Introduction
One of the most common misconceptions in modern database discussions is that relational databases cannot scale horizontally, while NoSQL databases can.
This statement is partially true but also misleading.
Traditional relational databases were originally designed to scale vertically, meaning they run on a single powerful server that increases performance by adding CPU, memory, and storage. However, modern architectures and new technologies have made it possible for relational databases to scale horizontally as well.
Horizontal scaling means distributing data and workload across multiple servers rather than relying on a single machine.
This article explores how relational databases scale horizontally, the techniques used to achieve this, and how these approaches compare with NoSQL systems.
What Is Horizontal Scaling?
Horizontal scaling refers to increasing system capacity by adding more machines to a cluster.
Instead of upgrading a single server, the system distributes data and requests across multiple nodes.
Example concept.
Application Layer
Load Balancer
Database Cluster
Node 1
Node 2
Node 3
Node 4
Each node handles a portion of the workload, allowing the system to scale as demand increases.
Horizontal scaling is critical for large scale systems such as
Global ecommerce platforms
Streaming services
Social media platforms
Cloud infrastructure systems
Vertical Scaling vs Horizontal Scaling
Understanding the difference between these two scaling strategies is essential.
| Scaling Method | Description |
|---|
| Vertical scaling | Increasing CPU, memory, and storage on a single machine |
| Horizontal scaling | Adding more servers to distribute workload |
Vertical scaling works well for moderate workloads but eventually reaches hardware limitations.
Horizontal scaling allows systems to grow almost indefinitely by adding more nodes.
Why Relational Databases Traditionally Struggled With Horizontal Scaling
Relational databases rely heavily on features that make horizontal scaling more complex.
These include
Strong ACID transactions
Complex joins across tables
Strict schema relationships
Centralized transaction management
When data is distributed across multiple nodes, maintaining consistency and executing joins across servers becomes difficult.
For this reason, NoSQL databases were designed with distributed architectures from the beginning.
However, relational databases have evolved significantly and now support several horizontal scaling techniques.
Horizontal Scaling Techniques for Relational Databases
Read Replicas
One of the most common scaling methods is read replication.
In this architecture, one primary database handles write operations while multiple replicas handle read queries.
Architecture example.
Primary Database
Replica 1
Replica 2
Replica 3
Applications send write operations to the primary node and read queries to replicas.
Advantages
Improves read performance
Reduces load on the primary server
Easy to implement in many relational systems
Popular relational databases that support replication include MySQL, PostgreSQL, SQL Server, and Oracle.
Database Sharding
Sharding is a technique that splits data across multiple databases.
Each shard contains a subset of the total dataset.
Example.
Shard 1 → Users with ID 1–1,000,000
Shard 2 → Users with ID 1,000,001–2,000,000
Shard 3 → Users with ID 2,000,001–3,000,000
Requests are routed to the correct shard based on a shard key.
Advantages
Distributes data across multiple machines
Improves write performance
Allows large datasets to scale
However, sharding introduces complexity because queries that span multiple shards require coordination.
Partitioning
Partitioning divides tables into smaller segments while keeping them within the same database system.
Example.
Orders Table
Partition 1 → Orders from 2023
Partition 2 → Orders from 2024
Partition 3 → Orders from 2025
Partitioning improves performance for large tables by reducing query scanning.
However, partitioning alone does not provide full horizontal scaling across machines.
Distributed SQL Databases
A new generation of databases known as Distributed SQL systems has emerged to combine relational capabilities with horizontal scalability.
These systems distribute relational data across clusters while maintaining SQL compatibility.
Examples include
| Database | Description |
|---|
| Google Spanner | Globally distributed relational database |
| CockroachDB | Cloud native distributed SQL database |
| YugabyteDB | Scalable distributed SQL database |
| TiDB | MySQL compatible distributed database |
These systems use advanced techniques such as consensus protocols and distributed transactions to maintain consistency.
How NoSQL Databases Handle Horizontal Scaling
NoSQL databases were designed from the beginning for distributed systems.
Key architectural characteristics include
Automatic data sharding
Built in replication
Eventual consistency models
Distributed query execution
Example NoSQL cluster.
Application
Load Balancer
Cluster Nodes
Node A
Node B
Node C
Node D
Because the data model avoids complex joins and strict relationships, distributing data across nodes becomes easier.
Comparison of Scaling Approaches
| Feature | Relational Databases | NoSQL Databases |
|---|
| Default architecture | Single node | Distributed cluster |
| Horizontal scaling | Possible but complex | Native capability |
| Data distribution | Manual sharding | Automatic sharding |
| Transaction support | Strong ACID | Often eventual consistency |
| Query complexity | High relational queries | Simple queries |
Both approaches have advantages depending on application requirements.
Real World Examples
Many large technology companies scale relational databases horizontally using advanced architectures.
Example 1: Instagram
Instagram uses sharding to distribute relational database workloads across multiple servers.
This allows them to handle millions of users and requests while maintaining relational data integrity.
Example 2: Google Spanner
Google developed Spanner as a distributed relational database capable of scaling globally while maintaining strong consistency.
Spanner powers many Google services.
Example 3: Financial Systems
Large banking systems often use replication and partitioning to scale relational databases while maintaining strict transactional guarantees.
Advantages of Horizontal Scaling in Relational Databases
| Advantage | Explanation |
|---|
| Increased capacity | Multiple servers handle workloads |
| Improved reliability | Failure of one node does not crash the system |
| Better read performance | Replicas handle queries |
| Scalability | Databases grow with demand |
These capabilities allow relational systems to support modern high scale applications.
Challenges of Horizontal Scaling in Relational Databases
| Challenge | Explanation |
|---|
| Sharding complexity | Data distribution requires careful design |
| Cross shard queries | Queries across shards can be slow |
| Transaction coordination | Maintaining ACID across nodes is difficult |
| Operational complexity | Requires sophisticated infrastructure |
These challenges explain why many distributed systems rely on NoSQL databases for massive scale workloads.
Frequently Asked Questions
Can relational databases scale horizontally?
Yes. Techniques such as replication, sharding, partitioning, and distributed SQL architectures allow relational databases to scale horizontally.
Why are NoSQL databases considered more scalable?
NoSQL databases were designed from the beginning for distributed environments, making horizontal scaling simpler.
What is the best way to scale a relational database?
Common strategies include read replicas, database sharding, partitioning, and using distributed SQL systems.
Are distributed SQL databases the future?
Distributed SQL databases are gaining popularity because they combine relational query capabilities with horizontal scalability.
Conclusion
Relational databases were historically designed for vertical scaling, but modern technologies have introduced multiple ways to scale them horizontally.
Techniques such as replication, sharding, partitioning, and distributed SQL architectures allow relational databases to support large scale applications.
While NoSQL databases provide simpler horizontal scaling for distributed workloads, relational databases remain essential for applications requiring strong consistency, structured data models, and powerful SQL queries.
Understanding these scaling strategies enables developers and architects to build systems that are both scalable and reliable.