ddadmin
335e5e7612
- Зачем: - диапазонные запросы Prometheus возвращали временные ряды, из-за чего Grafana не вычисляла алерты Airflow и Kafka. - Что: - между запросом Prometheus и Threshold добавлен Reduce(last) в семи правилах, а аннотации переведены на итоговое число. - добавлены регрессионные тесты конфигурации и зависимость PyYAML для их запуска. - в уроке 5 уточнено время срабатывания и восстановления алерта scheduler. - Проверка: - uv run --with-requirements generator/requirements.txt pytest tests/ -q -p no:cacheprovider: 33 passed. - uv run --with-requirements requirements.txt pytest tests/ -q -p no:cacheprovider из generator: 218 passed. - Docker Compose config и вычисление правил в Grafana 11.5.2 прошли успешно.
332 lines
9.7 KiB
YAML
332 lines
9.7 KiB
YAML
apiVersion: 1
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groups:
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- orgId: 1
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name: airflow_health_group
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folder: Airflow Alerts
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interval: 30s
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rules:
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# --- Alert: Airflow Scheduler Down ---
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- uid: airflow_scheduler_down
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title: Airflow Scheduler Down
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condition: C
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data:
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- refId: A
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relativeTimeRange:
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from: 300
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to: 0
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datasourceUid: prometheus_uid
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model:
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datasource:
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type: prometheus
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uid: prometheus_uid
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editorMode: code
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expr: rate(airflow_scheduler_heartbeat_total[5m])
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instant: false
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intervalMs: 1000
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legendFormat: __auto
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maxDataPoints: 43200
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range: true
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refId: A
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- refId: B
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params: []
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type: gt
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operator:
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type: and
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query:
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params:
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- B
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: A
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intervalMs: 1000
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maxDataPoints: 43200
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reducer: last
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refId: B
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type: reduce
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- refId: C
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params:
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- 0.1
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type: lt
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operator:
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type: and
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query:
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params:
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- C
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: B
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intervalMs: 1000
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maxDataPoints: 43200
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refId: C
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type: threshold
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noDataState: Alerting
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execErrState: Error
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for: 2m
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annotations:
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summary: "Airflow scheduler не отправляет heartbeats"
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description: "Scheduler possible down — rate(airflow_scheduler_heartbeat_total) < 0.1 в течение 2 минут"
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labels:
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severity: critical
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# --- Alert: Airflow Queue Backlog ---
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- uid: airflow_queue_high
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title: Airflow Queue Backlog
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condition: C
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data:
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- refId: A
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relativeTimeRange:
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from: 300
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to: 0
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datasourceUid: prometheus_uid
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model:
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datasource:
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type: prometheus
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uid: prometheus_uid
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editorMode: code
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expr: airflow_executor_queued_tasks
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instant: false
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intervalMs: 1000
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legendFormat: __auto
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maxDataPoints: 43200
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range: true
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refId: A
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- refId: B
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params: []
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type: gt
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operator:
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type: and
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query:
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params:
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- B
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: A
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intervalMs: 1000
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maxDataPoints: 43200
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reducer: last
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refId: B
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type: reduce
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- refId: C
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params:
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- 50
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type: gt
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operator:
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type: and
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query:
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params:
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- C
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: B
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intervalMs: 1000
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maxDataPoints: 43200
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refId: C
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type: threshold
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noDataState: OK
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execErrState: Error
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for: 5m
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annotations:
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summary: "В очереди Airflow > 50 задач"
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description: "airflow_executor_queued_tasks = {{ $values.B.Value }} (превышает 50 в течение 5 минут) — возможна перегрузка"
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labels:
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severity: warning
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# --- Alert: High Task Failure Rate ---
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- uid: airflow_task_failures_high
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title: High Task Failure Rate
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condition: C
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data:
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- refId: A
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relativeTimeRange:
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from: 300
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to: 0
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datasourceUid: prometheus_uid
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model:
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datasource:
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type: prometheus
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uid: prometheus_uid
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editorMode: code
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expr: rate(airflow_task_failures_total[5m])
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instant: false
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intervalMs: 1000
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legendFormat: __auto
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maxDataPoints: 43200
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range: true
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refId: A
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- refId: B
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params: []
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type: gt
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operator:
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type: and
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query:
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params:
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- B
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: A
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intervalMs: 1000
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maxDataPoints: 43200
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reducer: last
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refId: B
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type: reduce
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- refId: C
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params:
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- 0.1
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type: gt
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operator:
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type: and
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query:
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params:
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- C
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: B
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intervalMs: 1000
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maxDataPoints: 43200
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refId: C
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type: threshold
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noDataState: OK
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execErrState: Error
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for: 3m
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annotations:
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summary: "Высокий rate падений тасков"
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description: "rate(airflow_task_failures_total) = {{ $values.B.Value }} (превышает 0.1 в течение 3 минут) — проверьте логи DAG"
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labels:
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severity: warning
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# --- Alert: High DAG Parse Time ---
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- uid: airflow_parse_time_high
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title: High DAG Parse Time
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condition: C
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data:
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- refId: A
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relativeTimeRange:
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from: 300
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to: 0
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datasourceUid: prometheus_uid
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model:
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datasource:
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type: prometheus
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uid: prometheus_uid
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editorMode: code
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expr: airflow_dag_processing_total_parse_time
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instant: false
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intervalMs: 1000
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legendFormat: __auto
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maxDataPoints: 43200
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range: true
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refId: A
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- refId: B
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params: []
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type: gt
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operator:
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type: and
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query:
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params:
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- B
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: A
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intervalMs: 1000
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maxDataPoints: 43200
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reducer: last
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refId: B
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type: reduce
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- refId: C
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datasourceUid: __expr__
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model:
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conditions:
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- evaluator:
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params:
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- 30
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type: gt
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operator:
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type: and
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query:
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params:
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- C
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reducer:
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params: []
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type: last
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type: query
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datasource:
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type: __expr__
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uid: __expr__
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expression: B
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intervalMs: 1000
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maxDataPoints: 43200
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refId: C
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type: threshold
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noDataState: OK
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execErrState: Error
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for: 5m
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annotations:
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summary: "Парсинг DAG занимает > 30 секунд"
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description: "airflow_dag_processing_total_parse_time = {{ $values.B.Value }}s — возможно, есть тяжелые DAG"
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labels:
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severity: info
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