云详单管理系统的设计与实现

论文总字数：41172字

摘 要

随着云计算、大数据发展，各种新技术在IT系统中应用并逐渐成熟；为建设低成本、高可用、易扩展的业务支撑系统提供了更多借鉴。详单查询是一个典型的大数据应用，随着用户数增多、业务的发展，尤其是数据类业务的发展，传统的基于关系型数据库的集中式的详单查询技术已显现出乏力的迹象，在此背景下，中国移动集团公司出台了相关的规范，指导各省移动公司运用云计算技术应对不断涌来的海量数据，采用扩展性极佳的软硬件架构迎接大数据时代的到来。

用户可以通过用户消费详单随时了解自己当月的消费情况，选择适合自己的消费结构。随着电信业务量的急剧增长，电信网中各种新业务不断开放，当前各省用户数量庞大，历史详单数量逐年累积增加，尤其是流量业务的飞快增长，一个省每天的详单量达到10亿级别，这导致了详单管理变得极度复杂，数据处理速率越来越慢，查询速度变慢变差等一系列问题，且随着4G时代的到来，需要通过提升客户实时计费体验等方式来解决由于这些情况的DCC消息爆炸式增长问题，使得目前系统架构的拓展和扩容成本方面难以满足，需要进行进一步的架构优化和升级。

在移动第三代规划里提出了引入分布式数据库，基于X86设备实现详单处理的分布式架构，逐渐完成低成本的详单云化架构，提高系统的拓展能力，提供高速效率的查询访问和数据统计功能，降低系统整体建设成本。

本文结合实习期间我所参与的亚信云详单系统的相关模块的设计经验，介绍基于x86系统的MongoDB数据库和Docker容器云详单系统的设计方案与开发、测试情况，其中完成了在大数据的环境下对话单进行快速入库和完成基本的数据库查询处理操作，并对指定条件的话单文件进行出库。

关键词 ： 云详单；x86设备；分布式存储；详单管理；MongoDB，Docker容器

Abstract

With the development of cloud computing and large data, various new technologies are applied in IT system and matured. It provides more reference for building low cost, high availability and easy expansion of business support system. The detailed query is a typical large data application. With the increase of the number of users, the development of the business, especially the development of the data class business, the traditional centralized query system based on the relational database has shown signs of fatigue, In this context, China Mobile Group has introduced the relevant norms to guide the provinces to use cloud computing technology companies to cope with the massive incoming data, the use of highly scalable hardware and software architecture to meet the arrival of large data age.

Users can use the user's consumption details at any time to understand their own consumption of the month, choose their own consumption structure. With the rapid growth of telecommunications business, the telecommunications network in a variety of new business continued to open, the current huge number of users in the province, the history of the number of single-year accumulation, especially the rapid growth of traffic business, a province's daily single volume reached 1 billion Level, led directly to the details of the management has become extremely complex, more and more slow data processing, query speed becomes worse and a series of problems, and with the arrival of the 4G era, to enhance customer real-time billing experience to solve this Of the DCC news explosion growth problems, making the current system architecture expansion and expansion costs are difficult to meet, the need for further structural optimization and upgrading.

In the third generation of mobile planning proposed in the introduction of distributed database, based on the X86 device to achieve detailed processing of the distributed architecture, the progressive realization of low-cost single cloud structure, improve system scalability, provide high-speed query access and data Statistical functions, reduce the overall system construction costs.

In this paper, I will introduce the design experience of the related modules of the sub-letter cloud detailed system which I participated in during the internship, introduce the design scheme and development and test of the MongoDB database and the Docker container cloud detail system based on the x86 system. Environment, the dialog box for a quick storage and complete the basic database query processing operations, and specify the conditions of the single file to the database.

Keywords ： Cloud Detail ; x86 Device ; Distributed Storage ; Detail Management ; MongoDB ; Docker container

目 录

Abstract 4

第一章 绪论 7

1.1课题研究背景和意义 7

1.2数据库的使用与比较 7

1.3课题设计内容 7

第二章 相关知识概述 8

2.1 MongoDB数据库 8

2.2 Docker容器 8

2.3开发工具及开发环境简介 8

第三章 设计部分 10

3.1MongoDB集群部署 10

3.1.1 MongoDB集群结构 10

3.1.2 MongoDB数据库命名 12

3.1.3 Collection集合汇总 12

3.1.4 Collection集合汇总 12

3.2详单入库设计方案 12

3.2.1 入库流程 13

3.2.2入库系统类定义 13

3.2.3 入库配置信息 15

3.3详单出库设计方案 18

3.3.1 出库流程 18

3.3.2出库系统类定义 20

3.3.3 入库配置信息 20

3.4详单查询设计方案 22

3.4.1前端控制台部分 23

3.4.1.1控制台处理流程 23

3.4.1.2 控制台配置 24

3.4.2 数据分析部分 26

3.4.2.1 数据处理流程 27

3.4.2.2 数据分析配置 27

第四章 实验部分 29

测试环境 29

4.1 话单入库性能测试 30

4.1.1 测试内容 30

4.1.1.1 MongoDB单进程入库性能测试 30

4.1.1.2 MongoDB多进程入库性能测试 31

4.1.2 测试结果 33

4.2 话单出库性能测试 34

4.2.1测试内容 34

4.2.1.1 Oracle查询模式 34

4.2.1.2 本地查询模式 35

4.2.1.2.1 未建立索引情况下的本地查询 36

4.2.1.2.2 建立索引情况下的本地查询模式 37

4.2.2 测试结果 38

4.3 话单查询测试 38

致 谢 47

参考文献 48

第一章 绪论

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