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One of the most enticing parts about working in the technology industry is innovation and growth.
All professionals are forced to learn, use, and adapt to new technologies.
While in many other industries, learning new technology is a pain, nobody in the software or tech world would ever say such a thing.
The beautiful thing about technology is the ability to learn new things.
What is NoSQL UK Guide 🇬🇧
When it comes to data and storing data, one of the leading changes people must learn to utilize is NoSQL. This technology is diverse, powerful, and attractive. There are countless types and providers of NoSQL databases that only add to the draw.
To learn about NoSQL, one must know the basics of data storage by its counterpart, SQL.
What is SQL?
Highly structured databases 💽
SQL is an acronym that stands for the structured query language. This language is utilized by developers who want to interact with data and databases. Using SQL, people can access various data points, extract data, and manipulate data.
The power of SQL lies in the fact that the language, and subsequently the databases, are highly structured. Data is stored in tables that follow patterns that are specified and created before any data is stored.
Thus, to use an SQL database, you must have someone design and create the database architecture. This tells you and users how different data points will relate to each other.
Relational Databases 
This nature of SQL databases is what brings about the other common name they’re known by—relational databases. This inherent feature means that data can be extracted methodically, so users will always know the format of data they retrieve.
SQL databases follow ACID—atomicity, consistency, isolation, durability. These properties provide the following.
- Atomicity—all modifications to data are saved together as if there was one single change. It’s all or none.
- Isolation–transactions are isolated from one another.
- Durability—if a system failure occurs, any committed changes or transactions remain and are unaffected.
- Consistency— data maintains consistency from the beginning of an action to the end of an action. If an interruption occurs to a database or server during a change, the database maintains the original data.
Atomicity—all modifications to data are saved together as if there was one single change. It’s all or none.With an understanding of the principles of ACID, you will easily be able to differentiate the theory and ideas behind NoSQL databases and SQL databases.
Key Differences Between SQL and NoSQL
Not Only SQL 💾
While many people may think the term “NoSQL” means that databases that are NoSQL do not use the SQL language. However, that’s not quite true.
The SQL Language
Many NoSQL databases still support querying with the SQL language. The true acronym means “not only SQL”. Thinking of these databases as supporting additional features beyond SQL gives a greater insight into the nature of these databases.
Structure
As SQL is known to be a highly structured system, NoSQL can be thought of in the exact opposite manner—a highly unstructured system.
That’s not to say there is no pattern to data.
NoSQL systems are only unstructured in the sense that they do not follow rigid patterns of data storage.
They do not use schemas in the way that SQL databases do. NoSQL databases don’t follow predefined tables.
Nor do they have patterns of associations they must follow.
To create a database, developers do not need to create a database model at the start. Rather, developers can add to their database and create different relationships between stores as they go. Thus, a NoSQL database may start with only one store that holds data for a single user.
As the project grows, that store will begin to hold more users. When the project becomes more complex, and there is a need for data storage for a different type of data, developers can create new stores that hold completely unique data.
All the while, they can ensure the stores relate to one another through IDs, similar to how SQL databases use primary and secondary keys.
The bottom line about the structure of NoSQL databases is that it's fluid. It’s ever-changing, adaptable, and flexible.
Non-Relational Databases vs Relational Databases
The term non-relational database essentially describes what was just covered—the structure. Introducing this term, however, can further distinguish NoSQL databases from relational—SQL— databases.
When a database is non-relational, that means it doesn’t follow the typical pattern of rows and columns. Rather, the stored data is fluid and may be stored in a number of ways. Learning how to use NoSQL databases is conditioned by the way data is stored in a NoSQL database, which is dependent upon what kind of database you’re using.
BASE vs ACID
Just as ACID describes SQL databases, BASE describes most NoSQL databases. The keyword is most because some NoSQL databases do tend to stick to the ACID philosophy.
This acronym stands for Basically Available, Soft State, Eventual Consistency. These features mean the following:
Basically Available
The NoSQL system being used is always available even when a failure occurs. This is because NoSQL databases are spread out over nodes. When one node fails, the rest of the system is still operational. Users will always have a response to a request, whether it’s a successful response or a failure.
