Professional-Data-Engineer Exam Dumps | You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled.

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Google.Professional-Data-Engineer.v2024-07-17.q150
Question 143

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Question 143/150

You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled.
You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage One of the pipeline transforms reads CSV files and emits an element for every CSV line. The Job performance is low. the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

A. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.

B. Update the job to increase the maximum number of workers.

C. Enable Vertical Autoscaling to let the pipeline use larger workers.

D. Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Correct Answer: D

Fusion is an optimization technique that Dataflow applies to merge multiple transforms into a single stage.
This reduces the overhead of shuffling data between stages, but it can also limit the parallelism and scalability of the pipeline. By introducing a Reshuffle step, you can force Dataflow to split the pipeline into multiple stages, which can increase the number of workers that can process the data in parallel. Reshuffle also adds randomness to the data distribution, which can help balance the workload across workers and avoid hot keys or skewed data. References:
* 1: Streaming pipelines
* 2: Batch vs Streaming Performance in Google Cloud Dataflow
* 3: Deploy Dataflow pipelines
* 4: How Distributed Shuffle improves scalability and performance in Cloud Dataflow pipelines
* 5: Managing costs for Dataflow batch and streaming data processing

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