- 2073 Magh · 5 marks
Short note: Vertical and horizontal scalability
Short answer
Vertical scalability (scale up) increases a single machine's resources (CPU, RAM) but hits hardware limits. Horizontal scalability (scale out) adds more machines to a cluster, enabling near-unlimited growth with commodity hardware and better fault tolerance, as used in Hadoop.
Write a short note on Vertical and Horizontal Scalability.
Scalability is the ability of a system to handle growing amounts of data or load by adding resources.
Vertical scalability (scale up)
Increasing the capacity of a single machine by adding more CPU, RAM or disk, or replacing it with a stronger server.
- Advantages: simple, no change in the application, no distribution complexity.
- Disadvantages: has a hardware upper limit, expensive high-end machines, downtime during upgrade, and a single point of failure.
- Example: upgrading a database server from 16 GB to 128 GB RAM.
Horizontal scalability (scale out)
Increasing capacity by adding more machines (nodes) to a cluster so that data and work are shared among them.
- Advantages: almost unlimited growth, uses cheap commodity hardware, better fault tolerance because of replication, no downtime when adding nodes.
- Disadvantages: complex software for distribution, consistency and network communication; more nodes to manage.
- Example: adding DataNodes to a Hadoop cluster, adding Cassandra nodes.
Vertical: [small] -> [ BIG ]
Horizontal: [node] -> [node][node][node][node]
Comparison
| Point | Vertical | Horizontal |
|---|---|---|
| Method | Bigger machine | More machines |
| Limit | Hardware limit | Practically none |
| Cost | Very high at top end | Low per node |
| Fault tolerance | Low | High |
| Software | Simple | Needs distributed design |
Big data systems (Hadoop, GFS, NoSQL) prefer horizontal scaling.
✓ Answer checked by Claude Opus 5.5 (high effort)
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