Google Professional Data Engineer Question 265
Select 3Google Cloud PlatformYou are managing a data processing pipeline on Google Cloud that uses Dataflow for real-time stream processing. During a recent incident, one of the worker nodes failed unexpectedly, causing delays in processing. To ensure better awareness of failures and mitigate their impact in the future, which actions should you take?
- A
Set up Cloud Monitoring alerts to notify you when worker nodes fail or when processing latencies exceed thresholds.
- B
Enable Dataflow's autoscaling feature to automatically add more worker nodes during high demand and replace failed nodes.
- C
Use BigQuery to periodically query logs for errors instead of setting up continuous monitoring.
- D
Implement a retry mechanism in your pipeline code to handle transient failures gracefully.
- E
Enable Dataflow's snapshot feature to create regular backups of in-flight data for recovery purposes.
Show answer and explanation
Correct answers: A, B, D
Explanation
To maintain awareness of failures and mitigate their impact in a real-time data processing pipeline, you should use proactive monitoring and automated recovery mechanisms. Cloud Monitoring alerts provide real-time failure detection, Dataflow's autoscaling ensures resilience by replacing failed nodes, and retry mechanisms ensure transient failures are handled. Periodic querying of logs and Dataflow snapshots are not appropriate solutions in this scenario.
- A. Correct.
Setting up Cloud Monitoring alerts will allow you to detect failures or latency issues in real-time, enabling faster response times and minimizing impact.
- B. Correct.
Enabling Dataflow's autoscaling feature ensures that failed worker nodes are automatically replaced and that the pipeline can handle increased loads without manual intervention.
- C. Incorrect.
Using BigQuery to periodically query logs for errors is not a proactive or efficient approach for detecting failures in a real-time pipeline.
- D. Correct.
Implementing a retry mechanism in the pipeline code helps handle transient failures, reducing the risk of pipeline disruptions.
- E. Incorrect.
Dataflow's snapshot feature is not designed for mitigating the impact of worker node failures; it is primarily used for job resumption in batch processing scenarios.