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Question 272 - Professional Data Engineer discussion

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You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

A.
Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.
Answers
A.
Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.
B.
Log debug information in each ParDo function, and analyze the logs at execution time.
Answers
B.
Log debug information in each ParDo function, and analyze the logs at execution time.
C.
Insert output sinks after each key processing step, and observe the writing throughput of each block.
Answers
C.
Insert output sinks after each key processing step, and observe the writing throughput of each block.
D.
Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks
Answers
D.
Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks
Suggested answer: A

Explanation:

A Reshuffle operation is a way to force Dataflow to split the pipeline into multiple stages, which can help isolate the performance of each step and identify bottlenecks. By monitoring the execution details in the Dataflow console, you can see the time, CPU, memory, and disk usage of each stage, as well as the number of elements and bytes processed. This can help you diagnose where the pipeline is slowing down and optimize it accordingly.Reference:

1: Reshuffling your data

2: Monitoring pipeline performance using the Dataflow monitoring interface

3: Optimizing pipeline performance

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asked 18/09/2024
Vaniko Batiashvili
30 questions
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