In-memory processing

In computer science, in-memory processing (PIM) is a computer architecture for processing data stored in an in-memory database. In-memory processing improves the power usage and performance of moving data between the processor and the main memory. Older systems have been based on disk storage and relational databases using Structured Query Language, which 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 analyzed in real time, enabling faster reporting and decision-making in business. With 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 (RDBMS), often based on the structured query language (SQL), such as SQL Server, MySQL, Oracle and many others. RDBMS 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. Information technology (IT) staff spend substantial development time on optimizing databases, constructing indexes and aggregates, designing cubes and star schemas, data modeling, and query analysis.