Learn how to mitigate API quota errors during traffic spikes when your Cloud Run application queries a Cloud SQL database. Discover the best configuration change to optimize performance.
Table of Contents
Question
Your web application is hosted on Cloud Run and needs to query a Cloud SQL database. Every morning during a traffic spike, you notice API quota errors in Cloud SQL logs. The project has already reached the maximum API quota. You want to make a configuration change to mitigate the issue. What should you do?
A. Modify the minimum number of Cloud Run instances.
B. Use traffic splitting.
C. Modify the maximum number of Cloud Run instances.
D. Set a minimum concurrent requests environment variable for the application.
Answer
C. Modify the maximum number of Cloud Run instances.
Explanation
When dealing with a web application hosted on Cloud Run that queries a Cloud SQL database, API quota errors during traffic spikes can be a significant issue. The problem described in the question stems from the project reaching its maximum API quota, particularly during morning traffic spikes. To mitigate this issue, the most effective configuration change is to modify the maximum number of Cloud Run instances.
Here’s why this solution is the most appropriate:
- Scalability: Cloud Run is designed to scale automatically based on incoming requests. By increasing the maximum number of instances, you allow the service to handle higher concurrent loads during traffic spikes.
- API quota distribution: When you increase the maximum number of instances, you’re effectively distributing the API calls across more containers. This can help prevent hitting the API quota limits for a single instance.
- Performance improvement: More instances mean more resources available to handle incoming requests, reducing the likelihood of bottlenecks and improving overall performance during peak times.
- Cost-effective: Cloud Run only charges for the actual resources used, so increasing the maximum instances doesn’t necessarily increase costs if those instances aren’t needed.
Let’s examine why the other options are not as suitable:
A. Modify the minimum number of Cloud Run instances: While this might help to some extent, it’s not as effective as modifying the maximum number. The minimum number ensures a baseline of instances, but it doesn’t address the ability to scale during traffic spikes.
B. Use traffic splitting: Traffic splitting is typically used for gradual rollouts or A/B testing. It doesn’t address the core issue of API quota errors during traffic spikes.
D. Set a minimum concurrent requests environment variable for the application: This approach focuses on the application level rather than the infrastructure level. While it might help manage concurrent requests within a single instance, it doesn’t address the overall scaling needs during traffic spikes.
In conclusion, by modifying the maximum number of Cloud Run instances, you provide your application with the necessary scalability to handle traffic spikes effectively, distribute API calls more evenly, and reduce the likelihood of hitting API quota limits. This configuration change directly addresses the root cause of the problem while maintaining the flexibility and cost-effectiveness of Cloud Run.
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