- Зачем:
- генератор должен продолжать работу с места остановки после падения/рестарта
- нужно сохранять continuity тиков и состояние RNG для воспроизводимости
- Что:
- добавлен GeneratorState dataclass (tick, rng_state, last_batch_id, timestamp)
- добавлен KafkaStateManager для работы с compact topic generator_state
- топик создаётся с cleanup.policy=compact (хранится только последнее значение)
- интеграция в GeneratorService: восстановление при старте, сохранение после тика
- новые env: GEN_STATE_ENABLED (по умолчанию true), GEN_STATE_RESET (по умолчанию false)
- добавлены тесты test_state.py
- обновлена документация README.md
- Проверка:
- make generator-test (45 тестов проходят)
- docker compose restart generator - продолжает с сохранённого tick
- GEN_STATE_RESET=true - начинает с tick=1
- Зачем:
- ревью rev5: ClickHouse-интеграция была проблемной (порт 9000 native vs HTTP,
неработающий fallback, отсутствие DDL для базы meta)
- архитектурно чище: генератор остаётся pure Kafka producer,
история доступна для аналитики через стандартный ingestion
- Что:
- удален ClickHouseBatchHistory, clickhouse-connect зависимость
- добавлен KafkaBatchHistory с записью в топик generator_batch_history
- добавлен BatchRecord.to_dict() для JSON-сериализации
- добавлен рабочий fallback: Kafka → InMemory при недоступности
- удален pytest-asyncio (не использовался)
- добавлены тесты test_kafka_history.py (15 тестов) и test_service.py (6 тестов)
- обновлена документация: топик вместо таблицы ClickHouse
- Проверка:
- make generator-test: 44/44 тестов пройдено
- docker-compose валиден, генератор не зависит от clickhouse
- Зачем:
- нужен постоянный поток данных для демонстрации работы стека
- текущий batch-загрузчик не позволяет показать streaming-сценарии
- Что:
- добавлен сервис generator с режимом steady (Poisson-интенсивность)
- генератор публикует в 4 топика: browser/location/device/geo_events
- сохраняются связи event_id и click_id между событиями
- сборка через uv для скорости и компактности образа
- добавлены команды generator-* в Makefile
- комплексные тесты: валидация, статистика, формат сообщений
- Проверка:
- `docker run --rm -v $(pwd)/..:/workspace -w /workspace/generator generator:test python test_comprehensive.py` — 8/8 тестов
- `make generator-up` — 3 тика без ошибок, отправлено 2904 сообщения
- 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:
- Superset bootstrap used outdated ClickHouse URI format and did not fail fast on init errors.
- docs and exported dashboard metadata diverged from runtime connection settings.
- What:
- build ClickHouse URI from env vars and use clickhousedb:// in init script.
- refresh dataset metadata on existing datasets and surface import errors.
- run create_dashboard during superset-init startup and align docs/exported URI references.
- ignore node_modules in git.
- Check:
- python3 -m py_compile superset/init_superset.py
- manual dashboard smoke check in UI (charts render)
- Добавлена автоматическая инициализация Superset (подключение ClickHouse, 6 датасетов, 10 чартов, дашборд)
- Переведено хранение метаданных с SQLite на PostgreSQL (shared с Airflow)
- Добавлен superset_config.py для конфигурации PostgreSQL
- Обновлен Dockerfile.superset: postgresql-client, psycopg2-binary
- Обновлен docker-compose.yml: volume mount конфига, SUPERSET_CONFIG_PATH
- Исправлены скрипты init_superset.py и create_dashboard.py для работы с shell
- Обновлена документация в README.md: раздел Superset с инструкциями
Тестирование:
- Проверена работа после перезапуска (данные сохраняются)
- Проверен чистый запуск с нуля
- API и UI доступны
- Add port 9126 mapping for ClickHouse Prometheus metrics endpoint
(was configured in prometheus_ch.xml but not exposed in docker-compose.yml)
- Fix CPU Usage panel: use delta() instead of rate() for gauge metric
ClickHouseProfileEvents_OSCPUVirtualTimeMicroseconds is a gauge, not counter
- Add explicit datasource blocks to dashboard queries for consistency
ClickHouse ProfileEvents metrics correctly use rate() — they are counters.
