Question 6 of 890
hardMultiple ChoiceObjective-mapped
Dataflow Autoscaling for Pub/Sub Backlog
A company processes IoT sensor data in near real-time. They ingest data via Cloud Pub/Sub, then a Dataflow streaming pipeline writes to Bigtable for low-latency queries. Recently, they observed increased Pub/Sub message backlog during traffic spikes. What is the most effective scaling strategy?
Quick Answer
The answer is to increase the Dataflow worker count and adjust the autoscaling configuration. This is correct because a growing Pub/Sub backlog during traffic spikes signals that the Dataflow pipeline is the bottleneck—it cannot consume messages as fast as Pub/Sub publishes them. Dataflow autoscaling for Pub/Sub backlog relies on horizontal scaling; by raising the maximum worker count and fine-tuning the autoscaling parameters, the pipeline dynamically adds more workers to match the incoming throughput, thereby draining the backlog. On the Google Professional Data Engineer exam, this scenario tests your understanding of streaming pipeline throughput and the consumer-producer relationship in Pub/Sub. A common trap is to assume Pub/Sub itself is the problem, but Pub/Sub is built for high ingestion rates—the real issue is always the consumer’s processing capacity. Memory tip: “Backlog means the consumer is slow, not the source.”
⚠ Common exam trap
The trap here is that candidates mistakenly think Pub/Sub's throughput is limited by partitions (like Kafka) or that throttling the publisher is a valid scaling strategy, when in fact the bottleneck is the streaming pipeline's processing capacity, which must be scaled horizontally.
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
✓
Increase Dataflow worker count and adjust autoscaling configuration
The increased Pub/Sub backlog during traffic spikes indicates that the Dataflow pipeline is unable to consume messages as fast as they are being published. Increasing the Dataflow worker count and adjusting autoscaling configuration allows the pipeline to scale horizontally, processing more messages per second and reducing the backlog. Pub/Sub itself is designed to handle high throughput, so the bottleneck is the consumer (Dataflow), not the ingestion layer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase Pub/Sub subscription throughput by increasing the number of partitions
Why it's wrong here
Pub/Sub subscriptions do not have partitions; the backlog is at the subscription level and scaling workers is more effective.
- ✓
Increase Dataflow worker count and adjust autoscaling configuration
Why this is correct
Dataflow autoscaling can handle backlogs if enough workers are provisioned; increasing the max number of workers allows the pipeline to catch up during spikes.
- ✗
Use a Cloud Scheduler to throttle Pub/Sub publishing
Why it's wrong here
Throttling publishing defeats the real-time purpose and may cause data loss.
- ✗
Add a Cloud Function to pre-process messages before they are consumed by Dataflow
Why it's wrong here
Adding a Cloud Function adds an extra hop and does not directly improve Dataflow's processing capacity.
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Same concept, more angles
1 more way this is tested on PDE
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A gaming company uses Pub/Sub to ingest player events and Dataflow for real-time analytics. They notice that the Pub/Sub subscription backlog is growing despite the Dataflow pipeline running continuously. The pipeline has a 1-hour window for aggregations. What is the most effective way to reduce the backlog?
hard- ✓ A.Increase the Dataflow pipeline's worker count via autoscaling.
- B.Use a push subscription instead of pull.
- C.Decrease the window duration to 10 minutes.
- D.Enable Pub/Sub topic retention.
Why A: Increasing the Dataflow pipeline's worker count via autoscaling directly addresses the backlog by adding more parallel processing capacity to consume messages from the Pub/Sub subscription faster. Since the pipeline is continuously running but the backlog grows, the bottleneck is processing throughput, not pipeline availability. Autoscaling allows Dataflow to dynamically allocate more workers based on the backlog size, matching consumption rate to the incoming message rate.
Last reviewed: Jun 24, 2026
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