MySQL文档存储和Elasticsearch作为数据复制解决方案中的替代后端的性能和配置特性分析-外文翻译

外文原文

A Comparative Study of MongoDB and Document-Based MySQL

for Big Data Application Data Management

By: Győrödi Cornelia A.,DumşeBurescu Diana V.,Zmaranda Doina R.,Győrödi Robert Ş.

Source: [J]Big Data and Cognitive ComputingVolume 6, Issue 2. 2022. PP 49-49

1. Introduction

Currently, an explosion of data to be stored has been observed to originate from

social media, cloud computing services, and Internet of Things (IoT). The term,

“Internet of Things” actually refers to the combination of three distinct ideas: a large

Data applications have appeared in recent years.

The widespread deployment of IoT drives the high growth of data, both in

quantity and category, thus leading to a need for the development of Big Data

applications. The large volume of data from IoT has three characteristics that conform

to the Big Data paradigm: (i) Abundant terminals that generate a large volume of data;

(ii) the data generated from IoT is usually semi-structured or unstructured; (iii) the

data of IoT is only useful when it is analyzed.

As the volume of data has increased exponentially and applications must handle

millions of users simultaneously and process a huge volume of unstructured and

complex data sets, a relational database model has serious limitations when it has to

solutions. In today’s context of Big Data, the developments in NoSQL databases have

achieved the right infrastructure which can very much be well-adapted to support the

heavy demands of Big Data.

NoSQL databases are extensively useful when they are needed to access and

analyze huge amounts of unstructured data or data that are stored remotely on

multiple virtual servers. A NoSQL database does not store information in the

traditional relational format. NoSQL databases are not built on tables and, in some

cases, they do not fully satisfy the properties of atomicity, consistency, isolation, and

durability (ACID). A feature that is common to almost all NoSQL databases is that

they handle individual items, which are identified by unique keys. Additionally, their

databases (Neo4j, OrientDB and Allegro Graph). From the several NoSQL databases

that we have today, this paper focuses on document-based model databases, choosing

two well-known NoSQL databases, MongoDB and document-based MySQL, and

analyzing their behavior in terms of the performance of CRUD operations.

To perform performance analysis, a server application has been developed and

presented in this paper. The application serves as a backend for streamlining the

activity of small service providers, using the two document-based MongoDB and

MySQL data-bases, with an emphasis on how to use query operations through which

the CRUD operations are performed and tested, the analysis being performed on the

response times of these for a data volume of up to 100,000 items.

presented in Section 5. Discussions and the analysis of the obtained results are made

in Section 6, followed by some conclusions in Section 7.

2. Overview of MongoDB and the Document-Based MySQL

MongoDB is the most popular type of NoSQL database, with a continuous and

secure rise in popularity since its launch. It is a cross-platform, open-source NoSQL

database that is document-based (which is written in C++), completely schema-free,

and manages JSON-style documents. Improvements to each version, and its flexible

structure, which can change quite often during its development, provides automatic

scaling with high performance and availability. The document-based MySQL is not so

popular yet, with MySQL providing a solution for non-relational databases only since

2018, starting with version 8.0, which has several similarities but also several

with the package define their efficiency, both being optimized to operate upon a large

volume of data. However, in MongoDB, access is based on the roles defined for each

user, and in document-based MySQL, access is achieved by defining a username and

password, benefiting from all of the security features available in MySQL. Both

databases are available and free of charge, and can be used to develop individual or

small projects at no extra cost. In the case of large applications, monthly or annual

subscriptions appear for MongoDB, which involve a cost of several thousand dollars.

For document-based MySQL, this is not specified.

In terms of security, both databases provide security mechanisms.

Document-based MySQL is a relatively new database, but it benefits from all the

transactions, and scalability. The authors provide an overview of the different NoSQL

databases, in terms of the data model, query model, replication model, and

consistency model, without testing the CRUD operations performed upon them. In the

authors outlined the differences between the MySQL relational database and

MongoDB, a NoSQL database, through their integration in an online platform and

then through various operations being performed in parallel by many users. The

advantage of using the MongoDB database compared to relational MySQL was

highlighted by performed tests, concluding that the query times of the MongoDB

database were much lower than those of the relational MySQL.

The authors present in a comparative analysis between the NoSQL databases,

such as HBase, MongoDB, BigTable, and SimpleDB, and relational databases such as

MySQL, highlighting their limits in implementing a real application by performing

some tests on the databases, analyzing both simple and more complex queries. In the

analytics in terms of data collection, data parsing, and data prediction. In the paper,

the authors focused mainly on comparing the execution speed of writes/insert and

update/read operations upon different benchmark workloads for seven NoSQL

database engines such as Redis, Memcached, Voldemort, OrientDB, MongoDB,

HBase, and Cassandra.

The Cassandra and MongoDB database systems were described, presenting a

comparative study of both systems by performing the tests on various workloads. The

study involved testing the operations—reading and writing, through progressive

increases in client numbers to perform the operations, in order to compare the two

solutions in terms of performance.

under an open-source license, and it allows for the benchmarking and comparison of

multiple systems by creating “workloads”.

An analysis of the state of the security of the most popular open-source databases,

representing both the relational and NoSQL databases, is described, and includes

MongoDB and MySQL. From a security point of view, both these databases need to

be properly configured so as to significantly reduce the risks of data exposure and

intrusion.

Between MongoDB and MySQL, several comparisons exist in the literature, most

of them focusing on a comparison with relational MySQL, and not with

document-based MySQL; for example, a login system project developed using Python

programming language was used to analyze the performance of MongoDB and

relational MySQL, based on the data-fetching speed from databases. This paper

despite the fact that MongoDB has been approached in many scientific papers, to our

knowledge, at the time of writing this paper, no paper has focused directly on

comparing it with the document-based MySQL.

4. Method and Testing Architecture

For each database considered, an application was created in Java using IntelliJ

IDEA Community Edition (4 February 2020), which aims to develop a server for the

processing and storage of data on the frontend. When creating the testing architecture

setup, it was considered that it is very important to test the types of databases that

exactly fit the criteria that are imposed in an application that is similar to the one to be

developed, and not just by using their tools; such as for MongoDB, the MongoDB

(a process by which a method can be called automatically and repeatedly at a range

set by us, taking as a parameter a string that is composed of six digits separated by a