You use Spinnaker to deploy your application and have created a canary deployment stage in the pipeline.
Your application has an in-memory cache that loads objects at start time.
You want to automate the comparison of the canary version against the production version.
How should you configure the canary analysis?
Click on the arrows to vote for the correct answer
A. B. C. D.D.
https://cloud.google.com/solutions/automated-canary-analysis-kubernetes-engine-spinnakerSure, I'd be happy to provide a detailed explanation of the options provided and the most appropriate approach.
Spinnaker is a popular continuous delivery platform used to automate application deployments. It provides a canary deployment stage in the pipeline that enables developers to roll out changes to a subset of users, monitor the results, and then decide whether to roll out the changes to the rest of the users. Canary deployments allow you to test the impact of the new changes on a small subset of users before rolling out to the entire production environment.
When it comes to configuring canary analysis, there are different approaches you can take. The goal of canary analysis is to compare the canary version of the application (the new version being tested) with the production version and evaluate the impact of the changes made. Canary analysis helps determine if the canary version is performing better or worse than the production version and whether the changes made are ready to be rolled out to the entire production environment.
Let's discuss the options provided in the question and determine the most appropriate approach:
A. Compare the canary with a new deployment of the current production version. This option suggests comparing the canary version with a new deployment of the current production version. This approach means deploying a new version of the application in the production environment and then comparing it with the canary version. While this option could provide useful insights, it may not be the most efficient approach as deploying a new version in the production environment can be risky and may cause disruptions.
B. Compare the canary with a new deployment of the previous production version. This option suggests comparing the canary version with a new deployment of the previous production version. This approach means deploying the previous version of the application in the production environment and then comparing it with the canary version. This approach could provide more accurate insights into the impact of the changes made since you are comparing the canary version with the version currently in production. However, it may not account for any changes or improvements made to the production version since it was last deployed.
C. Compare the canary with the existing deployment of the current production version. This option suggests comparing the canary version with the existing deployment of the current production version. This approach means using the currently running production version and comparing it with the canary version. This approach could be the most efficient approach since you are not deploying a new version in the production environment. However, it may not account for any changes or improvements made to the production version since it was last deployed.
D. Compare the canary with the average performance of a sliding window of previous production versions. This option suggests comparing the canary version with the average performance of a sliding window of previous production versions. This approach means using a sliding window of previous production versions and averaging their performance before comparing them with the canary version. This approach could provide more insights into the impact of the changes made by comparing them to the average performance of previous versions. However, this approach may not account for any significant changes or improvements made to the current production version.
So, based on the options provided, the most appropriate approach would be to choose option C. Compare the canary with the existing deployment of the current production version. This approach is the most efficient since you are not deploying a new version in the production environment. However, it's essential to ensure that the existing deployment of the current production version is stable and has been running for a sufficient period to establish a baseline of performance metrics. Additionally, it's crucial to include appropriate metrics to evaluate the impact of the changes made, such as response times, error rates, and throughput.