Skip to main content

What is KubernetesPodOperator in Airflow

A KubernetesPodOperator is a type of operator in Apache Airflow that allows you to launch a Kubernetes pod as a task in an Airflow workflow. This can be useful if you want to run a containerized workload as part of your pipeline, or if you want to use the power of Kubernetes to manage the resources and scheduling of your tasks.

Here is an example of how you might use a KubernetesPodOperator in an Airflow DAG:

from airflow import DAG
from airflow.operators.kubernetes_pod_operator import KubernetesPodOperator
from airflow.utils.dates import days_ago

default_args = {
    'owner': 'me',
    'start_date': days_ago(2),
}

dag = DAG(
    'kubernetes_sample',
    default_args=default_args,
    schedule_interval=timedelta(minutes=10),
)

# Define a task using a KubernetesPodOperator
task = KubernetesPodOperator(
    namespace='default',
    image="python:3.6-slim",
    cmds=["python", "-c"],
    arguments=["print('hello world')"],
    labels={"foo": "bar"},
    name="test-pod",
    task_id="test-pod",
    is_delete_operator_pod=True,
    dag=dag,
)


In this example, we are defining a task that will launch a Kubernetes pod in the default namespace, using the python:3.6-slim Docker image. The pod will run a single command, print('hello world'), using the python interpreter. The task is given a label of foo: bar and a name of test-pod.

There are many other parameters that you can use to customize the behavior of the KubernetesPodOperator, such as setting resource limits and requests, specifying environment variables, and mounting volumes. You can find a full list of available parameters in the Airflow documentation.

Comments

Post a Comment

Popular posts from this blog

Building Scalable and Efficient Data Lakes with Apache Hudi

If you're looking to build a scalable and efficient data lake that can support both batch and real-time processing, Apache Hudi is a great tool to consider. In this blog post, we'll discuss what Apache Hudi is, how it works, and why it's a powerful tool for building data lakes. Apache Hudi is an open-source data management framework that provides several features to manage big data. It provides the ability to perform read and write operations on large datasets in real-time, while also supporting batch processing. With Hudi, you can also ensure data quality by performing data validation, data cleansing, and data profiling. One of the key advantages of Apache Hudi is its support for schema evolution. This means that as your data changes over time, Hudi can automatically update the schema of your data to accommodate these changes, without requiring any downtime or manual intervention. Another advantage of Hudi is its support for scalable and fault-tolerant data storage. Hudi p...
Post a Comment
Read more

Top 25 Data Engineer Interview Questions

In my last post  How to prepare for Data Engineer Interviews ,  I wrote about how one can prepare for the Data Engineer Interviews, and in this blog post, I am going to provide the  Top 25 Basic   data engineer interview questions  asked frequently and their brief answers. This is typically the first round of the Interview where the interviewer just wants to access whether you are aware of basic concepts or not and therefore you don't need to explain it in detail. Just a single statement would be sufficient. Let's get started Checkout the 5 Key Skills Data Engineers need in 2023 A. Programming  1. What is the Static method in Python? Static methods are the methods that are bound to the  Class  rather than the Class's Object. Thus, it can be called without creating objects of the class. We can just call it using the reference of the class. Also, all the objects of the class share only one copy of the static method. 2. What is a Decorator in Python?...
Post a Comment
Read more

What is CAP Theorem?

CAP Theorem states that a Distributed Database System can only have 2 out of 3 properties from Availability, Consistency, and Partition Tolerance . This means that every Big Data Engineer needs to do a trade-off between these three based on the use-case and Business requirements. It is very important for any Data engineer to understand the CAP Theorem and apply it when deciding the appropriate tools for the task in the hand. Let's discuss each of the properties in detail. 1. Availability   This condition states that every request (read/write) will get a response on Success or Failure. That means every node in the system must return a response in a reasonable amount of time. This could be only possible if the system remains operational all the time. Hence, the databases are time-independent as they should be available all the time. Therefore if any two records are added to the database we don't know which one was added first and the output could be either one of them. Now le...
Post a Comment
Read more