Exam Professional Data Engineer topic 1 question 263 discussion

- The Reshuffle operation is used in Dataflow pipelines to break fusion and redistribute elements, which can sometimes help improve parallelization and identify bottlenecks. - By inserting Reshuffle after each processing step and observing the pipeline's performance in the Dataflow console, you can potentially identify stages that are disproportionately slow or stalled. - This can help in pinpointing the step where the bottleneck might be occurring.

Reshuffle is the key.

Looks like A. However, this option does not provide any option of identifying the underlying cause. https://cloud.google.com/dataflow/docs/pipeline-lifecycle#prevent_fusion

B identifies where the problem lies.

Option A

It should be C

The best option is B Because create additional output to capturing and processing error data, will get error each step that allows you to observe the writing throughput of each block, which can help identify specific processing steps causing bottlenecks. Option A also is valid but can not directly address all bottlenecks, especially if the graph was merged.

From the Dataflow documentation: "There are a few cases in your pipeline where you may want to prevent the Dataflow service from performing fusion optimizations. These are cases in which the Dataflow service might incorrectly guess the optimal way to fuse operations in the pipeline, which could limit the Dataflow service's ability to make use of all available workers. You can insert a Reshuffle step. Reshuffle prevents fusion, checkpoints the data, and performs deduplication of records. Reshuffle is supported by Dataflow even though it is marked deprecated in the Apache Beam documentation."

A. Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.