Part 1: Understanding High-Level Trade-offs (System Design 101)

When delving into the world of system design, one of the fundamental concepts to grasp is the notion of high-level trade-offs. These trade-offs play a pivotal role in shaping the architecture and functionality of complex systems. In this article, we'll explore these trade-offs in-depth, examining key principles, performance metrics, and design considerations that product managers and developers need to be aware of.

Performance vs Scalability:

At the heart of system design lies the trade-off between performance and scalability. Performance refers to the speed and efficiency with which a system handles tasks or processes, whereas scalability pertains to the system's ability to accommodate increasing demands by adding resources. A system is deemed scalable if its performance improves proportionally to the resources added. However, it's essential to note that enhancing performance often comes at the cost of scalability and vice versa. For instance, vertically scaling a system (adding more resources to a single node) can boost performance but may limit scalability in the long run due to hardware constraints. On the other hand, horizontally scaling (adding more nodes) enhances scalability but may introduce complexities in maintaining consistent performance across nodes.

Latency vs Throughput:

Another critical trade-off is between latency and throughput. Latency refers to the time taken to perform a specific action or produce a result, while throughput measures the number of such actions or results per unit of time. Balancing these two factors is crucial, as maximizing throughput often involves reducing latency. However, achieving low latency may require sacrificing some throughput, especially in scenarios where real-time responses are prioritized over bulk processing.

Availability vs Consistency:

Understanding CAP Theorem

The CAP theorem, also known as Brewer's theorem, is a fundamental concept in computer science that explains the trade-offs between consistency, availability, and partition tolerance in distributed systems. It states that in the presence of network partitions, a distributed system can only achieve two out of the three properties:

1. Consistency: All nodes in the system have a consistent view of the data, ensuring that all clients see the same data at the same time, no matter which node they connect to.

2. Availability: The system remains available and can respond to client requests at all times.

3. Partition Tolerance: The system continues to operate even if there is a network partition, where nodes are unable to communicate with each other due to network failures.

During a network partition, the system must choose between prioritizing consistency or availability. We will often face a choice between CP (Consistency and Partition Tolerance) and AP (Availability and Partition Tolerance) trade-offs, depending on the application's specific requirements.

• CP systems: Prioritize data consistency but may experience temporary unavailability during network partitions while

• AP systems:Prioritize availability but may sacrifice strong consistency for immediate responsiveness.

If consistency is prioritized, the system may become unavailable until the partition is resolved. On the other hand, if availability is prioritized, the system may allow updates to the data, potentially leading to inconsistencies until the partition is resolved.

The CAP theorem highlights the trade-offs that people must consider when dealing with network partitions, which are inevitable in large-scale distributed systems. While the theorem provides a high-level understanding of these trade-offs, real-world implementations often require more nuanced approaches and additional considerations beyond the simplified model of the CAP theorem.

Resources:

1. Understanding Scalability

2. What’s the Difference Between Throughput and Latency?

3. CAP Theorem Simplified

Thank you for reading PM Tech House 🏠. This post is public so feel free to share it.