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Apache Airflow Python Client

Overview

To facilitate management, Apache Airflow supports a range of REST API endpoints across its objects. This section provides an overview of the API design, methods, and supported use cases.

Most of the endpoints accept JSON as input and return JSON responses. This means that you must usually add the following headers to your request:

Content-type: application/json
Accept: application/json

Resources

The term resource refers to a single type of object in the Airflow metadata. An API is broken up by its endpoint's corresponding resource. The name of a resource is typically plural and expressed in camelCase. Example: dagRuns.

Resource names are used as part of endpoint URLs, as well as in API parameters and responses.

CRUD Operations

The platform supports Create, Read, Update, and Delete operations on most resources. You can review the standards for these operations and their standard parameters below.

Some endpoints have special behavior as exceptions.

Create

To create a resource, you typically submit an HTTP POST request with the resource's required metadata in the request body. The response returns a 201 Created response code upon success with the resource's metadata, including its internal id, in the response body.

Read

The HTTP GET request can be used to read a resource or to list a number of resources.

A resource's id can be submitted in the request parameters to read a specific resource. The response usually returns a 200 OK response code upon success, with the resource's metadata in the response body.

If a GET request does not include a specific resource id, it is treated as a list request. The response usually returns a 200 OK response code upon success, with an object containing a list of resources' metadata in the response body.

When reading resources, some common query parameters are usually available. e.g.:

v1/connections?limit=25&offset=25
Query Parameter Type Description
limit integer Maximum number of objects to fetch. Usually 25 by default
offset integer Offset after which to start returning objects. For use with limit query parameter.

Update

Updating a resource requires the resource id, and is typically done using an HTTP PATCH request, with the fields to modify in the request body. The response usually returns a 200 OK response code upon success, with information about the modified resource in the response body.

Delete

Deleting a resource requires the resource id and is typically executing via an HTTP DELETE request. The response usually returns a 204 No Content response code upon success.

Conventions

  • Resource names are plural and expressed in camelCase.

  • Names are consistent between URL parameter name and field name.

  • Field names are in snake_case.

{
    \"name\": \"string\",
    \"slots\": 0,
    \"occupied_slots\": 0,
    \"used_slots\": 0,
    \"queued_slots\": 0,
    \"open_slots\": 0
}

Update Mask

Update mask is available as a query parameter in patch endpoints. It is used to notify the API which fields you want to update. Using update_mask makes it easier to update objects by helping the server know which fields to update in an object instead of updating all fields. The update request ignores any fields that aren't specified in the field mask, leaving them with their current values.

Example:

import requests

resource = requests.get("/resource/my-id").json()
resource["my_field"] = "new-value"
requests.patch("/resource/my-id?update_mask=my_field", data=json.dumps(resource))

Versioning and Endpoint Lifecycle

  • API versioning is not synchronized to specific releases of the Apache Airflow.
  • APIs are designed to be backward compatible.
  • Any changes to the API will first go through a deprecation phase.

Trying the API

You can use a third party client, such as curl, HTTPie, Postman or the Insomnia rest client to test the Apache Airflow API.

Note that you will need to pass credentials data.

For e.g., here is how to pause a DAG with curl, when basic authorization is used:

curl -X PATCH 'https://example.com/api/v1/dags/{dag_id}?update_mask=is_paused' \\
-H 'Content-Type: application/json' \\
--user \"username:password\" \\
-d '{
    \"is_paused\": true
}'

Using a graphical tool such as Postman or Insomnia, it is possible to import the API specifications directly:

  1. Download the API specification by clicking the Download button at top of this document.
  2. Import the JSON specification in the graphical tool of your choice.
  • In Postman, you can click the import button at the top
  • With Insomnia, you can just drag-and-drop the file on the UI

Note that with Postman, you can also generate code snippets by selecting a request and clicking on the Code button.

Enabling CORS

Cross-origin resource sharing (CORS) is a browser security feature that restricts HTTP requests that are initiated from scripts running in the browser.

For details on enabling/configuring CORS, see Enabling CORS.

