Tutorial on the TaskFlow API¶
This tutorial builds on the regular Airflow Tutorial and focuses specifically on writing data pipelines using the TaskFlow API paradigm which is introduced as part of Airflow 2.0 and contrasts this with DAGs written using the traditional paradigm.
The data pipeline chosen here is a simple ETL pattern with three separate tasks for Extract, Transform, and Load.
Example “TaskFlow API” ETL Pipeline¶
Here is a very simple ETL pipeline using the TaskFlow API paradigm. A more detailed explanation is given below.
import json
import pendulum
from airflow.decorators import dag, task
@dag(
schedule_interval=None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=['example'],
)
def tutorial_taskflow_api_etl():
"""
### TaskFlow API Tutorial Documentation
This is a simple ETL data pipeline example which demonstrates the use of
the TaskFlow API using three simple tasks for Extract, Transform, and Load.
Documentation that goes along with the Airflow TaskFlow API tutorial is
located
[here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html)
"""
@task()
def extract():
"""
#### Extract task
A simple Extract task to get data ready for the rest of the data
pipeline. In this case, getting data is simulated by reading from a
hardcoded JSON string.
"""
data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
order_data_dict = json.loads(data_string)
return order_data_dict
@task(multiple_outputs=True)
def transform(order_data_dict: dict):
"""
#### Transform task
A simple Transform task which takes in the collection of order data and
computes the total order value.
"""
total_order_value = 0
for value in order_data_dict.values():
total_order_value += value
return {"total_order_value": total_order_value}
@task()
def load(total_order_value: float):
"""
#### Load task
A simple Load task which takes in the result of the Transform task and
instead of saving it to end user review, just prints it out.
"""
print(f"Total order value is: {total_order_value:.2f}")
order_data = extract()
order_summary = transform(order_data)
load(order_summary["total_order_value"])
tutorial_etl_dag = tutorial_taskflow_api_etl()
It’s a DAG definition file¶
If this is the first DAG file you are looking at, please note that this Python script is interpreted by Airflow and is a configuration file for your data pipeline. For a complete introduction to DAG files, please look at the core Airflow tutorial which covers DAG structure and definitions extensively.
Instantiate a DAG¶
We are creating a DAG which is the collection of our tasks with dependencies between
the tasks. This is a very simple definition, since we just want the DAG to be run
when we set this up with Airflow, without any retries or complex scheduling.
In this example, please notice that we are creating this DAG using the @dag decorator
as shown below, with the Python function name acting as the DAG identifier.
@dag(
schedule_interval=None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=['example'],
)
def tutorial_taskflow_api_etl():
"""
### TaskFlow API Tutorial Documentation
This is a simple ETL data pipeline example which demonstrates the use of
the TaskFlow API using three simple tasks for Extract, Transform, and Load.
Documentation that goes along with the Airflow TaskFlow API tutorial is
located
[here](https://airflow.apache.org/docs/apache-airflow/stable/tutorial_taskflow_api.html)
"""
Tasks¶
In this data pipeline, tasks are created based on Python functions using the @task decorator
as shown below. The function name acts as a unique identifier for the task.
@task()
def extract():
"""
#### Extract task
A simple Extract task to get data ready for the rest of the data
pipeline. In this case, getting data is simulated by reading from a
hardcoded JSON string.
"""
data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
order_data_dict = json.loads(data_string)
return order_data_dict
The returned value, which in this case is a dictionary, will be made available for use in later tasks.
The Transform and Load tasks are created in the same manner as the Extract task shown above.
Main flow of the DAG¶
Now that we have the Extract, Transform, and Load tasks defined based on the Python functions, we can move to the main part of the DAG.
order_data = extract()
order_summary = transform(order_data)
load(order_summary["total_order_value"])
That’s it, we are done! We have invoked the Extract task, obtained the order data from there and sent it over to the Transform task for summarization, and then invoked the Load task with the summarized data. The dependencies between the tasks and the passing of data between these tasks which could be running on different workers on different nodes on the network is all handled by Airflow.
Now to actually enable this to be run as a DAG, we invoke the Python function
tutorial_taskflow_api_etl set up using the @dag decorator earlier, as shown below.
