airflow-xtended-api 0.1.6

Exposes 'Xtended' Apache Airflow management capabilities via secure API

Airflow Xtended API - Plugin

Apache Airflow plugin that exposes xtended secure API endpoints similar to the official Airflow API (Stable) (1.0.0), providing richer capabilities to support more powerful DAG and job management. Apache Airflow version 2.8.0 or higher is necessary.

Requirements

• apache-airflow
• pymongo
• boto3
• requests

Installation

python3 -m pip install airflow-xtended-api

Build from source

Build a custom version of this plugin by following the instructions in this doc

Screenshots

Authentication

Airflow Xtended API plugin uses the same auth mechanism as Airflow API (Stable) (1.0.0). So, by default APIs exposed via this plugin respect the auth mechanism used by your Airflow webserver and also complies with the existing RBAC policies. Note that you will need to pass credentials data as part of the request. Here is a snippet from the official docs when basic authorization is used:

curl -X POST 'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/dags/{dag_id}?update_mask=is_paused' \
-H 'Content-Type: application/json' \
--user "username:password" \
-d '{
    "is_paused": true
}'

Using the Custom API

After installing the plugin python package and restarting your airflow webserver, You can see a link under the 'Xtended API' tab called 'Reference Docs' on the airflow webserver homepage. All the necessary documentation for the supported API endpoints resides on that page. You can also directly navigate to that page using below link.

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/xtended_api/

All the supported endpoints are exposed in the below format:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/{ENDPOINT_NAME}

Following are the names of endpoints which are currently supported.

• deploy_dag
• create_dag
• s3_sync
• mongo_sync
• scan_dags
• purge_dags
• refresh_all_dags
• delete_dag
• upload_file
• restart_failed_task
• kill_running_tasks
• run_task_instance
• skip_task_instance

deploy_dag

Description:

• Deploy a new DAG File to the DAGs directory.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/deploy_dag

Method:

• POST

POST request Arguments:

• dag_file - file - Upload & Deploy a DAG from .py or .zip files
• force (optional) - boolean - Force uploading the file if it already exists
• unpause (optional) - boolean - The DAG will be unpaused on creation (Works only when uploading .py files)
• otf_sync (optional) - boolean - Check for newly created DAGs On The Fly!

Examples:

curl -X POST -H 'Content-Type: multipart/form-data' \
 --user "username:password" \
 -F 'dag_file=@test_dag.py' \
 -F 'force=y' \
 -F 'unpause=y' \
 http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/deploy_dag

response:

{
  "message": "DAG File [<module '{MODULE_NAME}' from '/{DAG_FOLDER}/exam.py'>] has been uploaded",
  "status": "success"
}

Method:

• GET

Get request Arguments:

• dag_file_url - file - A valid url for fetching .py, .pyc or .zip DAG files
• filename - string - A valid filename ending with .py, .pyc or .zip
• force (optional) - boolean - Force uploading the file if it already exists.
• unpause (optional) - boolean - The DAG will be unpaused on creation (Works only when uploading .py files)
• otf_sync (optional) - boolean - Check for newly created DAGs On The Fly!

Examples:

curl -X GET --user "username:password" \
'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/deploy_dag?dag_file_url={DAG_FILE_URL}&filename=test_dag.py&force=on&unpause=on'

response:

{
  "message": "DAG File [<module '{MODULE_NAME}' from '/{DAG_FOLDER}/exam.py'>] has been uploaded",
  "status": "success"
}

create_dag

Description:

• Create a new DAG File in the DAGs directory.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/create_dag

Method:

• POST

POST request Arguments:

• filename - string - Name of the python DAG file
• dag_code - string(multiline) - Python code of the DAG file
• force (optional) - boolean - Force uploading the file if it already exists
• unpause (optional) - boolean - The DAG will be unpaused on creation (Works only when uploading .py files)
• otf_sync (optional) - boolean - Check for newly created DAGs On The Fly!

s3_sync

Description:

• Sync DAG files from an S3 bucket.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/s3_sync

Method:

• POST

POST request Arguments:

• s3_bucket_name - string - S3 bucket name where DAG files exist
• s3_region - string - S3 region name where the specified bucket exists
• s3_access_key - string - IAM access key having atleast S3 bucket read access
• s3_secret_key - string - IAM secret key for the specifed access key
• s3_object_prefix (optional) - string - Filter results by object prefix
• s3_object_keys (optional) - string - Sync DAG files specifed by the object keys. Multiple object keys are seperated by comma (,)
• skip_purge (optional) - boolean - Skip emptying DAGs directory
• otf_sync (optional) - boolean - Check for newly created DAGs On The Fly!

Examples:

curl -X POST -H 'Content-Type: multipart/form-data' \
 --user "username:password" \
 -F 's3_bucket_name=test-bucket' \
 -F 's3_region=us-east-1' \
 -F 's3_access_key={IAM_ACCESS_KEY}' \
 -F 's3_secret_key={IAM_SECRET_KEY}' \
 -F 'skip_purge=y' \
 http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/s3_sync

response:

{
  "message": "dag files synced from s3",
  "sync_status": {
    "synced": ["test_dag0.py", "test_dag1.py", "test_dag2.py"],
    "failed": []
  },
  "status": "success"
}

mongo_sync

Description:

• Sync DAG files from a mongo db collection

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/mongo_sync

Method:

• POST

POST request Arguments:

• connection_string - string - Source mongo server connection string
• db_name - string - Source mongo database name
• collection_name - string - Collection name where DAG data exists in the specified db
• field_filename - string - DAGs are named using value of this document field from the specified collection
• field_dag_source - string - A document field referring the Python source for the yet-to-be created DAGs
• query_filter (optional) - string - JSON query string to filter required documents
• skip_purge (optional) - boolean - Skip emptying DAGs directory
• otf_sync (optional) - boolean - Check for newly created DAGs On The Fly!

