- 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
Слияние ветки с реализацией автоматизированной загрузки JSONL-файлов в Kafka
через Airflow DAG с валидацией, мониторингом и документацией.
- Что добавлено:
- dags/kafka_load_dag.py: TaskGroup-пайплайн загрузки 4 потоков данных
- dags/utils/kafka_helpers.py: хелперы для работы с Kafka (проверка,
создание топиков, загрузка с лимитом)
- airflow/requirements.txt: зависимость kafka-python==2.0.6
- .gitignore: полноценный шаблон для ETL-проекта
- Параметры DAG:
- limit: ограничение строк (0 = все)
- reset_topics: пересоздание топиков перед загрузкой
- load_browser/device/geo/location_events: выбор потоков
- Обновлена документация:
- README.md, AGENTS.md, docs/ARCHITECTURE.md
- plans/runbook.md, plans/airflow_dags_plan.md
- Why:\n - User-facing docs mixed Airflow and legacy CLI ingest paths and caused confusion\n- What:\n - Rework README quick start and status to use DAG chain ddl_init -> kafka_load -> etl_pipeline\n - Rewrite runbook as canonical Airflow-first execution flow\n - Sync architecture diagrams/sequence and DQ wording with current SQL and DAG behavior\n- Check:\n - Verified updated sections and removed stale markers with rg in README.md, docs/ARCHITECTURE.md, plans/runbook.md
- Why:
- For DE task we only need full ingest or limit-based sample.
- load_* and full_load params were redundant and unclear in current flow.
- What:
- Remove full_load and load_* params from kafka_load DAG contract.
- Simplify kafka helpers (validate/check files) to fixed 4-stream ingest.
- Sync AGENTS, README, runbook, architecture and airflow plan docs.
- Check:
- python3 -m py_compile dags/kafka_load_dag.py dags/utils/kafka_helpers.py
- Airflow smoke/full runs: ddl_init -> kafka_load -> etl_pipeline (all success).
- Legacy path: make data && make transform (success).
- Why:
- Align with Conventional Commits specification for consistency
- English is standard for open-source and team collaboration
- What:
- Change primary language to English (Russian still allowed)
- Add type and scope reference tables
- Add both English and Russian body templates
- Add good/bad examples section
- Add quick reference for common commit types
- Check:
- File renders correctly in markdown viewer
- Examples follow the new format rules
- Зачем:
- унифицировать стиль коммитов для всех участников проекта
- Что сделано:
- добавлен документ docs/COMMIT_RULES.md с форматом и примерами
- добавлена ссылка на правила в AGENTS.md
- Проверка:
- проверен staged diff перед коммитом
- 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
Move DDL files from flat ddl/ directory to sql/ddl/ with layer-based
subdirectories (stg, ods, dds, dm). Move batch transformation SQL from
jobs/ to sql/ layer directories. Update scripts and documentation to
reflect new paths for improved organization and Airflow integration.
Split Kafka ingestion into a dedicated `kafka_load` DAG to enable independent
experimentation with data loading without triggering the full ETL pipeline.
Restructure implementation phases: Stage 1 uses `make data` for MVP, Stage 2
adds the standalone Kafka DAG, Stage 3 adds monitoring. Update DAG numbering,
parameters, task groups, and acceptance criteria to reflect the new
architecture.
Consolidate the orchestration strategy by merging `kafka_load` and
`etl_batch_transform` into a unified `etl_pipeline`. Replace BashOperator
dependencies on Kafka CLI with PythonOperators utilizing `kafka-python`.
Add detailed technical specifications for helper functions, MVP stages,
and validation checks to align with current infrastructure constraints.
Detail the architecture for migrating ETL orchestration from make to
Airflow. Define DAG structures for database initialization, Kafka data
ingestion, batch transformation, and quality monitoring. Include
technical specifications, operator details, and implementation phases.
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.
Refine data flow diagrams and documentation to clarify error handling
in the ODS layer and partial data processing in the DDS layer. Add
detailed explanations for materialized views, batch SQL transformations,
and data quality metrics. Split DDS entity assembly diagrams for better
readability of event and click processing pipelines.
Add materialized views to capture parsing errors from browser, location,
device, and geo raw staging tables and route them to dedicated error
tables in the ODS layer. Refactor DDS refresh logic to handle partial
data arrivals where device and geo events may arrive independently by
using a unified click_id source with LEFT JOINs. Add TRUNCATE command
to prevent duplicate data accumulation in DQ summary table.
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.
Replace materialized view joins with batch SQL transformations to avoid
consistency issues with out-of-order data. Document the reasoning for
using batch processing for ODS to DDS layer, including handling of
eventual consistency and versioning in ReplacingMergeTree. Update
data flow diagrams and remove MV creation DDL for DDS tables. Add
documentation for error handling tables and batch transformation jobs.
Add comprehensive plan for migrating executable DDL statements from markdown
to separate SQL files organized by layer. The plan outlines artifact structure,
execution requirements via make/Airflow, and environment parameters.
Existing inline DDL content is now marked as legacy in an appendix section,
providing clear separation between planned implementation and current state.
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.
Update Kafka broker address to use internal Docker network port (29092)
instead of external port (9092). Add kafka_ingest_plan.md with detailed
implementation strategy and runbook.md with user instructions.
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.
- Описаны слои STG/ODS/DDS/DM и связи потоков (`event_id`/`click_id`)
- Добавлены DDL и MV-пайплайн для ingestion из Kafka (ClickHouse) + типизация/дедуп/DQ
- Добавлены витрины/VIEW для BI (Superset), mermaid-диаграмма и операционные заметки
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