Non-Relational Database

As applications grow, they produce large volumes of structured, semi-structured, and unstructured data types. Traditional relational databases can struggle to handle this variety effectively. Non-relational databases offer a solution to these data challenges.

What Is a Non-Relational Database? 

Non-relational databases, also called NoSQL databases, store data without enforcing a strict tabular schema. Rather, they use flexible data structures to manage different kinds of data.

They can store a customer profile, a sensor reading, and a video thumbnail in the same place without you having to redesign the entire data architecture.

Non-relational databases originated in the late 1990s. They became more popular due to the rise of big data and the need for real-time processing on social media platforms, IoT devices, and e-commerce systems.

Companies like Google and Amazon pioneered NoSQL database solutions to handle massive amounts of diverse data and remain available even under extreme loads.

How Does a Non-Relational Database Work? 

Non-relational databases store data in formats that reflect the structure of the data itself. They don't require fixed schemas, which makes them ideal for managing unstructured and semi-structured data.

In contrast, relational databases like MariaDB and MySQL organize data in neat rows and columns, with strict rules connecting different pieces of information.

Take an e-commerce platform, for example. In the relational model, there might be a customer table and an orders table, carefully linked by customer IDs. Relational database management systems work well when data is predictable and rarely changes.

But many applications work with a constant influx of unpredictable data that can't fit as easily into predefined columns.

Relational databases also tend to scale vertically, which can become expensive as your data grows. And if your data structure needs to change, you face system downtimes and complex migrations.

NoSQL databases solve these challenges. Instead of putting data into predefined structures, they adapt to manage unstructured and semi-structured data. 

ACID vs. BASE Model

Relational databases are built on the 'Atomicity, Consistency, Isolation, and Durability (ACID)' model, guaranteeing strict transaction integrity.

Non-relational databases follow the 'Basically Available, Soft State, Eventual' consistency (BASE) model.

BASE focuses on availability and scalability over strict consistency, so the system remains operational even during partial failures. Updates are asynchronous across distributed nodes, which is why there may be temporary inconsistencies before the system eventually reaches a consistent state.

This concept is based on the CAP theorem, which states that a database can only guarantee two of the following three properties at any given time: 

• Consistency: All nodes see the same data simultaneously.
• Availability: Every request receives a response.
• Partition tolerance: The system continues to operate despite network failures.

In financial applications, where transactional integrity is critical, non-relational databases are less suitable, such as debiting and crediting accounts simultaneously. However, non-relational databases excel in use cases like social media feeds, where availability and speed are more important.

An Example of How Data Retrieval Works in SQL and NoSQL Queries

SQL and NoSQL databases approach data retrieval differently. Here's an example using basic SQL and MongoDB (a NoSQL database).

In SQL, finding users over 30 looks like this:

SELECT * FROM users WHERE age > 30;

The same query in MongoDB is:

db.users.find({ age: { $gt: 30 } });

Here's a breakdown of the MongoDB query:

• db.users: This specifies the users collection.
• .find(...): This method searches for documents that match the criteria.
• { age: { $gt: 30 } }: This is the query filter, where $gt stands for "greater than." It filters documents where the age field is greater than 30.

There are two areas where they strongly differ:

• Syntax: SQL uses a structured language with keywords like SELECT, FROM, and WHERE, while MongoDB uses JavaScript-like syntax with methods like .find() and operators like $gt.
• Result handling: In SQL, results are returned as rows of data, while in MongoDB, results can be returned as BSON documents, making it easier to work with nested data structures.

NoSQL Databases and Application Development

To understand how non-relational databases work in application development, consider an audio or video livestreaming platform.

Such platforms generate vast amounts of user data, like:

• Profiles
• Viewing history
• Device preferences
• Recommendation algorithms

Relational databases, with their fixed schemas, struggle to handle this continuously changing and unstructured data.

In contrast, NoSQL databases make it easier to store and update varied data types. For example, a streaming service might use a document database to store user profiles as JSON documents containing fields like "watch history," "liked genres," and "device preferences." This flexibility allows the platform to adapt quickly to new features, such as adding a "continue watching" section without requiring schema changes.

Additionally, due to non-relational databases' horizontal scalability, the platform can serve millions of users simultaneously without performance issues. By distributing data across multiple servers, NoSQL databases maintain low latency for real-time operations like buffering videos or syncing playlists across devices.

Real-World Applications of NoSQL Databases

• Internet of Things (IoT) applications: Developers can use non-relational databases to manage vast amounts of data from IoT devices, such as smart home appliances and temperature sensors. Many NoSQL databases work well with common IoT protocols like the Message Queueing Telemetry Transport (MQTT) protocol and Advanced Message Queueing Protocol (AMQP).
• E-commerce platforms: Non-relational databases can store and manage large datasets, such as product catalogs, customer profiles, and transaction histories. They can also handle high traffic volumes during busy shopping seasons.
• Gaming platforms: Non-relational databases have fast read and write speeds, making them ideal for real-time gaming applications that require quick data access.
• Content Management Systems (CMSs): Non-relational databases are suited for managing content such as text, images, and videos for CMSs. They often do this faster than relational database management systems, improving user experience and increasing metrics like page speed.

Types of Non-Relational Databases 

There are seven types of non-relational databases, each with its strengths and weaknesses.

