PDE Ingesting and Processing the Data Practice Question
You are designing a streaming pipeline that must handle late-arriving data with a maximum lateness of 10 minutes. You need to ensure that all data is processed exactly once and that results are emitted after the watermark passes the window. Which Apache Beam concept should you use to achieve this?
⚠ Common exam trap
It's easy for candidates to confuse processing-time triggers with event-time watermarks; only watermark-based triggers respect event-time completeness and allowed lateness.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use fixed windows with allowed lateness set to 10 minutes and a trigger that fires when the watermark passes the end of the window.
Fixed windows with allowed lateness of 10 minutes and a watermark-based trigger ensure that data arriving up to 10 minutes late is included, and results are emitted only after the watermark passes the window end, meaning all on-time data has arrived. This combination provides exactly-once processing semantics when used with a runner that supports it.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use sliding windows with a period of 10 minutes and a trigger that fires when the watermark passes the end of the window.
Why it's wrong here
Sliding windows overlap and would produce multiple overlapping results, which may not be desired. While the trigger condition is correct, the window type does not align with the typical use case for fixed lateness handling, and allowed lateness is still needed to handle late data.
- ✗
Use a global window with a trigger that fires every 10 minutes.
Why it's wrong here
A global window with a periodic trigger does not account for event-time lateness or watermarks. It would emit results based on processing time, not event time, and would not guarantee that late data is included or that results are emitted after the watermark passes the window.
- ✗
Use session windows with a gap duration of 10 minutes and a trigger that fires on every element.
Why it's wrong here
Session windows group data based on gaps of inactivity, not fixed time intervals. Firing on every element would emit early results and not wait for the watermark, and session windows are not suitable for handling late data with a fixed lateness bound.
- ✓
Use fixed windows with allowed lateness set to 10 minutes and a trigger that fires when the watermark passes the end of the window.
Why this is correct
Setting allowed lateness to 10 minutes on fixed windows ensures that late data up to 10 minutes is not dropped. A trigger that fires when the watermark passes the end of the window emits results only after the watermark indicates all on-time data has arrived, satisfying the requirement.
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