High dimension data index structure design method based on solid state hard disk

Abstract

The invention relates to a high dimension data index structure design method based on a solid state hard disk, which includes the following steps: an index structure is divided into an original R tree area and a node difference log area which are respectively used for storing original version data and difference logs of the original version and the latest version; a Hashtable is designed in the internal storage to store nodes and update information in corresponding relation to the position of the node difference log area; once a new update is finished, update log of an earlier time at the node is read out, and then the log and the current log are combined and stored again to serve as all updated logs at the node so far. Based on the previous R tree, the node difference log area is added, node difference logs are designed, and random update operation is changed to random update so as to improve update efficiency. The node difference logs are capable of enabling logs aiming at a certainnode to be stored within a certain range, and read operation of the node difference log R tree is at most twice that of the previous R tree.

Description

technical field [0001] The invention relates to a database index structure, in particular to a high-dimensional data index structure design method based on a solid state disk. Background technique [0002] R tree is one of the most widely used high-dimensional data index structures. The traditional R-tree design assumes that the external storage is a disk. As flash memory technology matures, flash-based SSDs are widely used. Due to the characteristics of flash memory, the random update of SSD is relatively slow. Due to the large number of random update operations in the R-tree structure, the traditional R-tree design cannot well adapt to the characteristics of SSDs. [0003] From large data centers to small embedded systems, flash memory is an ideal substitute for hard disks and is widely used in different systems. Such a large-scale application mainly benefits from its excellent I / O characteristics, high reliability and low power consumption. Flash-based disks, such as...

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Problems solved by technology

However, the biggest problem with this method is that as the length of the log chain increases, the read operations on the log chain will increase dramatically

In particular, the closer the node is to the root node, the longer the log chain (because the update of the upper layer of the R tree is very frequent), and on the other hand, the upper layer nodes are read-intensive nodes, which will lead to a sharp increase in the cost of read operations

[0010] In recent years, the advancement of technology has reduced the gap between reading and writing of SSDs, and the introduction of too many read operations will reduce the overall performance

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