Authentication

To be able to meet the requirements of many organizations, Airflow supports many authentication methods, and it is even possible to add your own method.

If you want to check which auth backend is currently set, you can use airflow config get-value api auth_backends command as in the example below.

$ airflow config get-value api auth_backends
airflow.api.auth.backend.basic_auth

The default is to deny all requests.

For details on configuring the authentication, see API Authorization.

Errors

We follow the error response format proposed in RFC 7807 also known as Problem Details for HTTP APIs. As with our normal API responses, your client must be prepared to gracefully handle additional members of the response.

Unauthenticated

This indicates that the request has not been applied because it lacks valid authentication credentials for the target resource. Please check that you have valid credentials.

PermissionDenied

This response means that the server understood the request but refuses to authorize it because it lacks sufficient rights to the resource. It happens when you do not have the necessary permission to execute the action you performed. You need to get the appropriate permissions in other to resolve this error.

BadRequest

This response means that the server cannot or will not process the request due to something that is perceived to be a client error (e.g., malformed request syntax, invalid request message framing, or deceptive request routing). To resolve this, please ensure that your syntax is correct.

NotFound

This client error response indicates that the server cannot find the requested resource.

MethodNotAllowed

Indicates that the request method is known by the server but is not supported by the target resource.

NotAcceptable

The target resource does not have a current representation that would be acceptable to the user agent, according to the proactive negotiation header fields received in the request, and the server is unwilling to supply a default representation.

AlreadyExists

The request could not be completed due to a conflict with the current state of the target resource, e.g. the resource it tries to create already exists.

Unknown

This means that the server encountered an unexpected condition that prevented it from fulfilling the request.

This Python package is automatically generated by the OpenAPI Generator project:

  • API version: 2.9.0
  • Package version: 2.9.0
  • Build package: org.openapitools.codegen.languages.PythonClientCodegen

For more information, please visit https://airflow.apache.org

Requirements.

Python >=3.8

Installation & Usage

pip install

You can install the client using standard Python installation tools. It is hosted in PyPI with apache-airflow-client package id so the easiest way to get the latest version is to run:

pip install apache-airflow-client

If the python package is hosted on a repository, you can install directly using:

pip install git+https://github.com/apache/airflow-client-python.git

Import check

Then import the package:

import airflow_client.client

Getting Started

Please follow the installation procedure and then run the following:

import time
from airflow_client import client
from pprint import pprint
from airflow_client.client.api import config_api
from airflow_client.client.model.config import Config
from airflow_client.client.model.error import Error

# Defining the host is optional and defaults to /api/v1
# See configuration.py for a list of all supported configuration parameters.
configuration = client.Configuration(host="/api/v1")

# The client must configure the authentication and authorization parameters
# in accordance with the API server security policy.
# Examples for each auth method are provided below, use the example that
# satisfies your auth use case.

# Configure HTTP basic authorization: Basic
configuration = client.Configuration(username="YOUR_USERNAME", password="YOUR_PASSWORD")


# Enter a context with an instance of the API client
with client.ApiClient(configuration) as api_client:
    # Create an instance of the API class
    api_instance = config_api.ConfigApi(api_client)

    try:
        # Get current configuration
        api_response = api_instance.get_config()
        pprint(api_response)
    except client.ApiException as e:
        print("Exception when calling ConfigApi->get_config: %s\n" % e)