tutorial_etl_dag = tutorial_taskflow_api_etl()
But how?¶
For experienced Airflow DAG authors, this is startlingly simple! Let’s contrast this with how this DAG had to be written before Airflow 2.0 below:
import json
from textwrap import dedent
import pendulum
# The DAG object; we'll need this to instantiate a DAG
from airflow import DAG
# Operators; we need this to operate!
from airflow.operators.python import PythonOperator
with DAG(
'tutorial_etl_dag',
# These args will get passed on to each operator
# You can override them on a per-task basis during operator initialization
default_args={'retries': 2},
description='ETL DAG tutorial',
schedule_interval=None,
start_date=pendulum.datetime(2021, 1, 1, tz="UTC"),
catchup=False,
tags=['example'],
) as dag:
dag.doc_md = __doc__
def extract(**kwargs):
ti = kwargs['ti']
data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
ti.xcom_push('order_data', data_string)
def transform(**kwargs):
ti = kwargs['ti']
extract_data_string = ti.xcom_pull(task_ids='extract', key='order_data')
order_data = json.loads(extract_data_string)
total_order_value = 0
for value in order_data.values():
total_order_value += value
total_value = {"total_order_value": total_order_value}
total_value_json_string = json.dumps(total_value)
ti.xcom_push('total_order_value', total_value_json_string)
def load(**kwargs):
ti = kwargs['ti']
total_value_string = ti.xcom_pull(task_ids='transform', key='total_order_value')
total_order_value = json.loads(total_value_string)
print(total_order_value)
extract_task = PythonOperator(
task_id='extract',
python_callable=extract,
)
extract_task.doc_md = dedent(
"""\
#### Extract task
A simple Extract task to get data ready for the rest of the data pipeline.
In this case, getting data is simulated by reading from a hardcoded JSON string.
This data is then put into xcom, so that it can be processed by the next task.
"""
)
transform_task = PythonOperator(
task_id='transform',
python_callable=transform,
)
transform_task.doc_md = dedent(
"""\
#### Transform task
A simple Transform task which takes in the collection of order data from xcom
and computes the total order value.
This computed value is then put into xcom, so that it can be processed by the next task.
"""
)
load_task = PythonOperator(
task_id='load',
python_callable=load,
)
load_task.doc_md = dedent(
"""\
#### Load task
A simple Load task which takes in the result of the Transform task, by reading it
from xcom and instead of saving it to end user review, just prints it out.
"""
)
extract_task >> transform_task >> load_task
All of the processing shown above is being done in the new Airflow 2.0 dag as well, but it is all abstracted from the DAG developer.
Let’s examine this in detail by looking at the Transform task in isolation since it is in the middle of the data pipeline. In Airflow 1.x, this task is defined as shown below:
def transform(**kwargs):
ti = kwargs['ti']
extract_data_string = ti.xcom_pull(task_ids='extract', key='order_data')
order_data = json.loads(extract_data_string)
total_order_value = 0
for value in order_data.values():
total_order_value += value
total_value = {"total_order_value": total_order_value}
total_value_json_string = json.dumps(total_value)
ti.xcom_push('total_order_value', total_value_json_string)
As we see here, the data being processed in the Transform function is passed to it using XCom variables. In turn, the summarized data from the Transform function is also placed into another XCom variable which will then be used by the Load task.
Contrasting that with TaskFlow API in Airflow 2.0 as shown below.
@task(multiple_outputs=True)
def transform(order_data_dict: dict):
"""
#### Transform task
A simple Transform task which takes in the collection of order data and
computes the total order value.
"""
total_order_value = 0
for value in order_data_dict.values():
total_order_value += value
return {"total_order_value": total_order_value}
All of the XCom usage for data passing between these tasks is abstracted away from the DAG author in Airflow 2.0. However, XCom variables are used behind the scenes and can be viewed using the Airflow UI as necessary for debugging or DAG monitoring.
Similarly, task dependencies are automatically generated within TaskFlows based on the functional invocation of tasks. In Airflow 1.x, tasks had to be explicitly created and dependencies specified as shown below.
extract_task = PythonOperator(
task_id='extract',
python_callable=extract,
)
extract_task.doc_md = dedent(
"""\
#### Extract task
A simple Extract task to get data ready for the rest of the data pipeline.
In this case, getting data is simulated by reading from a hardcoded JSON string.
This data is then put into xcom, so that it can be processed by the next task.
"""
)
transform_task = PythonOperator(
task_id='transform',
python_callable=transform,
)
transform_task.doc_md = dedent(
"""\
#### Transform task
A simple Transform task which takes in the collection of order data from xcom
and computes the total order value.
This computed value is then put into xcom, so that it can be processed by the next task.
"""
)
load_task = PythonOperator(
task_id='load',
python_callable=load,
)
load_task.doc_md = dedent(
"""\
#### Load task
A simple Load task which takes in the result of the Transform task, by reading it
from xcom and instead of saving it to end user review, just prints it out.
"""
)
extract_task >> transform_task >> load_task
In contrast, with the TaskFlow API in Airflow 2.0, the invocation itself automatically generates the dependencies as shown below.
order_data = extract()
order_summary = transform(order_data)
load(order_summary["total_order_value"])
Reusing a decorated task¶
Decorated tasks are flexible. You can reuse a decorated task in multiple DAGs, overriding the task
parameters such as the task_id, queue, pool, etc.
