airflow-scheduler

I am not able see the logs attached to the tasks from the Airflow UI: Log related settings in airflow.cfg file are: remote_base_log_folder = base_log_folder = /home/my_projects/ksaprice_project/airflow/logs worker_log_server_port = 8793 child_process_log_directory = /home/my_projects/ksaprice_project/airflow/logs/scheduler Although I am setting remote_base_log_folter it is trying to fetch the log from http://:8793/log/tutorial/print_date/2017-08-02T00:00:00 - I don't understand this behavior. According to the settings the workers should store the logs at /home/my_projects/ksaprice_project

I have a working Airflow environment using Airflow version 1.9 that is running on an Amazon EC2-Instance. I need to upgrade to the latest version of Airflow which is 1.10. I have the option of either upgrading from version 1.9 or installing 1.10 freshly on a new server. Airflow version 1.10 is not listed on Pip so I'm installing it from Git via this command, pip-3.6 install git+git://github.com/apache/incubator-airflow.git@v1-10-stable This command successfully installs Airflow version 1.10. You can see that by running the command airflow version and viewing the output, ____________ __________

I have already seen this and this questions on SO and made the changes accordingly. However, my dependent DAG still gets stuck in poking state. Below is my master DAG: from airflow import DAG from airflow.operators.jdbc_operator import JdbcOperator from datetime import datetime from airflow.operators.bash_operator import BashOperator today = datetime.today() default_args = { 'depends_on_past': False, 'retries': 0, 'start_date': datetime(today.year, today.month, today.day), 'schedule_interval': '@once' } dag = DAG('call-procedure-and-bash', default_args=default_args) call_procedure =

I'm using Airflow v1.8.1 and run all components (worker, web, flower, scheduler) on kubernetes & Docker. I use Celery Executor with Redis and my tasks are looks like: (start) -> (do_work_for_product1) ├ -> (do_work_for_product2) ├ -> (do_work_for_product3) ├ … So the start task has multiple downstreams. And I setup concurrency related configuration as below: parallelism = 3 dag_concurrency = 3 max_active_runs = 1 Then when I run this DAG manually (not sure if it never happens on a scheduled task) , some downstreams get executed, but others stuck at "queued" status. If I clear the task from

I'm using Google Cloud Composer (managed Airflow on Google Cloud Platform) with image version composer-0.5.3-airflow-1.9.0 and Python 2.7, and I'm facing a weird issue : after importing my DAGs, they are not clickable from the Web UI (and there are no buttons "Trigger DAG", "Graph view", ...), while all works perfectly when running a local Airflow. Even if non usable from the webserver on Composer, my DAGs still exist. I can list them using CLI ( list_dags ), describe them ( list_tasks ) and even trigger them ( trigger_dag ). Minimal example reproducing the issue A minimal example I used to

Airflow is randomly not running queued tasks some tasks dont even get queued status. I keep seeing below in the scheduler logs [2018-02-28 02:24:58,780] {jobs.py:1077} INFO - No tasks to consider for execution. I do see tasks in database that either have no status or queued status but they never get started. The airflow setup is running https://github.com/puckel/docker-airflow on ECS with Redis. There are 4 scheduler threads and 4 Celery worker tasks. For the tasks that are not running are showing in queued state (grey icon) when hovering over the task icon operator is null and task details