Scalability in System Design

In its early days, as Twitter’s popularity increased and its user base grew, it became difficult for Twitter to keep up the system. The infamous fail whale, an error page whenever traffic overwhelmed Twitter, became a common sight for users. This happened because Twitter’s system wasn’t scalable enough to manage the surge in users and real-time activity. In today’s digital world, users become frustrated if the system cannot handle such a load, and some may even abandon our app in favor of a smoother experience elsewhere. That’s where scalability comes in, enabling the system’s responsiveness to handle spikes in user requests gracefully.

Key takeaways:

• Scalability is the ability of a system to handle a growing amount of users or work by adding resources to the system.

• Scalability of a system is driven by factors like user and data growth, geographic expansion, and new feature requirements.

• Techniques such as caching, load balancing, data replication and sharding, etc. are fundamental to scale systems.

• Google search, Netflix, Facebook, and Uber are some real-world examples of scalable systems being able to handle millions and billions of requests.

In System Design interviews, it is crucial to have a thorough understanding of scalability and providing authentic and proven strategies for scaling a system can be a plus point to pass the interview.

Let’s start with understanding scalability in System Design.

What is scalability?

Scalability is a system’s ability to handle more users or requests by adding resources or adjusting to their needs without compromising performance. Imagine a system like a balloon—the more we inflate it (increase workload), the larger it gets (scales) without bursting (performance degradation). When done right, scalability keeps things running smoothly even under pressure, preventing crashes or slowdowns. This ensures not only a better user experience but also a system architecture that can grow and adapt over time, driving long-term success.

Let’s now understand the importance of scalability in System Design.

Importance of scalability in System Design

Scalability is crucial because it ensures a system can adapt and grow in response to changing demands and requirements. Without scalability, a system may struggle to handle increased workloads, resulting in performance issues, downtime, and a bad user experience. Organizations can future-proof their systems by designing for scalability and accommodating growth without compromising performance or reliability.

When do we need to scale a system?

A system should be scalable considering the following factors:

• Anticipating future growth: We should anticipate future growth of users and data and scale our systems accordingly. For example, a social media platform like Twitter can struggle to keep up with the increased number of users and can lose users’ trust if it is not scalable enough.

• Boosting system performance: We should scale our system to boost performance by distributing the workload across available resources. This efficient resource utilization leads to faster response time and enhanced user experience. For example, an online retailer can lose revenue from a major sales event if its app’s performance is slow, making users bounce.

• Ensuring availability: We should scale our system to ensure that it remains up and running during high-traffic events. For example, if a streaming service does not scale, it can experience outages, frustrate viewers, and damage its reputation.

• Expansion to new regions: While expanding our system to new regions, it becomes vital to scale the system to support expansion and new users.

• Feature expansion: It also becomes critical to scale our system while introducing new features to encourage innovation, such as artificial intelligence.

• Integrating third-party services: If we want to integrate our system or application with third-party service providers, for example, a payment gateway for an e-commerce platform, we should scale our system to handle the requests for the integrated service.

Overall, scaling a system is essential in System Design, as it ensures that the system remains available and can grow over time to meet the business requirements.

How to achieve scalability in System Design?

There are two primary types of scalability: vertical and horizontal scaling.

Vertical scaling in System Design

Vertical scaling is adding more power or upgrading the hardware resources of the existing system to handle the increasing load. It may include increasing CPU cores, RAM, storage capacity, etc., to boost the system’s performance.

• The pros of vertical scaling include simpler implementation, improved performance, and easier management.

• The cons include a single machine’s limited capacity, downtime during upgrades, and higher costs for high-performance hardware.

Vertical scaling is suitable for applications with predictable workloads that need immediate performance boosts without concern for higher costs.

Horizontal scaling in System Design

Horizontal scaling means adding more resources into the system to distribute the workload among the available servers. For example, instead of increasing a single server’s capacity, we add more servers into the system.

• The pros of horizontal scaling include greater scalability, fault tolerance, and cost optimization.

• The cons of horizontal scaling are more complexity to manage and maintain, increased latency due to communication between servers, and the requirement of application architecture to support distributed systems.

Horizontal scaling suits applications expecting rapid growth or variable workloads requiring flexibility.

Instead of deciding which scaling type to use, modern systems prefer autoscaling, which automatically adjusts the number of resources allocated to a system based on real-time demand. It monitors CPU usage, network traffic, or queue length and then dynamically adds or removes resources vertically or horizontally to maintain optimal performance and efficiency.

