In-Memory Database Historization for OLTP-OLAP Consolidation
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Solution Overview
Problem
Traditional database management systems are inefficient in handling both transactional and analytical workloads, requiring separate databases and significant effort for reporting, which increases operational costs and reduces performance.
Innovation Solution
An in-memory database platform that processes transactions with validity times, updates a time-dependent data view to capture validity information, and stores this information in a historization table, enabling historical access and consolidating OLTP and OLAP functions into a single database, eliminating the need for separate reporting databases and tuning structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional database management systems are used for analytical workloads, then reporting and analytical functions can be performed, but significant effort is required to create and maintain tuning structures such as aggregates and indexes, and performance remains moderate at best
Solution Approach 1:
The patent merges OLTP and OLAP workloads into a single in-memory database platform, eliminating the need for separate reporting databases. The system consolidates transactional processing and analytical querying capabilities in one unified engine, removing the complexity of maintaining separate tuning structures like aggregates and indexes while delivering high performance for both workload types simultaneously.
Solution Approach 2:
The patent changes the fundamental parameter of data storage location by moving data from disk-based storage to in-memory storage. This parameter change enables the database to achieve high-speed analytical performance without requiring complex pre-computed tuning structures, as the in-memory architecture naturally provides fast access to raw data for analytical queries.
2Adaptability or versatility
If transactional database management systems are used for analytical workloads, then a single database can handle both types of operations, but performance for analytical queries deteriorates due to lack of specialized tuning structures
Solution Approach 1:
The patent creates a universal database engine that performs both OLTP and OLAP workloads with high performance. The in-memory architecture provides multi-functionality by enabling the same database instance to efficiently handle transactional operations, analytical queries, and ad-hoc reporting without requiring specialized tuning structures or separate systems, thus achieving versatility without sacrificing speed.
3Speed
If separate reporting databases are used to handle analytical workloads, then analytical performance can be improved, but operational costs increase and data must be transformed and loaded into the reporting database
Solution Approach 1:
The patent eliminates the need for separate reporting databases by merging analytical and transactional capabilities into a single in-memory database system. This consolidation removes the complexity of maintaining multiple databases and the associated data transformation and loading processes, while still delivering high analytical query performance through the in-memory architecture.
4Speed
If data is stored in slower disk drive devices, then storage costs are reduced, but access speed for real-time analytics deteriorates
Solution Approach 1:
The patent changes the storage medium parameter from disk-based to in-memory storage. This parameter change prioritizes speed over cost, using volatile memory resources to achieve real-time data access speeds required for modern analytics workloads. The in-memory architecture enables the database to hold working datasets in RAM, providing orders of magnitude faster access compared to traditional disk-based systems.
Data Source
AI summary
A method includes processing a transaction on an in memory database where data being processed has a validity time, updating a time dependent data view responsive to the transaction being processed to capture time validity information regarding the data, and storing the time validity information in a historization table to provide historical access to past time dependent data following expiration of the validity time.