Topic 1 Question 143
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?
Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to a new unique job name
Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code
Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
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コメント(17)
- 正解だと思う選択肢: D
It's D. → Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. New version is mayor changes. Stop and drain and then launch the new code is a lot is the safer way. We recommend that you attempt only smaller changes to your pipeline's windowing, such as changing the duration of fixed- or sliding-time windows. Making major changes to windowing or triggers, like changing the windowing algorithm, might have unpredictable results on your pipeline output. https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline#changing_windowing
👍 9odacir2022/12/08 - 正解だと思う選択肢: A
The answer is A. The question says "update", and on the documentation (https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline#UpdateMechanics) says explicitly on the second paragraph this: The replacement job preserves any intermediate state data from the prior job, as well as any buffered data records or metadata currently "in-flight" from the prior job. For example, some records in your pipeline might be buffered while waiting for a window to resolve.
So the update process prevent data loss.
👍 4josrojgra2022/10/13 - 正解だと思う選択肢: D
agree with odacir
👍 4jkhong2022/12/22
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