds": [ "rt-anomaly-mean-value" ], "groupIds": [] }, "lookbackInterval": null, "topNBuckets": null }, "producer": "ml", "revision": 0, "rule_type_id": "xpack.ml.anomaly_detection_alert", "tags": [], "uuid": "9e1d6bc0-5e10-11ee-8416-3bf48cca0922" }, "status": "active", "uuid": "c9c1f075-9985-4c55-8ff8-22349cb30269", "workflow_status": "open", "duration": { "us": "99021000000" }, "start": "2023-09-28T15:07:12.868Z", "time_range": { "gte": "2023-09-28T15:07:12.868Z" } }, "space_ids": [ "default" ], "version": "8.11.0" }, "@timestamp": "2023-09-28T15:08:51.889Z", "event": { "action": "active", "kind": "signal" }, "tags": [] } ``` </details> Now they look like: <details> <summary>Flattened</summary> ``` { "kibana.alert.url": "/app/ml/explorer/?_g=(ml%3A(jobIds%3A!(rt-anomaly-mean-value))%2Ctime%3A(from%3A'2023-09-28T15%3A03%3A00.000Z'%2Cmode%3Aabsolute%2Cto%3A'2023-09-28T15%3A23%3A00.000Z'))&_a=(explorer%3A(mlExplorerFilter%3A(filterActive%3A!t%2CfilteredFields%3A!(key%2Cthird-key)%2CinfluencersFilterQuery%3A(bool%3A(minimum_should_match%3A1%2Cshould%3A!((match_phrase%3A(key%3Athird-key)))))%2CqueryString%3A'key%3A%22third-key%22')%2CmlExplorerSwimlane%3A()))", "kibana.alert.reason": "Alerts are raised based on real-time scores. Remember that scores may be adjusted over time as data continues to be analyzed.", "kibana.alert.job_id": "rt-anomaly-mean-value", "kibana.alert.anomaly_score": 72.75515452061356, "kibana.alert.is_interim": false, "kibana.alert.anomaly_timestamp": 1695913980000, "kibana.alert.top_records": [{ "job_id": "rt-anomaly-mean-value", "record_score": 72.75515452061356, "initial_record_score": 72.75515452061356, "detector_index": 0, "is_interim": false, "timestamp": 1695913980000, "partition_field_name": "key", "partition_field_value": "third-key", "function": "mean", "actual": [ 0.5 ], "typical": [ 4.138745343296527 ] }], "kibana.alert.top_influencers": [{ "job_id": "rt-anomaly-mean-value", "influencer_field_name": "key", "influencer_field_value": "third-key", "influencer_score": 72.75515452061356, "initial_influencer_score": 72.75515452061356, "is_interim": false, "timestamp": 1695913980000 }], "kibana.alert.rule.category": "Anomaly detection alert", "kibana.alert.rule.consumer": "alerts", "kibana.alert.rule.execution.uuid": "17fef3d3-d595-4362-837e-b2a73650169e", "kibana.alert.rule.name": "rt-ad-alert-influencer", "kibana.alert.rule.parameters": { "severity": 5, "resultType": "influencer", "includeInterim": false, "jobSelection": { "jobIds": [ "rt-anomaly-mean-value" ], "groupIds": [] }, "lookbackInterval": null, "topNBuckets": null }, "kibana.alert.rule.producer": "ml", "kibana.alert.rule.revision": 0, "kibana.alert.rule.rule_type_id": "xpack.ml.anomaly_detection_alert", "kibana.alert.rule.tags": [], "kibana.alert.rule.uuid": "757c7610-5e11-11ee-8bc6-a95c3ced4757", "kibana.space_ids": [ "default" ], "@timestamp": "2023-09-28T15:14:52.057Z", "event.action": "active", "event.kind": "signal", "kibana.alert.action_group": "anomaly_score_match", "kibana.alert.flapping": false, "kibana.alert.flapping_history": [ true, false, false, false ], "kibana.alert.instance.id": "rt-anomaly-mean-value", "kibana.alert.maintenance_window_ids": [], "kibana.alert.status": "active", "kibana.alert.uuid": "ac1f0d7c-461b-4fc6-b4c3-04416ac876d3", "kibana.alert.workflow_status": "open", "kibana.alert.duration.us": "99028000000", "kibana.alert.start": "2023-09-28T15:13:13.028Z", "kibana.alert.time_range": { "gte": "2023-09-28T15:13:13.028Z" }, "kibana.version": "8.11.0", "tags": [] } ``` </details>
