Container Hot Upgrade Routing for Database Schema Changes
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Solution Overview
Problem
Existing hot patching methods in cloud environments cause service disruptions and runtime delays during database schema changes, leading to failed tasks and reduced throughput.
Innovation Solution
A method for seamless hot upgrades that involves identifying and separating tasks affected by a database schema change, storing unaffected tasks in the old container, executing unaffected tasks, and instantiating a new container for transformation and execution of affected tasks, allowing continuous service provision without stop-and-start operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional hot patching is used during database schema changes, then service continuity is maintained, but runtime delays and service disruptions occur
Solution Approach 1:
The patent segments tasks into two categories: those affected by database schema changes and those unaffected. The upgrade process is divided into phases where unaffected tasks continue executing in the old container while affected tasks are handled separately, eliminating runtime delays for unaffected operations while maintaining service continuity.
Solution Approach 2:
The system performs preliminary identification and labeling of tasks affected by database schema changes before the upgrade process begins. This allows proactive separation of tasks, enabling the old container to continue executing unaffected tasks without interruption while preparing the new container for affected tasks, thus preventing runtime delays.
2Adaptability or versatility
If database schema changes are implemented, then system functionality is improved, but service disruptions and failed tasks occur
Solution Approach 1:
The patent introduces a new container as an intermediary that handles tasks affected by database schema changes. The new container acts as a mediator between the old container and the upgraded database schema, transforming affected tasks to work with the new schema while the old container continues serving unaffected tasks, thus preventing service disruptions.
Solution Approach 2:
The system changes the operational parameters of task execution by introducing labeling to identify affected tasks and routing them differently. Tasks are transformed with new parameters to work with the updated database schema, allowing functionality improvement while maintaining service reliability through parameter-based task differentiation.
3Reliability
If continuous service provision during upgrades is maintained, then customer experience is improved, but throughput decreases
Solution Approach 1:
The patent ensures continuity of useful action by allowing the old container to continue executing unaffected tasks throughout the upgrade process. This maintains throughput for unaffected operations while the new container handles affected tasks, achieving both continuous service and sustained productivity without the trade-off present in traditional approaches.
Data Source
AI summary
A computer-implemented method for executing a hot upgrade process is provided. The computer-implemented method includes receiving a process upgrade request during executions of first and second tasks by an old container, determining that the process upgrade request affects the first task but not the second task, labeling the first task but not the second task and acting on the process upgrade request. The computer-implemented method further includes, based on the labeling, storing the first task by the old container and executing the second task by the old container, deleting the old container and instantiating a new container for transformation and execution of the first task.