SQL vs NoSQL on the example of MySQL and MongoDB

SQL vs NoSQL on the example of MySQL and MongoDB

When you need to select a DBMS, the main question is usually to select a relational (SQL) or non-relational (NoSQL) structure. Both options have their advantages, as well as several key features that should be kept in mind when choosing.

MAIN DIFFERENCES BETWEEN SQL AND NOSQL

Language

Now imagine the City of B, where all the inhabitants speak different languages. They interact with the surrounding world in completely different ways, and for them, there are no “universal” means of communication.

These two examples demonstrate the differences between relational and non-relational databases, and behind these differences are the key features of both DBMSs.

Relational databases use Structured Query Language (SQL) to define and process data. On the one hand, this opens up great development opportunities: SQL is one of the most flexible and common query languages, so its choice allows minimizing the number of risks, and will be especially useful if you have to work with complex queries. On the other hand, SQL has some limitations. The construction of queries in this language requires to predetermine the data structure and, as in the case of City A, subsequent changes to the data structure can be detrimental to the entire system.

Non-relational databases, in turn, offer a dynamic data structure, which can be stored in several ways: column-oriented, document-oriented, graph-based, or key-value-based. This flexibility means the following:

  • You can create documents without having to set their structure in advance;
  • Each document may have its structure;
  • Each database may have its syntax;
  • You can add fields while working with the data.

Scalability

Structure

DBMS for SQL databases include MySQL, Oracle, PostgreSQL, and Microsoft SQL Server. MongoDB, BigTable, Redis, RavenDB Cassandra, HBase, Neo4j, and CouchDB are suitable for working with NoSQL.

SQL VS. NOSQL: MYSQL OR MONGODB

MySQL: relational DBMS

  • Time-tested: MySQL is a highly developed DBMS, which means there is a large community around it, many examples, and high reliability;
  • Compatibility: MySQL is available on all major platforms including Linux, Windows, Mac, BSD, and Solaris. It also has libraries for languages like Node.js, Ruby, C#, C++, Java, Perl, Python, and PHP;
  • Payback: This is an open-source DBMS, freely available;
  • Replicability: The MySQL database can be distributed across multiple nodes, thus reducing the load and improving the scalability and availability of the application;
  • Sharing: While shredding is not possible on most SQL databases, MySQL is an exception.

MongoDB: non-relational DBMS

  • Dynamic scheme: As mentioned above, this DBMS allows you to work flexibly with the data scheme without having to change the data itself;
  • Scalability: MongoDB is horizontally scalable, making it easy to reduce the load on servers with large data volumes;
  • Ease of management: The DBMS does not require a separate database administrator. Due to its sufficient usability, it can be easily used by both developers and system administrators;
  • Speed: High performance when performing simple queries;
  • Flexibility: You can add fields or columns to MongoDB without damaging existing data, its structure, and DBMS performance.

WHICH DBMS SHOULD I CHOOSE?

Especially if you can’t define a schema for your database, none of the other DBMS is suitable for you, or it is constantly changing in your projects, such as in the case of mobile applications, real-time analytics systems, or content management.

WISSENSCHAFTSCOACHING: BERATUNG, COACHING, LEKTORAT, ÜBERSETZUNG