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
Imagine a city — let it be called City A, where all speak the same language. All business is conducted in it, it is used in any form of communication — in general, it is the only means of interaction and understanding for the inhabitants of the city. Changing the language in any of the activities will confuse everyone.
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.
In most cases, SQL databases are vertically scalable, that is, you can increase the load on a single server by increasing the capacity of CPUs, RAM, or storage systems. And NoSQL databases are horizontally scalable. This means that you can increase traffic by distributing it or adding more servers to your DBMS. It’s like adding more floors to your building or adding more buildings on the street. In the second case, the system can become much larger and more powerful, choosing a NoSQL database preferred for large or constantly changing data structures.
In relational DBMS data are presented as tables, while in non-relational DBMS data are presented as documents, key-value pairs, graphs, or wide-column storages. This makes SQL databases the best choice for applications that involve transactions with multiple records — such as the accounting system — or for legacy systems that were built for relational structures.
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
Having grasped the key structural differences between SQL and NoSQL databases, it is worth carefully consider their functional features on the example of MySQL and MongoDB.
MySQL: relational DBMS
Advantages of MySQL:
- 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
Advantages of MongoDB:
- 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?
MySQL is the right choice for any project that can rely on a predefined structure and specified schemes. On the other hand, MongoDB is a great choice for fast-growing projects without a certain data schema.
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.