airflow: Скилл для управления и отладки

--- name: airflow description: Queries, manages, and troubleshoots Apache Airflow using the af CLI. Covers listing DAGs, triggering runs, reading task logs, diagnosing failures, debugging DAG import errors, checking connections, variables, pools, and monitoring health. Also routes to sub-skills for writing DAGs, debugging, deploying, and migrating Airflow 2 to 3. Use when user mentions "Airflow", "DAG", "DAG run", "task log", "import error", "parse error", "broken DAG", or asks to "trigger a pipeline", "debug import errors", "check Airflow health", "list connections", "retry a run", or any Airflow operation. Do NOT use for warehouse/SQL analytics on Airflow metadata tables — use analyzing-data instead. --- # Airflow Operations Use `af` commands to query, manage, and troubleshoot Airflow workflows. ## Astro CLI The [Astro CLI](https://www.astronomer.io/docs/astro/cli/overview) is the recommended way to run Airflow locally and deploy to production. It provides a containerized Airflow environment that works out of the box: ```bash # Initialize a new project astro dev init # Start local Airflow (webserver at http://localhost:8080) astro dev start # Parse DAGs to catch errors quickly (no need to start Airflow) astro dev parse # Run pytest against your DAGs astro dev pytest # Deploy to production astro deploy # Full deploy (image + DAGs) astro deploy --dags # DAG-only deploy (fast, no image build) ``` For more details: - **New project?** See the **setting-up-astro-project** skill - **Local environment?** See the **managing-astro-local-env** skill - **Deploying?** See the **deploying-airflow** skill --- ## Running the CLI Run all `af` commands using uvx (no installation required): ```bash uvx --from astro-airflow-mcp af <command> ``` Throughout this document, `af` is shorthand for `uvx --from astro-airflow-mcp af`. ## Instance Configuration Manage multiple Airflow instances with persistent configuration: ```bash # Add a new instance af instance add prod --url https://airflow.example.com --token "$API_TOKEN" af instance add staging --url https://staging.example.com --username admin --password admin # List and switch instances af instance list # Shows all instances in a table af instance use prod # Switch to prod instance af instance current # Show current instance af instance delete old-instance # Auto-discover instances (use --dry-run to preview first) af instance discover --dry-run # Preview all discoverable instances af instance discover # Discover from all backends (astro, local) af instance discover astro # Discover Astro deployments only af instance discover astro --all-workspaces # Include all accessible workspaces af instance discover local # Scan common local Airflow ports af instance discover local --scan # Deep scan all ports 1024-65535 # IMPORTANT: Always run with --dry-run first and ask for user consent before # running discover without it. The non-dry-run mode creates API tokens in # Astro Cloud, which is a sensitive action that requires explicit approval. # Override instance for a single command af --instance staging dags list ``` Config file: `~/.af/config.yaml` (override with `--config` or `AF_CONFIG` env var) Tokens in config can reference environment variables using `${VAR}` syntax: ```yaml instances: - name: prod url: https://airflow.example.com auth: token: ${AIRFLOW_API_TOKEN} ``` Or use environment variables directly (no config file needed): ```bash export AIRFLOW_API_URL=http://localhost:8080 export AIRFLOW_AUTH_TOKEN=your-token-here # Or username/password: export AIRFLOW_USERNAME=admin export AIRFLOW_PASSWORD=admin ``` Or CLI flags: `af --airflow-url http://localhost:8080 --token "$TOKEN" <command>` ## Quick Reference | Command | Description | |---------|-------------| | `af health` | System health check | | `af dags list` | List all DAGs | | `af dags get <dag_id>` | Get DAG details | | `af dags explore <dag_id>` | Full DAG investigation | | `af dags source <dag_id>` | Get DAG source