Soft State
The system is subject to change with time. This is a result of the following feature.
Eventual Consistency
The system will develop consistency over time. When one node/server is updated, those updates will spread to the rest of the system given time and no other changes.
The key difference between BASE and ACID is that BASE gives up its dedication to always being available and always being consistent. Why do they give up these properties? Due to the CAP Theorem.
The CAP Theorem says that no distributed system can guarantee that it will provide users with consistency, availability, and partition tolerance all at once. Rather, systems can only guarantee 2 out of the 3 in any given instance.
These three features are described by the following.
Consistency
When a system is consistent, every person using that system at the same time will see the same data. To reach consistency, when data is updated at one node, those changes must be pushed to the rest of the system before the transaction is complete.
Availability
When a user requests the database, they should be able to get that data, even if there is a malfunction in some node. The user must get a response, whether it is the most up to date piece of information they require.
Partition Tolerance
A system must be able to continue its operations even when there are complications, such as delays in communication, between various nodes in the system.
Although most NoSQL databases prioritize availability and partition tolerance, there are still some databases that do put consistency ahead of the other two. The next section will cover the various types of NoSQL databases relating to how they overcome the problem defined by the CAP Theorem.
Overcoming CAP Theorem with NoSQL
Innovation is key 🔑
Since the three key features from CAP Theorem must be paired, three types of databases remain. These databases prioritize two characteristics each. As you’ll soon see, only two of these three types of database work in the real world.
CP Databases
A database that prioritizes consistency and partition tolerance must give up 100% availability by nature.
That means, when a user writes something to the database, this change is processed at one node and must be pushed to all the other nodes.
If a break occurs in the communication between two nodes—a partition—the system is unavailable until that break can be fixed.
An example of a database that follows this philosophy is MongoDB.
AP Databases
Databases that give up consistency to keep partition tolerance and remain available are AP databases. In the case of a partition within an AP system, all of the nodes that are not involved in the partition remain available. Once the partition is fixed, whatever changes occurred to the database are then pushed to the rest of the system.
A good example of an AP database is Cassandra.
CA Databases
Theoretically, a CA database is one that maintains consistency and remains available at all times. However, this is virtually impossible in practice. If a partition occurs, it must be handled, and partitions definitely occur in all systems, ask any developer!
Types of NoSQL Storage Methods
Databases on Top of Databases 🗄️
Beyond the theoretical aspect of these systems and what they prioritize, there are other factors that differentiate the types of NoSQL databases.
When selecting one out of the many free and open-source databases, users have four primary types to choose from—document stores, key-value stores, column-oriented databases, and graph databases.
Document Stores
Many popular NoSQL databases are document stores.
They’re simple to use, powerful, and flexible. This type of database is especially enticing to beginners because they store data in JSON-like documents.
Thus, developers should easily understand the storage patterns and methods of retrieving the necessary data from any given document.
Additionally, documents can be nested within each other, further adding flexibility. When creating a document store, developers can structure the individual documents however they like.
There is no pattern that must be followed. This allows developers to create varying documents as a project grows in complexity, and data storage needs change over time. Another advantage for developers is the fact that they can create documents with whatever kind of data they have.
For example, a developer could create a document to store website users’ information. One user may have their name, email, phone number, address, and profile picture stored. Another user could be created that only has a name and an email. No structure needs to be followed, which is extremely attractive to developers.
Key-Value Stores
Key-value databases are the most basic of NoSQL systems. These databases store pairs of information. The key in the database is often used to describe what the value being stored is—it acts as a unique identifier. The value can be anything the database designer wants it to be. A value may store an image, a string, or even a document.
As an example, a key-value database may be used for an e-Commerce website to store users’ shopping cart information. A single user will have a unique identifier for their shopping cart that then relates to what the user holds in the shopping cart.
Column-Oriented Databases
This type of database uses a structure that can be thought of as a hybrid table used by SQL databases. These structures are, however, much more fluid in terms of integrity. One row— specified by a key— in a table in a column-oriented database may have several different columns it uses. Thus, there is no rigid structure that must be followed.
