Choosing the correct database for your projects is no easy decision. After all, picking one is a long-term commitment, and realizing you’ve made the wrong choice may force you to undertake a risky and costly procedure. In this article, we’ll discuss the types of databases there are, their advantages and disadvantages, and other topical points to help you decide what’s the right choice for you.
Although, before we begin, let’s define some general terms you should know before we start overviewing the topic. The most important ones are relational and non-relational databases.
Sometimes referred to as a NoSQL (Not Only SQL) database, it is a type of database management system. A non-relational database's primary distinguishing characteristic is that its data organization is not based on the conventional relational model. These databases have flexible schemas and are built to manage enormous volumes of both structured and unstructured data. Some core use cases for non-relational databases include applications where speed, scalability, and high performance are key. Also, check our article if you’d like to read more on structured vs unstructured data.
Unlike non-relational, relational databases organize data into tables consisting of rows and columns. The tables are related, resulting in data units having a fixed place. Therefore, the benefit of using relational databases is that they provide a simple and clear-cut structure that can be more easily dealt with by developers. Relational databases are also queried using structured query language (SQL), so they are often referred to as SQL databases.
While in the above section, we mentioned that relational databases are often referred to as SQL databases, they aren’t the same. SQL is a programming language built for managing and manipulating relational databases. Notably, it provides a standardized way to interact with relational databases, allowing you to define, control, and query the stored data. However, it itself isn't a database.
On the other hand, NoSQL and non-relational databases are the same thing. NoSQL simply stands for “Not Only SQL,” suggesting that these databases do not exclusively use traditional SQL for data management. What makes NoSQL potentially attractive for software engineers is that it delivers where SQL fails. NoSQL can be the correct choice when you must handle unstructured or semi-structured data quickly and on a large scale. Crucially, though, you miss out on the convenient and standardized way of querying with SQL by choosing NoSQL.
Something else you should consider when choosing NoSQL is the various types of databases it contains, starting with key-value.
Commonly seen as the simplest type of NoSQL database, key-value databases have data sets with no predefined structure, resulting in exceptionally flexible data formats. The foundation of this database lies in its approach to storing data as a collection of key-value pairs. Each data value gets assigned its designed key, and upon requiring a specific key, said data value is retrieved.
Because, comparably, it's so simple, key-value databases become highly resilient to failure, easy to scale, quick, and crucially capable of handling high loads. They also excel at simple read-and-write operations, thus cases where high-performance data retrieval and low-latency access are where these databases excel.
Overall, the features of key-value databases make them ideally suited for systems requiring quick data access and simple querying. Caching, session management, user profiles, real-time analytics, and high-scale distributed systems are just some of the examples where it excels at.
Instead of rows, this type of database focuses on columns. Curiously though, the structure of this database isn’t that much different from row-oriented databases. One of the primary aspects of column-oriented databases is how a single cell of a table row contains multiple types of data about a singular object. Imagine you want to easily retrieve information on an employee working for your firm. By requesting the appropriate row, you’ll get a multitude of columns, each providing different information like age, position, name, etc.
The convenience of having such a database is that it allows you to find things by their categories. For example, going back to our imaginary employee, let’s say you want to see all the people in the firm who have the same position as the said employee. By using a column-oriented database, a single request will retrieve all the results, whereas, with a row-oriented database, you’d have to scan row by row to find the necessary information. As such, this type of database is excellent at dealing with big data and real-time analytics.
As the name suggests, data is stored within documents in these databases, usually in JSON or BSON (binary JSON) formats. Each document can have a unique structure and completely independent types of fields. Notably, due to the documents' independence, they don’t require a predefined schema.
Because this database allows for documents of varying types to be collected into buckets (essentially groups where similar documents are housed together) and uses a schema-less approach, it is considered highly flexible. You can update, change or remove different attributes without worrying about ruining your schema.
Document-oriented databases are usually chosen by software engineers when scalability and flexibility are needed in areas like content management solutions, e-commerce platforms, and collaborative applications.
The basis for a graph database is that the data is modeled as nodes connected by edges. The nodes represent data entities, for example, objects or people, while the edges show their relationships. Where such a database shines is in graph traversal. Finding related information becomes straightforward since the relationships between nodes can be easily followed.
What sets graph databases apart is how effectively they avoid multiple indexing and referencing as the connections between numerous relationships are already reflected. Because of these features, graph databases are becoming widely used in many industries, from data intelligence and fraud detection to AI and Machine Learning.
Having examined the various types of NoSQL databases and their unique features, let’s further delve into what makes SQL different, and to do so, we needn't look further than the ACID (Atomicity, Consistency, Isolation, and Durability) properties.
Before delving into each feature separately, note that these 4 properties define a transaction. Therefore, if a database operation has these properties, it can be considered an ACID transaction, and the data storage systems that apply the operations are thus considered transactional systems.
