System Design Principle: CAP Theorem

Published in

AyoKoding

7 min readJun 22, 2023

Introduction

Have you ever wondered why every system can’t be perfect? Why we can’t have a distributed system that is always available, provides only the latest data, and tolerates network failures? This is where the CAP theorem, also known as Brewer’s theorem, comes into play. Coined by computer scientist Eric Brewer in 2000, the CAP theorem is a cornerstone concept in distributed computing. It argues that a distributed data system can’t simultaneously provide all three of these guarantees:

Consistency: Every read receives the most recent write or an error. So if you update some data, any subsequent access will return that updated data.
Availability: Every request receives a non-error response without a guarantee that it contains the most recent write. The system will always process your request and return a result, even if it’s not the most recent.
Partition Tolerance: This means the system continues to function despite network failures between some servers.

Understanding CAP Theorem with an Analogy

Imagine a neighborhood where messages are delivered through a phone tree system.

Consistency is like ensuring that everyone in the neighborhood gets the same message. If the message is changed at some point, everyone receives the new updated message, not the old one.
Availability is like ensuring that everyone in the neighborhood is reachable by phone, no matter what. Even if the message they receive differs from the most updated one, they still receive one.
Partition Tolerance is like ensuring the message gets delivered even if a few phones aren’t working or there’s an issue with the telephone lines in some neighborhoods.

Now, why can’t we have all three — Consistency, Availability, and Partition Tolerance — at the same time?

Suppose a storm knocks out the phone lines in part of the neighborhood (this represents a network partition).

If we prioritize Consistency and Availability (everyone gets a message, and it’s the same message), we’re in trouble because we can’t reach the people in the part of the neighborhood where the phone lines are down. So, we’ve lost Partition Tolerance.
If we prioritize Consistency and Partition Tolerance (everyone gets the same message, and we account for broken phone lines), we might stop all messages until the phone lines are fixed. But in doing this, we’ve lost Availability because we’ve stopped all messaging.
If we prioritize Availability and Partition Tolerance (everyone gets a message no matter what, and we account for broken phone lines), we might continue sending messages even though some people might get an outdated message because of the broken phone lines. In this case, we’ve lost Consistency.

In reality, distributed systems often have to deal with network partitions, so Partition Tolerance is usually a necessity, and the choice often comes down to favoring either Consistency or Availability during these times of partition. So, when designing a system, you must decide which of these three properties is most important based on your specific needs.

Consistency vs. Availability vs. Partition Tolerance

In distributed systems, there is often a trade-off between Consistency and Availability. While Consistency ensures data integrity, Availability ensures that users can always access the system. This trade-off becomes even more apparent in specific use cases like banking systems, where ensuring data integrity takes precedence over anything else, and Consistency is a top priority.

Traditional relational databases (RDBMS) also typically prioritize Consistency over Availability due to their design principles. On the other hand, NoSQL databases are known for prioritizing Availability over Consistency. NoSQL databases are often used for web applications and other use cases requiring high Availability. For instance, content delivery networks (CDNs) prioritize Availability to ensure users can access the content they need, even if the system is experiencing high traffic.

Despite this trade-off, modern distributed systems aim to prioritize Partition Tolerance. This means the system can continue functioning even if some parts are unavailable. This is especially important in large-scale systems prone to failures where Partition Tolerance ensures that the system can recover quickly and continue to provide services to users.

CAP Theorem and Real-World Examples

Google’s Spanner and Amazon Dynamo are prime examples of systems that have made specific choices to achieve their desired Consistency, Availability, and partition tolerance guarantees.

Spanner employs a complex clock synchronization mechanism to achieve global Consistency, putting Consistency at the forefront. This ensures that all nodes in the system are in sync and there are no conflicts between them. However, this approach can come with some trade-offs, including increased latency and use of resources.

On the other hand, Amazon Dynamo prioritizes Availability using an eventual consistency model and decentralized control. This ensures the system remains operational and responsive, even if the accessed data is not the most recent. While this approach may lead to inconsistencies, high Availability and performance benefits outweigh the risks in many applications.

