PDE Designing Data Processing Systems Practice Question
You are designing a data pipeline that processes streaming events with late-arriving data (up to 2 hours late). The pipeline must compute hourly aggregations and emit results as soon as possible, but must also accurately update results when late data arrives. You want to minimize overall processing cost. Which Dataflow windowing and trigger configuration should you use?
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
✓
Fixed windows of 1 hour with allowed lateness of 2 hours and trigger every 5 minutes (early) and on watermark (late) with accumulating fired panes
Session windows are ideal for capturing bursts of user activity but not for fixed hourly aggregations. The best approach is to use fixed windows with allowed lateness of 2 hours and triggering early every N minutes (e.g., 5 minutes) and also on watermark advancement. This provides early results while allowing late data to update the window. Using accumulating and discarding late panes (or just accumulating) depends on the use case; but here, accumulating fired panes is typical for correctness.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Fixed windows of 1 hour with allowed lateness of 2 hours and trigger every 5 minutes (early) and on watermark (late) with accumulating fired panes
Why this is correct
Fixed windows match the hourly aggregation requirement. Allowed lateness of 2 hours handles late data. Early triggers provide near-real-time results. Accumulating fired panes ensures updates are included.
- ✗
Global window with triggers every 5 minutes
Why it's wrong here
Global window does not provide hourly boundaries; it would aggregate all data together, not by hour.
- ✗
Sliding windows of 1 hour with 30-minute offset
Why it's wrong here
Sliding windows create overlapping windows, which is not what is needed for distinct hourly aggregations.
- ✗
Session windows with 10-minute gap duration
Why it's wrong here
Session windows group events based on inactivity gaps, not fixed hourly intervals.
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