Topic 1 Question 57
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?
They have not assigned the timestamp, which causes the job to fail
They have not set the triggers to accommodate the data coming in late, which causes the job to fail
They have not applied a global windowing function, which causes the job to fail when the pipeline is created
They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
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コメント(17)
Answer: D Description: Caution: Beam’s default windowing behavior is to assign all elements of a PCollection to a single, global window and discard late data, even for unbounded PCollections. Before you use a grouping transform such as GroupByKey on an unbounded PCollection, you must do at least one of the following: —->>>>>>Set a non-global windowing function. See Setting your PCollection’s windowing function. Set a non-default trigger. This allows the global window to emit results under other conditions, since the default windowing behavior (waiting for all data to arrive) will never occur. —->>>>If you don’t set a non-global windowing function or a non-default trigger for your unbounded PCollection and subsequently use a grouping transform such as GroupByKey or Combine, your pipeline will generate an error upon construction and your job will fail. So it looks like D
👍 60[Removed]2020/03/27Global windowing is the default behavior, so I don't think C is right. An error can occur if a non-global window or a non-default trigger is not set. I would say D. (https://beam.apache.org/documentation/programming-guide/#windowing)
👍 13jvg6372020/03/15Correct Answer: D Explanation:-This option is correct as with unbounded (Streaming) Pub/Sub collection you need to apply the non-global windowing function.
👍 9VishalB2020/07/26
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