feat(monitoring): add Airflow monitoring via statsd-exporter

- Add statsd-exporter service to docker-compose.yml (prom/statsd-exporter:v0.27.1)
- Add StatsD env vars to airflow-default-env for metrics export
- Add airflow job to prometheus.yml scrape configs
- Add Airflow Overview dashboard (Grafana provisioning)
- Add Airflow alert rules: scheduler down, queue backlog, failures, parse time
- Add configs/statsd_mapping.yml for StatsD → Prometheus conversion
- Use Prometheus naming convention (_total for counters, _seconds for timers)
- Add monitoring plan at plans/monitoring_airflow_plan.md
- Update OPERATIONS.md and Makefile for airflow monitoring

Tested: all 3 jobs (airflow, clickhouse, kafka) showing UP in Prometheus,
metrics flowing (dagbag_size=3, executor slots, heartbeats with _total suffix),
all 4 alert rules loaded in Grafana
This commit is contained in:
2026-02-08 22:27:32 +03:00
parent 8e31b06241
commit 4917a9a6ad
8 changed files with 1701 additions and 8 deletions
@@ -0,0 +1,331 @@
apiVersion: 1
groups:
- orgId: 1
name: airflow_health_group
folder: Airflow Alerts
interval: 30s
rules:
# --- Alert: Airflow Scheduler Down ---
- uid: airflow_scheduler_down
title: Airflow Scheduler Down
condition: C
data:
- refId: A
relativeTimeRange:
from: 300
to: 0
datasourceUid: prometheus_uid
model:
datasource:
type: prometheus
uid: prometheus_uid
editorMode: code
expr: rate(airflow_scheduler_heartbeat_total[5m])
instant: false
intervalMs: 1000
legendFormat: __auto
maxDataPoints: 43200
range: true
refId: A
- refId: B
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 0.1
type: lt
operator:
type: and
query:
params:
- B
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: A
intervalMs: 1000
maxDataPoints: 43200
refId: B
type: threshold
- refId: C
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 0
type: gt
operator:
type: and
query:
params:
- C
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: B
intervalMs: 1000
maxDataPoints: 43200
refId: C
type: threshold
noDataState: Alerting
execErrState: Error
for: 2m
annotations:
summary: "Airflow scheduler не отправляет heartbeats"
description: "Scheduler possible down — rate(airflow_scheduler_heartbeat_total) < 0.1 в течение 2 минут"
labels:
severity: critical
# --- Alert: Airflow Queue Backlog ---
- uid: airflow_queue_high
title: Airflow Queue Backlog
condition: C
data:
- refId: A
relativeTimeRange:
from: 300
to: 0
datasourceUid: prometheus_uid
model:
datasource:
type: prometheus
uid: prometheus_uid
editorMode: code
expr: airflow_executor_queued_tasks
instant: false
intervalMs: 1000
legendFormat: __auto
maxDataPoints: 43200
range: true
refId: A
- refId: B
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 50
type: gt
operator:
type: and
query:
params:
- B
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: A
intervalMs: 1000
maxDataPoints: 43200
refId: B
type: threshold
- refId: C
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 0
type: gt
operator:
type: and
query:
params:
- C
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: B
intervalMs: 1000
maxDataPoints: 43200
refId: C
type: threshold
noDataState: OK
execErrState: Error
for: 5m
annotations:
summary: "В очереди Airflow > 50 задач"
description: "airflow_executor_queued_tasks = {{ $values.A }} (превышает 50 в течение 5 минут) — возможна перегрузка"
labels:
severity: warning
# --- Alert: High Task Failure Rate ---
- uid: airflow_task_failures_high
title: High Task Failure Rate
condition: C
data:
- refId: A
relativeTimeRange:
from: 300
to: 0
datasourceUid: prometheus_uid
model:
datasource:
type: prometheus
uid: prometheus_uid
editorMode: code
expr: rate(airflow_task_failures_total[5m])
instant: false
intervalMs: 1000
legendFormat: __auto
maxDataPoints: 43200
range: true
refId: A
- refId: B
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 0.1
type: gt
operator:
type: and
query:
params:
- B
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: A
intervalMs: 1000
maxDataPoints: 43200
refId: B
type: threshold
- refId: C
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 0
type: gt
operator:
type: and
query:
params:
- C
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: B
intervalMs: 1000
maxDataPoints: 43200
refId: C
type: threshold
noDataState: OK
execErrState: Error
for: 3m
annotations:
summary: "Высокий rate падений тасков"
description: "rate(airflow_task_failures_total) = {{ $values.A }} (превышает 0.1 в течение 3 минут) — проверьте логи DAG"
labels:
severity: warning
# --- Alert: High DAG Parse Time ---
- uid: airflow_parse_time_high
title: High DAG Parse Time
condition: C
data:
- refId: A
relativeTimeRange:
from: 300
to: 0
datasourceUid: prometheus_uid
model:
datasource:
type: prometheus
uid: prometheus_uid
editorMode: code
expr: airflow_dag_processing_total_parse_time
instant: false
intervalMs: 1000
legendFormat: __auto
maxDataPoints: 43200
range: true
refId: A
- refId: B
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 30
type: gt
operator:
type: and
query:
params:
- B
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: A
intervalMs: 1000
maxDataPoints: 43200
refId: B
type: threshold
- refId: C
datasourceUid: __expr__
model:
conditions:
- evaluator:
params:
- 0
type: gt
operator:
type: and
query:
params:
- C
reducer:
params: []
type: last
type: query
datasource:
type: __expr__
uid: __expr__
expression: B
intervalMs: 1000
maxDataPoints: 43200
refId: C
type: threshold
noDataState: OK
execErrState: Error
for: 5m
annotations:
summary: "Парсинг DAG занимает > 30 секунд"
description: "airflow_dag_processing_total_parse_time = {{ $values.A }}s — возможно, есть тяжелые DAG"
labels:
severity: info