Canary Deployment Automation via Changeset Complexity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current canary deployment methods require manual orchestration and lack automation, leading to inefficient traffic routing based on changeset complexity, resulting in either slow deployments for simple changes or rapid exposure of complex changes to users, with a wide 'blast radius' and potential user impact.
Innovation Solution
Implementing a system that analyzes changeset complexity using static analysis and instrumentation to generate a progressive deployment plan, automatically directing traffic to a new microservice version based on complexity, ensuring gradual and verified transitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual canary deployment orchestration is used, then deployment control is maintained, but deployment efficiency decreases and manual labor increases
Solution Approach 1:
The system performs self-service by automatically analyzing changeset complexity through static analysis and instrumentation, then autonomously generating and executing deployment plans without requiring manual intervention. The deployment system analyzes its own workload and makes decisions about traffic routing based on complexity metrics.
Solution Approach 2:
The patent replaces manual mechanical orchestration with automated computational analysis. Instead of humans manually managing deployment processes, the system uses static analysis tools and instrumentation mechanisms to automatically determine complexity and generate deployment plans, substituting human operations with automated technical processes.
2Reliability
If traffic routing is based on changeset complexity without automation, then deployment speed can be controlled, but manual labor and error increase
Solution Approach 1:
The system implements feedback loops where static analysis and instrumentation continuously monitor changeset complexity, and this information feeds back into automatic deployment plan generation and traffic routing decisions. The system adapts its behavior based on real-time complexity assessment.
Solution Approach 2:
The patent introduces static analysis and instrumentation as intermediary components between code changes and deployment execution. These intermediaries automatically assess complexity and translate it into deployment parameters, mediating the interaction between developers and the deployment system.
3Productivity
If simple changes are deployed rapidly, then productivity increases, but user impact and blast radius increase
Solution Approach 1:
The system dynamically changes deployment parameters such as traffic percentage and rollout speed based on the analyzed complexity of changesets. Simple changes receive faster deployment with higher traffic percentages, while complex changes receive slower, more controlled rollouts with lower initial traffic percentages, automatically adjusting parameters to balance speed and safety.
4Speed
If complex changes are deployed with high traffic immediately, then deployment speed increases, but harmful effects on users increase
Solution Approach 1:
The patent implements dynamic deployment strategies where traffic routing percentages and rollout velocities are not fixed but adapt based on changeset complexity analysis. The system continuously adjusts deployment parameters in real-time, transitioning from conservative to aggressive rollout strategies based on analyzed complexity metrics.
Data Source
AI summary
A system can identify that computer code that is executable to operate at least one microservice that is part of a group of microservices has been modified. The system can determine complexity data representative of a complexity of changes to the computer code. The system can determine conditions under which the changes to the computer code are invoked based on at least one of performing a static analysis of the computer code or instrumenting the computer code. The system can generate a progressive deployment plan for the at least one microservice based on the complexity of changes. The system can progressively direct traffic to the at least one microservice based on the progressive deployment plan, and the conditions under which the changes to the computer code are invoked.