Column-oriented databases are often used by businesses involved in warehousing and fulfillment. That’s because all the data for one item is stored together allowing for quick results when querying for data.
Graph Databases
Graph databases offer a unique style of storage compared to all other NoSQL systems. These databases store data through nodes and links.
You can think of a web of information. Points exist which hold data that can connect to other points on the web. These links between points—or nodes—also hold information. This information, though, is shared between the nodes.
Graph databases are rarely used on their own. However, they partner well with other NoSQL systems to power complex projects. For example, a social media network may use a graph database to store user’s information and to show how information is shared between these users.
Benefits of NoSQL
Cloud-Based Among Other Things ☁️
One of the most attractive features of NoSQL is the fact that these systems scale horizontally.
Horizontal Scaling
Their counterpart, SQL databases, scale vertically.
This can be costly and cumbersome as the scaling requires adding additional hardware or improved components.
As NoSQL is essentially synonymous with cloud storage, horizontal scaling is a pain-free process.
When the time comes when a NoSQL project needs more data storage capability, nodes and computers can be easily added.
Many NoSQL database providers even offer services where databases can scale on an as-needed basis. For instance, if a website experiences abnormally high traffic over an hour, a NoSQL database can scale out to handle the growth. When the traffic has decreased, the NoSQL system will automatically drop nodes.
Flexibility
The beauty of NoSQL lies in the fluidity and flexibility. These systems can change the way data is stored on the fly. One NoSQL database may store endless amounts of information about one user, and in the same database, there may be users with only their names stored.
NoSQL databases provide developers with the freedom to store data how they want. When storage needs change, developers don’t need to design a new table or database to handle the data. Rather, stores can be as complex or as basic as a developer wants. They can store all kinds of data, JSON files, images, maps, strings, binary values, and more.
Developer Friendly
NoSQL was built to be intuitive for developers. These systems have a small learning curve, especially considering data can be stored in JSON formats. Storing data in this structure makes it extremely easy for developers. When they work with data, it’s just like they’re working with code or extracting information from an API.
What’s more, there is a strong community of developers who are supporting the growth and advancement of NoSQL systems.
Cloud Storage
The future is in the unlimited cloud storage. Businesses are looking to cloud storage providers to save money, space, energy, and time.
The great thing about NoSQL databases is their reliance on the cloud.
As a developer or project owner, you never need to worry about hardware, downtime, and the costs associated with database maintenance. NoSQL systems thrive in cloud environments where they can scale out as needed.
When to Use NoSQL
Variety of Scenarios 📚
In reality, NoSQL can be used anytime a project needs to store data. There are several types of NoSQL systems that may fit a variety of scenarios better than others. However, NoSQL databases are often used in tandem with other NoSQL or SQL databases.
There are no clear-cut situations where NoSQL should be used over a traditional relational database. To know if your project would benefit from NoSQL ask yourself the following questions.
Do You Want a System Easy to Use?
NoSQL has a small learning curve. It’s easy to get started with, and developers find themselves advancing quickly within the system. NoSQL systems are easily modified and updated, and they follow Agile principles such as flexibility, results-focused workflows, and speed.
Do You See Your Data Storage Needs Changing?
At the beginning of any project, it’s tough to see what kind of data you’ll be storing. You never know what the future holds for your project—maybe it grows exponentially, and you expand on your offerings. In scenarios where your project changes and grows, you’ll have different data storage needs.
Such a case is perfect for a NoSQL database where your data storage system is flexible.
Do You Expect Your Data to Grow?
Piggybacking off the last question, you may not be able to predict the number of users your project will see. Your project may hit unexpected growth which requires quick scaling. NoSQL databases offer the perfect solution to scaling issues. With quick and seamless horizontal scaling, your project will always be ready to handle growth.
Conclusion
NoSQL is a powerful technology that is taking the software industry by storm. Developers are becoming more and more intrigued by this technology due to the variety of benefits. NoSQL is powerful, versatile, and flexible. It’s easy to get started with, easy to scale, and adaptable to all kinds of projects.
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