Backend:
* Update the Grafana Alerting engine to provide feedback to HysteresisCommand. The feedback information is stored in state.Manager as a fingerprint of each state. The fingerprint is persisted to the database. Only fingerprints that belong to Pending and Alerting states are considered as "loaded" and provided back to the command.
- add ResultFingerprint to state.State. It's different from other fingerprints we store in the state because it is calculated from the result labels.
- add rule_fingerprint column to alert_instance
- update alerting evaluator to accept AlertingResultsReader via context, and update scheduler to provide it.
- add AlertingResultsFromRuleState that implements the new interface in eval package
- update getExprRequest to patch the hysteresis command.
* Only one "Recovery Threshold" query is allowed to be used in the alert rule and it must be the Condition.
Frontend:
* Add hysteresis option to Threshold in UI. It's called "Recovery Threshold"
* Add test for getUnloadEvaluatorTypeFromCondition
* Hide hysteresis in panel expressions
* Refactor isInvalid and add test for it
* Remove unnecesary React.memo
* Add tests for updateEvaluatorConditions
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Co-authored-by: Sonia Aguilar <soniaaguilarpeiron@gmail.com>
* add metrics and tracing to state manager
* propagate tracer to state manager
* add scheduler metrics
* fix backtesting
* add test for state metrics
* remove StateUpdateCount
* update docs
* metrics can be null
* add tracer to new tests
* introduce a function checkIfSeriesNeedToBeFixed to scan all value fields in the response and provide a function that updates Series so they can be uniquely identifiable. Only Graphite and TestData are checked.
* update `convertDataFramesToResults` to run this function and provide it to WideToMany
* update WideToMany to run the fix function if it is not nil
* add test for the bug
* update backtesting evaluators to accept a number of evaluations instead of `to` to have control over the number evaluations in one place
This commit adds support for concurrent queries when saving alert
instances to the database. This is an experimental feature in
response to some customers experiencing delays between rule evaluation
and sending alerts to Alertmanager, resulting in flapping. It is
disabled by default.
* Implement backtesting engine that can process regular rule specification (with queries to datasource) as well as special kind of rules that have data frame instead of query.
* declare a new API endpoint and model
* add feature toggle `alertingBacktesting`
