In-memory processing

In computer science, in-memory processing is a developing technology for processing of data stored in an in-memory database. Older systems have been based on disk storage and relational databases using SQL query language, but these are increasingly regarded as inadequate to meet business intelligence (BI) needs. Because stored data is accessed much more quickly when it is placed in Random Access Memory (RAM) or flash memory, in-memory processing allows data to be analysed in real time, enabling faster reporting and decision-making in business.

With the hitherto prevalent disk-based technology, data is loaded on to the computer's hard disk in the form of multiple tables and multi-dimensional structures against which queries are run. Disk based technologies are Relational Database Management Systems (RDMS), often based on the structured query language (SQL), such as SQL Server, MySQL, Oracle and many others. RDMS are designed for the requirements of transactional processing. Using a database that supports insertions and updates as well as performing aggregations, joins (typical in BI solutions) are typically very slow. Another drawback is that SQL is designed to efficiently fetch rows of data, while BI queries usually involve fetching of partial rows of data involving heavy calculations.

To improve query performance, multidimensional databases or OLAP cubes - also called multidimensional online analytical processing (MOLAP) - are constructed. Designing a cube is an elaborate and lengthy process, and changing the cube's structure to adapt to dynamically changing business needs may be cumbersome. Cubes are pre-populated with data to answer specific queries and although they increase performance they are still not suitable for answering ad hoc queries.

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