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…tened alerts docs (#167691) Resolves #166946 ## PRs to this feature branch * #167439 * #167583 ## Summary The rule registry has traditionally written out AAD docs with flattened keys, like ``` { "kibana.alert.rule.name": "test" } ``` The framework alerts client has been writing out AAD docs as objects, like ``` { "kibana": { "alert": { "rule": { "name": "test" } } } } ``` We've identified a few places where we're updating the docs where having this divergence makes things more difficult, so this is to switch the framework to writing flattened alert docs before onboarding more rule types. This PR is targeted for 8.11, which is also when we onboarded the index threshold rule type and the ML anomaly detection rule type to FAAD. For the ES query rule, which started writing unflattened AaD docs in 8.10, this PR adds special handling to ensure that those unflattened docs are correctly updated with flattened fields. ## To Verify ### ES Query and Index Threshold AaD Create these rules that trigger alerts and verify that their AaD docs are written out as flattened. For the ES Query rule type, select a Metrics/Logs consumer and verify that they appear on the O11y alerts table. ### ML alerts ML alerts added in #166349 looked like: <details> <summary>Unflattened</summary> ``` { "kibana": { "alert": { "url": "/app/ml/explorer/?_g=(ml%3A(jobIds%3A!(rt-anomaly-mean-value))%2Ctime%3A(from%3A'2023-09-28T14%3A57%3A00.000Z'%2Cmode%3Aabsolute%2Cto%3A'2023-09-28T15%3A17%3A00.000Z'))&_a=(explorer%3A(mlExplorerFilter%3A(filterActive%3A!t%2CfilteredFields%3A!(key%2Cthird-key)%2CinfluencersFilterQuery%3A(bool%3A(minimum_should_match%3A1%2Cshould%3A!((match_phrase%3A(key%3Athird-key)))))%2CqueryString%3A'key%3A%22third-key%22')%2CmlExplorerSwimlane%3A()))", "reason": "Alerts are raised based on real-time scores. Remember that scores may be adjusted over time as data continues to be analyzed.", "job_id": "rt-anomaly-mean-value", "anomaly_score": 73.63508175828011, "is_interim": false, "anomaly_timestamp": 1695913620000, "top_records": [{ "job_id": "rt-anomaly-mean-value", "record_score": 73.63516446528412, "initial_record_score": 73.63516446528412, "detector_index": 0, "is_interim": false, "timestamp": 1695913620000, "partition_field_name": "key", "partition_field_value": "third-key", "function": "mean", "actual": [ 3 ], "typical": [ 4.187715468532429 ] }], "top_influencers": [{ "job_id": "rt-anomaly-mean-value", "influencer_field_name": "key", "influencer_field_value": "third-key", "influencer_score": 73.63508175828011, "initial_influencer_score": 73.63508175828011, "is_interim": false, "timestamp": 1695913620000 }], "action_group": "anomaly_score_match", "flapping": false, "flapping_history": [ true, false, false, false ], "instance": { "id": "rt-anomaly-mean-value" }, "maintenance_window_ids": [], "rule": { "category": "Anomaly detection alert", "consumer": "alerts", "execution": { "uuid": "e9e681d4-c8e4-43eb-82e5-a58bdf7ffe12" }, "name": "rt-ad-alert-influencer", "parameters": { "severity": 5, "resultType": "influencer", "includeInterim": false, "jobSelection": { "jobIds": [ "rt-anomaly-mean-value" ], "groupIds": [] }, "lookbackInterval": null, "topNBuckets": null }, "producer": "ml", "revision": 0, "rule_type_id": "xpack.ml.anomaly_detection_alert", "tags": [], "uuid": "9e1d6bc0-5e10-11ee-8416-3bf48cca0922" }, "status": "active", "uuid": "c9c1f075-9985-4c55-8ff8-22349cb30269", "workflow_status": "open", "duration": { "us": "99021000000" }, "start": "2023-09-28T15:07:12.868Z", "time_range": { "gte": "2023-09-28T15:07:12.868Z" } }, "space_ids": [ "default" ], "version": "8.11.0" }, "@timestamp": "2023-09-28T15:08:51.889Z", "event": { "action": "active", "kind": "signal" }, "tags": [] } ``` </details> Now they look like: <details> <summary>Flattened</summary> ``` { "kibana.alert.url": "/app/ml/explorer/?_g=(ml%3A(jobIds%3A!(rt-anomaly-mean-value))%2Ctime%3A(from%3A'2023-09-28T15%3A03%3A00.000Z'%2Cmode%3Aabsolute%2Cto%3A'2023-09-28T15%3A23%3A00.000Z'))&_a=(explorer%3A(mlExplorerFilter%3A(filterActive%3A!t%2CfilteredFields%3A!(key%2Cthird-key)%2CinfluencersFilterQuery%3A(bool%3A(minimum_should_match%3A1%2Cshould%3A!