Warning about missing _total suffix is expected (ClickHouse naming convention).
- 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
- Why:
- dashboard showed offset as throughput and produced misleading values
- kafka-exporter metric/label naming was inconsistent across alerts/docs
- consumer-group-missing alert was noisy for demo runs
- What:
- switch throughput panel to rate(kafka_topic_partition_current_offset[5m]) aggregated by topic and exclude __* topics
- align lag metric/labels to kafka_consumergroup_lag + consumergroup
- remove Kafka Consumer Group Missing alert from provisioning
- pin kafka-exporter image to v1.9.0 and update OPERATIONS.md checks
- Check:
- airflow dags list-import-errors -> No data found
- Prometheus targets: clickhouse up, kafka up
- PromQL kafka_consumergroup_lag returns series
- Grafana dashboards provisioning reload returns success
- Why:
- students hit permission denied after pull and grafana restart-loop with readonly db
- What:
- run grafana as default non-root user
- mount provisioning directory as read-only
- add troubleshooting for git permission issues and grafana volume reset
- normalize file modes for data jsonl and docs/DE-task.md to 100644
- Check:
- docker compose config
- docker compose up -d grafana
- curl -u admin:admin http://localhost:3000/api/health
- 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
- Why:
- keep Airflow artifacts under a single airflow/ directory
- align repository layout with intended project structure
- What:
- move dags/ to airflow/dags/ and update compose mounts
- make SQL root resolution work in container and local runs
- update DAG path references in README, AGENTS, ARCHITECTURE, and plans
- remove tracked Python cache artifacts from old DAG location
- Check:
- airflow dags list
- airflow dags list-import-errors
- e2e success: ddl_init, kafka_load(limit=50), etl_pipeline
- Изменен путь volume с /tmp/kraft-combined-logs на /var/lib/kafka/data
- Решена проблема с правами доступа при старте Kafka в KRaft mode
- Kafka теперь корректно инициализирует метаданные при первом запуске
Add persistent volume for ClickHouse to preserve data across container
restarts. The volume `clickhouse-data` is mounted to `/var/lib/clickhouse`,
ensuring data remains when containers are recreated.
Add comprehensive DAG implementation for ClickHouse schema initialization
and ETL pipeline orchestration. The ddl_init_dag manages database schema
creation across stg/ods/dds/dm layers with verification capabilities. The
etl_pipeline_dag implements full ODS to DDS to DM transformation flow with
data quality checks, branching logic for full/incremental loads, and
timeout handling for data availability.
Additional changes:
- Upgrade Airflow from 2.9.3 to 2.10.5
- Fix ClickHouse connection to use native protocol port 9000
- Mount SQL directory in docker-compose for DAG execution
- Update project requirements and documentation comments
- Remove unused pandas dependency
Update Airflow configuration to integrate with ClickHouse DWH instead of
PostgreSQL training database. Changes include:
- Switch Airflow dependencies from PostgreSQL to ClickHouse connector
- Update docker-compose to use ClickHouse connection and correct Dockerfile
- Refactor airflow/requirements.txt to include only essential packages
- Add DAGs directory for ETL pipeline orchestration
- Update documentation to reflect Airflow integration and access credentials
- Adjust service dependencies to wait for ClickHouse startup
Add Apache Airflow infrastructure with webserver, scheduler, and metadata
database to enable DAG-based pipeline orchestration. Includes optimized
requirements file and Docker configuration for Airflow 2.9.3.
Add Apache Superset for data visualization to the docker-compose setup.
The custom Dockerfile installs additional tools and the clickhouse-connect
driver. The service is configured with health check, persistent volumes,
and environment variables.
Fix network definition name from 'ch_replicated' to 'cs_dwh' to match
service references. Comment out hardcoded container names to allow
Docker to generate unique names automatically and avoid conflicts.
ddadminandDmitry Dementiev