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Scaling & Distributed Systems
Scalability is a critical factor in Big Data and cloud computing. As workloads grow, systems must adapt.
There are two main ways to scale infrastructure:
vertical scaling and horizontal scaling. These often relate to how distributed systems are designed and deployed.
Vertical Scaling (Scaling Up)
Vertical scaling means increasing the capacity of a single machine.
Like upgrading your personal computer — adding more RAM, a faster CPU, or a bigger hard drive.
Pros:
- Simple to implement
- No code or architecture changes needed
- Good for monolithic or legacy applications
Cons:
- Hardware has physical limits
- Downtime may be required during upgrades
- More expensive hardware = diminishing returns
Used In:
- Traditional RDBMS
- Standalone servers
- Small-scale workloads
Horizontal Scaling (Scaling Out)
Horizontal scaling means adding more machines (nodes) to handle the load collectively.
Like hiring more team members instead of just working overtime yourself.
Pros:
- More scalable: Keep adding nodes as needed
- Fault tolerant: One machine failure doesn’t stop the system
- Supports distributed computing
Cons:
- More complex to configure and manage
- Requires load balancing, data partitioning, and synchronization
- More network overhead
Used In:
- Distributed databases (e.g., Cassandra, MongoDB)
- Big Data platforms (e.g., Hadoop, Spark)
- Cloud-native applications (e.g., Kubernetes)
Distributed Systems
A distributed system is a network of computers that work together to perform tasks. The goal is to increase performance, availability, and fault tolerance by sharing resources across machines.
Analogy:
A relay team where each runner (node) has a specific part of the race, but success depends on teamwork.
Key Features of Distributed Systems
| Feature | Description |
|---|---|
| Concurrency | Multiple components can operate at the same time independently |
| Scalability | Easily expand by adding more nodes |
| Fault Tolerance | If one node fails, others continue to operate with minimal disruption |
| Resource Sharing | Nodes share tasks, data, and workload efficiently |
| Decentralization | No single point of failure; avoids bottlenecks |
| Transparency | System hides its distributed nature from users (location, access, replication) |
Horizontal Scaling vs. Distributed Systems
| Aspect | Horizontal Scaling | Distributed System |
|---|---|---|
| Definition | Adding more machines (nodes) to handle workload | A system where multiple nodes work together as one unit |
| Goal | To increase capacity and performance by scaling out | To coordinate tasks, ensure fault tolerance, and share resources |
| Architecture | Not necessarily distributed | Always distributed |
| Coordination | May not require nodes to communicate | Requires tight coordination between nodes |
| Fault Tolerance | Depends on implementation | Built-in as a core feature |
| Example | Load-balanced web servers | Hadoop, Spark, Cassandra, Kafka |
| Storage/Processing | Each node may handle separate workloads | Nodes often share or split workloads and data |
| Use Case | Quick capacity boost (e.g., web servers) | Large-scale data processing, distributed storage |
Vertical scaling helps improve single-node power, while horizontal scaling enables distributed systems to grow flexibly. Most modern Big Data systems rely on horizontal scaling for scalability, reliability, and performance.