If you are new to database coding, then you are probably aware of the two predominant styles of data structure: SQL and NoSQL. But in the wide world of coding, it is easy to hear terminology over and over again without anyone ever explaining what it means. In fact, you might have heard SQL and NoSQL said out loud as “Sequel” and “No-Sequel” but never seen them written out before now.
With a limited exposure comes limited knowledge. So, what we are going to do today is break down what SQL and NoSQL both are, what they are used for, and the key differences between them. Because despite both being related to data structures, they both have very different use cases and tools.
Table of Contents:
What are SQL and NoSQL?
SQL Style: Relational Databases
Relational Databases are, arguably, one of the best reasons to use SQL to construct your databases. These types of databases are basically as old as computers themselves. The idea behind them is simple, but their functionality is rather complex. It all comes down to things called “keys” and “tables”.
In just about any type or style of database, information is assigned a “key”. Think of this key as the main datapoint that tells you where the information is located relative to all the other information on the table. However, it should be noted that the same information can have multiple keys.
In order to examine how these two differ from each other, it is first important to understand their similarities. The most important similarity is the fact that they are both data structures. What does that mean? Simply put, they create databases of information and allow you to use certain tools to navigate those databases. The “certain tools” in question are also a known quantity, and there are five in total.
The five tools of databases are the commands “insert”, “select”, “update”, “delete”, and “where”. The names of these commands give you a pretty good idea of what they do, but in case it’s not clear:
- “Insert” is used to add a new datapoint to the database
- “Select” is used to pick a particular data point within a database
- “Update” is used to change something within the selected datapoint
- “Delete” is used to get rid of the selected datapoint
- “Where” is used to find a particular datapoint
Both SQL and NoSQL databases have these tools. If they don’t have these tools, they’re not a database. Making databases that can do this is the essential function of both SQL and NoSQL.
So, if that’s their core similarity, how are they different? The main answer to that is style. How SQL organises and relates information is very different from how NoSQL does the same thing. To expand on this, we are going to take a look at each style of database and examine what exactly those “styles” are.
But in an SQL database, there is another layer of organization called a “table”. A table is a certain boundary of keys. A table can contain multiple keys organizing multiple types of information that are related by the keys assigned to them. That’s a lot of jargon. Let’s get to an example.
An SQL Example
It’s a lot easier to understand SQL if you do so from the top down rather than the ground up. What does that mean? Well, don’t worry as much about how keys exactly function. Instead, think of what they are trying to do, and you can begin to see the logic they have to follow to get there.
For example, imagine you are trying to organise a digital bookshelf. There are a lot of ways to organise a bookshelf. You could organise by the length of each book, the ISBN number, both the first and the last name of the author, not to mention ordering them alphabetically. Each of those potential ways of ordering the bookshelf represents a key. The bookshelf itself is the table.
The books themselves are also tables, however, since they contain multiple keys within them. Within the book, a datapoint like its word count would be called a “primary key”, since the key relates to information about the book itself. That key would also exist in the bookshelf, where it would be identified as a “foreign key”, meaning it is a key belonging to a different table.
By building a network of tables, primary keys, and foreign keys, SQL allows you to easily organise and reorganise your data points however you want.
NoSQL Style: Non-Relational Databases
So, SQL is useful and intuitive. Why ever use NoSQL then?
NoSQL is used when there is a lot of data being stored, but the data doesn’t necessarily have any similarities. This means that the method of storage and organization can be more closely related to the content of each individual datapoint.
And in a database, more specific organization means more specific searching, organizing, and editing tools. It is more difficult, but sometimes more necessary.
This is not to say that a NoSQL database lacks keys. It does have keys; it just doesn’t have tables. That means a NoSQL database is not going to be able to reorder its data points based on similar keys.
So, what can NoSQL do?
Let’s go back to that bookshelf again. But this time, we are not trying to sort the books on the bookshelf in any particular way. Now, we are looking for one particular passage from one particular book.
That would require one of the keys associated with each book to basically include the entire contents of the book. An SQL database will not allow for keys of that size. A NoSQL database, however, will allow for document storage within keys. These documents are in plain text, allowing them to be easily searchable.
You can also store data inside a NoSQL database that is subject to change. One of the most popular things to store using NoSQL databases is sales figures of individual products, as these will often contain developing graphs, mathematics, and information fed to them remotely through the internet.
