SQL vs NoSQL (MySQL vs MongoDB)
We can freely say that data is more important than code. We can claim this on a premise that in the end of the day you look at data, you use data, you make decisions based on the information from that data as a source. And what code does – roughly said it transforms that data in some way or another, so that is more readable and understandable. Even if the code fails, it is not such a big disaster compared to when the data fails (or misleads).
The main focuses of this story are SQL (Structured Query Language) and NoSQL (Not only Structured Query Language), their differences and advantages over each other.
Relational databases held the lead for quite a time. There are some famous names in this field: MySQL, MsSQL, Oracle DB, Postgres… just to name some. During the time demand and requirements changed in the line of more diversity and scalability. NoSQL databases raised and took a significant part of the market. Let’s see the difference between SQL and NoSQL and a faceoff between two dominant solutions that are close in popularity: MySQL from the SQL and MongoDB from the NoSQL world.
Structure and schema
Great thing about MongoDB is that there are no restrictions on schema design. It is possible to just drop a couple of documents within a collection and there is no need to have relations between those documents. The only restriction with this is supported data structures. The payoff is: due to the absence of joins and transactions you need to frequently optimize your schema based on how the application will be accessing the data.
On the other hand, before you can store anything in MySQL, you need to clearly define tables and columns, and every row in the table should have the same column. Clearly, there isn’t much space for flexibility in the manner of storing data in case that normalization is followed (which is definitely recommended).
MongoDB creates schemaless documents which can store any information you want though it may cause problems with data consistency. MySQL creates a strict schema-template and it is less prone to mistakes in this manner.
MongoDB uses an unstructured query language. To build a query in JSON documents, you need to specify a document with properties you wish the results to match.
It is typically executed using a very rich set of operators that are linked to each other using JSON. MongoDB treats each property as having an implicit boolean AND. It natively supports boolean OR queries, but you must use a special operator ($or) to achieve it.
MySQL uses the structured query language SQL to communicate with the database. Despite its simplicity, it is indeed a very powerful language which consists mainly of two parts: data definition language (DDL) and data manipulation language (DML).
MongoDB doesn’t support JOIN — at least, it has no equivalent. On the contrary, it supports multi-dimensional data types such as arrays and even other documents. The placement of one document inside another is known as embedding.
One of the best parts about MySQL is the JOIN operations. To put it in simple terms, JOIN makes the relational database relational. JOIN allows the user to link data from two or more tables in a single query with the help of single SELECT command. For example, we can easily obtain related data in multiple tables using a single SQL statement.
Performance and Speed
Probably the most important benefit that MongoDB has over MySQL is its ability to handle large unstructured data. It is magically faster because it allows users to query in a different manner that is more sensitive to workload. We noted in practice that MySQL is slower in comparison to MongoDB when it comes to dealing with large databases. It is unable to cope with large amounts of data in a really efficient way. When it comes to calculation and aggregation, MySQL is winner over Mongo.
In any case, there is no “standard” benchmark that can tell which database is “faster”. Only the demands, structure and way of handling data, and infrastructure can tell in which direction to go.
MongoDB uses a role-based access control with a flexible set of privileges. Its security features include authentication, auditing, and authorization. Moreover, it is also possible to use Transport Layer Security (TLS) and Secure Sockets Layer (SSL) for encryption purposes. This ensures that it is only accessible and readable by the intended client.
MySQL uses a privilege-based security model. This means it authenticates a user and facilitates it with user privileges on a particular database such as CREATE, SELECT, INSERT, UPDATE, and so on. On the transport layer, it uses encrypted connections between clients and the server using SSL.
When – what?
There is no one general rule when to use which technology, but there are some guidelines that can help:
|If data is well structured||If volume of data is huge and data is unstructured or less structured|
|If ACID principle (atomic, consistency, isolation and durability) needs to be ensured for a data being processed.||If leveraging cloud provided services and offerings|
|If there are Complex queries, inner joins, outer joins, complex value calculations and a lot of||If low cost and high performance are requirements|
|If scaling ad performance are not the biggest factors||If scaling and rapid development are aimed|
To answer the question, “Who is the winner?” it is needed to take into consideration project goals and many other things.
MySQL is highly organized, structured, it has great performance, reliable data protection, and ease of managing data. Proper data indexing can resolve issues with performance, facilitate interaction and ensure robustness.
If data is not structured and complex to handle, or if scaling is a big concern, the better choice would be MongoDB. What’s more, if you’re required to handle a large volume of data and store it as documents, MongoDB will help you a lot!
The final result would be: One isn’t necessarily better than the other. Depending on the case: MongoDB will be a great choice for some requirements and MySQL for another.