Prioritized Database Artifact Deployment via Dependency Graphs
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Solution Overview
Problem
The deployment of database artifacts in large database systems is complex and time-consuming due to the need for manual determination of the correct order of deployment, especially when dealing with hundreds or thousands of artifacts with complex dependencies, and the lack of clear execution order in dependency graphs.
Innovation Solution
A deployment infrastructure that uses a declarative approach and prioritized processing to ensure correct deployment order through the creation of dependency graphs with additional 'push-forward' edges and data structures to handle predecessor and successor nodes, allowing for parallel processing and transactional deployment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual deployment of database artifacts is performed, then deployment accuracy can be maintained, but deployment time and complexity increase significantly
Solution Approach 1:
The system performs self-service by automatically analyzing artifact dependencies and determining deployment order without human intervention. The deployment infrastructure autonomously processes artifact metadata, builds dependency graphs, and executes deployment sequences, eliminating the need for manual deployment while maintaining accuracy through systematic automated analysis.
Solution Approach 2:
The patent replaces manual mechanical deployment processes with an automated computational system. Instead of manually tracking and deploying artifacts in correct order, the system uses algorithmic dependency analysis and automated deployment infrastructure to determine and execute the proper deployment sequence, substituting human effort with computational automation.
2Productivity
If automated deployment is implemented, then deployment speed increases, but deployment errors may occur due to incorrect ordering
Solution Approach 1:
The system performs preliminary action by building complete dependency graphs and determining deployment sequences before actual deployment begins. Artifact metadata is analyzed upfront, dependencies are resolved in advance, and the deployment order is predetermined, ensuring that when deployment executes, artifacts are deployed in the correct sequence without errors.
Solution Approach 2:
The deployment infrastructure incorporates feedback mechanisms by continuously monitoring deployment status and artifact dependencies. The system tracks which artifacts have been deployed, updates the dependency graph in real-time, and uses this feedback to determine the next artifact to deploy, ensuring correct ordering and preventing deployment errors.
3Manufacturing precision
If dependency graphs are enhanced with additional edges and data structures, then deployment order accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces intermediary elements such as dependency graphs and metadata structures that mediate between artifact definitions and deployment execution. These intermediaries organize and represent dependency relationships in a structured format, making the complex relationships manageable and enabling accurate deployment order determination without directly increasing operational complexity.
4Productivity
If parallel processing is enabled, then deployment efficiency increases, but coordination complexity and error potential increase
Solution Approach 1:
The system applies segmentation by dividing the deployment process into independent parallel tasks based on dependency analysis. Artifacts with no dependencies or whose dependencies are already satisfied can be deployed in parallel, while the coordination complexity is managed by segmenting the work according to the pre-analyzed dependency graph, allowing safe parallel execution.
Data Source
AI summary
Systems and techniques for ordering deployment of artifacts to a database include collecting predecessor node information for nodes in a dependency graph. The dependency graph includes available edges connecting the nodes of the dependency graph, where each node in the dependency graph represents at least one artifact. A data structure contained within each of the nodes is populated with the predecessor node information. Successor node information is determined using the predecessor node information and an updated dependency graph is created by adding additional edges to the dependency graph using the predecessor node information in the data structure within each of the nodes and the successor node information. The artifacts are ordered for deployment to the database using the updated dependency graph.