How To Download Airflow Logs
Vida Hubbert
By default, Airflow supports logging into the local file system. These include logs from the Web server, the Scheduler, and the Workers running tasks. This is suitable for development environments and for quick debugging.
How can I add my own logs onto the Apache Airflow logs that are automatically generated? any print statements wont get logged in there, so I was wondering how I can add my logs so that it shows up on the UI as well?
However, if you look at the BashOperator: -airflow/blob/master/airflow/operators/bash_operator.py#L79-L94, the STDOUT/STDERR from there is logged along with the airflow logs. So, if logs are important to you, I suggest adding the python code in a separate file and calling it using the BashOperator.
Streaming logs: These logs are a superset of the logs in Airflow. Toaccess streaming logs, you can go to the logs tab of Environment detailspage in Google Cloud console, use the Cloud Logging, or use Cloud Monitoring.
When you create an environment, Cloud Composer creates aCloud Storage bucket and associates the bucket with your environment.Cloud Composer stores logs for single DAG tasks in logs folder in the bucket.
The logs folder includes folders for each workflow that has runin the environment. Each workflow folder includes a folder for its DAGs and sub-DAGs. Each folder contains log files for each task. The task filename indicates when the task started.
Amazon MWAA can send Apache Airflow logs to Amazon CloudWatch. You can view logs for multiple environments from a single location to easily identify Apache Airflow task delays or workflow errors without the need for additional third-party tools. Apache Airflow logs need to be enabled on the Amazon Managed Workflows for Apache Airflow console to view Apache Airflow DAG processing, tasks, Web server, Worker logs in CloudWatch.
Amazon MWAA creates a log group for each Airflow logging option you enable, and pushes the logs to the CloudWatch Logs groups associated with an environment. Log groups are named in the following format: YourEnvironmentName-LogType. For example, if your environment's named Airflow-v202-Public, Apache Airflow task logs are sent to Airflow-v202-Public-Task.
You can enable Apache Airflow logs at the INFO, WARNING, ERROR, or CRITICAL level. When you choose a log level, Amazon MWAA sends logs for that level and all higher levels of severity. For example, if you enable logs at the INFO level, Amazon MWAA sends INFO logs and WARNING, ERROR, and CRITICAL log levels to CloudWatch Logs.
You can view Apache Airflow logs for the Scheduler scheduling your workflows and parsing your dags folder. The following steps describe how to open the log group for the Scheduler on the Amazon MWAA console, and view Apache Airflow logs on the CloudWatch Logs console.
My main goal is to parse apache airflow logs into particular fields using logstash, feed it into elasticsearch and visualise them using kibana. There is no particular grok pattern available for airflow logs. I'm fairly new to elk stack. Need any help possible to parse important info from airflow logs.
Airflow supports logging for the web server. The logs can provide valuable information about the operation of the web server, including any errors or issues that may occur. The logs can be viewed directly in the Airflow web interface, or they can be exported to an external logging service for more detailed analysis.
Airflow logs detailed information about the operation of the Scheduler. These logs can be found in the AIRFLOW_HOME/logs/scheduler directory. You can configure the level of detail in these logs using the logging_level configuration option in the airflow.cfg file.
Airflow can be configured to send metrics to StatsD or Prometheus, which can then be visualized using tools like Grafana. This provides a real-time view of the Scheduler's operation. To enable this, you need to set the statsd_on configuration option to True in the airflow.cfg file and provide the appropriate statsd_host and statsd_port values.
Airflow can be configured to send email alerts when tasks fail, are retried, or succeed. This can be set up by providing the appropriate SMTP server details in the airflow.cfg file and setting the email_on_failure and email_on_retry configuration options to True.
By default, Airflow stores logs locally on the machine where the tasks are run. The logs are located in the AIRFLOW_HOME/logs directory. Each task has its own log file, which is named after the task instance's dag_id, task_id, and execution_date.
Please note that the logs generated by Airflow workers can contain sensitive information, so it's important to secure access to them. You can do this by configuring access controls on your logging service or by encrypting the logs.
The Apache Airflow Scheduler is a key component of the Airflow architecture, responsible for scheduling and triggering tasks. It generates logs that provide valuable insights into its operations and can be crucial for debugging and monitoring.
