Data storage structure conversion method and device

Abstract

The invention provides a conversion scheme of a data storage structure, and comprises the following steps: extracting the position identifier of each piece of data to be converted in the same linked list on the basis of an original linked list storage structure, and the linked list identifier of the linked list, and independently combining the linked list identifier with the position identifier of each piece of data to be converted to determine a key in a conversion storage structure corresponding to each piece of data to be converted; subsequently, combining the extracted position identifiers in the left adjacent data and the right adjacent data of each piece of data to be converted in the linked list with a value data unit in each piece of extracted data to be converted to determine as a value in the conversion storage structure of each piece of data to be converted; and according to the conversion storage structure, storing a data key value formed by the key and the value into a key value database. A storage system which is high in data storage processing efficiency, supports various types of data storage structures and is free from the limitation of a physical memory can be finally realized.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular, the present invention relates to a data storage structure conversion method and device. Background technique [0002] With the rapid development of the information age, not only large enterprises, but even some small enterprises have accumulated terabytes of data. Organizations of all sizes have begun to have the need to deal with large-scale and multiple types of data, while the traditional relationship SQL databases (SQL databases) are almost at their limits in terms of data storage scalability. In order to solve the challenges brought by large-scale data collections and multiple data types and the problems of big data applications, non-relational databases (NoSQL databases) emerged as the times require. [0003] Commonly used non-relational databases include KV (Key-Value, key-value) storage databases, where data is organized, indexed, and stored in the form of key-va...

AI Technical Summary

Problems solved by technology

The KV storage method is very suitable for data storage services that do not involve too many data relationships and business relationships. It can effectively reduce the number of reads and writes to the disk, and has better read and write performance than SQL database storage. The KV storage database can be realized by Redis And LevelDB, etc., LevelDB can support billion-level data volume, and can maintain very high data storage and processing performance at this level. At the same time, LevelDB can store data to disk, which is conducive to permanent storage of data; however, currently The interface of LevelDB only supports key-value (key-value pair) data without deformation, the interface is relatively single, and the scalability to business is poor

Redis is a high-performance key-value storage system that supports a variety of value-type data storage structures, including string (string) type, list (linked list) type, set (set) type, zset (ordered set) ) type and hash (hash) type, however, in order to ensure high data storage and processing efficiency, its data is cached in memory, that is, Redis is an in-memory database, so this storage method is limited by physical memory and cannot Realize big data storage

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