PDE Ingesting and Processing the Data Practice Question
A company uses BigQuery's Storage Write API in committed mode to stream data. They notice that some writes are failing with 'DEADLINE_EXCEEDED' errors during peak traffic. The pipeline is a Dataflow job using the Beam SDK. What is the MOST likely cause and solution?
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
✓
The default RPC timeout is too low for the write throughput; increase the timeout in the Storage Write API configuration.
Committed mode requires immediate acknowledgment from BigQuery. Under high traffic, the default timeout may be exceeded. The solution is to increase the timeout or switch to buffered mode, which provides higher throughput by batching. The error is not due to schema mismatch or permissions; those would cause different errors. Pub/Sub is not involved in the write path.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Dataflow workers lack sufficient memory; increase worker memory.
Why it's wrong here
Memory issues would cause OOM or backpressure, not deadline exceed errors.
- ✓
The default RPC timeout is too low for the write throughput; increase the timeout in the Storage Write API configuration.
Why this is correct
High traffic can cause RPCs to exceed the default timeout; increasing the timeout allows more time for acknowledgment.
- ✗
The Pub/Sub subscription is not sending acknowledgments; check the subscription.
Why it's wrong here
Pub/Sub is not used in the write path; the error is from BigQuery, not Pub/Sub.
- ✗
The row schema has changed; update the schema before writing.
Why it's wrong here
Schema mismatch causes 'INVALID_ARGUMENT' errors, not 'DEADLINE_EXCEEDED'.
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