- make superset-init run via dedicated init service\n- tolerate missing dm views during early metadata refresh\n- add clickhouse dependency for init service\n- document clean-reset behavior and re-init flow
- Why:
- intensive development needs quick cluster stop/cleanup commands
- current Makefile had only up and pipeline/monitoring targets
- What:
- add make target down for standard docker compose shutdown
- add make target clean for full cleanup with volumes and orphans
- update OPERATIONS runbook with new make commands
- Check:
- make -n down clean
- Why:
- during intensive development monitoring can get stuck (No data, out of bounds)
- regular reload is not always enough to recover Prometheus + StatsD pipeline
- What:
- add make target recover-monitoring for hard recovery path
- recreate prometheus and statsd-exporter, restart airflow scheduler/webserver
- keep Grafana provisioning reload and target checks in one command
- document when to use recover-monitoring in OPERATIONS runbook
- Check:
- run make recover-monitoring
- verify Prometheus targets for airflow/clickhouse/kafka are up
- Why:
- Airflow task metrics were mapped to non-emitted StatsD keys
- reload-monitoring did not restart statsd-exporter after mapping changes
- What:
- update StatsD mapping for Airflow 2.10.5 metric names
- remove problematic catch-all mapping that produced inconsistent series
- restart statsd-exporter in reload-monitoring flow
- sync operations runbook and airflow monitoring plan with actual metrics
- Check:
- make reload-monitoring
- Prometheus targets: airflow/clickhouse/kafka are UP
- trigger ddl_init and verify airflow_task_duration_seconds_count
- verify airflow_task_success_total and airflow_task_failures_total in Prometheus
- 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
- Add kafka-exporter service to docker-compose.yml
- Add kafka job to prometheus.yml scrape configs
- Add Kafka Overview dashboard (Grafana provisioning)
- Add Kafka alert rules (broker down, consumer lag, etc.)
- Add make reload-monitoring command for easy updates
- Update OPERATIONS.md with TL;DR and troubleshooting
API verified via Context7:
- /danielqsj/kafka_exporter for exporter config
- /prometheus/docs for scrape_configs format
Add batch ETL pipeline with ODS→DDS→DM transformation jobs and scripts.
Create DDL infrastructure with automated database schema application.
Update Makefile with transform target for executing batch processes.
Rewrite README with complete Russian documentation including architecture
diagrams, quick start guide, and data flow visualization.
Add build automation via Makefile with targets for docker compose
management, DDL application, and data ingestion. Implement a robust bash
script for loading JSONL demo data into Kafka topics with configurable
options for limits, full dataset loading, and topic reset behavior.
ddadmin