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Hii Lee2532, In your Airflow setup on Kubernetes, if you're experiencing out-of-memory (OOM) issues with tasks running through the basic Python operator, you can manage resource allocation by defining resource requests and limits directly in your task definitions. When you define a task in Airflow using the KubernetesPodOperator, you can specify resource requests and limits as part of the pod's configuration. However, for tasks that utilize the basic PythonOperator, you need to ensure these are managed at the Kubernetes level, typically through Kubernetes Executor configs if you're using this executor. Here's an example of how you might set this up in your DAG: from airflow.decorators import dag, task
from airflow.utils.dates import days_ago
from airflow.kubernetes.pod import Resources
default_args = {
'owner': 'airflow',
}
@dag(default_args=default_args, schedule_interval=None, start_date=days_ago(2))
def resource_managed_dag():
@task(resources={'request_memory': '500Mi', 'request_cpu': '500m', 'limit_memory': '1000Mi', 'limit_cpu': '1000m'})
def process_data():
# Python code to process data here
pass
process_data()
dag = resource_managed_dag() In this example, the It's also recommended to monitor your tasks' resource usage to adjust these settings optimally over time. Tools like Kubernetes metrics-server or Prometheus can be helpful for this. |
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I'm using airflow in my Kubernetes environment
Some tasks allocate resources through kpo, but some tasks run through the basic Python operator without doing anything else. However, sometimes those jobs cause OOM, and I want to allocate resources in advance to prevent this, is there a way?
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