Documentation for API Endpoints

All URIs are relative to /api/v1

Class Method HTTP request Description
ConfigApi get_config GET /config Get current configuration
ConnectionApi delete_connection DELETE /connections/{connection_id} Delete a connection
ConnectionApi get_connection GET /connections/{connection_id} Get a connection
ConnectionApi get_connections GET /connections List connections
ConnectionApi patch_connection PATCH /connections/{connection_id} Update a connection
ConnectionApi post_connection POST /connections Create a connection
ConnectionApi test_connection POST /connections/test Test a connection
DAGApi delete_dag DELETE /dags/{dag_id} Delete a DAG
DAGApi get_dag GET /dags/{dag_id} Get basic information about a DAG
DAGApi get_dag_details GET /dags/{dag_id}/details Get a simplified representation of DAG
DAGApi get_dag_source GET /dagSources/{file_token} Get a source code
DAGApi get_dags GET /dags List DAGs
DAGApi get_task GET /dags/{dag_id}/tasks/{task_id} Get simplified representation of a task
DAGApi get_tasks GET /dags/{dag_id}/tasks Get tasks for DAG
DAGApi patch_dag PATCH /dags/{dag_id} Update a DAG
DAGApi patch_dags PATCH /dags Update DAGs
DAGApi post_clear_task_instances POST /dags/{dag_id}/clearTaskInstances Clear a set of task instances
DAGApi post_set_task_instances_state POST /dags/{dag_id}/updateTaskInstancesState Set a state of task instances
DAGRunApi clear_dag_run POST /dags/{dag_id}/dagRuns/{dag_run_id}/clear Clear a DAG run
DAGRunApi delete_dag_run DELETE /dags/{dag_id}/dagRuns/{dag_run_id} Delete a DAG run
DAGRunApi get_dag_run GET /dags/{dag_id}/dagRuns/{dag_run_id} Get a DAG run
DAGRunApi get_dag_runs GET /dags/{dag_id}/dagRuns List DAG runs
DAGRunApi get_dag_runs_batch POST /dags/~/dagRuns/list List DAG runs (batch)
DAGRunApi get_upstream_dataset_events GET /dags/{dag_id}/dagRuns/{dag_run_id}/upstreamDatasetEvents Get dataset events for a DAG run
DAGRunApi post_dag_run POST /dags/{dag_id}/dagRuns Trigger a new DAG run
DAGRunApi set_dag_run_note PATCH /dags/{dag_id}/dagRuns/{dag_run_id}/setNote Update the DagRun note.
DAGRunApi update_dag_run_state PATCH /dags/{dag_id}/dagRuns/{dag_run_id} Modify a DAG run
DagWarningApi get_dag_warnings GET /dagWarnings List dag warnings
DatasetApi get_dataset GET /datasets/{uri} Get a dataset
DatasetApi get_dataset_events GET /datasets/events Get dataset events
DatasetApi get_datasets GET /datasets List datasets
DatasetApi get_upstream_dataset_events GET /dags/{dag_id}/dagRuns/{dag_run_id}/upstreamDatasetEvents Get dataset events for a DAG run
EventLogApi get_event_log GET /eventLogs/{event_log_id} Get a log entry
EventLogApi get_event_logs GET /eventLogs List log entries
ImportErrorApi get_import_error GET /importErrors/{import_error_id} Get an import error
ImportErrorApi get_import_errors GET /importErrors List import errors
MonitoringApi get_health GET /health Get instance status
MonitoringApi get_version GET /version Get version information
PermissionApi get_permissions GET /permissions List permissions
PluginApi get_plugins GET /plugins Get a list of loaded plugins
PoolApi delete_pool DELETE /pools/{pool_name} Delete a pool
PoolApi get_pool GET /pools/{pool_name} Get a pool
PoolApi get_pools GET /pools List pools
PoolApi patch_pool PATCH /pools/{pool_name} Update a pool
PoolApi post_pool POST /pools Create a pool
ProviderApi get_providers GET /providers List providers
RoleApi delete_role DELETE /roles/{role_name} Delete a role
RoleApi get_role GET /roles/{role_name} Get a role
RoleApi get_roles GET /roles List roles
RoleApi patch_role PATCH /roles/{role_name} Update a role
RoleApi post_role POST /roles Create a role
TaskInstanceApi get_extra_links GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/links List extra links
TaskInstanceApi get_log GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/logs/{task_try_number} Get logs
TaskInstanceApi get_mapped_task_instance GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/{map_index} Get a mapped task instance
TaskInstanceApi get_mapped_task_instances GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/listMapped List mapped task instances
TaskInstanceApi get_task_instance GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id} Get a task instance
TaskInstanceApi get_task_instances GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances List task instances
TaskInstanceApi get_task_instances_batch POST /dags//dagRuns//taskInstances/list List task instances (batch)
TaskInstanceApi patch_mapped_task_instance PATCH /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/{map_index} Updates the state of a mapped task instance
TaskInstanceApi patch_task_instance PATCH /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id} Updates the state of a task instance
TaskInstanceApi set_mapped_task_instance_note PATCH /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/{map_index}/setNote Update the TaskInstance note.
TaskInstanceApi set_task_instance_note PATCH /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/setNote Update the TaskInstance note.
UserApi delete_user DELETE /users/{username} Delete a user
UserApi get_user GET /users/{username} Get a user
UserApi get_users GET /users List users
UserApi patch_user PATCH /users/{username} Update a user
UserApi post_user POST /users Create a user
VariableApi delete_variable DELETE /variables/{variable_key} Delete a variable
VariableApi get_variable GET /variables/{variable_key} Get a variable
VariableApi get_variables GET /variables List variables
VariableApi patch_variable PATCH /variables/{variable_key} Update a variable
VariableApi post_variables POST /variables Create a variable
XComApi get_xcom_entries GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries List XCom entries
XComApi get_xcom_entry GET /dags/{dag_id}/dagRuns/{dag_run_id}/taskInstances/{task_id}/xcomEntries/{xcom_key} Get an XCom entry

