Variable Airflow

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In the last entry on airflow(which was many moons ago!) we had created a simple DAG and executed it. It was simple to get us started. However, it did not allow us any flexibility. We could not change the behaviour of the DAG. In the real world scenarios, we may need to change the behaviour of our workflow based on certain parameters. This is accomplished by Airflow Variables

Airflow Variables are simple key-value pairs which are stored in the database which holds the airflow metadata. These variables can be created & managed via the airflow UI or airflow CLI.

Airflow WebUI -> Admin -> Variables

Some of the features of Airflow variables are below

  • Can be defined as a simple key-value pair
  • One variable can hold a list of key-value pairs as well!
  • Stored in airflow database which holds the metadata
  • Can be used in the Airflow DAG code as jinja variables.

Define a single key-value variable

Airflow variables can be created using three ways

  1. Using Airflow Web UI
  2. Airflow CLI
  3. File Import

Using Airflow Web UI

To define a variable to hold a single value is done in the following steps

Step 1 – Navigate to Airflow UI->Admin->Variables

Step 2 – Press Create

Step 3 – Define the variable

Press Save to create the variable.

Airflow CLI

Variables can also be created via Airflow CLI. Use the following command to create variable via CLI

The variable is then also visible in the Airflow Web UI

The CLI allows all creation, deletion and import of variables and is quite easy. Here is the documentation link

Usage

Variables can be quite easily be used in the code as jinja variables. Below is an example of a DAG which uses one of the variables we have defined earlier – v_name

You can access a variable using the following API

var.value.<variable name>

Airflow Variable Example

For example var.value.v_name. See Below

# Filename: hello_world_variables.py
from airflow import DAG
from airflow.operators.bash_operatorimport BashOperator
fromdatetimeimportdatetime, timedelta
default_args ={
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2019,7,13),
'email': ['[email protected]'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
}
dag = DAG('hello_world_variables',
schedule_interval='0 0 * * *',
default_args=default_args
)
# Use of variable in the command
create_command ='echo {{var.value.v_name}}'
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag
)
t2 = BashOperator(
task_id='echo_my_variable',
bash_command=create_command,
dag=dag
)
t2.set_upstream(t1)

Let’s execute the DAG and see the output

Once the DAG is executed you can see the logs using the following steps

Click on the DAG

Click on Graph View and then echo_my_variable

Click on View Log

The log of the task is shown below with the output of our command highlighted

Define a variable – list of key-value pairs

If your DAG uses more than one variable then(depending upon the use case) they can be stored as a list of key-value pairs in an airflow variable. The variables can be created via UI and CLI or imported as well.

Airflow UI

Navigate to Airflow Web UI ->Admin -> Variables and create a variable called another_var. Observe how the key-value pairs are added as a JSON. See Below

Save and you have a variable which is a list of key-value pairs

Airflow CLI

From the command line, the variables with a list of key-value pairs can be created using the following command

airflow variables --set my_new_var1 '{ 'v_command': 'ls /var/log', 'v_some_var': 'HELLO FROM CLI' }'

The variable is now also available in the Airflow Web UI.

Usage

Let’s now see how the variables with a list of key-value pairs are used in the code. The DAG below uses a key-value pair from the variable another_var. See Below

# Filename: hello_world_variables_2.py
from airflow import DAG
from airflow.operators.bash_operatorimport BashOperator
fromdatetimeimportdatetime, timedelta
default_args ={
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2019,7,19),
'email': ['[email protected]'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
}
dag = DAG('hello_world_variables_2',
schedule_interval='0 0 * * *',
default_args=default_args
)
create_command ='{{var.json.another_var.v_command}}'
t1 = BashOperator(
task_id='print_date',
bash_command='date',
dag=dag
)
t2 = BashOperator(
task_id='run_my_command',
bash_command=create_command,
dag=dag
)
t2.set_upstream(t1)

Once the DAG is executed you can see the logs using the following steps

Click on DAG

Click on Graph View and then run_my_variable

Click on Log

The log is shown below

This brings us to the end of this post. I hope you found it useful. Next up how we can do branching. Till next time…byeeeeee

2011-04-12
This paper describes rationale for determining the apportionment of variable or ‘shuttered’ airflow and non-variable or static airflow through openings in the front of a vehicle as needed for vehicle cooling. Variable airflow can be achieved by means of a shutter system, which throttles airflow through the front end and into the Condenser, Radiator, and Fan Module, (CRFM). Shutters originated early in the history of the auto industry and acted as a thermostat [1]. They controlled airflow as opposed to coolant flow through the radiator. Two benefits that are realized today are aerodynamic and thermal gains, achieved by restricting unneeded cooling airflow. Other benefits exist and justify the use of shutters; however, there are also difficulties in both execution and practical use. This paper will focus on optimizing system performance and execution in terms of the two benefits of reduced aerodynamic drag and reduced mechanical drag through thermal control. For a device(s) to receive credit for improved drag and fuel economy, performance in the EPA Coast Down test must be improved. If the device(s) are open in the Coast Down test, then the benefit will not be reflected in the fuel economy label. If the device(s) are over-utilized to exploit possible gains on the Coast Down, then the resulting label value will be better than the real world gain. In other words, to have an optimized system, the measured fuel economy value must be derived in a manner consistent with typical consumer use. To that end, this paper will explore the determination of, as well as the difference between every day use and severe use, and how to best provide the consumer the means of using only the fuel required to cool the vehicle, given the operating conditions.
DOI: https://doi.org/10.4271/2011-01-1340
Citation: Charnesky, S., Fadler, G., and Lockwood, T., 'Variable and Fixed Airflow for Vehicle Cooling,' SAE Int. J. Mater. Manuf. 4(1):1286-1296, 2011, https://doi.org/10.4271/2011-01-1340.
Download Citation
Author(s): Scott Charnesky, Gregory Fadler, Thomas Lockwood
Pages: 11
ISSN: 1946-3979
Also in: SAE International Journal of Materials and Manufacturing-V120-5, SAE International Journal of Materials and Manufacturing-V120-5EJ
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