By default, these logs are stored in the AIRFLOW_HOME/logs/scheduler directory. The naming convention for the log files is scheduler.log.YYYY-MM-DD, where YYYY-MM-DD is the date when the logs were generated.
In addition to the local file system, Airflow also supports remote logging to services like Elasticsearch, Google Cloud Storage, and Amazon S3. This can be configured in the airflow.cfg file under the [elasticsearch] section 1.
Airflow workers' logs can provide detailed information about the execution of tasks. These logs can be used to debug issues and understand the performance of your tasks. Airflow supports storing logs in various locations including local storage, remote storage like S3, GCS, or Azure Blob Storage, or a distributed file system like HDFS.
Apache Airflow provides robust logging capabilities for the Scheduler, which is a critical component of any Airflow setup. The Scheduler is responsible for triggering the tasks at the right time and handling task dependencies. Therefore, having detailed logs is crucial for debugging and monitoring the Scheduler's performance.
Airflow uses Python's built-in logging module, and the configuration is located in the airflow.cfg file under the [logging] section. By default, the Scheduler logs are stored in the AIRFLOW_HOME/logs/scheduler directory.
In addition to the local file logging, Airflow also supports remote logging to services like Elasticsearch, Google Cloud Storage, S3, or Stackdriver. This can be enabled by setting remote_logging = True in the airflow.cfg file and configuring the appropriate remote log location.
Airflow can be configured to read task logs from Elasticsearch and optionally write logs to stdout in standard or json format. These logs can later be collected and forwarded to the Elasticsearch cluster using tools like fluentd, logstash or others.
You can choose to have all task logs from workers output to the highest parent level process, instead of the standard file locations. This allows for some additional flexibility in container environments like Kubernetes, where container stdout is already being logged to the host nodes. From there a log shipping tool can be used to forward them along to Elasticsearch. To use this feature, set the write_stdout option in airflow.cfg. You can also choose to have the logs output in a JSON format, using the json_format option. Airflow uses the standard Python logging module and JSON fields are directly extracted from the LogRecord object. To use this feature, set the json_fields option in airflow.cfg. Add the fields to the comma-delimited string that you want collected for the logs. These fields are from the LogRecord object in the logging module. Documentation on different attributes can be found here.
We have a RHEL 8 server where we have a few python applications installed for data management and analysis, notably JupyterHub and Airflow. We have these set up with systemd unit files so we can start, stop, restart them using systemctl and we access their logs using journalctl. Our infrastructure folks in charge of the server have kindly asked to make sure our applications send their log messages to somewhere other than /var/log/messages so they can debug server health without having our applications "polluting" the log. But we have not been able to identify a solution to this.
I want to change logging location to a different folder. For that I changed base_log_folder = /code/python/airflow/airflow/logs from base_log_folder = /home/cel/sm/airflow/logs(Airflow_Home) in airflow.cfg file.
After doing this when I'm restarting scheduler and webserver using sudo systemctl restart airflow-scheduler and sudo systemctl restart airflow-webserver; logging is still happening in old log folder i.e /home/cel/sm/airflow/logs.
TLDR: By changing base_log_folder value you only changed the logs directory for tasks, you also need to change logs directory for scheduler by changing child_process_log_directory value in airflow.cfg
Restarting airflow-scheduler and airflow-webserver processes with systemd reloads configuration. So I guess logs still written in your old logs directory are only the scheduler's logs and you should be able to correct it by changing child_process_log_directory value as follow:
You should restart the workers as well (in case you're using celery or something like that). For me, I just restarted the the EC2 and the logs redirection worked. Try restarting the server if it's possible.
I am using GCP Composer(airflow). What I want to do is to change Airflow, in all its components, to reflect my Time Zone: "Europe/Lisbon". I know that, by default, Composer uses timedates in UTC timezone, so I alredy proceed on some steps to change that, but, without being able to change in all components.
The Airflow scheduler and workers, independent processes running on different computers than the Composer environment, produce the Airflow logs may be one factor contributing to this problem. To change the timezone for certain components, altering the Composer settings might not be sufficient.
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