Automated Code Rollback via Error Rate Monitoring
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Solution Overview
Problem
Updated versions of code and APIs often result in increased error rates and latency, leading to disruptions in service performance for end-user devices due to changes in cloud-based computing resource configurations, necessitating a method to efficiently rollback to previous versions when issues arise.
Innovation Solution
An automated rollback system that monitors error rates and other performance metrics to detect rollback conditions, allowing for the seamless transition from updated to previous versions of code or APIs, thereby minimizing latency and resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If updated versions of code or APIs are deployed to provide new functionality and fixes, then the functionality and problem-solving capability are improved, but the error rate increases and service reliability deteriorates
Solution Approach 1:
The system dynamically switches between different versions of code or APIs based on real-time performance monitoring. When the updated version exhibits high error rates, the system automatically routes traffic to the previous stable version, making the version selection flexible and adaptive rather than static
Solution Approach 2:
The system changes the version parameter of the deployed code or API based on performance metrics. By monitoring error rates and automatically switching version parameters between updated and previous versions, the system resolves the contradiction between adopting new functionality and maintaining reliability
2Adaptability or versatility
If updated versions of code or APIs are deployed, then new functionality is added, but latency increases and service performance deteriorates
Solution Approach 1:
The system implements continuous feedback loops that monitor latency and performance metrics of updated code versions. When latency exceeds thresholds, the feedback mechanism triggers automatic rollback to previous versions, ensuring performance degradation is quickly detected and corrected
Solution Approach 2:
The system dynamically adjusts the deployed version based on real-time latency measurements, making the version selection adaptive to current performance conditions rather than fixed
3Adaptability or versatility
If configurations of computing resources are changed in cloud-based architecture, then new capabilities are implemented, but the operation of end-user devices is adversely affected
Solution Approach 1:
The system takes preliminary protective action by maintaining previous stable versions ready for immediate rollback. Before fully deploying updated configurations that may affect end-user devices, the system has backup versions prepared to counteract potential harmful effects
Solution Approach 2:
The system introduces a version management intermediary layer between the updated code deployment and end-user devices. This intermediary monitors performance and can switch to previous versions, buffering the impact on end-user devices
Data Source
AI summary
Technologies are described for determining when an updated version of a configuration of a computing resource may be rolled back to a previous version. The computing resource can include code for a function or the computing resource can be used to implement calls of an API. In some cases, the computing resource can be tagged to indicate that rollback functionality is applicable to the computing resource. The rollback to a previous version of code for a function or a previous version of an API may take place based on a rollback condition being satisfied that is related to error rates that take place when updated versions of a function or an API are utilized. The systems and methods described herein may be implemented in conjunction with a cloud-based storage system.