Below is an example of how you can reuse a decorated task in multiple DAGs:
from airflow.decorators import task, dag
from datetime import datetime
@task
def add_task(x, y):
print(f"Task args: x={x}, y={y}")
return x + y
@dag(start_date=datetime(2022, 1, 1))
def mydag():
start = add_task.override(task_id="start")(1, 2)
for i in range(3):
start >> add_task.override(task_id=f"add_start_{i}")(start, i)
@dag(start_date=datetime(2022, 1, 1))
def mydag2():
start = add_task(1, 2)
for i in range(3):
start >> add_task.override(task_id=f"new_add_task_{i}")(start, i)
first_dag = mydag()
second_dag = mydag2()
You can also import the above add_task and use it in another DAG file.
Suppose the add_task code lives in a file called common.py. You can do this:
from common import add_task
from airflow.decorators import dag
from datetime import datetime
@dag(start_date=datetime(2022, 1, 1))
def use_add_task():
start = add_task.override(priority_weight=3)(1, 2)
for i in range(3):
start >> add_task.override(task_id=f"new_add_task_{i}", retries=4)(start, i)
created_dag = use_add_task()
Using the TaskFlow API with Docker or Virtual Environments¶
If you have tasks that require complex or conflicting requirements then you will have the ability to use the TaskFlow API with either a Docker container (since version 2.2.0) or Python virtual environment (since 2.0.2). This added functionality will allow a much more comprehensive range of use-cases for the TaskFlow API, as you will not be limited to the packages and system libraries of the Airflow worker.
To use a docker image with the TaskFlow API, change the decorator to @task.docker
and add any needed arguments to correctly run the task. Please note that the docker
image must have a working Python installed and take in a bash command as the command argument.
Below is an example of using the @task.docker decorator to run a Python task.
@task.docker(image='python:3.9-slim-bullseye', multiple_outputs=True)
def transform(order_data_dict: dict):
"""
#### Transform task
A simple Transform task which takes in the collection of order data and
computes the total order value.
"""
total_order_value = 0
for value in order_data_dict.values():
total_order_value += value
return {"total_order_value": total_order_value}
It is worth noting that the Python source code (extracted from the decorated function) and any callable args are sent to the container via (encoded and pickled) environment variables so the length of these is not boundless (the exact limit depends on system settings).
Note
Using @task.docker decorator in one of the earlier Airflow versions
Since @task.docker decorator is available in the docker provider, you might be tempted to use it in
Airflow version before 2.2, but this is not going to work. You will get this error if you try:
AttributeError: '_TaskDecorator' object has no attribute 'docker'
You should upgrade to Airflow 2.2 or above in order to use it.
If you don’t want to run your image on a Docker environment, and instead want to create a separate virtual
environment on the same machine, you can use the @task.virtualenv decorator instead. The @task.virtualenv
decorator will allow you to create a new virtualenv with custom libraries and even a different
Python version to run your function.
@task.virtualenv(
use_dill=True,
system_site_packages=False,
requirements=['funcsigs'],
)
def extract():
"""
#### Extract task
A simple Extract task to get data ready for the rest of the data
pipeline. In this case, getting data is simulated by reading from a
hardcoded JSON string.
"""
import json
data_string = '{"1001": 301.27, "1002": 433.21, "1003": 502.22}'
order_data_dict = json.loads(data_string)
return order_data_dict
These two options should allow for far greater flexibility for users who wish to keep their workflows more simple and Pythonic.
Multiple outputs inference¶
Tasks can also infer multiple outputs by using dict Python typing.
@task
def identity_dict(x: int, y: int) -> Dict[str, int]:
return {"x": x, "y": y}
By using the typing Dict for the function return type, the multiple_outputs parameter
is automatically set to true.
Note, If you manually set the multiple_outputs parameter the inference is disabled and
the parameter value is used.
Adding dependencies between decorated and traditional tasks¶
The above tutorial shows how to create dependencies between TaskFlow functions. However, dependencies can also
be set between traditional tasks (such as BashOperator
or FileSensor) and TaskFlow functions.
Building this dependency is shown in the code below:
@task()
def extract_from_file():
"""
#### Extract from file task
A simple Extract task to get data ready for the rest of the data
pipeline, by reading the data from a file into a pandas dataframe
"""
order_data_file = "/tmp/order_data.csv"
order_data_df = pd.read_csv(order_data_file)
file_task = FileSensor(task_id="check_file", filepath="/tmp/order_data.csv")
order_data = extract_from_file()
file_task >> order_data
In the above code block, a new TaskFlow function is defined as extract_from_file which
reads the data from a known file location.
In the main DAG, a new FileSensor task is defined to check for this file. Please note
that this is a Sensor task which waits for the file.
Finally, a dependency between this Sensor task and the TaskFlow function is specified.
