What is a distributed SQL database and how does it differ from a single-node database?

Choosing the right database system is crucial in system design. A distributed SQL database runs on multiple servers (allowing horizontal scaling and fault tolerance), whereas a single-node database runs on just one server (relying on vertical scaling as demand grows).

This article compares these two approaches in terms of SQL scalability, consistency (think CAP theorem trade-offs), and architecture. You'll also find real-world examples and technical interview tips to help you prepare for system design interviews.

What is a Distributed SQL Database?

A distributed SQL database is a relational database that operates across a cluster of multiple servers rather than a single machine. It still uses SQL for queries, but it spreads data across nodes, keeping them in sync so the whole cluster acts like one large database.

Key features:

• Horizontal scalability: Scale out by adding servers. The system distributes data and queries across nodes, avoiding the hard limits of a single machine as your data or user count grows.
• High availability: Data is replicated on multiple nodes, so the database can keep running even if one node fails. Failover is often automatic, minimizing downtime.

Single-Node Databases Explained

A single-node database runs entirely on one server. Traditional databases like MySQL often start this way, with one machine handling all data storage and queries.

Key characteristics:

• Vertical scaling & simplicity: To handle more load, you upgrade the server's hardware (scale up). The design is simple – all data is on one machine, which means low query latency and straightforward maintenance.
• Single point of failure: If that one server crashes, the database goes down. You can add replicas or backups for failover, but writes still funnel through a single primary node. And if you outgrow one server’s capacity, you’ll need to implement sharding (splitting the data across multiple databases), which adds complexity at the application level.

Key Differences Between Distributed SQL and Single-Node Databases

Scalability and Performance

Distributed SQL can scale out horizontally by adding servers to a cluster (allowing virtually unlimited growth, with some added network overhead for coordination). In contrast, a single-node database must scale up vertically by using a more powerful server. This works well until you hit the limits of that one machine.

Availability and Fault Tolerance

A distributed database has built-in replication, so it can stay online even if one node fails (the other nodes have copies of the data). A single-node database is a potential single point of failure – if the lone server crashes, the database is unavailable (unless you have a standby setup, which isn’t as seamless).

Consistency and CAP Theorem

Distributed SQL systems prioritize strong consistency across nodes, even if it means sacrificing some availability during network partitions (as noted by the CAP theorem trade-offs). Single-node databases don’t have this concern – with only one node, data is always consistent as long as the server is running (there’s no multi-node conflict scenario).

System Architecture and Complexity

Distributed SQL databases rely on a complex cluster architecture (multiple nodes, data sharding, consensus algorithms) which can be harder to manage and optimize. Single-node databases have a simpler architecture – one server handles everything – making them easier to set up and maintain, but they lack the built-in scalability and resilience of a distributed approach.

Real-World Examples

• Google Spanner: A globally distributed relational database service by Google, known for strong consistency and worldwide scale.
• CockroachDB: An open-source distributed SQL database that automatically replicates data and scales out across nodes, designed to survive server failures.
• MySQL: A popular single-node SQL database. It’s simple and reliable on one server, but scaling beyond one machine requires vertical upgrades or sharding (and replicas for high availability).

Best Practices and Considerations

Keep it simple until you need to scale. If a single server can meet your needs, start with a single-node database for its simplicity and low overhead. Move to a distributed SQL solution only when your application outgrows what one machine can handle or when you require the extra fault tolerance. Always weigh the added complexity against the benefits.

Tips for Technical Interviews

• Explain core concepts: Be ready to discuss horizontal vs. vertical scaling and the basics of the CAP theorem in simple terms. For example, explain how adding more servers (horizontal scaling) can increase capacity.
• Discuss trade-offs: When comparing solutions, outline the pros and cons of each choice. For instance: "Distributed SQL offers high uptime and scale-out capability but adds complexity, whereas a single-node database is simpler and faster but can be a single point of failure." Practice in mock interviews to deliver such answers confidently and concisely.

FAQs: Distributed vs. Single-Node Databases

Q1. What is a distributed SQL database?

A distributed SQL database is a relational database that runs on a cluster of servers rather than a single machine. It uses SQL for queries but stores data across multiple nodes. This allows it to scale out horizontally and remain available even if one server fails.

Q2. How is a distributed SQL database different from a single-node database?

A distributed SQL database runs on many servers, whereas a single-node database runs on just one. Distributed SQL can handle more data and traffic by adding nodes (with built-in replication for fault tolerance). A single-node database is simpler and often faster for smaller workloads, but it's limited by one machine’s capacity and can go completely offline if that machine fails.

Q3. When should I use a distributed database over a single-node database?

Use a distributed SQL database when you need to scale beyond what one server can handle or when high availability is critical (no single point of failure). Conversely, stick with a single-node database when a single machine can easily handle your workload or when simplicity and low latency are top priorities.

Conclusion

In the end, choosing distributed SQL vs. single-node comes down to your application’s requirements. Distributed SQL shines when you need massive scale and high availability – it can grow across many servers and tolerate failures. Single-node databases excel for simplicity and low latency at smaller scale – they are easy to manage on one machine but have limits. Many teams start with a single-node setup for simplicity, then move to a distributed database as their data and traffic grow.

To learn more and strengthen your system design skills, check out Grokking the System Design Interview – our course that covers scalability, databases, and other key concepts with hands-on examples and mock interviews.