Horizontal scaling

Definition

Horizontal scaling, often referred to as 'scale out,' is the process of adding more machines or nodes to a system to handle an increased load. This approach is crucial in environments that require high availability and performance, especially when dealing with large volumes of data. In the context of NoSQL databases, horizontal scaling allows systems to efficiently distribute data across multiple servers, enabling better resource utilization and fault tolerance.

5 Must Know Facts For Your Next Test

1. Horizontal scaling is particularly beneficial for NoSQL databases, as they are designed to handle distributed data across many nodes seamlessly.
2. This method allows for cost-effective expansion since adding new machines can often be cheaper than upgrading existing hardware.
3. With horizontal scaling, if one node fails, the system can continue operating, as other nodes can take over the workload, enhancing reliability.
4. In cloud environments, horizontal scaling is a common practice because it provides flexibility to adapt to changing demands without significant downtime.
5. Many NoSQL databases like MongoDB and Cassandra natively support horizontal scaling, making them popular choices for applications that require scalability.

Review Questions

• How does horizontal scaling differ from vertical scaling in terms of system architecture and performance?
  • Horizontal scaling differs from vertical scaling primarily in its approach to increasing capacity. While vertical scaling enhances a single machine's performance by adding resources like CPU or RAM, horizontal scaling distributes the load across multiple machines. This not only improves performance by handling more requests concurrently but also increases fault tolerance since the failure of one node does not incapacitate the entire system. This makes horizontal scaling a preferred choice for large-scale applications needing high availability.
• Discuss the advantages of horizontal scaling in NoSQL databases and its impact on handling large datasets.
  • The advantages of horizontal scaling in NoSQL databases include improved flexibility and cost-effectiveness when dealing with large datasets. By distributing data across numerous nodes, these databases can efficiently manage vast amounts of information without bottlenecks. This method also enhances fault tolerance since if one node goes down, others can still function, ensuring data availability. As a result, applications can maintain high performance and reliability even as data grows.
• Evaluate how horizontal scaling can influence the design and management of distributed systems in modern cloud environments.
  • Horizontal scaling significantly influences the design and management of distributed systems by promoting a microservices architecture that can dynamically adjust resources based on demand. In modern cloud environments, this allows developers to deploy applications that can automatically scale out or in according to real-time needs without affecting overall performance. This adaptability not only optimizes resource usage but also reduces costs associated with underutilized servers, ultimately leading to more efficient and resilient systems.

Related terms

Vertical Scaling: Vertical scaling, or 'scale up,' involves adding more power (CPU, RAM) to an existing machine to improve its performance.

Sharding:

Sharding is a database architecture pattern that involves splitting a dataset into smaller, more manageable pieces called shards, which can be distributed across multiple servers.

Load Balancing:

Load balancing is the practice of distributing network or application traffic across multiple servers to ensure no single server becomes overwhelmed with too much demand.