In-Memory Database Calculation Scenario Recreation
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Solution Overview
Problem
In-memory databases used for OLAP systems face data loss when restarted due to volatile memory storage, leading to the loss of calculation scenarios essential for multidimensional analytical data processing.
Innovation Solution
A dynamic recreation mechanism is implemented, where a multidimensional analytical data view compiler recreates calculation scenarios in the in-memory computing engine if they are not available, utilizing MDAV metadata to redeploy and restore the necessary data views and operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is stored in volatile memory for fast access in in-memory databases, then query performance is improved, but data is lost when the system shuts down or restarts
Solution Approach 1:
The system performs preliminary actions by pre-compiling and caching calculation scenarios in the in-memory database before system shutdown. When the system restarts, these pre-prepared calculation scenarios are immediately available, eliminating the need to re-execute complex queries and thus resolving the contradiction between fast query performance and data persistence across restarts.
Solution Approach 2:
The patent creates a copy of calculation scenarios by serializing them to disk storage during system shutdown, while maintaining the original volatile memory copies for active queries. This copying mechanism allows the system to recover from restarts without sacrificing the speed benefits of in-memory processing, as the copied scenarios can be quickly loaded back into memory.
2Productivity
If calculation scenarios are stored in volatile memory for rapid execution, then analytical processing speed is improved, but scenarios are lost during system restart
Solution Approach 1:
The system implements a feedback mechanism where the in-memory database monitors its own state during shutdown and automatically triggers serialization of calculation scenarios to disk. Upon restart, the system queries for the availability of these scenarios and reconstructs them if necessary. This feedback loop ensures that calculation scenarios are preserved across restarts while maintaining rapid execution performance during operation.
3Reliability
If the system checks availability of calculation scenarios before execution, then data loss is prevented, but system complexity and execution time increase
Solution Approach 1:
The in-memory database performs self-service by automatically managing the serialization and reconstruction of calculation scenarios without requiring external intervention or complex manual checks. The system monitors its own state, serializes scenarios during shutdown, and reconstructs them upon restart, thereby ensuring data availability while avoiding the complexity of external availability checking mechanisms.
Data Source
AI summary
According to one aspect of systems and methods for dynamic recreation of multidimensional analytical data, lost sets of calculation scenarios that provide multidimensional analytical data results after aggregations and transformations of the multidimensional analytical data are recreated in the volatile storage of an in-memory computing engine. A multidimensional analytical data view (MDAV) compiler is triggered to read the MDAV metadata stored in an intermediate buffer in the MDAV compiler. The read MDAV metadata is compiled into a calculation scenario including calculation view metadata. The calculation view metadata is stored in the intermediate buffer. The recreated set of calculation scenarios is deployed on the in-memory computing engine.