There are certain things with all the tools you use that you won’t know even after using it for a long time. And once you know it you are like “I wish I knew this before” as you had already told your client that it can’t be done in any better way 🤦🤦. Airflow like other tool is no different, there are some hidden gems that can make your life easy and make DAG development fun.

You might already know some of them and if you know them all — well you are a PRO then🕴🎩.

(1) DAG with context Manager

Were you annoyed with yourself when you forgot to add dag=dag to your task and Airflow error’ed? Yes, it is easy to forget adding it for each task. It is also redundant to add the same parameter as shown in the following example ( example_dag.py file):

The example ( example_dag.py file) above just has 2 tasks, but if you have 10 or more then the redundancy becomes more evident. To avoid this you can use Airflow DAGs as context managers to automatically assign new operators to that DAG as shown in the above example ( example_dag_with_context.py ) using with statement.

(2) Using List to set Task dependencies

When you want to create the DAG similar to the one shown in the image below, you would have to repeat task names when setting task dependencies.

As shown in the above code snippet, using our normal way of setting task dependencies would mean that task_two and end are repeated 3 times. This can be replaced using python lists to achieve the same result in a more elegant way.

(3) Use default arguments to avoid repeating arguments

Airflow allows passing a dictionary of parameters that would be available to all the task in that DAG.

For example, at DataReply, we use BigQuery for all our DataWareshouse related DAGs and instead of passing parameters like labels , bigquery_conn_id to each task, we simply pass it in default_args dictionary as shown in the DAG below.

This is also useful when you want alerts on individual task failures instead of just DAG failures which I already mentioned in my last blog post on Integrating Slack Alerts in Airflow.

(4) The “params” argument

“params” is a dictionary of DAG level parameters that are made accessible in templates. These params can be overridden at the task level.

This is an extremely helpful argument and I have been personally using it a lot as it can be accessed in templated field with jinja templating using params.param_name . An example usage is as follows:

It makes it easy for you to write parameterized DAG instead of hard-coding values. Also as shown in the examples above params dictionary can be defined at 3 places: (1) In DAG object (2) In default_args dictionary (3) Each task.

(5) Storing Sensitive data in Connections

Most users are aware of this but I have still seen passwords stored in plain-text inside the DAG. For goodness sake — don’t do that. You should write your DAGs in a way that you are confident enough to store your DAGs in a public repository.

By default, Airflow will save the passwords for the connection in plain text within the metadata database. The crypto package is highly recommended during Airflow installation and can be simply done by pip install apache-airflow[crypto] .

You can then easily access it as follows:

from airflow.hooks.base_hook import BaseHook

slack_token = BaseHook.get_connection('slack').password

(6) Restrict the number of Airflow variables in your DAG

Airflow Variables are stored in Metadata Database, so any call to variables would mean a connection to Metadata DB. Your DAG files are parsed every X seconds. Using a large number of variable in your DAG (and worse in default_args ) may mean you might end up saturating the number of allowed connections to your database.

To avoid this situation, you can either just use a single Airflow variable with JSON value. As an Airflow variable can contain JSON value, you can store all your DAG configuration inside a single variable as shown in the image below:

As shown in this screenshot you can either store values in separate Airflow variables or under a single Airflow variable as a JSON field

You can then access them as shown below under Recommended way:

(7) The “context” dictionary

Users often forget the contents of the context dictionary when using PythonOperator with a callable function.

The context contains references to related objects to the task instance and is documented under the macros section of the API as they are also available to templated field.

{

'dag': task.dag,

'ds': ds,

'next_ds': next_ds,

'next_ds_nodash': next_ds_nodash,

'prev_ds': prev_ds,

'prev_ds_nodash': prev_ds_nodash,

'ds_nodash': ds_nodash,

'ts': ts,

'ts_nodash': ts_nodash,

'ts_nodash_with_tz': ts_nodash_with_tz,

'yesterday_ds': yesterday_ds,

'yesterday_ds_nodash': yesterday_ds_nodash,

'tomorrow_ds': tomorrow_ds,

'tomorrow_ds_nodash': tomorrow_ds_nodash,

'END_DATE': ds,

'end_date': ds,

'dag_run': dag_run,

'run_id': run_id,

'execution_date': self.execution_date,

'prev_execution_date': prev_execution_date,

'next_execution_date': next_execution_date,

'latest_date': ds,

'macros': macros,

'params': params,

'tables': tables,

'task': task,

'task_instance': self,

'ti': self,

'task_instance_key_str': ti_key_str,

'conf': configuration,

'test_mode': self.test_mode,

'var': {

'value': VariableAccessor(),

'json': VariableJsonAccessor()

},

'inlets': task.inlets,

'outlets': task.outlets,

}

(8) Generating Dynamic Airflow Tasks

I have been answering many questions on StackOverflow on how to create dynamic tasks. The answer is simple, you just need to generate unique task_id for all of your tasks. Below are 2 examples on how to achieve that:

(9) Run “airflow upgradedb” instead of “airflow initdb”

Thanks to Ash Berlin for this tip in his talk in the First Apache Airflow London Meetup.