code | | `af dags pause <dag_id>` | Pause DAG scheduling | | `af dags unpause <dag_id>` | Resume DAG scheduling | | `af dags errors` | List import errors | | `af dags warnings` | List DAG warnings | | `af dags stats` | DAG run statistics | | `af runs list` | List DAG runs | | `af runs get <dag_id> <run_id>` | Get run details | | `af runs trigger <dag_id>` | Trigger a DAG run | | `af runs trigger-wait <dag_id>` | Trigger and wait for completion | | `af runs delete <dag_id> <run_id>` | Permanently delete a DAG run | | `af runs clear <dag_id> <run_id>` | Clear a run for re-execution | | `af runs diagnose <dag_id> <run_id>` | Diagnose failed run | | `af tasks list <dag_id>` | List tasks in DAG | | `af tasks get <dag_id> <task_id>` | Get task definition | | `af tasks instance <dag_id> <run_id> <task_id>` | Get task instance | | `af tasks logs <dag_id> <run_id> <task_id>` | Get task logs | | `af config version` | Airflow version | | `af config show` | Full configuration | | `af config connections` | List connections | | `af config variables` | List variables | | `af config variable <key>` | Get specific variable | | `af config pools` | List pools | | `af config pool <name>` | Get pool details | | `af config plugins` | List plugins | | `af config providers` | List providers | | `af config assets` | List assets/datasets | | `af api <endpoint>` | Direct REST API access | | `af api ls` | List available API endpoints | | `af api ls --filter X` | List endpoints matching pattern | ## User Intent Patterns ### Getting Started - "How do I run Airflow locally?" / "Set up Airflow" -> use the **managing-astro-local-env** skill (uses Astro CLI) - "Create a new Airflow project" / "Initialize project" -> use the **setting-up-astro-project** skill (uses Astro CLI) - "How do I install Airflow?" / "Get started with Airflow" -> use the **setting-up-astro-project** skill ### DAG Operations - "What DAGs exist?" / "List all DAGs" -> `af dags list` - "Tell me about DAG X" / "What is DAG Y?" -> `af dags explore <dag_id>` - "What's the schedule for DAG X?" -> `af dags get <dag_id>` - "Show me the code for DAG X" -> `af dags source <dag_id>` - "Stop DAG X" / "Pause this workflow" -> `af dags pause <dag_id>` - "Resume DAG X" -> `af dags unpause <dag_id>` - "Are there any DAG errors?" -> `af dags errors` - "Create a new DAG" / "Write a pipeline" -> use the **authoring-dags** skill ### Run Operations - "What runs have executed?" -> `af runs list` - "Run DAG X" / "Trigger the pipeline" -> `af runs trigger <dag_id>` - "Run DAG X and wait" -> `af runs trigger-wait <dag_id>` - "Why did this run fail?" -> `af runs diagnose <dag_id> <run_id>` - "Delete this run" / "Remove stuck run" -> `af runs delete <dag_id> <run_id>` - "Clear this run" / "Retry this run" / "Re-run this" -> `af runs clear <dag_id> <run_id>` - "Test this DAG and fix if it fails" -> use the **testing-dags** skill ### Task Operations - "What tasks are in DAG X?" -> `af tasks list <dag_id>` - "Get task logs" / "Why did task fail?" -> `af tasks logs <dag_id> <run_id> <task_id>` - "Full root cause analysis" / "Diagnose and fix" -> use the **debugging-dags** skill ### Data Operations - "Is the data fresh?" / "When was this table last updated?" -> use the **checking-freshness** skill - "Where does this data come from?" -> use the **tracing-upstream-lineage** skill - "What depends on this table?" / "What breaks if I change this?" -> use the **tracing-downstream-lineage** skill ### Deployment Operations - "Deploy my DAGs" / "Push to production" -> use the **deploying-airflow** skill - "Set up CI/CD" / "Automate deploys" -> use the **deploying-airflow** skill - "Deploy to Kubernetes" / "Set up Helm" -> use the **deploying-airflow** skill - "astro deploy" / "DAG-only deploy" -> use the **deploying-airflow** skill ### System Operations - "What version of Airflow?" -> `af config version` - "What connections exist?" -> `af config connections` - "Are pools full?" -> `af config pools` - "Is Airflow healthy?" -> `af health` ### API Exploration - "What API endpoints are available?" -> `af api ls` - "Find variable endpoints" -> `af api ls --filter variable` - "Access XCom values" / "Get XCom" -> `af api xcom-entries -F dag_id=X -F task_id=Y` - "Get event logs" / "Audit trail" -> `af api event-logs -F dag_id=X` - "Create connection via API" -> `af api connections -X POST --body '{...