Database Artifact Redeployment via Change Detection
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Solution Overview
Problem
The HANA Deployment Infrastructure (HANA DI) often performs unnecessary or non-optimal re-deployment of database objects during deployment, leading to increased application development and installation times, CPU load, and database traffic due to the execution of unnecessary operations.
Innovation Solution
The methodology determines if database objects need to be recreated by using an IS_VALID indicator and supports a redeployment option for build plugins, optimizing the re-deployment process by skipping unnecessary drop and create operations, and processing artifacts according to an execution graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If HANA DI automatically calculates all dependent database objects and re-deploys them to achieve consistent target state, then deployment consistency is improved, but application development and installation times increase
Solution Approach 1:
The patent applies partial action by determining whether database objects actually need to be re-deployed based on change detection, rather than re-deploying all dependent objects unconditionally. The system performs selective re-deployment only for objects that have actual changes, reducing unnecessary operations while maintaining deployment consistency for objects that require updates.
Solution Approach 2:
The patent implements self-service through automated change detection mechanisms that identify which database objects have actually changed. The system uses dependency graphs and change tracking to autonomously determine the minimal set of objects requiring re-deployment, eliminating the need for manual intervention while optimizing the re-deployment process.
2Reliability
If HANA DI re-deploys all dependent database objects, then deployment completeness is improved, but CPU load increases
Solution Approach 1:
The system performs partial re-deployment by identifying and processing only those database objects that have actual changes. The change detection mechanism compares current object states with previous states, and the deployment engine processes only the minimal necessary subset of objects, significantly reducing CPU load while ensuring completeness for changed objects.
3Reliability
If HANA DI re-deploys all dependent database objects, then deployment consistency is improved, but database traffic increases
Solution Approach 1:
The patent reduces database traffic by performing partial re-deployment of only changed objects. The system tracks which objects have modifications and limits database operations to those specific objects, avoiding unnecessary data transfers and operations on unchanged objects while maintaining consistency for the affected subset.
4Ease of manufacture
If unnecessary drop and create operations are executed, then artifact processing is simplified, but runtime increases
Solution Approach 1:
The patent extracts the essential re-deployment operations by removing unnecessary drop and create steps. The system analyzes object changes and executes only the minimal required operations (such as alter statements for schema changes or selective recreations), eliminating redundant full drop-create cycles while maintaining artifact processing effectiveness.
Data Source
AI summary
Whether database objects corresponding to an artifact to deploy already exists in a database is determined. Depending artifacts are calculated using a prior-generated dependency graph. Whether any database objects corresponding to the calculated depending artifacts need to be redeployed are determined. Whether a build plugin corresponding to the database objects needing to be redeployed supports a redeployment option is determined. The artifacts are processed according to an execution graph.