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Google Professional Data Engineer Exam - Topic 3 Question 94 Discussion

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
B) Increase the memory available to the Airflow workers. and C) Increase the maximum number of workers and reduce worker concurrency.
A) Increase the directed acyclic graph (DAG) file parsing interval.
D) Increase the memory available to the Airflow triggerer.
E) Increase the Cloud Composer 2 environment size from medium to large.

Google Professional Data Engineer Exam - Topic 3 Question 94 Discussion

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

Show Suggested Answer Hide Answer
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

by Socorro at Sep 16, 2024, 03:36 PM

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Charlene
9 months ago
Wait, are worker pod evictions really that common?
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Brande
9 months ago
Increasing worker concurrency could help with the evictions.
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Lorenza
10 months ago
Not sure about the DAG parsing interval being the issue here.
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Georgiann
10 months ago
I think increasing the environment size is a good move too!
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Stephanie
10 months ago
Definitely increase the memory for the Airflow workers.
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Vernice
10 months ago
I’m a bit confused about the worker concurrency part. Should we really reduce it if we’re also increasing the number of workers?
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Marvel
11 months ago
I feel like we had a practice question about scaling the environment size. Increasing it from medium to large could be a good move.
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Leah
11 months ago
I'm not entirely sure, but I think increasing the DAG file parsing interval might not really help with memory issues.
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Thurman
11 months ago
I remember we discussed increasing the memory for Airflow workers in class. That seems like a solid option here.
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Malinda
11 months ago
Increasing the environment size from medium to large could be an easy fix, but I want to make sure I understand the underlying issues first before jumping to that solution.
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Dominga
11 months ago
Increasing the maximum number of workers and reducing concurrency might also help distribute the load and free up resources. I'll need to weigh the pros and cons of that approach.
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Joaquin
11 months ago
Increasing the memory available to the workers sounds like a good place to start. If the tasks are consuming too much memory, that could be the root cause of the failures and pod evictions.
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Jesusita
11 months ago
Hmm, increasing the DAG file parsing interval doesn't seem directly related to the memory usage problem. I'm leaning more towards options that address the worker resource constraints.
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Trinidad
11 months ago
This seems like a tricky one. I'll need to carefully consider the options and think through the potential causes of the memory issues.
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Leigha
11 months ago
This is a good opportunity to showcase my knowledge of GRUB. I'll review the options and try to eliminate the false statements.
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Rodrigo
2 years ago
B and C are the clear winners here. Gotta give those workers some more breathing room and add some reinforcements. Wait, why would we want to increase the memory for the Airflow triggerer (D)? That's like trying to fit an elephant in a phone booth.
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Lai
2 years ago
I'd say B and E are the way to go. Boost that worker memory and scale up the whole environment. Can't have those tasks failing on our watch! Although, increasing the DAG parsing interval (A) might be an interesting experiment, just to see how long it takes for everything to grind to a halt.
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Novella
2 years ago
Definitely go for B and C. Increasing the memory and worker capacity should help handle the increased processing load. I wonder if the engineers had a bet going on how many worker pods they could evict before someone noticed.
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Luisa
2 years ago
E) Increase the Cloud Composer 2 environment size from medium to large.
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Diane
2 years ago
That sounds like a good plan. Hopefully, it will help stabilize the data processing jobs.
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Alaine
2 years ago
C) Increase the maximum number of workers and reduce worker concurrency.
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Mitsue
2 years ago
B) Increase the memory available to the Airflow workers.
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Catalina
2 years 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
2 years ago
I agree with Patria. That could help resolve the memory usage issues.
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Patria
2 years ago
I think we should increase the memory available to the Airflow workers.
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Luann
2 years 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!
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Devora
2 years ago
Sounds like a plan. Let's monitor the environment after making the changes to ensure everything runs smoothly.
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Catina
2 years ago
Great suggestions! Let's make those adjustments to improve the performance of our data processing jobs.
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Francoise
2 years 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
2 years ago
I agree, increasing the memory available to the Airflow workers (B) should help with the memory usage.
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