Documentation For Models

Documentation For Authorization

By default the generated client supports the three authentication schemes:

  • Basic
  • GoogleOpenID
  • Kerberos

However, you can generate client and documentation with your own schemes by adding your own schemes in the security section of the OpenAPI specification. You can do it with Breeze CLI by adding the --security-schemes option to the breeze release-management prepare-python-client command.

Basic "smoke" tests

You can run basic smoke tests to check if the client is working properly - we have a simple test script that uses the API to run the tests. To do that, you need to:

  • install the apache-airflow-client package as described above
  • install rich Python package
  • download the test_python_client.py file
  • make sure you have test airflow installation running. Do not experiment with your production deployment
  • configure your airflow webserver to enable basic authentication In the [api] section of your airflow.cfg set:
[api]
auth_backend = airflow.api.auth.backend.session,airflow.api.auth.backend.basic_auth

You can also set it by env variable: export AIRFLOW__API__AUTH_BACKENDS=airflow.api.auth.backend.session,airflow.api.auth.backend.basic_auth

  • configure your airflow webserver to load example dags In the [core] section of your airflow.cfg set:
[core]
load_examples = True

You can also set it by env variable: export AIRFLOW__CORE__LOAD_EXAMPLES=True

  • optionally expose configuration (NOTE! that this is dangerous setting). The script will happily run with the default setting, but if you want to see the configuration, you need to expose it. In the [webserver] section of your airflow.cfg set:
[webserver]
expose_config = True

You can also set it by env variable: export AIRFLOW__WEBSERVER__EXPOSE_CONFIG=True

  • Configure your host/ip/user/password in the test_python_client.py file
import airflow_client

# Configure HTTP basic authorization: Basic
configuration = airflow_client.client.Configuration(
    host="http://localhost:8080/api/v1", username="admin", password="admin"
)

  • Run scheduler (or dag file processor you have setup with standalone dag file processor) for few parsing loops (you can pass --num-runs parameter to it or keep it running in the background). The script relies on example DAGs being serialized to the DB and this only happens when scheduler runs with core/load_examples set to True.

  • Run webserver - reachable at the host/port for the test script you want to run. Make sure it had enough time to initialize.

Run python test_python_client.py and you should see colored output showing attempts to connect and status.

Notes for Large OpenAPI documents

If the OpenAPI document is large, imports in client.apis and client.models may fail with a RecursionError indicating the maximum recursion limit has been exceeded. In that case, there are a couple of solutions:

Solution 1: Use specific imports for apis and models like:

  • from airflow_client.client.api.default_api import DefaultApi
  • from airflow_client.client.model.pet import Pet

Solution 2: Before importing the package, adjust the maximum recursion limit as shown below:

import sys

sys.setrecursionlimit(1500)
import airflow_client.client
from airflow_client.client.apis import *
from airflow_client.client.models import *

Authors

[email protected]