}'` - "Create variable via API" -> `af api variables -X POST -F key=name -f value=val` ## Common Workflows ### Validate DAGs Before Deploying If you're using the Astro CLI, you can validate DAGs without a running Airflow instance: ```bash # Parse DAGs to catch import errors and syntax issues astro dev parse # Run unit tests astro dev pytest ``` Otherwise, validate against a running instance: ```bash af dags errors # Check for parse/import errors af dags warnings # Check for deprecation warnings ``` ### Investigate a Failed Run ```bash # 1. List recent runs to find failure af runs list --dag-id my_dag # 2. Diagnose the specific run af runs diagnose my_dag manual__2024-01-15T10:00:00+00:00 # 3. Get logs for failed task (from diagnose output) af tasks logs my_dag manual__2024-01-15T10:00:00+00:00 extract_data # 4. After fixing, clear the run to retry all tasks af runs clear my_dag manual__2024-01-15T10:00:00+00:00 ``` ### Morning Health Check ```bash # 1. Overall system health af health # 2. Check for broken DAGs af dags errors # 3. Check pool utilization af config pools ``` ### Understand a DAG ```bash # Get comprehensive overview (metadata + tasks + source) af dags explore my_dag ``` ### Check Why DAG Isn't Running ```bash # Check if paused af dags get my_dag # Check for import errors af dags errors # Check recent runs af runs list --dag-id my_dag ``` ### Trigger and Monitor ```bash # Option 1: Trigger and wait (blocking) af runs trigger-wait my_dag --timeout 1800 # Option 2: Trigger and check later af runs trigger my_dag af runs get my_dag <run_id> ``` ## Output Format All commands output JSON (except `instance` commands which use human-readable tables): ```bash af dags list # { # "total_dags": 5, # "returned_count": 5, # "dags": [...] # } ``` Use `jq` for filtering: ```bash # Find failed runs af runs list | jq '.dag_runs[] | select(.state == "failed")' # Get DAG IDs only af dags list | jq '.dags[].dag_id' # Find paused DAGs af dags list | jq '[.dags[] | select(.is_paused == true)]' ``` ## Task Logs Options ```bash # Get logs for specific retry attempt af tasks logs my_dag run_id task_id --try 2 # Get logs for mapped task index af tasks logs my_dag run_id task_id --map-index 5 ``` ## Direct API Access with `af api` Use `af api` for endpoints not covered by high-level commands (XCom, event-logs, backfills, etc). ```bash # Discover available endpoints af api ls af api ls --filter variable # Basic usage af api dags af api dags -F limit=10 -F only_active=true af api variables -X POST -F key=my_var -f value="my value" af api variables/old_var -X DELETE ``` **Field syntax**: `-F key=value` auto-converts types, `-f key=value` keeps as string. **Full reference**: See [api-reference.md](api-reference.md) for all options, common endpoints (XCom, event-logs, backfills), and examples. ## Related Skills | Skill | Use when... | |-------|-------------| | **authoring-dags** | Creating or editing DAG files with best practices | | **testing-dags** | Iterative test -> debug -> fix -> retest cycles | | **debugging-dags** | Deep root cause analysis and failure diagnosis | | **checking-freshness** | Checking if data is up to date or stale | | **tracing-upstream-lineage** | Finding where data comes from | | **tracing-downstream-lineage** | Impact analysis -- what breaks if something changes | | **deploying-airflow** | Deploying DAGs to production (Astro, Docker Compose, Kubernetes) | | **migrating-airflow-2-to-3** | Upgrading DAGs from Airflow 2.x to 3.x | | **managing-astro-local-env** | Starting, stopping, or troubleshooting local Airflow | | **setting-up-astro-project** | Initializing a new Astro/Airflow project |