Full-text database architecture, bitmap index creation method, data query method, server and medium

Abstract

The invention discloses a full-text database architecture, a bitmap index creating method, a data query method, a server and a medium. The full-text database architecture of the invention comprises: a data module, which is used for recording database table names of a full-text database and index field information thereof; a storage module, which includes a plurality of independent data blocks, and used for storing data and index information of database tables; a loading module, which is used for loading the data of all the database tables into the data blocks according to the index information in the metadata module, and when one of the data blocks is written fully, marking the data block as a steady state; an index creation module, which is used for generating an index of the data block marked as the steady state; and a query module, which is used for carrying out searching in the metadata module according to a query statement, searching indexes of all the data blocks if a matching index field is found, and then determining, according to the index of the data block, whether the data block meets query conditions. The architecture greatly increases the query speed and database throughput.

Description

technical field [0001] The invention relates to a full-text database architecture and its bitmap index creation method, data query method, server and medium, in particular to a lucene-based full-text index system, which adopts metadata unified identification, uses bitmap indexes to improve data filtering efficiency, and improves Query speed, the key technology to improve database throughput, belongs to the field of big data retrieval. Background technique [0002] Database technology was born in the late 1960s and early 1970s. Its main purpose is to effectively manage and access large amounts of data resources. Database technology mainly studies how to store, use and manage data. Over the past few years, the data stored in the database has grown from GB to TB. In the near future, the data that many enterprises will store will reach the PB level. The amount of data is getting larger and larger, requiring a larger database for support. With the growth of data volume, simple...

AI Technical Summary

Problems solved by technology

[0017] BloomFilter is an efficient random data structure, which is generated on the basis of bitmap, and is used to detect whether an element is an element in a collection. This detection will only misjudge the data in the collection, and No misjudgment will be made for data that is not in the collection, so that each detection request returns two situations: "in the collection (may be wrong)" and "not in the collection (absolutely not in the collection)", that is, if it judges that the element is not in In the collection, this element must not be an element in the collection. If it is judged that the element is in the collection, there may be a certain error rate

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