Running airflow tasks/dags in parallel

Running airflow tasks/dags in parallel

Apache Airflow allows you to run tasks in parallel by utilizing its task parallelism feature. Task parallelism means running multiple tasks concurrently, which can improve the overall execution time of your workflows. Here's how you can achieve parallelism in Apache Airflow:

  1. Configure Concurrency:

    In your Airflow configuration file (airflow.cfg), you can configure the maximum number of task instances that can run concurrently across all your tasks. Look for the parallelism and dag_concurrency settings. Adjust these settings according to your available resources and the level of parallelism you want to achieve.

  2. Use Multiple Operators:

    In your DAG definition, you can use multiple operators to define tasks that can run concurrently. Airflow allows you to define tasks with different dependencies and run them in parallel.

    For example:

    from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

def task_1():
    print("Task 1 executed.")

def task_2():
    print("Task 2 executed.")

def task_3():
    print("Task 3 executed.")

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
}

dag = DAG('parallel_example', default_args=default_args, schedule_interval=None)

t1 = PythonOperator(task_id='task_1', python_callable=task_1, dag=dag)
t2 = PythonOperator(task_id='task_2', python_callable=task_2, dag=dag)
t3 = PythonOperator(task_id='task_3', python_callable=task_3, dag=dag)

t1 >> [t2, t3]

    In the example above, task_2 and task_3 are defined as parallel tasks because they have the same level of indentation and are connected with the >> operator.

  3. Use TaskGroups (Airflow 2.0+):

    In Airflow 2.0 and later versions, you can use TaskGroup to define and manage parallel tasks more cleanly. TaskGroup provides better visualization and organization of parallel tasks in the DAG graph.

    from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.utils.task_group import TaskGroup
from datetime import datetime

def task_1():
    print("Task 1 executed.")

def task_2():
    print("Task 2 executed.")

def task_3():
    print("Task 3 executed.")

default_args = {
    'owner': 'airflow',
    'start_date': datetime(2023, 1, 1),
}

dag = DAG('parallel_example', default_args=default_args, schedule_interval=None)

with TaskGroup("parallel_tasks") as parallel_tasks:
    t1 = PythonOperator(task_id='task_1', python_callable=task_1, dag=dag)
    t2 = PythonOperator(task_id='task_2', python_callable=task_2, dag=dag)
    t3 = PythonOperator(task_id='task_3', python_callable=task_3, dag=dag)

parallel_tasks

    Using TaskGroup, tasks within the group are automatically parallelized.

  4. Use SubDAGs:

    For more complex parallelism scenarios, you can use SubDAGs to encapsulate a group of tasks within a separate DAG.

Remember that while you can define tasks to run in parallel, the level of actual parallelism achieved depends on the resources available in your Airflow environment (like the number of worker nodes) and the configuration settings you've defined.

Keep in mind that running tasks in parallel might require careful consideration of resource utilization, dependencies, and any potential race conditions or contention issues. Always test and monitor your workflows to ensure they're behaving as expected.

Examples

  1. "How to run Airflow tasks in parallel?"

    • Description: This query explores the concept of running Airflow tasks in parallel, which increases throughput and efficiency.
    • Code:
      from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago

# Define a simple DAG with parallel tasks
with DAG("example_parallel_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    task1 = DummyOperator(task_id="task1")
    task2 = DummyOperator(task_id="task2")
    task3 = DummyOperator(task_id="task3")

    # Run tasks 1, 2, and 3 in parallel
    [task1, task2, task3]

  2. "Running Airflow DAGs in parallel?"

    • Description: This query explores running multiple DAGs in parallel in Airflow.
    • Explanation: Running DAGs in parallel requires proper configuration of Airflow's Executor and other settings to ensure parallel execution.
    • Code:
      from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago

# Define two DAGs to run in parallel
with DAG("parallel_dag_1", start_date=days_ago(1), schedule_interval="@daily") as dag1:
    task1 = DummyOperator(task_id="task1")

with DAG("parallel_dag_2", start_date=days_ago(1), schedule_interval="@daily") as dag2:
    task2 = DummyOperator(task_id="task2")
  3. Configuration:
    # airflow.cfg configuration to support parallelism
[core]
parallelism = 32  # Number of tasks to run in parallel
dag_concurrency = 16  # Number of DAG runs in parallel
  4. Code:
    from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago

# Create a DAG that uses a pool with 3 slots
with DAG("pooled_parallel_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    task1 = DummyOperator(task_id="task1", pool="my_pool")
    task2 = DummyOperator(task_id="task2", pool="my_pool")
    task3 = DummyOperator(task_id="task3", pool="my_pool")

    [task1, task2, task3]

  5. "Running Airflow tasks in parallel with SubDAGs?"

