What is CAP Theorem?

Summary

In this video, Jamil Spain from IBM explores the CAP Theorem, a fundamental concept in distributed systems and cloud-native design. The theorem, introduced by Eric Brewer, posits that a distributed system can only guarantee two out of three desirable properties: consistency, availability, and partition tolerance. Spain uses MongoDB and Apache Cassandra as examples to illustrate how different databases prioritize these properties, emphasizing the trade-offs involved in system design. He also extends the discussion to microservices architecture, suggesting that the CAP principles can guide decisions about service responsibilities and resilience.

Takeaways

• 🍰 The CAP Theorem is a fundamental concept in distributed systems, illustrating the trade-offs between consistency, availability, and partition tolerance.
• 👤 The theorem was developed by Eric Brewer during his Ph.D. at MIT in the early 2000s, focusing on cloud native design and distributed architectures.
• 🔤 The acronym CAP stands for Consistency, Availability, and Partition Tolerance, which are the three key aspects of distributed systems design.
• 🔄 Consistency ensures all clients get the same data at the same time, reflecting a synchronized state across the system.
• 🚀 Availability means that every request receives a response, without guarantee that it contains the most recent version of the information.
• 🔄 Partition Tolerance refers to the system's ability to continue operating when some nodes have lost communication with others.
• 🚫 According to the CAP Theorem, it's impossible for a distributed system to simultaneously achieve all three properties; at most, two can be fully realized.
• 📚 The choice between the three depends on the specific requirements and priorities of the system being designed.
• 🌐 MongoDB is highlighted as an example of a database that prioritizes Consistency and Partition Tolerance, using a primary-secondary replication model.
• 🌐 Apache Cassandra is presented as an example of an AP (Availability and Partition Tolerance) system, where all nodes are equal and synchronization is eventual.
• 🤔 The CAP Theorem challenges designers to make deliberate decisions about trade-offs in their distributed systems, considering the importance of each property.
• 🛠 The principles of the CAP Theorem can also be applied to microservices architecture, guiding decisions on how to balance consistency, availability, and partition tolerance in different service components.

Q & A

What is the CAP Theorem?

The CAP Theorem, also known as Brewer's Theorem, states that in a distributed system, it is impossible for a database to simultaneously provide more than two out of the following three guarantees: Consistency, Availability, and Partition Tolerance.

Who developed the CAP Theorem?

Eric Brewer developed the CAP Theorem while he was getting his Ph.D. at MIT in the early 2000s.

What does the acronym 'CAP' stand for in the context of the CAP Theorem?

In the CAP Theorem, 'C' stands for Consistency, 'A' for Availability, and 'P' for Partition Tolerance.

What does Consistency mean in the CAP Theorem?

Consistency in the CAP Theorem means that all clients will get the same data at the same time, ensuring that the data is synchronized across the system.

What does Availability mean in the CAP Theorem?

Availability in the CAP Theorem refers to the system's ability to serve read and write requests at all times, even in the event of a node failure.

What is Partition Tolerance in the CAP Theorem?

Partition Tolerance in the CAP Theorem is the system's ability to continue operating even when nodes lose communication with each other, ensuring that the system can recover and re-synchronize.

Why can only two out of the three aspects of the CAP Theorem be achieved at any given time?

The reason only two out of three can be achieved is due to the inherent trade-offs in distributed systems. Prioritizing one aspect often comes at the expense of the other two, as they are mutually exclusive in certain scenarios.

Can you provide an example of a database that focuses on Consistency and Partition Tolerance?

MongoDB is an example of a database that focuses on Consistency and Partition Tolerance, with a primary node handling all writes and secondary nodes replicating from the primary to ensure data synchronization.

How does MongoDB handle node failures to ensure Partition Tolerance?

In the event of a primary node failure in MongoDB, an election process occurs where one of the secondary nodes becomes the new primary, ensuring the system continues to operate and maintain data consistency.

What is an example of a distributed system that focuses on Availability and Partition Tolerance?

Apache Cassandra is an example of a distributed system that focuses on Availability and Partition Tolerance, allowing all nodes to independently serve read and write requests and synchronize data eventually.

How does the CAP Theorem apply to microservices architecture?

The CAP Theorem can be applied to microservices architecture by considering the trade-offs between Consistency, Availability, and Partition Tolerance when designing individual components. For example, a web frontend might prioritize Availability to ensure user requests are always served, while the backend might focus on Consistency to maintain accurate data state.