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Google Exam Professional-Data-Engineer Topic 3 Question 94 Discussion

Actual exam question for Google's Google Cloud Certified Professional Data Engineer exam
Question #: 94
Topic #: 3
[All Google Cloud Certified Professional Data Engineer Questions]

You recently deployed several data processing jobs into your Cloud Composer 2 environment. You notice that some tasks are failing in Apache Airflow. On the monitoring dashboard, you see an increase in the total workers' memory usage, and there were worker pod evictions. You need to resolve these errors. What should you do?

Choose 2 answers

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Suggested Answer: B, C

To resolve issues related to increased memory usage and worker pod evictions in your Cloud Composer 2 environment, the following steps are recommended:

Increase Memory Available to Airflow Workers:

By increasing the memory allocated to Airflow workers, you can handle more memory-intensive tasks, reducing the likelihood of pod evictions due to memory limits.

Increase Maximum Number of Workers and Reduce Worker Concurrency:

Increasing the number of workers allows the workload to be distributed across more pods, preventing any single pod from becoming overwhelmed.

Reducing worker concurrency limits the number of tasks that each worker can handle simultaneously, thereby lowering the memory consumption per worker.

Steps to Implement:

Increase Worker Memory:

Modify the configuration settings in Cloud Composer to allocate more memory to Airflow workers. This can be done through the environment configuration settings.

Adjust Worker and Concurrency Settings:

Increase the maximum number of workers in the Cloud Composer environment settings.

Reduce the concurrency setting for Airflow workers to ensure that each worker handles fewer tasks at a time, thus consuming less memory per worker.


Cloud Composer Worker Configuration

Scaling Airflow Workers

Contribute your Thoughts:

Catalina
8 days ago
I also think we should increase the Cloud Composer 2 environment size from medium to large to handle the increased workload.
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Jacinta
12 days ago
I agree with Patria. That could help resolve the memory usage issues.
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Patria
16 days ago
I think we should increase the memory available to the Airflow workers.
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Luann
24 days ago
Hmm, I think increasing the memory available to the Airflow workers (B) and the maximum number of workers (C) would be a good place to start. We don't want those poor workers to get evicted like a bad tenant!
upvoted 0 times
Francoise
2 days ago
Yes, and increasing the maximum number of workers and reducing worker concurrency (C) can also prevent worker pod evictions.
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Brice
6 days ago
I agree, increasing the memory available to the Airflow workers (B) should help with the memory usage.
upvoted 0 times
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