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**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".

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