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Which Scalability approach is right for our Application? - System Design

What is Scalability and How to achieve it?

Last Updated : 03 Dec, 2024

The ability of a system to accommodate a growing load or demand without compromising performance is referred to as scalability. Scalability guarantees that systems can adjust without difficulty, whether a website's user base is growing or a corporation is extending its operations. Using distributed architectures like cloud computing, code optimization techniques, hardware enhancements, and other techniques are necessary to achieve scalability.

What is Scalability and How to achieve Scalability - Learn System Design

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What is Scalability?

Scalability is the capacity of a system to support growth or to manage an increasing volume of work.

When a system's workload or scope rises, it should be able to maintain or even improve its performance, efficiency, and dependability. This is known as scalability.
A system must be scalable in order to accommodate growing user traffic, data volumes, or computing demands without suffering a major performance hit or necessitating a total redesign.

Importance of Scalability in System Design

Scalability is an important factor in system design, which offers many benefits such as:

Managing Growth: When a system is scalable, it can handle more users, data, and traffic without sacrificing speed or reliability. Businesses that want to grow over time and draw in more customers need to do this.
Increasing Performance: By dividing up the load among several servers or resources, a scalable system increases overall performance. This leads to faster processing speeds, faster reaction times, and an intuitive user interface.
Ensuring Availability: By guaranteeing that systems continue to operate even in the event of unexpected spikes in traffic or component failures, scalability ensures availability. This is particularly crucial for mission-critical systems, which must always be operational to guarantee that clients can get the services they require.
Cost-effectiveness: Scalable systems are able to adjust their resources to meet demand variations by adding or subtracting resources as needed. This flexibility helps avoid oversupply and leads to significant cost savings by using just the resources that are required.
Encouraging Innovation: Scalability encourages the development of new features or services by lowering infrastructure obstacles. Because of this flexibility, businesses can swiftly adapt to changes in the market and keep a competitive edge.

How to achieve Scalability?

Now scalability is achieved via two methods in systems.

1. Vertical Scaling

Vertical scaling, also known as scaling up, refers to the process of increasing the capacity or capabilities of an individual hardware or software component within a system.

You can add more power to your machine by adding better processors, increasing RAM, or other power-increasing adjustments.
Vertical scaling aims to improve the performance and capacity of the system to handle higher loads or more complex tasks without changing the fundamental architecture or adding additional servers.

What is Scalability and How to achieve it?

Example of Vertical Scaling

Let's say you have a web application running on a server with 4 CPU cores and 8GB of RAM.

As the application grows in popularity and starts receiving more traffic, you notice that the server is starting to struggle to handle the increased load. To address this, you decide to vertically scale your server by upgrading it to a new server with 8 CPU cores and 16GB of RAM.

Advantages of Vertical Scaling

Increased Capacity: You can greatly improve a server's performance and capacity to handle more requests by improving its hardware, for as by installing more RAM or a faster CPU.
Simplified Management: Since vertical scaling concentrates on improving a single server, it is simple to implement. This can be less complicated and more user-friendly to handle than managing several servers.

Disadvantages of Vertical Scaling

Limited Scalability: The maximum hardware capacity of a single server places restrictions on vertical scaling. Physical bounds are reached at a certain point in the vertical scaling process.
Increased Costs: Compared to adding many, less expensive servers, upgrading server hardware, particularly with high-end components, might be more expensive. Because of this, choosing vertical scaling may end up costing more in the long term.
Single Point of Failure: When one server is used, any incoming requests are impacted by its failure, which causes downtime. Because of this, vertical scaling is more sensitive than horizontal scaling, which spreads the load over a number of servers to lower the chance of a complete failure.

2. Horizontal Scaling

Horizontal scaling, also known as scaling out, refers to the process of increasing the capacity or performance of a system by adding more machines or servers to distribute the workload across a larger number of individual units.

In this approach, there is no need to change the capacity of the server or replace the server.
Also, like vertical scaling, there is no downtime while adding more servers to the network

What is Scalability and How to achieve it?

Example of Horizontal Scaling

Imagine you own a small delivery service company that initially operates with a single delivery van. As your business grows, you start receiving more orders and delivering to a larger area. However, you quickly realize that the single van is not sufficient to handle the increasing demand efficiently.

To address this issue, you decide to horizontally scale your delivery operation by adding more delivery vehicles to your fleet. Instead of relying on just one van, you purchase several additional vans and hire more drivers.
By doing so, you’re able to distribute the workload across multiple vehicles, enabling your company to handle a greater volume of packages and serve a larger customer base.

Advantages of Horizontal Scaling

Expanded Capacity: By adding more servers or instances, horizontal scaling allows your system to handle a larger number of incoming requests. This helps accommodate growing traffic and demand without compromising performance.
Enhanced Performance: With multiple servers, the workload can be distributed evenly using load balancing. This prevents any single server from becoming overloaded, ensuring smooth and efficient performance across the system.
Greater Fault Tolerance: Horizontal scaling improves system reliability. If one server goes down, other servers can take over the load, minimizing the risk of downtime and maintaining service availability.

Disadvantages of Horizontal Scaling

Increased Complexity: Managing a network of multiple servers or instances can be more challenging compared to handling a single server. This requires robust infrastructure and management tools to ensure everything runs smoothly.
Higher Costs: Adding more servers or instances generally increases the overall cost of the system. This includes not just the hardware or cloud costs but also the expenses related to maintenance and management of the additional resources.

