Research And Application Of Data Storage Technology Based On MongoDB

The arrival of the third-generation sequencing technology has landmark significance for the study of genes.The further exploration of the mysteries embedded in the genes plays an indispensably important role in helping us understand the process of human evolution,exposing the nature of life phenomena,preventing major diseases,and providing accurate treatment for diseases.On the one hand,the combination of traditional gene technology and big data and cloud computing has produced a huge amount of genetic data.Not only does the data need to be stored forever,but also they need to be stored and accelerated quickly.On the other hand,there are higher requirements for the framework of database system for storing massive genetic data.The traditional relational database can no longer meet the demand of the storage of massive genetic data,and it cannot achieve high-speed access and large-scale concurrent queries under massive data.Especially when it meets the horizontal expansion of database clusters and the diversity of data types,it seems to be ineffective.The NoSQL database mentioned in this thesis is known for its support for high-speed concurrent read and write of massive data,high-efficiency data storage,high database availability,and high scalability of database clusters,and it better compensates for the shortcomings of traditional relational databases.As today's most popular non-relational database,MongoDB not only has the same advantages as NoSQL database,but also can achieve the index and query function of traditional databases.This article takes MongoDB as an example to study the following:Firstly,this thesis introduces the definition,types and characteristics of Nosql database,and makes a theoretical comparison with traditional relational database.The conclusion is that Nosql database has advantages in mass storage and access.Also,there is an introduction about the definition of load balancing algorithm and the commonly-used load balancing algorithm.Secondly,this thesis studies the slicing mechanism and load balancing algorithm of MongoDB,and proposes improvements for load balancing algorithms that can only achieve the balance of quantity.By adding two reference indexes: node real-time load and chunk operation heat parameters,an improved load balancing algorithm is proposed,and its effectiveness is verified through comparative experiments.Last,aiming at the defects that the current genomic storage system cannot achieve high expansion,high availability,high concurrency in massive data storage and access,this paper designed a MongoDB genome storage system based on improved algorithm,and made a more comprehensive assessment of the performance of the system by the experiment.