Our airflow installation is using CeleryExecutor. The concurrency configs were
# The amount of parallelism as a setting to the executor. This defines
# the max number of task instances that should run simultaneously
# on this airflow installation
parallelism = 16
# The number of task instances allowed to run concurrently by the scheduler
dag_concurrency = 16
# Are DAGs paused by default at creation
dags_are_paused_at_creation = True
# When not using pools, tasks are run in the "default pool",
# whose size is guided by this config element
non_pooled_task_slot_count = 64
# The maximum number of active DAG runs per DAG
max_active_runs_per_dag = 16
[celery]
# This section only applies if you are using the CeleryExecutor in
# [core] section above
# The app name that will be used by celery
celery_app_name = airflow.executors.celery_executor
# The concurrency that will be used when starting workers with the
# "airflow worker" command. This defines the number of task instances that
# a worker will take, so size up your workers based on the resources on
# your worker box and the nature of your tasks
celeryd_concurrency = 16
We have a dag that executes daily. It has around some tasks in parallel following a pattern that senses whether the data exists in hdfs then sleep 10 mins, and finally upload to s3.
Some of the tasks has been encountering the following error:
2019-05-12 00:00:46,212 ERROR - Executor reports task instance <TaskInstance: example_dag.task1 2019-05-11 04:00:00+00:00 [queued]> finished (failed) although the task says its queued. Was the task killed externally?
2019-05-12 00:00:46,558 INFO - Marking task as UP_FOR_RETRY
2019-05-12 00:00:46,561 WARNING - section/key [smtp/smtp_user] not found in config
This kind of error occurs randomly in those tasks. When this error happens, the state of task instance is immediately set to up_for_retry, and no logs in the worker nodes. After some retries, they execute and finished eventually.
This problem sometimes gives us large ETL delay. Anyone knows how to solve this problem?
We fixed this already. Let me answer myself question:
We have 5 airflow worker nodes. After installing flower to monitor the tasks distributed to these nodes. We found out that the failed task was always sent to a specific node. We tried to use airflow test command to run the task in other nodes and they worked. Eventually, the reason was a wrong python package in that specific node.