    • Description: This query explores using SubDAGs to run tasks in parallel within a parent DAG.
    • Code:
      from airflow import DAG
from airflow.operators.subdag import SubDagOperator
from airflow.utils.dates import days_ago
from airflow.operators.dummy import DummyOperator

def create_subdag(parent_dag_name, child_dag_name, start_date):
    with DAG(f"{parent_dag_name}.{child_dag_name}", start_date=start_date, schedule_interval="@daily") as dag:
        task1 = DummyOperator(task_id="task1")
        task2 = DummyOperator(task_id="task2")
        [task1, task2]
    return dag

# Define a parent DAG with a SubDAG
with DAG("parent_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    subdag = SubDagOperator(
        task_id="subdag",
        subdag=create_subdag("parent_dag", "subdag_1", days_ago(1))
    )
    end = DummyOperator(task_id="end")
    
    subdag >> end

  6. "How to run Airflow tasks in parallel with TriggerRules?"

    • Description: This query discusses using TriggerRules to control when tasks run in parallel.
    • Code:
      from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.trigger_rule import TriggerRule

# Define a DAG with tasks running in parallel based on trigger rules
with DAG("trigger_rule_parallel_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    start = DummyOperator(task_id="start")
    task1 = DummyOperator(task_id="task1")
    task2 = DummyOperator(task_id="task2")
    task3 = DummyOperator(task_id="task3")
    end = DummyOperator(task_id="end", trigger_rule=TriggerRule.ALL_DONE)
    
    start >> [task1, task2, task3] >> end

  7. "Running Airflow tasks in parallel with different start times?"

    • Description: This query explores running tasks in parallel with staggered start times.
    • Code:
      from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago, timedelta

# Define a DAG with tasks starting at different times
with DAG("staggered_parallel_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    task1 = DummyOperator(task_id="task1")
    task2 = DummyOperator(task_id="task2", start_date=dag.start_date + timedelta(minutes=10))
    task3 = DummyOperator(task_id="task3", start_date=dag.start_date + timedelta(minutes=20))
    
    [task1, task2, task3]

  8. "Using Airflow XComs to communicate between parallel tasks?"

    • Description: This query discusses using XComs to share data between parallel tasks in Airflow.
    • Code:
      from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.operators.python import PythonOperator
from airflow.utils.dates import days_ago

def push_data(**context):
    context['ti'].xcom_push(key='key', value='value')

def pull_data(**context):
    value = context['ti'].xcom_pull(key='key')
    print("Pulled data:", value)

# Define a DAG with XCom data sharing between parallel tasks
with DAG("xcom_parallel_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    push_task = PythonOperator(task_id="push_data", python_callable=push_data)
    pull_task = PythonOperator(task_id="pull_data", python_callable=pull_data)
    
    push_task >> pull_task
  9. Configuration:
    # airflow.cfg configuration for managing parallelism
[core]
dag_concurrency = 8  # Control number of concurrent DAG runs
[pools]
default_pool_slots = 8  # Limit the number of parallel tasks

  10. "Running parallel tasks in Airflow with Task Groups?"

    • Description: This query discusses using Task Groups to organize and run parallel tasks in Airflow.
    • Code:
      from airflow import DAG
from airflow.operators.dummy import DummyOperator
from airflow.utils.dates import days_ago
from airflow.utils.task_group import TaskGroup

# Define a DAG with parallel tasks using Task Groups
with DAG("task_group_parallel_dag", start_date=days_ago(1), schedule_interval="@daily") as dag:
    start = DummyOperator(task_id="start")
    end = DummyOperator(task_id="end")
    
    with TaskGroup("group1") as group1:
        task1 = DummyOperator(task_id="task1")
        task2 = DummyOperator(task_id="task2")
    
    with TaskGroup("group2") as group2:
        task3 = DummyOperator(task_id="task3")
        task4 = DummyOperator(task_id="task4")
    
    start >> [group1, group2] >> end

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