Which is Better? SQL or NoSQL?
This is the million-dollar question most people are looking for. But as you can see, it is not as clear-cut as “one is better than the other for everything”. They both have different use-cases, meaning that SQL will be useful in some circumstances, but struggle in other circumstances.
So, what kind of circumstances does each one prefer?
When is SQL Better?
SQL is better when you have small, strict data points to compare. A SQL database is great for listing a storefront page, as the only data points it needs are things like price, alphabetical name, and possible date of release. Any information on a product that can be rendered in letter and numbers can be easily input and organised by an SQL system, as the key on a table of content.
This also makes SQL better at ordering things faster, since the data that it processes is individually small. That means that even if the volume of data is enormous, none of it is too daunting to process.
When is NoSQL Better?
But you are not always going to have the luxury of small data points made up of simple strings. When we give the example of the word count of a novel as opposed to the actual contents of a novel, that is the exact situation where a NoSQL database is better for organizing that data than a SQL database. Why?
Because NoSQL databases can contain larger amounts of information per datapoint. This makes them slower, but it also makes them more detailed in the individual complexity of the data points they contain. Perhaps the most important thing a NoSQL database can contain is something we alluded to.
A NoSQL database can contain programming within the data points it contains. That means a few compelling things for people setting up these databases. For one, as we said before, it means that these data points can contain processes that allow for the execution of functions. Your database might contain a sales ledger that needs to be updated every day. Only a NoSQL Database can contain that.
While SQL servers can certainly be secured, NoSQL servers can have far greater degrees of encryption due to the encryption being secured within the datapoint itself, rather than in the whole system. As a result, if one datapoint is compromised, other parts of the database are still safe.
SQL and NoSQL Safety
While NoSQL is safer in the sense that it has more options for the encryption of individual files, that does not necessarily mean it is your only option if you want a secure database. After all, that level of security is extremely complex, and it slows things down. You might simply not have the option to use that kind of security. It benefits you to be aware of all your options with database security.
Part of the advantage of an SQL database is its speed. A high-speed database is far less likely to be compromised due to an issue in transmission called “packet loss”. Packet loss occurs when a network connection weakens. It can happen for lots of reasons, but the end result is that data goes missing.
The security risk involved with packet loss is that both the sender and the receiver of the message will send information along with their files to “rebuild” the files in the event of packet loss. This is one of the most common vulnerabilities caught by hackers. By reading these files, a hacker can have an idea of what kind of information was sent. SQL databases are better at avoiding this issue.
Simply put, because SQL deals in such small data points (letters and numbers, primarily) they really don’t need a very fast or stable internet connection to secure a transmission. You can set up a SQL database to completely avoid sending these “rebuild” files, thereby avoiding that exact vulnerability.
NoSQL databases can be set up with similar options, but the sheer size of most of the files that they send means that lacking those files will lead to data corruption during transmissions.
Is it Ever Right to Use Both SQL and NoSQL?
It would be an incredibly daunting task that would definitely require the work of more than one programmer, but yes. You can absolutely use both SQL and NoSQL simultaneously. In fact, you can use them to contain each other. A SQL database can contain a NoSQL database, and the other way around.
The most well-known use of SQL and NoSQL simultaneously is Google Drive. In that case, an SQL database keeps a record of all the files on your Drive, while a NoSQL database contains the files themselves. The SQL database is contained within a NoSQL database that can edit it to add listings of new files and remove listings of files that are no longer found on the Drive.
Another example is Netflix, which does a similar thing with its movies. The list of movies is a simple list of titles, lengths, and other data in a SQL database. The movies themselves are played out of a database that contains a video player and the data for the movie itself.
The key difference between SQL and NoSQL is rigidity vs flexibility. A rigid system of organization will let you order things by tons of different metrics that are shared throughout the data points. Most if not all data points will have some keys of the same kind, and you can almost always navigate to any datapoint from any other datapoint by going from one table to another. But things are not always that simple.
If each individual data point has anything larger or more complex underlying it, then NoSQL is the way to go. Its flexibility means that no two data points in a database need to be that similar. You will not be able to organise things as easily, but you will still be able to search things out.
Which style you use is up to you, just go in with a good idea of what your organizational needs are.