![]() ![]() To solve this you can simply mount a volume for the logs directory so that all the airflow containers have access to the logs file, as the dags file but for logs. ![]() In this case the log is being created on one container and tiring to be read it on an other container. Unfortunately i don't have access to the logs anymore. The default path for the logs is at /opt/airflow/logs. By default, logs are placed in the AIRFLOWHOME directory. ![]() Using KubernetesExecutor connected to EKS. Configuring logging For the default handler, FileTaskHandler, you can specify the directory to place log files in airflow.cfg using baselogfolder. Other Docker-based deployment Deployment details Ubuntu Versions of Apache Airflow Providers Note that Airflow Scheduler in versions prior to 2.1.4 generated a lot of Page Cache memory used by log files (when the log files were not removed). airflow-maintenance-dags The other recommended option is to write logs to cloud watch, and then you can set retention whatever suit you. Create a second dag using the default pool for a single task. log-cleanup A maintenance workflow that you can deploy into Airflow to periodically clean out the task logs to avoid those getting too big. Behind the scenes, the scheduler spins up a subprocess, which monitors and stays in sync with all DAGs in the specified DAG directory. How to reproduceĬreate a dag with 64 concurrent tasks, and set a pool that doesnt exist. Scheduler The Airflow scheduler monitors all tasks and DAGs, then triggers the task instances once their dependencies are complete. The scheduler to continue running correctly-configured tasks, ignoring the incorrectly configured ones, rather than blocking. ![]() When i created the missing pool the scheduler started the tasks and started clearing the queue. This allows you to run a local Apache Airflow. The CLI builds a Docker container image locally that’s similar to an Amazon MWAA production image. The command line interface (CLI) utility replicates an Amazon Managed Workflows for Apache Airflow environment locally. The log showed that it was attempting to run 64 tasks, and that every one was trying to use a pool that didn't exist. Step one: Test Python dependencies using the Amazon MWAA CLI utility. The question is that also when I go to look at the logs that I have in the persistent volume claim I have noticed that all the logs have been deleted until that same date, 4 weeks from today, but there are also some pipelines that It has lost the logs from the 26th backwards.Our airflow instance was not scheduling any tasks, even simple ones using the default pools. Python_callable=delete_old_database_entries, Print("Skipping truncation until explicitly enabled.")ĭelete_old_database_entries_by_model(TaskInstance, TaskInstance.end_date)ĭelete_old_database_entries_by_model(DagRun, DagRun.end_date)ĭelete_old_database_entries_by_model(BaseJob, BaseJob.end_date)ĭelete_old_database_entries_by_model(Log, Log.dttm)ĭelete_old_database_entries_by_model(TaskReschedule, TaskReschedule.end_date)Ĭleanup_old_database_entries = PythonOperator( Print("To enable this, create an Airflow Variable called ENABLE_DB_TRUNCATION set to 'True'") Print("This DAG will delete all data older than %s weeks.", EXPIRATION_WEEKS) If Variable.get("ENABLE_DB_TRUNCATION", "") != "True": Print(f"Deleting old database entries from ") Streaming logs: a superset of the logs in Airflow, for example, uncategorised logs that Airflow pods generate and the Airflow scheduler logs, the full list can be found here. I want to change logging location to a different folder. This is the pipe: from datetime import datetime, timezone, timedeltaįrom airflow.models import DAG, Log, DagRun, TaskInstance, TaskReschedule, Variableįrom _job import BaseJobįrom import PythonOperatorįrom import ENV, BASIC_CONFIG_FACTORYĭef delete_old_database_entries_by_model(table, date_col):ĭelete old database entries where the date is older than EXPIRATION_WEEKS.Įxpiration_date = datetime.now(timezone.utc) - timedelta(weeks=EXPIRATION_WEEKS) Im running airflow 1.10.11 as systemd service on Centos 7 server. I have a pipeline that is in charge of deleting the logs in the airflow database (I have the BBDD running on Postgress on AWS). Hello good afternoon I have encountered a curious occurrence in Airflow 2.3.0 ![]()
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