Vertical Scaling vs. Horizontal Scaling

Now that we have looked into the details of each type of scaling, let us compare them with respect to different parameters:

What is Scalability and How to achieve it? — Vertical Scaling vs. Horizontal Scaling

Aspect	Vertical Scaling (Scale-Up)	Horizontal Scaling (Scale-Out)
Definition	Increasing the capacity of a single server (CPU, RAM, storage)	Adding more servers or machines to the system
Cost	Higher, as larger and more powerful hardware is expensive	Lower per server, but cost increases with more servers
Complexity	Easier to implement, as it involves upgrading a single machine	More complex, as it requires managing multiple machines
Limitations	Limited by the hardware capabilities of a single server	Can be scaled infinitely by adding more servers
Performance Impact	Performance improves by adding resources to a single server	Performance improves through distribution of load across servers
Downtime	May require downtime for upgrades or maintenance	Can be scaled without downtime (if properly configured)
Use Case	Suitable for small applications with less traffic	Suitable for large-scale systems and high-traffic applications
Elasticity	Less elastic, harder to quickly add/remove capacity	Highly elastic, capacity can be adjusted easily by adding/removing servers

Factors Affecting Scalability

Below are the factors that affects the scalability with their explanation:

Performance Bottlenecks:
- Performance bottlenecks are points in a system where the performance is significantly lower than expected.
- These bottlenecks can be caused by various factors such as slow database queries, inefficient algorithms, or resource contention.
Resource Utilization:
- Efficiently using resources such as CPU, memory, and disk space is essential for scalability.
- Inefficient resource utilization can lead to bottlenecks and limit the system's ability to scale.
Network Latency:
- Network latency refers to the delay in transmission of data over a network.
- High network latency can impact the scalability of distributed systems by causing delays in communication between nodes.
Data Storage and Access:
- The way data is stored and accessed can impact scalability.
- Using scalable data storage solutions such as distributed databases or caching mechanisms can improve scalability.
Concurrency and Parallelism:
- By enabling the system to manage several activities or requests at once, concurrency and parallelism design can increase scalability by increasing throughput and decreasing response times.
System Architecture:
- The overall architecture of the system, including how components are structured and connected, can impact scalability.
- Using a modular, loosely coupled architecture that can be scaled horizontally (adding more instances) or vertically (upgrading existing instances) can improve scalability.

Components that help to increase Scalabilty

Below are some of the main components that help to increase the scalability:

Load Balancer: A load balancer distributes incoming network traffic across multiple servers or resources to ensure no single resource is overwhelmed. This helps improve performance and availability by distributing the workload evenly.
Caching: Caching involves storing frequently accessed data in a cache to reduce the need to access the original source of the data. This can significantly improve performance by reducing latency and the load on backend systems.
Database Replication: Database replication involves copying data from one database to another in real-time. This helps improve performance and availability by ensuring that multiple copies of the data are available across different locations or servers.
Database Sharding: Database sharding involves partitioning a database into smaller, more manageable parts called shards. Each shard contains a subset of the data, which helps improve scalability by distributing the data across multiple database instances.
Microservices Architecture: Microservices architecture involves breaking down a monolithic application into smaller, more independent services. This helps improve scalability by allowing each service to be scaled independently based on its workload.
Data Partitioning: Data partitioning involves dividing data into smaller, more manageable parts based on certain criteria (such as geographic location or user ID). This can improve scalability by distributing the data across multiple storage devices or database instances.
Content Delivery Networks (CDNs): CDNs can improve scalability by caching and delivering content from servers that are geographically closer to users, reducing latency and improving performance.
Queueing Systems: Queueing systems can improve scalability by decoupling components and allowing requests to be processed asynchronously. This can help manage spikes in traffic and prevent overload on backend systems.

Real-World Examples of Scalable Systems

There are many real-world examples of scalable systems that demonstrate the importance and impact of scalability in modern technology.

Google: Google's infrastructure is highly scalable, allowing it to handle billions of search queries and serve millions of users worldwide. Google uses a distributed system architecture and technologies like Bigtable, MapReduce, and Spanner to achieve scalability.
Amazon Web Services (AWS): AWS provides a wide range of scalable cloud computing services, including computing power, storage, and databases. AWS's scalable infrastructure allows businesses to quickly scale resources up or down based on demand.
Netflix: Netflix's streaming service is built on a highly scalable architecture that allows it to deliver high-quality video content to millions of users simultaneously. Netflix uses a combination of caching, microservices, and cloud computing to achieve scalability.

These examples demonstrate the importance of scalability in enabling companies to provide reliable and high-performance services to a large number of users. Scalability allows these companies to grow their user base, handle spikes in traffic, and innovate rapidly without being limited by the constraints of their infrastructure

Challenges and Trade-offs in Scalability

Challenges and trade-offs in scalability are important considerations when designing and implementing scalable systems. Some of the key challenges and trade-offs include:

Cost vs. Scalability: Adding extra resources is a common step in system scaling, which might raise expenses. A trade-off exists between the advantages of increased performance and availability and the expense of scaling.
Complexity: Systems typically get more complex as they grow in size. Because of its complexity, the system may be more difficult to maintain and debug, which could result in higher operating cost.
Latency vs. Throughput: There is often a trade-off between latency and throughput. Optimizing for low latency may reduce throughput, and vice versa.
Data Partitioning Trade-offs: Partitioning data can improve scalability by distributing it across multiple nodes. However, choosing the right partitioning strategy involves trade-offs, such as balancing the size of partitions, minimizing data movement, and ensuring data locality.

Conclusion

Scalability is a key factor in system design that allows applications and services to grow smoothly as demand increases. By building scalable systems, businesses can ensure that their platforms handle more traffic, data, and users without compromising performance or reliability. Achieving scalability involves using strategies like vertical scaling, horizontal scaling, and distributed architectures, which help distribute workloads efficiently and improve overall system resilience.

Which Scalability approach is right for our Application? - System Design

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