Data has become a valuable asset for businesses of all sizes in today's technology-driven world.
As a result, organizations seek more efficient ways to store, manage and retrieve data. One such technology that has emerged recently to solve this problem is NoSQL databases.
In this conceptual post, I go over what NoSQL databases are, how they differ from traditional relational databases, their various types, and their use cases.
So, whether you're a software developer or someone curious about databases, this article will provide you with a comprehensive overview of NoSQL databases.
First, let's take a closer look at what a NoSQL database is.
What is a NoSQL Database?
The term NoSQL (also referred to as not-only-SQL), is used to describe an array of database management systems that uses data models other than the traditional tabular or relational database model used in SQL(Structured Query Language) databases.
In contrast to relational databases, NoSQL databases are designed to handle large amounts of unstructured and semi-structured data. They attempt to solve the problem of scalability and availability, as opposed to atomicity and consistency which relational database management systems (RDBMS) adhere to.
What are the differences between NoSQL and Relational Databases?
There are a couple of properties that differentiate the NoSQL databases from the SQL databases. The following points highlight some of the differences:
Structure and Data Organization:
NoSQL databases use a flexible schema to represent and store data. In contrast, its SQL counterpart (relational databases) uses a fixed schema to represent data in tables - partitioned into rows and columns, with defined relationships between them.
In addition to flexible schemas, there is a variety of data storage options available in the NoSQL space, such as documents, key-value stores, columns, or graphs. Also, NoSQL databases prioritize data availability and partition tolerance in distributed environments. This means they are designed to handle large amounts of data and work well in distributed environments where the data is spread across multiple servers.
On the other hand, relational databases enforce data consistency through constraints and foreign key relationships. This means that they prioritize making sure the data is accurate and consistent, even if the data is spread across multiple tables or databases.
Querying and Data Manipulation:
Relational databases use SQL (Structured Query Language) to query and manipulate data while NoSQL uses other query languages and APIs from other programming languages to query and manipulate data.
Scalability and Performance:
Relational databases can scale vertically by increasing the horsepower of the hardware. On the other hand, NoSQL databases can scale horizontally by increasing the database servers in the pool of resources to reduce load.
What are the different data storage types in NoSQL Databases?
There are several types of NoSQL databases, each with its approach to storing and retrieving data. Let's look at some of the main ones in the following section.
Key-Value Stores:
Key-value (KV) databases are conceptually the simplest NoSQL data models. These databases store data as a collection of key-value pairs. The key serves as a unique identifier for the value. The value can be anything such as text, JSON or XML document, or even an image. KV databases group key-value pairs into logical groups called "buckets".
A key-value database is not concerned about the content it's storing, it simply stores it. As a result, they do not support complex queries owing to the fact that the database is unaware of the content of the value component. Only get, store, and delete operations are done on a KV database.
Unlike in a relational database, relationships cannot be tracked among the keys as there are no foreign keys. This makes KV databases extremely fast and scalable for basic data processing.
Some examples of databases that fall within this category include DynamoDB, Redis, Riak, and Voldemort.
Document-Oriented Databases:
A document database is a type of NoSQL database that stores data as tagged documents, which are often encoded in formats like JSON or BSON. In a document database, each document is typically stored as a key-value pair, where the key is a unique identifier for the document and the value is the actual document data, which can be complex and nested.
Document databases organize documents into logical groups called collections, similar to how KV databases organize key-value pairs into logical groups called buckets.
The data is self-contained within each document and is represented as a key-value pair, where the key and value represent the field and value respectively. Think of the field as the column in a relational database and its corresponding value as the row. Here's an example of data in a document, stored in a document-oriented database like MongoDB:
{
"firstname": "John",
"lastname": "Doe",
"email": "johndoe@example.com",
"city": "jupyter"
}
Examples of databases within this category include MongoDB, CouchDB, OrientDB, and RavenDB.