Now, let’s examine the ACID properties one by one. Atomicity means that each statement in a transaction is treated as a single, indivisible unit, for example, deleting, writing, or updating data. This means that either all the changes are executed or none of them. The benefit of Atomicity is it helps prevent data loss and corruption from happening as partial updates (which can lead to data corruption) are avoided.
Consistency ensures that transactions bring the database from one consistent state to another. Therefore the changes to tables are done in predefined, predictable ways. With Consistency, errors in your data don’t affect the overall integrity of your table.
Isolation is done to avoid concurrent transactions interfering with each other. Each request occurs individually, meaning even if multiple users are reading or adjusting the table in any way, non-repeatable and phantom reads can't happen.
Durability is arguably the most straightforward aspect of ACID as it simply guarantees that changes to your data upon successfully executed transactions are saved so that even if a failure like a crash or power outage occurs, no data gets lost.
|SQL databases||NoSQL databases|
|Schema||There is strict schema enforcement.||No predefined structure, dynamic.|
|Scalability||Vertical scalability, chiefly limited by hardware.||Horizontal scalability, can easily scale with nodes.|
|Data Integrity||Data integrity and consistency is ensured.||Less data consistency comparatively.|
|Examples||PostgreSQL, Oracle, and MySQL.||CouchDB, Redis, and MongoDB.|
|Query Language||Structured Query Language (SQL).||Query language dependent on the database technology.|
|Transactions||ACID-compliant (Atomicity, Consistency, Isolation, Durability).||Eventual consistency (BASE: Basically Available, Soft state, Eventual consistency).|
|Use Cases||Standard applications with complex relationships and structured data, for example, data warehousing.||Rapid development, large-scale applications, unstructured data, and real-time analytics, for example, big data and real-time analytics.|
Overall, if we were to define the benefits and limitations of these 2 differing approaches to databases, the key takeaways would be:
NoSQL is wonderful for scalability. It’s made with horizontal scaling in mind, and distributing data among multiple nodes is a simplistic process. Handling large data volumes along with traffic loads becomes seamless as well.
Beyond scalability, NoSQL databases also offer flexibility and high performance. The core of its flexibility lies in NoSQL handling unstructured and semi-structured data without relying on predefined schema.
Regarding performance, use cases needing concurrent read/write operations or high loads are perfect for NoSQL databases as they’re made to optimize these specific data access patterns.
NoSQL limitations are primarily limited joins, complex queries, limited standardization, and lacking consistency. The difficult queries and subpar joins result in use cases like performing complex data analysis becoming unnecessarily complicated (when compared to SQL).
Standardization is an inherent NoSQL issue as it has no universal query language and data model. Therefore, switching between databases or even integrating aspects of a database into your existing one can be challenging.
Lastly, eventual consistency leads to data reaching nodes in a potentially slow manner, as NoSQL focuses primarily on scalability and availability, not consistency.
Unsurprisingly, many of the areas where NoSQL fails or is limited in its capacity are where SQL shines. Strict schema and the resulting data integrity, for example, are key features of SQL databases. Providing constraints like foreign and primary keys also helps create a foundation aimed at preventing anomalies.
A developed ecosystem is another benefit that NoSQL cannot use. Mature tooling combined with development frameworks and ORMs (Object-Relational Mapping) creates a thriving, long-established ecosystem for SQL which is perfect for developer support.
Having a standardized querying language is no slight advantage as well. Advanced data analysis, an otherwise deeply complex use case, is greatly simplified by SQL joins, data manipulations, and aggregations.
The limitations of SQL are primarily vertical scalability, schema rigidity, and impedance mismatch.
SQL databases tend to be limited by the hardware capacity of a single server as they primarily scale vertically. Therefore, scaling with SQL can become an increasingly costly endeavor.
If you have changing data requirements, SQL may not be optimal as its predefined schema will struggle to adapt to constantly evolving data needs. The common way of modifying a schema is schema migration, and that’s sadly both a difficult and time-consuming task.
Finally, data retrieval can become inefficient when dealing with hierarchical data structures commonplace in SQL databases. This is because mapping these structures to a relational model may create an impedance mismatch.
We’ve looked at SQL’s and NoSQL's various features, advantages, and limitations. Both were compared, and specific conclusions have been reached regarding which use cases should use what database. However, when considering what database is relevant for you, we highly recommend doing in-depth research into your specific project needs and the benefits each database offers. After all, switching databases, even if they’re the same type, is never a simplistic task.
About the author
Danielius Radavičius is a Copywriter at Oxylabs. Having grown up in films, music, and books and having a keen interest in the defense industry, he decided to move his career toward tech-related subjects and quickly became interested in all things technology. In his free time, you'll probably find Danielius watching films, listening to music, and planning world domination.
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