((match_phrase%3A(key%3Athird-key)))))%2CqueryString%3A'key%3A%22third-key%22')%2CmlExplorerSwimlane%3A()))", "kibana.alert.reason": "Alerts are raised based on real-time scores. Remember that scores may be adjusted over time as data continues to be analyzed.", "kibana.alert.job_id": "rt-anomaly-mean-value", "kibana.alert.anomaly_score": 72.75515452061356, "kibana.alert.is_interim": false, "kibana.alert.anomaly_timestamp": 1695913980000, "kibana.alert.top_records": [{ "job_id": "rt-anomaly-mean-value", "record_score": 72.75515452061356, "initial_record_score": 72.75515452061356, "detector_index": 0, "is_interim": false, "timestamp": 1695913980000, "partition_field_name": "key", "partition_field_value": "third-key", "function": "mean", "actual": [ 0.5 ], "typical": [ 4.138745343296527 ] }], "kibana.alert.top_influencers": [{ "job_id": "rt-anomaly-mean-value", "influencer_field_name": "key", "influencer_field_value": "third-key", "influencer_score": 72.75515452061356, "initial_influencer_score": 72.75515452061356, "is_interim": false, "timestamp": 1695913980000 }], "kibana.alert.rule.category": "Anomaly detection alert", "kibana.alert.rule.consumer": "alerts", "kibana.alert.rule.execution.uuid": "17fef3d3-d595-4362-837e-b2a73650169e", "kibana.alert.rule.name": "rt-ad-alert-influencer", "kibana.alert.rule.parameters": { "severity": 5, "resultType": "influencer", "includeInterim": false, "jobSelection": { "jobIds": [ "rt-anomaly-mean-value" ], "groupIds": [] }, "lookbackInterval": null, "topNBuckets": null }, "kibana.alert.rule.producer": "ml", "kibana.alert.rule.revision": 0, "kibana.alert.rule.rule_type_id": "xpack.ml.anomaly_detection_alert", "kibana.alert.rule.tags": [], "kibana.alert.rule.uuid": "757c7610-5e11-11ee-8bc6-a95c3ced4757", "kibana.space_ids": [ "default" ], "@timestamp": "2023-09-28T15:14:52.057Z", "event.action": "active", "event.kind": "signal", "kibana.alert.action_group": "anomaly_score_match", "kibana.alert.flapping": false, "kibana.alert.flapping_history": [ true, false, false, false ], "kibana.alert.instance.id": "rt-anomaly-mean-value", "kibana.alert.maintenance_window_ids": [], "kibana.alert.status": "active", "kibana.alert.uuid": "ac1f0d7c-461b-4fc6-b4c3-04416ac876d3", "kibana.alert.workflow_status": "open", "kibana.alert.duration.us": "99028000000", "kibana.alert.start": "2023-09-28T15:13:13.028Z", "kibana.alert.time_range": { "gte": "2023-09-28T15:13:13.028Z" }, "kibana.version": "8.11.0", "tags": [] } ``` </details> --------- Co-authored-by: kibanamachine <42973632+kibanamachine@users.noreply.github.com>
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Labels
Feature:Alerting/Alerts-as-Data
Issues related to Alerts-as-data and RuleRegistry
Team:ResponseOps
Label for the ResponseOps team (formerly the Cases and Alerting teams)
Currently, the rule registry writes out documents with flattened keys like:
The alerting framework writes out documents as an object
When considering this, we looked at what might happen if we had mixed formats in the same index, where doc1 was flattened and doc2 was unflattened and it seemed to make no difference when reading. Reading and writing sources as objects also allow us to apply typescript typings to the ES search requests.
However, when performing an update by query on these documents, we could potentially get into a mixed state within the same document. For example, updating an object source with flattened keys:
We would have to account for this scenario in the painless scripts we use to update alerts, which is possible but is it necessary? Currently we have just the stack rules onboarded onto the framework alerts-as-data so the only manual update that would be performed on them would be in this new PR #164788. Should we switch to writing flattened alerts before onboarding more rule types?
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