What Are NoSQL And SQL?

NoSQL is sometimes called a non-relational data structure or non-SQL structure. It means that storing, manipulating, optimizing, and maintaining data is not dependent on each other.

For example, consider that you have a dataset of food dishes for a particular cuisine. There may be a variety of dishes within a cuisine, but instead of making a table of the dishes separately, we will make them a subset of the cuisine’s dataset. 

So, in case we want to remove a dish from the collection, it will not affect the entire dataset. But it can lead to a big data structure, which may be tricky to maintain, optimize, and scale. The data becomes easy to duplicate and may become obsolete.

SQL stands for Structured Query Language. The dataset under this must have a predefined structure, to begin with; it is a rich language that provides support and enhances relationships. Since it is a relational data, we will have to make different tables for different entities and then relate them with each other using keys. 

And this way, it becomes easy to connect the dots and find the related entities very quickly. But when the tables become large, it consumes a lot of time to execute the queries, and in the end, it leads to consuming the memory of the server.

What Is The Difference Between SQL And NoSQL?

What’s In A Name? 

An important difference between these two – is in their names; the SQL data structure follows a structured pattern to store the data. Before we begin storing data, we must create a proper schema. In many cases, the schema undergoes multiple changes – while the project is in the development phase. And this happens due to the project’s need, where we must invest a large amount of time before starting the project to finalize the schema of the database. In the case of NoSQL datasets, they don’t have any kind of fixed structure and schema. You can define a schema if you want to have a designed one. And in the future, if we wish to change the schema, we can easily do that. 

How Do They Handle Complex Queries?

The second difference between these two, which troubles the developers, is the ability to handle complex queries. The clear winner here is SQL, as it is made with an idea to manage complex data sets that have connections across all tables. So for mining, the data which may be having all sorts of joins and conditions, SQL defeats NoSQL by a very big margin.

Powerful Data Structures

The next difference is that data structure can perform a lot of functions other than storing and manipulating data. Generating reports, performing analysis based on the data, handling big data, and processing it faster, without harming the data and the server, are a few of its functions. We can store NoSQL data sets either in key-value pairs or in document-subdocument structure. It can help in storing data that the users may not be able to manipulate. Hence, in a smaller database, NoSQL can give surprising results in executing the queries, but when it comes to larger datasets or Big Data, SQL can handle it more efficiently. And as it follows ACID properties, it is a better option when we want to store, access, and manipulate the data.

Now let’s talk about the DBMS.

The first one that we are going to discuss is the vastly used NoSQL DBMS, MongoDB.


MongoDB came into the picture when the database storage costs decreased marginally. In the late 90s, developers had to use SQL databases because they were easy to normalize, and therefore the size of the overall database was lesser than NoSQL databases. But with the changing scenario in today’s time, the storage cost has drastically decreased, and it is now affordable for an organization or a developer to use the speed and functionality of NoSQL DBMS like MongoDB.

MongoDB stores the data in the document type. MongoDB being a NoSQL DBMS, can also handle relationships between different entities differently, so it breaks the misconception of a NoSQL database being a non-relational database.

MongoDB also follows ACID properties, which means you can have apples and oranges both. MongoDB facilitates the flexibility of managing the database and the rich functionality of SQL. The developers’ community is friendly with JSON structures, which helps in working with MongoDB as it stores data in a JSON document.

But this doesn’t mean that it can’t perform queries of searching a nested field or apply to sort on that, it can easily do that, because the queries are also in the form of JSON, isn’t it cool?

It also supports a left outer join, which is a value addition when working with a non-relational database.

Let’s move our discussion further to MySQL, it is the most famous free and open-source relational database management system, managed by Oracle. 


It stores the data in table formats with rows and columns structure. MySQL can become very useful when the project is well planned, and the project team already knows the needed schema and its design. It becomes very hard to change the schema in the middle of project development. It increases the time and cost of the project, as well.

It provides rich aggregation methods, joins, views, triggers, procedures, and allows us to modify the core of MySQL, as per our need, as it is open-source software. 

MySQL wins the race over MongoDB when it comes to complex and high relations requirements between the entities. It may fall short if the data is unstructured, and we don’t know how to store and access our data in a normalized way.

It was and still is the most used database management system in the world, used by many tech giants like Facebook, Twitter, and Youtube. [Source: Wikipedia]

Let’s now look at how the queries look and if they’re similar to each other. 

Below are examples of queries in a Films dataset, which is a table in the case of MySQL, and is called a collection in terms of MongoDB.

Inserting data in MongoDB:

    title: “Pirates of the Caribbean”,
    description: “Pirates of the Caribbean is a series of fantasy swashbuckler films produced by Jerry Bruckheimer and based on Walt Disney’s theme park attraction of the same name. The film series serves as a major component of the eponymous media franchise.”,
    category: “Fiction”

Inserting data in MySQL DB:

INSERT INTO films (title, description, category) VALUES (“Pirates of the Caribbean”, “Pirates of the Caribbean is a series of fantasy swashbuckler films produced by Jerry Bruckheimer and based on Walt Disney’s theme park attraction of the same name. The film series serves as a major component of the eponymous media franchise.”, “Fiction”);

Let’s consider this example; the algorithm fetches the films that fall under the fiction category. The first argument of a MongoDB find a query is an object of the conditions, and the second parameter is an object of the fields to be fetched.

If the value in the object for a field is 1, then it will be fetched, and if it is 0, then it will be omitted. On the other hand, MySQL queries are easy to understand because of its simpler structure.

Fetching data in MongoDB:

db.films.find( { category: “Fiction” }, { title:1 } )

Fetching data in MySQL:

SELECT title FROM films WHERE category=”Fiction”;

MongoDB is winning the hearts of developers showing an uptrend in Stackoverflow questions in recent times.


[Source: Stackoverflow]

And another comparison from DB-engines ranking shows the uptrend of MongoDB as well.


[Source: DB-Engines]

When it comes to scalability, MongoDB is vertically scalable. It means that if we want to increase the size and performance of the database, then we can add more resources i.e., RAM, SDD, or CPU to your server. MySQL is horizontally scalable, which means you will have to add more servers to scale your database. Generally, the NoSQL databases are vertically scalable, and SQL databases are horizontally scalable.

Wrapping Up

There is never a clear answer to the question, which one to choose? It always depends on the project needs, requirements, resources, and budget. If I had to sum up this article in one line, I would say go with your instinct because these both have large community support and the ability to handle any kind of complex data.

Some web developers can turn the unstructured data into structured and vice versa. MySQL has a solid community because many websites and applications are running over it for more than 15 years now. But the generations to come are and will continue to love the simpler yet powerful MongoDB which the trends are showing.

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There is always a room for discussion over such comparisons, let me know what you think about it in the comments below.