Column-Oriented Databases:
A column-oriented database is a NoSQL database that organizes data in key-value pairs with keys mapped to a set of columns in the value component.
A column is a key-value pair similar to a cell in a relational database. The key is the name of the column and the value component is the data that is stored in the column.
These databases are somehow similar to the relational database model, in the sense that they both present data in logical tables. However, while the relational database follows a row-centric storage approach, the column-oriented database follows a column-centric storage approach.
The image above shows how a column-oriented database differs from a row-oriented database. In the row-oriented database example, the data for each row is stored together, with all the columns for that row stored in a single row of the database. In contrast, in the column-oriented database example, the data for each column is stored together, with all the rows for that column stored in a single column of the database.
One advantage of a column-oriented database is that it can be more efficient for certain types of operations, such as aggregations or calculations that involve only a subset of columns. Because all the data for a given column is stored together, the database can read only the relevant columns and ignore the rest, which can lead to faster performance and better use of hardware resources.
Some examples of databases that fall within this category include Cassandra, HBase, and Google Bigtable.
Graph-Oriented Database:
Graph databases are primarily designed to model and store data about relationships. The relationships are stored natively alongside the data itself. The primary components of a graph database are nodes, edges, and properties(entities).
node - is a specific instance of something we want to keep data about.
edge - is a relationship between nodes.
properties - they are like attributes, that is, they are the data we need to store about the node.
The edges show relationships between the entities, while the nodes store the entities themselves. An edge can have a start node, end node, type, and direction. A node is not limited to the number of relationships it can have.
In a graph database, a query is known as a traversal. It is possible to navigate the graph database along particular edge types or throughout the entire graph. Because the associations between the nodes are stored in the database rather than being computed at query time, traversing relationships is very fast.
Graph-oriented databases excel in applications where the relationship is as important as the data. Typical use cases are social networking and recommendation engines.
Some examples of databases that fall within this category include Neo4j and GraphBase.
Use Cases of NoSQL Databases
Both SQL and NoSQL databases have where they stand out, hence choosing between both depends on an organization's present and long-term objectives.
In certain cases, development teams will employ both, either in their cloud data environment or within the same application, with both deployed to service the areas they each excel at.
Here are some example use cases for a NoSQL database:
Inventory and catalog management
E-commerce and social networking applications
Fraud detection and Identity management
Big data and real-time applications
Financial services and payment
Content management system
Internet of Things (IoT) and sensor data
What are the Advantages and Disadvantages of NoSQL Databases?
The Pros:
Flexibility: NoSQL databases allow for flexible data models that handle all data types - structured, semi-structured, unstructured, and polymorphic, which can improve performance by reducing the need for complex joins and data transformations.
Data Storage Options: With NoSQL databases, there are several options you could choose to employ - document, key-value, column, and graph databases, to fit your specific data needs.
Scalability: Can scale horizontally using distributed clusters of hardware which is less expensive than scaling up a SQL database.
Data model modification is easier with NoSQL databases. You do not need to redefine the model in other to add new data types and fields to the database.
The Cons:
Lack of standardization: Each category of NoSQL database has its own API, query language, and data schema. As a result, it can be difficult to switch between different NoSQL databases. For relational databases, there is a standardized query language, which is SQL.
Data Consistency Issues: NoSQL databases do not strictly adhere to ACID (Atomicity, Consistency, Isolation, and Durability) principles. As a result, there may be potential data inconsistency issues, especially for applications with high-write throughput.
Limited Querying Capabilities: When it comes to sophisticated joins and aggregations, NoSQL databases have limited querying options than SQL databases. This makes it more challenging to perform ad-hoc queries or analytics on data stored in a NoSQL database.
Conclusion
In this post, you have learned about what a NoSQL database is, its various types, and its use cases. While there are some drawbacks to NoSQL databases, they still offer many benefits for modern application development and data management.
I hope you found this article useful.
Have any questions for me? Drop it in the comment section or reach out to me on Twitter.