Despite their different approaches, both systems have successfully met their respective goals. Google has used Spanner for large-scale transaction processing, while Amazon has used Dynamo for its highly scalable web services. Overall, the choices made by these systems demonstrate the importance of carefully selecting the right Consistency, Availability, and partition tolerance guarantees for a given application.

Comparison of Distributed Databases

To fully understand the implications of the CAP theorem, it’s essential first to know what Consistency, Availability, and Partition Tolerance stands for. These three properties are critical to ensuring the reliable operation of distributed database systems.

Look at a few distributed database systems and how they fare regarding these properties. For example, Cassandra is a popular choice due to its tunable consistency levels and emphasis on availability and partition tolerance. These features make it an excellent option for use cases requiring high scalability and fault tolerance.

On the other hand, MongoDB, while offering tunable Consistency, differs from Cassandra’s approach to partition tolerance. It uses sharding, allowing horizontal scaling by dividing data across multiple servers. This can be especially useful for managing large datasets.

By comparing these two systems, developers can better understand which suits their application requirements. This is an important consideration when implementing a distributed database system, as choosing the wrong database system can lead to serious performance issues and data inconsistencies. Therefore, it’s crucial to carefully evaluate the trade-offs between Consistency, availability, and partition tolerance when selecting a database system.

Practical Implementation Strategies

Considering the CAP theorem, which stands for Consistency, Availability, and Partition tolerance, is crucial when designing distributed systems. To achieve a balance between Consistency and Availability, several practical strategies can be employed.

For example, replication and synchronization mechanisms, such as multi-master replication, can enable data to be stored and updated in multiple locations while maintaining Consistency. Distributed consensus algorithms like Raft or Paxos help ensure that all nodes in the system agree on the same value. These mechanisms help achieve the desired balance between Consistency and Availability and ensure fault tolerance against network partitions.

Another technique that can be used to achieve eventual Consistency while minimizing conflicts is conflict-free replicated data types (CRDTs). These data structures are designed to converge to a consistent state even if nodes are updated concurrently.

In addition to these strategies, it is essential to consider other factors, such as the system’s size, the amount of data to be stored, and the required level of fault tolerance. By taking these factors into account and using the appropriate strategies, designers can create distributed systems that are both efficient and reliable.

CAP Theorem in Modern Technologies

The CAP theorem, first proposed in 2000, has stood the test of time and remains relevant in modern fields such as edge computing and Internet of Things (IoT) platforms. Although initially formulated to guide distributed database design, the theorem’s principles have since been applied to a wide range of distributed systems.

In edge computing, where data is processed at the network’s edge rather than in centralized data centers, the CAP theorem is crucial in ensuring data availability and system responsiveness. Due to its distributed nature and intermittent connectivity, edge computing necessitates careful consideration of CAP trade-offs, including the trade-off between Consistency and Availability. To address this trade-off, edge computing systems may utilize eventual Consistency or conflict-free replicated data types (CRDTs) to ensure data consistency while maintaining high Availability.

By applying CAP theorem principles, IoT platform designers can ensure that their systems are designed to handle the unique challenges of distributed deployments, such as intermittent connectivity and high data volumes. Similarly, IoT platforms often involve distributed deployments and may require tailored consistency and availability approaches based on the specific use cases. With its continued relevance in these cutting-edge fields, the CAP theorem will likely remain an important concept in distributed systems design for years.

Conclusion

Understanding the CAP theorem is crucial for anyone designing or managing distributed systems. It’s a fundamental framework that emphasizes balancing Consistency, Availability, and partition tolerance based on specific use cases and requirements. As you continue your journey into distributed systems, we encourage you to explore how different systems handle these trade-offs. Real-world examples, comparisons of distributed databases, practical implementation strategies, and considerations for emerging technologies all provide insights into the practical applications of the CAP theorem. With a solid understanding of the CAP theorem, you are well-equipped to design distributed systems that meet your desired goals.