Airflow scheduler logs11/19/2023 In Amazon MWAA this is a CeleryExecutorĪIRFLOW_CORE_LOAD_EXAMPLES – Used to activate, or deactivate, the loading of example DAGs.ĪIRFLOW_METRICS_METRICS_BLOCK_LIST – Used to manage which Apache Airflow metrics are emitted and captured by Amazon MWAA in CloudWatch. MWAA_AIRFLOW_COMPONENT – Used to identify the Apache Airflow component with one of the following values: scheduler, worker, or webserver.ĪIRFLOW_WEBSERVER_SECRET_KEY – The secret key used for securely signing session cookies in the Apache Airflow web server.ĪIRFLOW_CORE_FERNET_KEY – The key used for encryption and decryption of sensitive data stored in the metadata database, for example, connection passwords.ĪIRFLOW_HOME – The path to the Apache Airflow home directory where configuration files and DAG files are stored locally.ĪIRFLOW_CELERY_BROKER_URL – The URL of the message broker used for communication between the Apache Airflow scheduler and the Celery worker nodes.ĪIRFLOW_CELERY_RESULT_BACKEND – The URL of the database used to store the results of Celery tasks.ĪIRFLOW_CORE_EXECUTOR – The executor class that Apache Airflow should use. Therefore, you can't update the environment's Python version When running yum update in a startup script, you must exclude Python using -exclude=python* as shown in the example.įor your environment to run, Amazon MWAA installs a specific version of Python compatible with your environment. Use a startup script to update the operating system of an Apache Airflow component, and install additional runtime libraries to use with your workflows.įor example, the following script runs yum update to update the operating system. Install Linux runtimes using a startup script You can repeat the previous steps to view worker and web server logs. For example, for scheduler logs, you will the following:įinished running startup script. On the Log events pane, you will see the output of the command printing the value for MWAA_AIRFLOW_COMPONENT. In the CloudWatch console, from the Log streams list, choose a stream with the following prefix: startup_script_exection_ip. In the Monitoring pane, choose the log group for which you want to view logs, for example, Airflow scheduler log group. Open the Environments page on the Amazon MWAA console. # match on timestamp.To check the Apache Airflow log stream (console) Replacement: 'integrations/apache-airflow' Ensure that the instance under the agent statsd_exporter matches the instance labels under the logs static_configs as well as the pipeline_stages match selector. instance label must be set to a value that uniquely identifies your Apache Airflow system.Ensure that the job under the agent relabel_configs matches the job labels under the logs static_configs as well as the pipeline_stages match selector. job must be set to integrations/apache-airflow.job and instance label values must match for the Apache Airflow integration and logs scrape config in your agent configuration file. If you want to show logs and metrics signals correlated in your dashboards as a single pane of glass, ensure the following: Make sure to change listen_udp in the snippet according to your environment. Post-install configuration for the Apache Airflow integrationĪfter enabling the metrics generation, instruct the Grafana Agent to scrape your Apache Airflow system. Click Install to add this integration’s pre-built dashboard and alerts to your Grafana Cloud instance, and you can start monitoring your Apache Airflow setup.Review the prerequisites in the Configuration Details tab and set up Grafana Agent to send Apache Airflow metrics and logs to your Grafana Cloud instance.Find Apache Airflow and click its tile to open the integration.In your Grafana Cloud stack, click Connections in the left-hand menu.Statsd_prefix = airflow Install Apache Airflow integration for Grafana Cloud
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |