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Packing method based on line storage database engine

A database and engine technology, applied in the field of database systems, can solve problems such as high R&D costs

Inactive Publication Date: 2012-06-13
天津神舟通用数据技术有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0012] The above database products all combine row storage and column storage very well, and a unified database management system provides transparent storage services, but they all design new page management structures at the physical storage layer to store column storage data, and high R&D costs

Method used

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  • Packing method based on line storage database engine
  • Packing method based on line storage database engine
  • Packing method based on line storage database engine

Examples

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Embodiment Construction

[0031] Attached below figure 1 And the specific embodiments further describe the present invention:

[0032] 1. Such as figure 1 As shown, first create a compression table through CREAET TABLE table_name COMPRESS compress_level. The user can specify the compression level (0-9). In theory, the higher the level, the greater the compression ratio but the slower the speed;

[0033] 2. Import the CSV format data file into the created compression table through the BULK INSERT statement. The user can specify the number of rows cached during batch import. The larger the value, the better the compression effect and speed, but the more memory usage;

[0034] 3. Such as figure 2 As shown, the compressed table is externally represented as an ordinary data table, which can support usual DML / DDL operations; internally, it is implemented as a master HOT data segment and a slave LOB large object segment, where the master HOT segment is used to store internally defined Property row, the definition ...

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Abstract

The invention discloses a packing method based on a line storage database engine, which comprises a line storage database, and following operations are carried out based on the line database: creating a compress table, specifying a corresponding compressing level, and importing data files with CSV (Comma Separated Value) format into the created compress table. The method is characterized in that: the compress table is a common data table showed externally for supporting general DML / DDL (Data Manipulation Language / Data Definition Language) operation; the compress table comprises a master HOT data segment and a slave LOB large object segment realized internally, wherein the master HOT segment is used for storing internally defined attribute lines, and the slave LOB segment is used for storing a compression method control structure and compressed data content; the compress table is used for supporting various query languages, and when in query, even though all fields in the line need to be read into memory, the related line data only needs to be decompressed and queried; the compress table is used for supporting update operation, and only decompressing updated lines when in update; and the compress table is used for supporting speedy deletion, only delete mark is made, and when no record is in a slave line, a master line is automatically deleted; and the compress table is deleted. According to the invention, through a line storage database technology, without changing a storage engine physical result, the technology of compression and storage according to line is realized, and the effect of high compression ratio of the line storage database is achieved.

Description

Technical field [0001] The invention relates to a column storage method based on a row storage database engine, belonging to the field of database systems. Background technique [0002] With the improvement of enterprise informatization, the application of analytical databases has become more and more common, and higher requirements have been put forward for the efficiency of OLAP queries for massive data. Column-stored databases that have emerged in recent years, such as commercial database SybaseIQ, open source databases C-Store, and MonetDB, have been proven to have very good performance in read-only data warehouse query processing. Many experiments have also shown that the performance of column storage is much better than row storage for OLAP queries with high selection rates, high projection rates and a large number of aggregate calculations. However, due to the different underlying physical storage modes of the two methods, users cannot switch between row storage and colum...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F17/30
Inventor 关刚冯柯刘碧楠何清法顾云苏王嘉春蒋志勇李阳周宇
Owner 天津神舟通用数据技术有限公司
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