Managed Airflow Migrations

Managed Service for Apache Airflow (formerly Cloud Composer) Migration Guide

This skill guides you through the process of adjusting Airflow DAGs from an existing Managed Service for Apache Airflow (formerly Cloud Composer) environment (or available locally) to make them compatible with Airflow 2.11.1 (MSAA Gen 2 or 3) or Airflow 3 (MSAA Gen 3).

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read Usage Attribution. For commands executed directly in the terminal (never in generated scripts, Terraform, or user code), prefix gcloud with CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:managed-airflow-migrations)" and append agent-skills/1.0 (skill:managed-airflow-migrations) to User-Agent headers.

Phase 1: Discovery & Download

Before making any changes, download the existing DAG files if explicitly requested. Inspect the source environment to confirm source version only if explicitly requested. For detailed instructions about environment inspection and downloading files check references/environment-inspection.md.

Phase 2: Target Version & Dependency Mapping

2.1 Airflow 2.11.1+ Dependency Mapping

If migrating to Airflow 2.11.1 (MSAA Gen 2) or Airflow 3, use the list below to trace the version progression of key dependencies. The list covers changes needed to get to Airflow 2.11.1. Take them into account when migrating from Airflow 2 (earlier than 2.11.1) to Airflow 3.

Composer 2.10.0 (Airflow 2.10.2)

• Google Provider: 10.26.0

• SSH Provider: 3.14.0

• HTTP Provider: 4.13.3

• Breaking Changes: Baseline for oldest fully documented source.

Composer 2.15.3 (Airflow 2.10.5)

• Google Provider: 18.0.0

• SSH Provider: 4.1.4

• HTTP Provider: 5.3.4

• Breaking Changes:

  • SSH Provider 4.0.0: Hook timeout removed; get_conn() context manager.

  • HTTP Provider 5.0.0: SimpleHttpOperator -> HttpOperator.

  • Google Provider 11.0.0: BigQueryExecuteQueryOperator removed.

  • Google Provider 12.0.0: Legacy Data Pipeline operators removed.

  • Google Provider 13.0.0: AutoMLBatchPredictOperator removed.

  • Google Provider 17.0.0: BigQueryCreateEmptyTableOperator and BigQueryCreateExternalTableOperator removed; Life Sciences operators removed.

  • Google Provider 18.0.0: Legacy DV360 operators removed.

Composer 2.16.1 (Airflow 2.10.5)

• Google Provider: 19.0.0

• SSH Provider: 4.1.6

• HTTP Provider: 5.5.0

• Breaking Changes: Google Provider 19.0.0: AutoML operators removed (use Vertex AI).

Composer 2.17.0 (Target Airflow 2.11.1)

• Google Provider: 20.0.0

• SSH Provider: 5.0.0

• HTTP Provider: 6.0.2

• Breaking Changes:

  • SSH Provider 5.0.0: sshtunnel removed (native tunneling).

  • HTTP Provider 6.0.0: JSON serialization.

  • Google Provider 20.0.0: ADLS Gen2 migration.

2.2 Airflow 3 Migration

If migrating to Airflow 3 (MSAA Gen 3), note that this is a major version upgrade with significant changes, including:

• Decoupled Task SDK (imports change from airflow to airflow.sdk).

• Removal of direct metadata DB access.

• Renaming of Dataset to Asset.

• Removal of SubDAGs and SLAs.

• Changes to context variables availability.

Take into account all applicable changes within Airflow 2 (e.g. when migrating from Airflow 2.10.2, apply changes needed to move to Airflow 2.11.1 and Airflow 3 migration changes on top of that).

Phase 3: Analysis & Remediation (Scanning Downloaded Files)

Run the scan commands from the root of your local workspace (./migration_workspace unless indicated otherwise).

3.1 Airflow 2.11.1 Core & Dependency checks

Use these scans if migrating to Airflow 2.11.1+ (intermediate step when migrating to Airflow 3).

3.1.1 Dataset Scheduling (Airflow 2.11.0)

• Change: DAGs scheduled on datasets only trigger if events occur while the DAG is unpaused.

• Scan Command: grep -rn "Dataset(" ./dags

• Remediation: You MUST document that these DAGs must remain unpaused to catch events, or plan manual triggers for catch-up.

3.1.2 HTML in Descriptions (Airflow 2.11.0)

• Change: Raw HTML in DAG docs / params is escaped by default.

• Scan Command:

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  grep -rn -E "doc_md.*<|doc_md.*>|description.*<|description.*>" ./dags

• Remediation: Convert HTML to Markdown, or set AIRFLOW__WEBSERVER__ALLOW_RAW_HTML_DESCRIPTIONS=True in target.

3.1.3 Teardown Tasks (Airflow 2.10.5)

• Change: Teardowns always run when a DAG is marked failed.

• Scan Command: grep -rn "as_teardown" ./dags

• Remediation: Ensure teardown tasks are idempotent.

3.1.4 Pendulum 3 Upgrade (Airflow 2.11.0)

• Change: Period renamed to Interval, testing helpers removed.

• Scan Command (Code):

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  grep -rn -E "pendulum\.Period|pendulum\.period" ./dags

• Scan Command (Tests):

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  grep -rn -E "\.test\(|set_test_now\(" ./tests 2>/dev/null || true

• Remediation: Replace Period with Interval, and period(...) with interval(...).

