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).
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:
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):
grep -rn -E "pendulum\.Period|pendulum\.period" ./dags
-
Scan Command (Tests):
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):
grep -rn "BigQueryExecuteQueryOperator" ./dags
- Remediation: Replace with
BigQueryInsertJobOperator (use
configuration dict).
-
Scan Command (BigQuery table):
grep -rn -E "BigQueryCreateEmptyTableOperator|BigQueryCreateExternalTableOperator" ./dags
- Remediation: Replace with
BigQueryCreateTableOperator (use
table_resource dict).
-
Scan Command (AutoML):
grep -rn -E "AutoMLTrainModelOperator|AutoMLPredictOperator|AutoMLCreateDatasetOperator|AutoMLBatchPredictOperator" ./dags
- Remediation: Migrate to Vertex AI operators.
-
Scan Command (Dataflow):
grep -rn -E "CreateDataPipelineOperator|RunDataPipelineOperator" ./dags
- Remediation: Replace with
DataflowCreatePipelineOperator/DataflowRunPipelineOperator.
-
Scan Command (Life Sciences):
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>.
-
Upload DAGs:
gcloud storage cp -r ./dags/* gs://<target-bucket-name>/dags/
-
Upload Other Bucket Dependencies (If applicable):
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.
-
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.
gcloud composer environments run <TARGET_ENV> \
--location <TARGET_REGION> \
dags list
-
Check for Import Errors: If some DAGs are missing from the list, or to
ensure there are no parsing issues, check for import errors:
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.