What is database scaling?

Database scaling is the process of increasing its performance and throughput as load grows (data volume, request count, users).

There are two main approaches:

#Vertical scaling (Scale-up)

The power of a single server increases, adding more CPU, RAM, an SSD.

• Pros: simple to implement, no architectural changes needed.
• Cons: there's a physical ceiling, it's expensive, and it's a single point of failure.

Example: upgrading a PostgreSQL server from 16 GB RAM to 64 GB.

#Horizontal scaling (Scale-out)

The load gets spread across several servers (nodes).

• Pros: flexible, fault-tolerant, you can add capacity as you grow.
• Cons: harder to configure and keep data in sync.

Example solutions:

• Sharding, the database gets split into parts by user, region, and so on.
• Replication, data gets copied to several servers: one primary, the rest for reads.
• Caching, some requests get served from memory (Redis, Memcached).

Conclusion: scaling is a way to make the database faster and more resilient under load, choosing between "add more power" and "add more servers".