Dataflow Pipeline Deployment System for Distributed Applications
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Solution Overview
Problem
Current software development processes, particularly in distributed applications, face frequent outages and integration conflicts due to complex dependencies across components, leading to downtime during updates, as changes in one process require updates in upstream and downstream processes, and the longer development continues on a branch without merging back to the mainline, the higher the risk of integration conflicts and failures.
Innovation Solution
A dataflow pipeline deployment system and method that manages pipelines across multiple environments by using a dataflow pipeline deployer, Git repository, secret store, and pipeline flow registry to selectively update and promote source code, secrets, and configurations, pausing only affected components during updates to minimize downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional software update processes are used in distributed applications, then updates can be implemented, but system downtime occurs and integration conflicts arise due to complex dependencies across components
Solution Approach 1:
The patent segments the monolithic update process into independent, modular components. Each microservice can be updated independently through version control branches, allowing selective deployment without requiring system-wide downtime. The build automation system handles each component separately, enabling continuous availability of unaffected services.
Solution Approach 2:
The patent implements preliminary actions by creating version-controlled branches before updates and performing automated builds and validations in advance. Dependencies are identified and prepared beforehand, and test deployments are executed in staging environments prior to production updates, preventing integration conflicts and reducing actual downtime.
2Productivity
If developers work on branches without frequent merging to mainline, then development productivity is improved, but integration conflicts and failures increase
Solution Approach 1:
The patent implements continuous feedback mechanisms through automated build systems that constantly validate code against the mainline. Version control systems provide real-time feedback on integration status, and automated testing gives immediate feedback on compatibility issues. This allows developers to maintain productivity on branches while ensuring integration reliability through continuous validation.
Solution Approach 2:
The patent makes the development process dynamic by allowing flexible branching strategies while maintaining automated synchronization with the mainline. The system adapts to different development workflows, enabling long-lived branches when needed while automatically managing dependency updates and integration validation, thus balancing productivity with integration success.
3Stability of the object's composition
If updates are propagated across all environments sequentially, then environment consistency is maintained, but deployment time increases
Solution Approach 1:
The patent implements periodic automated synchronization actions that propagate updates across environments at scheduled intervals rather than sequentially. The build automation system periodically checks for updates, validates dependencies, and deploys across development, staging, and production environments in a coordinated manner, maintaining consistency while reducing total deployment time through parallel processing.
Data Source
AI summary
A computer-based method for managing a dataflow pipeline across a plurality of environments, includes the steps of: receiving, for a first environment, a check-in comprising source code, a task definition, a configuration, and a flow definition; receiving a definition of an application secret and an associated credential; storing the application secret and credential in a dataflow pipeline deployer data store; creating a data flow pipeline package, further comprising the steps of: merging the source code, the configuration, and the secrets; and defining a pipeline graph in the first environment pipeline flow registry.