Relational data cache and inquiry method and device

Abstract

An embodiment of the invention discloses a relational data cache and inquiry method and device. The cache method includes the steps: receiving relational data to be cached and then caching each of associated sub-data into a corresponding column according to the corresponding relation between each of the associated sub-data and each column in a column cluster; updating bitmap identifiers of corresponding lines in a bitmap index according to the condition whether relational data are cached in each column or not. As the relational data are cached based on the corresponding relation table of the bitmap index and each column and divided into a plurality of associated sub-data to be respectively cached, the cache efficiency of the relational data is improved. Moreover, the method is applied to a memory of an electronic device, and the use ratio of the memory is increased.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a relational data cache and query method and device. Background technique [0002] The data exchange buffer of computer hardware technology is generally a storage module between the memory and the CPU, prior to the memory, so that the CPU can quickly access the required data. Cache in software technology generally opens up an area in the memory and solid-state hard disk to store some frequently accessed data to supplement the difference in memory and the problem of slow disk access. [0003] The current mainstream caching systems include Memcache d system and Redis system. The Memcached system caches data based on Key-Value. Key-Value is a storage and access method for Key and data Value. For example, <1, a>, <2, b> can access data a through key values ​​1 and 2. and b. In addition to supporting Key-Value for data caching, the Redis system can also support ...

AI Technical Summary

Problems solved by technology

However, the HBase database is a database based on external memory, and the carrier of the database cache data is still disk. Although a small amount of data can be cached in the memory, when the read data exceeds the amount of data cached in the memory, the read performance will decrease. significantly decreased

In addition, for the Hbase database, when caching relational data, the relational data needs to be stored in different columns. In the existing caching system, a corresponding Key-Value is set for each column, thus causing Key Redundancy, resulting in waste of storage space

In addition, when performing data storage and search, you can only find Value through Key, but cannot realize the search of the entire relational data

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