Exam Professional Data Engineer topic 1 question 57 discussion

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

Global 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)

The job fails because triggers are not set to handle late-arriving data, causing the pipeline to mishandle or drop delayed records.

A. They have not assigned the timestamp, which causes the job to fail Analysis: Cloud Dataflow relies on timestamps to perform windowing operations. Without proper event-time timestamps, windowing cannot be applied correctly, and the job may fail or behave unpredictably. This is a common issue when processing streaming data from Google Cloud Pub/Sub, as timestamps must be explicitly assigned if not already embedded in the data. This is the most likely cause.

This option is very likely, as without timestamps assigned to streaming data, the system cannot properly process time windows. Timestamps are crucial for the correct time handling in Dataflow pipelines

The most likely cause of this problem is A. They have not assigned the timestamp, which causes the job to fail. Here's why: Importance of Timestamps in Windowing: Windowing in Dataflow relies on timestamps to group elements into windows. If timestamps are not explicitly assigned or extracted from the data, Dataflow cannot determine which elements belong to which windows, leading to failures in the job. Let's look at the other options: B. They have not set the triggers to accommodate the data coming in late: While triggers are important for managing late data, not setting them would not cause the job to fail for all streaming inserts. It might affect the accuracy of the results, but the job would still run. C & D. Global vs. Non-global Windowing: The choice between global and non-global windowing depends on the specific requirements of the analysis. While incorrect windowing choices can lead to unexpected results, they would not typically cause the job to fail completely.

D You have to apply a non-global windowing function because the global windowing function is a default windowing function for every pub/sub stream or batch data.

option B: They have not set the triggers to accommodate the data coming in late, which causes the job to fail. In a streaming data processing pipeline, it's common to encounter data that arrives late, meaning it arrives after the event time has passed for the associated window. If you don't handle late data appropriately by setting triggers, it can cause issues in your pipeline, including job failures.

gpt: The most likely cause of the problem is A, that they have not assigned the timestamp. In streaming data processing, timestamps are essential for proper windowing and triggering of data. Without timestamps, the system cannot correctly determine which window a particular piece of data belongs to, or when it is safe to trigger processing of a window. If the engineers did not assign timestamps to the data, the Cloud Dataflow job would not be able to process the data correctly, and it would fail. Option B, not setting triggers to accommodate late data, is also an important consideration for streaming data processing. However, it is less likely to cause the job to fail outright than missing timestamps. Option C, not applying a global windowing function, and Option D, not applying a non-global windowing function, are also important considerations for windowing in Cloud Dataflow. However, neither of these would cause the job to fail when the pipeline is created. Instead, they would affect the performance and correctness of the data processing.

what about A? This can cause the job to fail

A: note that without a correct timestamp, the pipeline still run fine with the default timestamp. The result maybe incorrect but the job will not fail. D: For unbound collection, this will fail if any aggregation function is done.

Answer: A All Streaming Insert failed, because there is no TimeStamp added, otherwise there is already a DEFAULT global windowing function and can execute without assigning any windowing function. I mean first there should be Timestamp in the data, then according to our aggregation outcome either its full time (global) or batch/chunks time aggregation(non global) will be performed.

https://beam.apache.org/documentation/programming-guide/#windowing 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.

Answer is D

Correct answer is D. C does not make sense because for unbounded source like Pub/Sub, the global functions are applied by default. The reason for failure would be they are using specific aggregations that require non-global window functions, e.g. tumbling or hopping windows.

C is the answer. https://beam.apache.org/documentation/programming-guide/#windowing-bounded-collections 8.2.4. The single global window By default, all data in a PCollection is assigned to the single global window, and late data is discarded. If your data set is of a fixed size, you can use the global window default for your PCollection (not our case because we are streaming). You can use the single global window if you are working with an unbounded data set (e.g. from a streaming data source) but use caution when applying aggregating transforms such as GroupByKey and Combine. The single global window with a default trigger generally requires the entire data set to be available before processing, which is not possible with continuously updating data. To perform aggregations on an unbounded PCollection that uses global windowing, you should specify a non-default trigger for that PCollection.

Why A won't cause an error?