Scalability techniques

Along with scaling in terms of hardware, some techniques can also help to scale the system to handle increased workloads:

• Load balancing: Load balancing ensures that users’ traffic is evenly distributed among the available servers without overloading a single server by monitoring each server’s load. In this way the system can handle increased load as no single server overwhelms and fails.

• Caching and CDNs: Caching stores commonly accessed data in temporary storage like memory or a dedicated caching server, reducing the need for repeated requests to the database or other backend services. Additionally, distributing static content like videos, images, and web pages through CDNs close to users further reduces the system’s load. Both caching and CDN allow the system to handle a larger volume of user requests without experiencing performance degradation.

• Data replication and sharding: Data replication duplicates data across different servers for scalability and fault tolerance, while sharding divides data into smaller partitions across databases to enhance performance and scalability.

• Microservices: Microservices architecture improves scalability by breaking down applications into smaller, independent services that can be handled or managed independently. It also allows each service to scale independently according to demands without affecting the performance of other services.

By understanding and implementing the right scalability techniques effectively, we can build systems that are future-proof, survive the ever-increasing demand, and be available to meet upcoming challenges. Scalable and flexible systems can ensure seamless performance and enhance user experience.

Best practices for System Design scalability

The techniques can help us achieve scalability, and following the best practices in action can help us make our system more resilient and scalable to ever-increasing demand:

• Mitigating performance bottlenecks: We should analyze and determine the performance bottlenecks in our system. These bottlenecks can be caused by factors like inefficient database queries, algorithms, etc. We should identify and mitigate such bottlenecks to ensure the system’s scalability.

• Efficient resource utilization: The available resources should be utilized efficiently, and no single resource should be overburdened, as it can become a bottleneck later. We should implement a queueing mechanism to process the incoming requests and opt for workers’ servers to handle background tasks independently. Similarly, effective caching can also help us improve the efficiency of our system.

• Minimizing network latency: We all know that communication is key to a system’s performance, and if there is a delay due to network latency, it can impact the system’s performance. So, reducing network latency through minimizing network hops, utilizing caching, and optimizing data transfer can also help to scale our system.

• Improving data storage and access: How we store and access data can also impact scalability. Scalable and distributed storage, caching, efficient data access patterns, indexing, data replication, and partitioning can help improve a system’s scalability and performance.

• Choosing the right and modern technologies: Last but not least, choosing the right and modern technologies can be a big plus for scalability. For example, choosing the most efficient algorithm, having optimized database queries, choosing SSD over HDD, etc., can greatly affect the system.

Note: We have designed dozens of real-world applications and discussed how to ensure the scalability of systems to handle millions of concurrent requests in our comprehensive course, Grokking the Modern System Design Interview.

Scalability challenges and trade-offs

Along with the techniques and best practices for scaling a system, we can face challenges as System Design engineers, as listed below:

• Cost: To scale a system, we often need to add more resources, which can directly increase its cost. This can be a direct trade-off to achieve scalability.

• Consistency: Data consistency becomes difficult across the distributed systems as the system scales.

• Security: Ensuring consistent security policies across service and data becomes challenging as the system grows.

• Complexity: Systems become more complex to manage and troubleshoot as we scale.

Scalability in action: Real-world examples

The following are some examples of scalable real-world systems:

• Google search: Google’s search is built on a massively scalable architecture that processes daily billions of queries.

• Netflix: Netflix is another well-known scalable system that uses cloud-based infrastructure (Amazon Web Services (AWS), another example of a real-world scalable system in action) to handle millions of concurrent requests and provide seamless streaming to users.

• Facebook: Facebook is another highly scalable system in action that handles requests and data from millions of users across the globe.

• Uber: Uber’s system is another highly scalable system designed to handle millions of ride requests globally.

Conclusion

Scalability in System Design is not just a technical requirement—it’s a vital element determining how well a system can grow, adapt, and perform under increasing demands. Whether scaling to accommodate more users, expanding to new regions, or introducing new features, maintaining performance and reliability is key to ensuring a seamless user experience. By implementing best practices like load balancing, sharding, and data replication and understanding the trade-offs involved, businesses can ensure their systems remain resilient and responsive in the face of growth.

As we look to the future, AI and machine learning are becoming more prominent in scaling systems intelligently. These technologies can automate resource management, predict traffic patterns, and optimize infrastructure in ways that were previously unimaginable. So, how do you think AI and machine learning can further revolutionize scalability in the systems you’re building?