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Scalability
On This Page
Horizontal Scaling
Vertical Scaling
The Difference That Decides It
Scalability is the ability of a system to handle an increasing workload, either by adding more resources or by making the existing ones bigger.
Those are the two directions, and they have names.
Horizontal Scaling
Horizontal scaling, also called scaling out, means adding more machines or nodes to a system so the workload is distributed evenly across them.
No single node has to absorb the growth. More requests arrive, more machines share them.
This suits distributed systems well. Capacity can be added while the system runs, which makes it a cost-effective way to handle workloads that rise and fall, and having many machines is what makes high availability possible in the first place.
Cassandra and MongoDB are good examples. Both are built so you add machines to meet growing demand.
Vertical Scaling
Vertical scaling, also called scaling up, means increasing the capacity of an individual node by upgrading its hardware: more CPU, more memory, more storage.
The same single machine now handles more work.
MySQL is a good example. Moving from a smaller machine to a bigger one is a straightforward way to get more capacity, and the process usually involves downtime.
The Difference That Decides It
Horizontal scaling is easier to do dynamically. You add machines to the pool that is already running.
Vertical scaling is bounded by a single server's capacity. Growing past that ceiling usually means downtime, and there is an upper limit you eventually hit no matter what you spend.
| Horizontal (scaling out) | Vertical (scaling up) | |
|---|---|---|
| What changes | Number of machines | Size of one machine |
| Ceiling | Practically far away | The biggest machine available |
| Adding capacity | Add nodes to the running pool | Usually needs downtime |
| Failure of one node | The others carry on | Everything is on that node |
| Examples given | Cassandra, MongoDB | MySQL |
Read the last two rows together, because that is where the risk lives. Relying only on vertical scaling has two problems at once: there is a physical ceiling on how big one machine can get, and putting everything on that one machine creates a single point of failure. The system gets faster right up until the moment it is completely down.
💡 In an interview, do not just say "I would scale horizontally." Say what makes it possible. Horizontal scaling only works if requests can go to any node, which means saying where the state lives. "The service is stateless, so I can put any number of instances behind a load balancer, and session state goes in a shared cache" is the actual design.
Key takeaway: Scalability is handling more work by adding resources. Horizontal scaling adds machines and spreads the workload across them, which is the approach distributed systems are built for. Vertical scaling makes one machine bigger, which is simple but limited by that machine's ceiling, usually requires downtime, and concentrates everything into a single point of failure.
The next lesson, Availability, covers what it takes to keep a system reachable once it is spread across many machines.
Fayaz S
· 3 years ago
what should be our approach or things to consider or how to start with if we are trying to design a scalable and high performing platform and cloud analytic monitoring solutions
Shrikrishna jagdale
· a year ago
Normally in spring boot application we write validation checks for the request content, e.g headers, request body fields. What if we offload this validation to api gateway and let the application assume that only valid requests will land on the application's controller?
dinko.osrecki
· a month ago
What is missing here is diagonal scaling (combined horizontal and vertical scaling of the same workload).
Imagine an application that is processing tasks of variable complexity and load. It can happen that servers are hit with a huge batch of highly complex tasks (while normally it processes steady number of tasks of lower complexity). In this case it makes sense to scale both vertically (more CPU/RAM to have capacity to handle a complex task) and horizontally (many tasks to process).
On This Page
Horizontal Scaling
Vertical Scaling
The Difference That Decides It