1. Key-Value Stores

Key-value stores are the simplest form of non-relational databases since they store data as key-value pairs. This model is ideal for applications that need fast lookups and simple data retrieval.

Pros:

• High-speed retrieval
• Scalability
• Easy to implement and manage

Cons:

• Limited querying capabilities
• No support for complex relationships

Use Cases:

• Caching systems
• Session management
• Real-time analytics

Redis is a popular key-value store often used to cache web session data to improve application performance.

2. Document Databases

Document databases store data in flexible document formats like JSON or BSON. Each document can have unique fields and structures, making them suitable for hierarchical or semi-structured data.

Pros:

• Schema flexibility
• Easy to manage dynamic data
• Supports hierarchical data storage

Cons:

• Querying nested documents can be complex
• Less efficient for transactional operations

Use Cases:

• CMSs
• E-commerce platforms with significantly varying product details
• Mobile apps

MongoDB is a well-known document database developers often use in CMSs where data structures change frequently.

3. Column-Family Stores

Column-family databases organize data into columns grouped into families rather than rows. They are designed to handle massive amounts of data efficiently.

Pros:

• Excellent performance for analytics
• Scalable architecture
• Optimized for write-heavy operations

Cons:

• Limited support for complex queries
• Requires careful schema design

Use Cases:

• Time-series data
• Real-time analytics
• IoT applications, particularly for scenarios where data is mostly written and occasionally read, such as sensor data collection

Apache Cassandra is a column-family store often used in IoT applications to handle large volumes of sensor data.

4. Graph Databases

Graph databases represent data as nodes (entities) and edges (relationships). They are ideal for applications where relationships between data points are critical.

Pros:

• Can handle complex relationships
• Fast traversal of connected data
• Supports querying based on relationships

Cons:

• Difficult to scale horizontally
• Requires specialized query languages

Use Cases:

• Social networks
• Fraud detection
• Recommendation systems

Neo4j is a popular graph database used in social networks to analyze user connections.

5. Time-Series Databases

Time-series databases are optimized for storing and querying time-stamped data, such as logs or sensor readings.

Pros:

• High performance for sequential data
• Efficient storage of time-based information
• Optimized for real-time analytics

Cons:

• Less suitable for non-time-series data
• Complex queries may be difficult to manage

Use Cases:

• IoT applications
• Monitoring systems
• Financial analytics

InfluxDB is a time-series database used in IoT applications to monitor sensor data over time.

6. Search Databases

Search databases are designed for full-text search and indexing of large datasets. They allow users to query unstructured text.

Pros:

• Optimized for text-based searches
• Supports advanced query capabilities like filtering and ranking results

Cons:

• Not suitable for transactional operations or structured data storage
• Requires additional indexing

Use Cases:

• Search engines
• Log analysis tools
• Text-based analytics

Elasticsearch is a popular search database used in search engines for fast text-based queries.

7. In-Memory Databases

In-memory databases store data entirely in RAM rather than disk storage, enabling ultra-fast read/write operations.

Pros:

• High-speed performance
• Low latency
• Suitable for real-time applications

Cons:

• May have limited scalability due to memory constraints
• Higher cost compared to disk-based storage
• Volatile with the risk of data loss in case of power failure

Use Cases:

• Real-time applications like gaming leaderboards
• Financial trading systems
• Real-time analytics

In addition to being a key-value store database, Redis is one of the most popular in-memory databases.

Best Practices for Using NoSQL Databases 

Here are some best practices when working with non-relational databases.

Understand Your Data Model Needs

Pick your database type based on what your application needs. For instance, graph databases excel with relationship-heavy data, while document databases handle semi-structured data well.

In a video streaming platform, user profiles and watch histories are often accessed together. 

Instead of storing this data in separate collections or tables, a document database can store the user profile and watch history in a single JSON document. This cuts query complexity and improves retrieval speed.

Implement Sharding for Horizontal Scalability

Sharding splits your data across multiple servers or nodes. It's essential for large datasets or high-traffic applications.

Social media platforms with billions of posts can shard by user ID in a column-family database. Each server handles just a subset of users, boosting query performance and smoothing scaling as the user base grows.

Optimize Queries

Index frequently accessed fields to improve query performance, especially in document-oriented and column-family databases.

In an e-commerce application using a search database, you can create indexes on fields like product name, category, and price range for quick filtering and ranking of search results.

However, avoid indexing less frequently queried fields, such as product descriptions, to balance performance and storage efficiency.

Leverage Built-In Replication for High Availability

Many non-relational databases offer built-in replication features for data availability, even during server failures. Use these features to maintain uptime in mission-critical applications.

In a real-time multiplayer gaming app using an in-memory database, replication allows player scores to remain accessible even if one server goes offline. This prevents disruptions during gameplay.

Monitor and Maintain Data Consistency

Regularly analyze query patterns and improve them to prevent performance degradation as your application grows. Implement mechanisms like versioning or eventual consistency checks for reliable data integrity in distributed systems.

In an IoT monitoring system using a time-series database like InfluxDB, database administrators or developers should optimize queries that aggregate sensor data over time with downsampling techniques, like summarizing hourly data instead of querying raw minute-level data. This reduces query load while maintaining meaningful insights.

Frequently Asked Questions