Method and device for quickly inquiring and calculating MangoDB cluster

Abstract

The invention discloses a method and a device for quickly inquiring and calculating a MangoDB cluster, which is applied to computers, communication and associated fields. The method comprises the following steps of S1, determining a query condition, wherein the query condition is used for inquiring subgroup summary data of a designated subgroup corresponding to the query condition in a designated data list of all fragments of MongoDB; S2, inquiring the designated data list according to the query condition, and acquiring data in the designated subgroup corresponding to the query condition; S3, storing the data into a first temporary data cache table on a server; and S4, carrying out subgroup query on the data in the first temporary data cache table and calculating a query result. According to the method, an original fragment query way is improved to a query way on one server, so that the query speed is greatly improved on the premise of guaranteeing the accuracy of the query result.

Description

technical field [0001] The invention relates to a database cluster technology for distributed file storage, in particular to a method and device for fast query and calculation of a MongoDB cluster. Background technique [0002] With the further deepening of informatization, in various fields, such as telecommunications, transportation, finance, retail, aerospace, medicine, etc., the magnitude of data is showing a rapid growth trend. How to store, analyze, understand and utilize these large-scale Data has become a critical issue. In order to deal with the problem of large-scale data processing, database cluster technology came into being. Database cluster technology is to connect multiple databases together to form a distributed database cluster, and "divide and conquer" massive data. As a member of the distributed database, MongoDB performs very well. In the MongoDB database cluster, you can use the find statement to query data, and the query results can be returned in th...

AI Technical Summary

Problems solved by technology

In this way, there are the following disadvantages: 1. MapReduce supports fragmented database queries, and the processing speed is slow. MapReduce is not suitable for systems that require real-time response; 2. Compared with systems with complete indexes, MapReduce no longer has advantages. Because it needs to match each piece of data and extract the data that matches the search conditions, and if the index system is used, it does not need to traverse all the data

The query speed of the Group by statement is much faster than that of MapReduce, but the Group by statement can only be used for data query and calculation on one server, and cannot be applied to the query of distributed databases

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