Today, we generate unprecedented volumes of data, precisely over 2.5 quintillion bytes of data every day! With each passing day, this number is only going to increase. However, the data we produce is generally raw and unstructured – it is a compilation of unorganized, random facts that lack coherence and meaning. Thus, it is essential to clean, organize, process, analyze, and contextualize the data to convert into meaningful information. This is where databases and database management systems (DBMS) enter the picture.
There are primarily two types of databases that act as a base for the many different databases we have now. They are SQL and NoSQL. Both of them are opposite binaries. Primarily, SQL served as the foundation for relational databases. Although SQL dominated the database domain for a very long time, the steady upsurge in data over the years created a need for a DBMS that can scale exponentially. This need resulted in the birth of the NoSQL database.
Following the Internet boom in the mid-90s, relational databases could not handle the sudden increase in the influx and generation of different data types by users around the globe. Thus, NoSQL (non-relational database) was developed to replace SQL’s organized storage with flexibility.
Today’s post is all about looking at relational and non-relational databases, with a particular focus on the difference between SQL and MongoDB.
Table of Contents
SQL vs. NoSQL: A detailed discussion
SQL (Structured Query Language) was developed back in the 1970s for accessing and manipulating relational databases. SQL commands are used to perform a host of functions on databases, such as updating data or retrieving the data stored in a database. The main focus of creating SQL databases was to avoid data duplication to reduce storage costs. Usually, SQL databases feature a set and complex tabular schemas. They require vertical scaling (migrating data to larger servers), which is quite expensive.
Oracle, MySQL, PostgreSQL, and Microsoft SQL Server are some of the most popular SQL databases.
Advantages of SQL
- Efficient data retrieval – SQL allows you to retrieve large volumes of data quickly. You can also perform operations like insertion, deletion, selection, etc. to cater to your database needs in real-time.
- Easy learning curve – You need not be an expert coder to use SQL. There’s no need to write long lines of code for SQL operations. You can use standard SQL commands like “Select”, “Insert”, “Update”, “Delete”, “Create”, and “Drop” to perform specific operations on a database..
- Standardized language – The American National Standards Institute (ANSI) declared SQL as the standard language for relational database management systems (RDBMS). Thanks to extensive documentation and upgrades over the years, SQL offers a consistent experience to all users.
- Platform compatible – SQL is compatible with all the major operating platforms, including Windows, Linux, macOS, and Unix systems. You can use SQL to write code for PCs, laptops, and servers, totally independent of any OS. Moreover, you can also integrate SQL commands with other applications.
NoSQL databases came into the mainstream picture in the early 2000s. They were designed for flexibility, scalability, and high-speed querying. The unique aspect of NoSQL is that it allows for frequent application updates powered by agile and DevOps best practices. Unlike SQL databases, NoSQL databases can scale horizontally across commodity servers, making it both time and cost-efficient.
The most widely used NoSQL databases are MongoDB, CouchDB, DynamoDB, Cassandra, HBase, and Amazon Neptune.
Advantages of NoSQL
- Fast query processing – Typically, NoSQL databases allow for rapid query processing than SQL databases. This is because, in a NoSQL database, data is stored in a manner that optimizes it for query execution. Contrary to this, in SQL databases, the data is normalized. For accessing the data, you need to merge data from multiple tables. This process is called “join.” Thus, all queries for a single object demands that you combine data from various tables. When the tables grow in size, joining the data becomes very expensive. In NoSQL databases, data is that is accessed together is always stored together, thereby eliminating the need for joins.
- Easy mapping – Typically, NoSQL databases like MongoDB map their data structures to that of popular programming languages like Python, Java, R, etc. Hence, programmers can store the data in the way they use it in application code. This mapping reduces the development time and eliminates bugs.
- Flexible schemas – NoSQL databases boast of flexible schemas that allow developers to make changes to a database as and when needed. These schemas enable quick iteration and continuous integration of new features into an application.
- Horizontal scaling – NoSQL databases let you scale-out horizontally, meaning when the current server’s capacity requirements exceed, you can add cheaper, commodity servers as required.