Consuming XComs between decorated and traditional tasks¶
As noted above, the TaskFlow API allows XComs to be consumed or passed between tasks in a manner that is abstracted away from the DAG author. This section dives further into detailed examples of how this is possible not only between TaskFlow functions but between both TaskFlow functions and traditional tasks.
You may find it necessary to consume an XCom from traditional tasks, either pushed within the task’s execution
or via its return value, as an input into downstream tasks. You can access the pushed XCom (also known as an
XComArg) by utilizing the .output property exposed for all operators.
By default, using the .output property to retrieve an XCom result is the equivalent of:
task_instance.xcom_pull(task_ids="my_task_id", key="return_value")
To retrieve an XCom result for a key other than return_value, you can use:
my_op = MyOperator(...)
my_op_output = my_op.output["some_other_xcom_key"]
# OR
my_op_output = my_op.output.get("some_other_xcom_key")
Note
Using the .output property as an input to another task is supported only for operator parameters
listed as a template_field.
In the code example below, a SimpleHttpOperator result
is captured via XComs. This XCom result, which is the task output, is then passed
to a TaskFlow function which parses the response as JSON.
get_api_results_task = SimpleHttpOperator(
task_id="get_api_results",
endpoint="/api/query",
do_xcom_push=True,
http_conn_id="http",
)
@task
def parse_results(api_results):
return json.loads(api_results)
parsed_results = parse_results(api_results=get_api_results_task.output)
The reverse can also be done: passing the output of a TaskFlow function as an input to a traditional task.
@task(retries=3)
def create_queue():
"""This is a Python function that creates an SQS queue"""
hook = SqsHook()
result = hook.create_queue(queue_name="sample-queue")
return result["QueueUrl"]
sqs_queue = create_queue()
publish_to_queue = SqsPublishOperator(
task_id="publish_to_queue",
sqs_queue=sqs_queue,
message_content="{{ task_instance }}-{{ execution_date }}",
message_attributes=None,
delay_seconds=0,
)
Take note in the code example above, the output from the create_queue TaskFlow function, the URL of a
newly-created Amazon SQS Queue, is then passed to a SqsPublishOperator
task as the sqs_queue arg.
Finally, not only can you use traditional operator outputs as inputs for TaskFlow functions, but also as inputs to
other traditional operators. In the example below, the output from the SalesforceToS3Operator
task (which is an S3 URI for a destination file location) is used an input for the S3CopyObjectOperator
task to copy the same file to a date-partitioned storage location in S3 for long-term storage in a data lake.
BASE_PATH = "salesforce/customers"
FILE_NAME = "customer_daily_extract_{{ ds_nodash }}.csv"
upload_salesforce_data_to_s3_landing = SalesforceToS3Operator(
task_id="upload_salesforce_data_to_s3",
salesforce_query="SELECT Id, Name, Company, Phone, Email, LastModifiedDate, IsActive FROM Customers",
s3_bucket_name="landing-bucket",
s3_key=f"{BASE_PATH}/{FILE_NAME}",
salesforce_conn_id="salesforce",
aws_conn_id="s3",
replace=True,
)
store_to_s3_data_lake = S3CopyObjectOperator(
task_id="store_to_s3_data_lake",
aws_conn_id="s3",
source_bucket_key=upload_salesforce_data_to_s3_landing.output,
dest_bucket_name="data_lake",
dest_bucket_key=f"""{BASE_PATH}/{"{{ execution_date.strftime('%Y/%m/%d') }}"}/{FILE_NAME}""",
)
Accessing context variables in decorated tasks¶
When running your callable, Airflow will pass a set of keyword arguments that can be used in your
function. This set of kwargs correspond exactly to what you can use in your Jinja templates.
For this to work, you need to define **kwargs in your function header, or you can add directly the
keyword arguments you would like to get - for example with the below code your callable will get
the values of ti and next_ds context variables. Note that when explicit keyword arguments are used,
they must be made optional in the function header to avoid TypeError exceptions during DAG parsing as
these values are not available until task execution.
With explicit arguments:
@task
def my_python_callable(ti=None, next_ds=None):
pass
With kwargs:
@task
def my_python_callable(**kwargs):
ti = kwargs["ti"]
next_ds = kwargs["next_ds"]
Also, sometimes you might want to access the context somewhere deep in the stack, but you do not want to pass
the context variables from the task callable. You can still access execution context via the get_current_context
method.
from airflow.operators.python import get_current_context
def some_function_in_your_library():
context = get_current_context()
ti = context["ti"]
Current context is accessible only during the task execution. The context is not accessible during
pre_execute or post_execute. Calling this method outside execution context will raise an error.
What’s Next?¶
You have seen how simple it is to write DAGs using the TaskFlow API paradigm within Airflow 2.0. Please do
read the Concepts section for detailed explanation of Airflow concepts such as DAGs, Tasks,
Operators, and more. There’s also a whole section on the TaskFlow API and the @task decorator.