3.2 Path A: Airflow 2.11.1 Provider Package Scan

3.2.1 SSH Provider (SSH 4.0.0 & 5.0.0)

• Scan Command (Timeout): grep -rn "SSHHook" ./dags | grep "timeout"

• Scan Command (Context Manager): grep -rn "with SSHHook" ./dags

• Scan Command (Tunnel Attributes): grep -rn "\.get_tunnel" ./dags

• Remediation:

  • Replace timeout with conn_timeout in SSHHook.

  • Replace with hook as conn: with with hook.get_conn() as conn:.

  • Use get_tunnel() as context manager: with hook.get_tunnel(...) as tunnel:.

3.2.2 HTTP Provider (HTTP 5.0.0 & 6.0.0)

• Scan Command: grep -rn "SimpleHttpOperator" ./dags

• Remediation: Replace SimpleHttpOperator with HttpOperator.

3.2.3 Google Provider (v11 to v20)

• Scan Command (BigQuery query):

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  grep -rn "BigQueryExecuteQueryOperator" ./dags

  • Remediation: Replace with BigQueryInsertJobOperator (use configuration dict).

• Scan Command (BigQuery table):

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  grep -rn -E "BigQueryCreateEmptyTableOperator|BigQueryCreateExternalTableOperator" ./dags

  • Remediation: Replace with BigQueryCreateTableOperator (use table_resource dict).

• Scan Command (AutoML):

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  grep -rn -E "AutoMLTrainModelOperator|AutoMLPredictOperator|AutoMLCreateDatasetOperator|AutoMLBatchPredictOperator" ./dags

  • Remediation: Migrate to Vertex AI operators.

• Scan Command (Dataflow):

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  grep -rn -E "CreateDataPipelineOperator|RunDataPipelineOperator" ./dags

  • Remediation: Replace with DataflowCreatePipelineOperator/DataflowRunPipelineOperator.

• Scan Command (Life Sciences):

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  grep -rn "LifeSciencesRunPipelineOperator" ./dags`

  • Remediation: Migrate to Google Cloud Batch operators (BatchCreateJobOperator).

• Scan Command (ADLS to GCS): grep -rn "ADLSToGCSOperator" ./dags

  • Remediation: Ensure file_system_name is provided.

3.3 Airflow 3 Migration checks

Use instructions from references/airflow-3.md when migrating to Airflow 3.

Phase 4: Deployment & Verification

Perform deployment and verification steps only if explicitly requested to do so.

4.1 Static Verification (when migrating to Airflow 3)

After applying code changes for Airflow 3, verify syntax correctness. If available in the development environment, run static lint checks:

ruff check {target_dag_file} --select AIR30

Resolve any reported deprecation warnings before finalization. If ruff is not available, recommend installing one.

4.2 Deployment to MSAA

4.2.1 Get Target GCS Bucket Path (only when requested)

gcloud composer environments describe <TARGET_ENV> \
    --location <TARGET_REGION> \
    --format="value(config.dagGcsPrefix)"

Expected Output: gs://<target-bucket-name>/dags

4.2 Upload Modified DAGs and Bucket Dependencies (Only when requested)

Perform this step only if explicitly requested to do so. Copy the modified DAGs and any backed-up bucket dependencies from your local workspace to the target GCS bucket. If you skipped the inspection step, ensure you have the correct <target-bucket-name>.

1. Upload DAGs:

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   gcloud storage cp -r ./dags/* gs://<target-bucket-name>/dags/

2. Upload Other Bucket Dependencies (If applicable):

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   gcloud storage cp -r ./migration_workspace/<dependency-folder> gs://<target-bucket-name>/<dependency-folder>

4.3 Verify DAGs via Airflow CLI

Perform this step only if explicitly requested to upload modified DAGS to a target environment (and after uploading).

You can verify that your DAGs have been successfully uploaded, parsed, and registered by the Airflow scheduler in the target environment using the Airflow CLI.

1. List Registered DAGs: Run the following command to list all DAGs registered in the target environment. Verify that your migrated DAGs appear in this list.

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   gcloud composer environments run <TARGET_ENV> \
       --location <TARGET_REGION> \
       dags list

2. Check for Import Errors: If some DAGs are missing from the list, or to ensure there are no parsing issues, check for import errors:

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   gcloud composer environments run <TARGET_ENV> \
       --location <TARGET_REGION> \
       dags list-import-errors

   Expected Output:

   • If there are no errors, the command will output No data found.

   • If there are errors, it will list the file path and the traceback of the error.

Note: It may take a couple of minutes for the Airflow scheduler to parse the new files and for changes to reflect in these commands.

4.4 Verify in Cloud Logging

Perform this step only if explicitly requested to upload modified DAGS to a target environment (and after uploading). Monitor Cloud Logging for the target environment to detect any runtime errors or import errors.

Run the following query in the GCP Cloud Logging Console (or via gcloud logging read):

resource.type="cloud_composer_environment"
resource.labels.environment_name="<TARGET_ENV>"
log_id("airflow-scheduler")
severity>=ERROR

Appendix: Local Environment Verification

If you want to verify your changes locally before deploying to the target environment, you can use the Composer Local Development CLI tool (composer-dev). Use references/local-development-environment.md as a reference for interactions with local development environments.