Examples:

curl -X POST -H 'Content-Type: multipart/form-data' \
 --user "username:password" \
 -F 'connection_string={MONGO_SERVER_CONNECTION_STRING}' \
 -F 'db_name=test_db' \
 -F 'collection_name=test_collection' \
 -F 'field_dag_source=dag_source' \
 -F 'field_filename=dag_filename' \
 -F 'skip_purge=y' \
 http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/mongo_sync

response:

{
  "message": "dag files synced from mongo",
  "sync_status": {
    "synced": ["test_dag0.py", "test_dag1.py", "test_dag2.py"],
    "failed": []
  },
  "status": "success"
}

refresh_all_dags

Description:

• Refresh all DAGs in the webserver.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/refresh_all_dags

Method:

• GET

GET request Arguments:

• None

Examples:

curl -X GET --user "username:password" \
http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/refresh_all_dags

response:

{
  "message": "All DAGs are now up-to-date!!",
  "status": "success"
}

scan_dags

Description:

• Check for newly created DAGs.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/scan_dags

Method:

• GET

GET request Arguments:

• otf_sync (optional) - boolean - Check for newly created DAGs On The Fly!

Examples:

curl -X GET --user "username:password" \
http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/scan_dags

response:

{
  "message": "Ondemand DAG scan complete!!",
  "status": "success"
}

purge_dags

Description:

• Empty DAG directory.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/purge_dags

Method:

• GET

GET request Arguments:

• None

Examples:

curl -X GET --user "username:password" \
http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/purge_dags

response:

{
  "message": "DAG directory purged!!",
  "status": "success"
}

delete_dag

Description:

• Delete a DAG in the web server from Airflow database and filesystem.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/delete_dag

Method:

• GET

GET request Arguments:

• dag_id (optional)- string - DAG id
• filename (optional) - string - Name of the DAG file that needs to be deleted
• Note: Atleast one of args 'dag_id' or 'filename' should be specified

Examples:

curl -X GET --user "username:password" \
'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/delete_dag?dag_id=test_dag&filename=test_dag.py'

response:

{
  "message": "DAG [dag_test] deleted",
  "status": "success"
}

upload_file

Description:

• Upload a new File to the specified directory.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/upload_file

Method:

• POST

POST request Arguments:

• file - file - File to be uploaded
• force (optional) - boolean - Force uploading the file if it already exists
• path (optional) - string - Location where the file is to be uploaded (Default is the DAGs directory)

Examples:

curl -X POST -H 'Content-Type: multipart/form-data' \
 --user "username:password" \
 -F 'file=@test_file.py' \
 -F 'force=y' \
 http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/upload_file

response:

{
  "message": "File [/{DAG_FOLDER}/dag_test.txt] has been uploaded",
  "status": "success"
}

restart_failed_task

Description:

• Restart failed tasks with downstream.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/restart_failed_task

Method:

• GET

GET request Arguments:

• dag_id - string - DAG id
• run_id - string - DagRun id

Examples:

curl -X GET --user "username:password" \
'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/restart_failed_task?dag_id=test_dag&run_id=test_run'

response:

{
  "message": {
    "failed_task_count": 1,
    "clear_task_count": 7
  },
  "status": "success"
}

kill_running_tasks

Description:

• Kill running tasks having status in ['none', 'running'].

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/kill_running_tasks

Method:

• GET

GET request Arguments:

• dag_id - string - DAG id
• run_id - string - DagRun id
• task_id - string - If task_id is none, kill all tasks, else kill the specified task.

Examples:

curl -X GET --user "username:password" \
'http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/kill_running_tasks?dag_id=test_dag&run_id=test_run&task_id=test_task'

response:

{
  "message": "tasks in test_run killed!!",
  "status": "success"
}

run_task_instance

Description:

• Create DagRun, run the specified tasks, and skip the rest.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/run_task_instance

Method:

• POST

POST request Arguments:

• dag_id - string - DAG id
• run_id - string - DagRun id
• tasks - string - task id(s), Multiple tasks are separated by comma (,)
• conf (optional)- string - Optional configuartion for creating DagRun.

Examples:

curl -X POST -H 'Content-Type: multipart/form-data' \
 --user "username:password" \
 -F 'dag_id=test_dag' \
 -F 'run_id=test_run' \
 -F 'tasks=test_task' \
 http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/run_task_instance

response:

{
  "execution_date": "2021-06-21T05:50:19.740803+0000",
  "status": "success"
}

skip_task_instance

Description:

• Skip one task instance.

Endpoint:

http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/skip_task_instance

Method:

• GET

GET request Arguments:

• dag_id - string - DAG id
• run_id - string - DagRun id
• task_id - string - task id

Examples:

curl -X GET http://{AIRFLOW_HOST}:{AIRFLOW_PORT}/api/v1/xtended/skip_task_instance?dag_id=test_dag&run_id=test_run&task_id=test_task

response:

{
  "message": "<TaskInstance: test_dag.test_task 2021-06-21 19:59:34.638794+00:00 [skipped]> skipped!!",
  "status": "success"
}

Acknowledgements

Huge shout out to these awesome plugins that contributed to the growth of Airflow ecosystem, which also inspired this plugin.

• airflow-rest-api-plugin
• airflow-rest-api-plugin
• simple-dag-editor
• airflow-code-editor