Greetings Airflowers. I'm evaluating Airflow 1.9.0 for our distributed
orchestration needs (using CeleryExecutor and RabbitMQ), and I am seeing
something strange.
I made a dag that has three main stages: 1) start, 2) fan out and run N tasks
concurrently, 3) finish.
N can be large, maybe up to 10K. I would expect to see N tasks get dumped onto
the Rabbit queue when stage 2 begins. Instead I am seeing only a few hundred
added at a time. As the workers process the tasks and the queue gets smaller,
then more get added to Celery/Rabbit. Eventually, it does finish, however I
would really prefer that it dump ALL the work (all 10K tasks) into Celery
immediately, for two reasons:
1. The current way makes the scheduler long-lived and stateful. The
scheduler might die after only 5K have completed, in which case the remaining
5K tasks would never get added (I verified this)
2. I want to use the size of the Rabbit queue as metric to trigger
autoscaling events to add more workers. So I need a true picture of how much
outstanding work remains (10K, not a few hundred)
I assume the scheduler has some kind of throttle that keeps it from dumping all
10K messages simultaneously? If so is this configurable?
FYI I have already set “parallelism” to 10K in the airflow.cfg
Here is my test dag:
# This dag tests how well airflow fans out
from airflow import DAG
from datetime import datetime, timedelta
from airflow.operators.bash_operator import BashOperator
default_args = {
'owner': 'airflow',
'depends_on_past': False,
'start_date': datetime(2015, 6, 1),
'email': ['airf...@example.com'],
'email_on_failure': False,
'email_on_retry': False,
'retries': 1,
'retry_delay': timedelta(minutes=5),
}
dag = DAG('fan_out', default_args=default_args, schedule_interval=None)
num_tasks = 1000
starting = BashOperator(
task_id='starting',
bash_command='echo starting',
dag=dag
)
all_done = BashOperator(
task_id='all_done',
bash_command='echo all done',
dag=dag)
for i in range(0, num_tasks):
task = BashOperator(
task_id='say_hello_' + str(i),
bash_command='echo hello world',
dag=dag)
task.set_upstream(starting)
task.set_downstream(all_done)
--
Regards,
Kevin Pauli
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