Salt-based massive mongodb slow log management system

Abstract

The invention discloses a massive mongodb slow log management system based on the salt. The system includes a salt management end, a mongodb node and a distributed type memory. The salt management end is used for registering and cancelling a salt client and sending a command to the salt client; the mongodb node is a server used for deploying mongodb service, and each mongodb node needs to deploy the salt client; the slat client receives a command of the salt management end, completes the corresponding operation and returns the result to the salt management end; the mongodb node includes a slow log management module; the distributed type memory is a distributed type file system used for storing a mass of small files and includes a slow log storage module. The system adopts the distributed type memory, and large-scale mongodb slow log storage can be supported; an automation mode is adopted for managing slow logs, and the maintenance cost is low; the mongodb slow logs are analyzed, a slow query low in efficiency can be easily seen, and the performance optimization effect is greatly improved. A platform mode is adopted to show the slow log analysis file, the result is more intuitive, and the visualization effect is better.

Description

technical field [0001] The invention belongs to the fields of slow log management and performance tuning of a mongodb database, and is mainly applied to massive storage and performance tuning scenarios in the Internet industry. Background technique [0002] With the vigorous development of the Internet industry, business needs for databases are more diverse. Among them, mongodb database, as a non-relational database, is more and more widely used in production environments. For large-scale mongodb clusters, managing massive slow logs is becoming more and more important. [0003] Disadvantages of existing management slow log systems: [0004] (1) High maintenance cost: It requires more manual maintenance of mongodb log management work, which is inefficient. [0005] (2) The function is simple: the mongodb slow log cannot be parsed and displayed, and the historical information cannot be viewed. [0006] (3) Lack of automation: the degree of automation is low and requires a l...

AI Technical Summary

Problems solved by technology

[0004] (1) High maintenance cost: more manual maintenance of mongodb log management work is required, and the efficiency is low

[0005] (2) The function is simple: the mongodb slow log cannot be parsed and displayed, and the historical information cannot be viewed

[0006] (3) Lack of automation: the degree of automation is low and a large amount of human operation is required

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