Intelligent memory block replacement

Abstract

A framework for intelligent memory replacement of loaded data blocks by requested data blocks is provided. For example, various factors are taken into account to optimize the selection of loaded data blocks to be discarded from the memory, in favor of the requested data blocks to be loaded into the memory. In some implementations, correlations between the requested data blocks and the loaded data blocks are used to determine which of the loaded data blocks may become candidates to be discarded from memory.

Description

technical field [0001] The present disclosure relates generally to databases, and more specifically, to replacing or exchanging data in memory. Background technique [0002] In-memory databases were developed to take full advantage of modern hardware to improve performance. By maintaining all relevant data in main memory (eg, random access memory (RAM)), data processing operations can be significantly accelerated. [0003] More and more enterprises and institutions have applied in-memory databases to process and analyze big data (bigdata). Since all relevant data is kept in memory, in-memory databases undoubtedly surpass traditional databases in terms of computing speed. However, keeping all the data in memory can also be problematic when the available memory size (RAM) is less than the total data size of the database. For example, in this case less than the entire database is loaded into memory at a time. Memory replacement techniques are generally used in such situatio...

AI Technical Summary

Problems solved by technology

In such cases, traditional page-based memory replacement techniques usually do not work

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