Read: SQL Interview Questions
MongoDB vs. MySQL
MongoDB and MySQL lie at the two extremes of the database domain. While MongoDB is a NoSQL database that is primarily concerned with handling raw and unstructured data, MySQL is an SQL database designed for handling organized, structured data.
What are the main differences between MySQL and MongoDB?
MySQL is an RDBMS hosted, managed, and offered by the Oracle Corporation. It uses SQL for communicating with the database and accessing stored data. Like all relational databases, MySQL stores data in tables, within columns and rows. In MySQL, the database schema needs to be pre-defined. One also has to set the rules determining the relationship between fields in the tables within a database.
MongoDB is a NoSQL database wherein data is stored in the form of JSON documents. It uses MongoDB Query Language (MQL) to access data. All the documents that hold related information are stacked together in MongoDB. You need not declare the structure of documents to the database system since they are self-describing. The fields usually vary from document to document.
Now, let’s focus on the other aspects of the difference between MongoDB vs. MySQL.
MySQL stores data in a tabular format. The tables contain columns that represent the attribute, and rows denote specific records. On the other hand, MongoDB stores data in collections that are similar to tables. However, these collections consist of multiple documents in JSON format having key-value.
In MySQL, different tables relate to each other via primary keys or foreign keys. For instance, in a database containing employee records, the EmployeeID column is the “Employee” table’s primary key. However, it will function as the foreign key in the “Payments” table. This defined relational rule ensures that the Employee table does not contain any entry regarding payments. Hence the name relational database.
In MongoDB, there’s no need to build such a relationship between the unstructured data contained in the collections. This is what makes it a non-relational database.
Like any SQL database, MySQL follows the ACID (Atomicity, Consistency, Isolation, and Durability) theorem. All four properties ensure that transactions occur consistently and reliably in the database.
On the contrary, MongoDB is governed by the principles of the CAP (Consistency, Availability, and Partition) theorem. The CAP approach focuses more on the availability of data in the database.
Thus, while MySQL ensures secure and reliable transactions, MongoDB assures high availability of data.
Checkout: SQL Project Ideas
As we mentioned earlier, SQL databases can scale only vertically. This means, to scale MySQL, you need to increase the memory size, disk space, and computing power of the server. As the database’s size increases, high query volume, and vertical scaling can lead to increased costs.
Unlike MySQL, MongoDB supports horizontal scaling, wherein instead of increasing the memory size or computation power of the server, you can add a new server for scalability. This is less expensive since incorporating a cluster of low-cost commodity hardware is a cost-effective option to support high query volumes.
MySQL has a pre-defined schema that dictates data compliance. You need to define the number of columns in a table and its data type while creating the table. Any data that you wish to store in the table must match the defined structure, failing which, it shows an error.
MongoDB does not require you to pre-define schemas. You can store varied data types in a collection without any hassle. This feature is extremely useful in the present scenario since most of the data generated today is unstructured and cannot be saved in SQL databases.
In MySQL, you can write queries using SQL. The biggest advantage of SQL queries is that they are user-friendly. Like most other relational databases, MySQL also follows ANSI SQL standards. You can use SQL queries to perform advanced analytics operations, including joins, merge, and data aggregation. Thus, SQL is an excellent analytics tool.
Although MongoDB lacks the support for traditional SQL-like queries, it does support document querying. However, this being a developing feature has many limitations. For instance, unlike MySQL, MongoDB does not support joins, which is pivotal for collecting data from disparate sources.
There’s no clear winner in the MongoDB vs. MySQL debate. Each database boasts of unique features and advantages, while also having certain limitations. The choice between MongoDB and MySQL essentially depends on your data storage needs and scalability requirements.
If you’re interested to learn more about full-stack software development, check out upGrad & IIIT-B’s PG Diploma in Full-stack Software Development which is designed for working professionals and offers 500+ hours of rigorous training, 9+ projects, and assignments, IIIT-B Alumni status, practical hands-on capstone projects & job assistance with top firms.
We hope this helps!