In the realm of distributed systems, one of the foundational principles that every system designer must grasp is the CAP Theorem. This theorem, introduced by Eric Brewer in 2000, provides a framework for understanding the trade-offs involved in designing distributed systems. Let's dive into the CAP Theorem and explore its implications in high-level system design.
Caption: Distributed systems are foundational to modern applications, powering everything from cloud services to global data storage solutions.
This is the example of a distributed system where the system can allocate any node to the user based on nearest availability or any other mechanism. The user does not know which node is replying to the query requests and can only see reponses.
Response types:
Success
Error
What is the CAP Theorem?
The CAP Theorem states that in any distributed data store, you can only achieve two out of the following three guarantees:
Consistency: Every read receives the most recent write.
Availability: Every request (read or write) receives a (non-error) response, without guarantee that it contains the most recent write.
Partition Tolerance: The system continues to operate and respond despite an arbitrary number of messages being dropped or delayed by the network between nodes.
The 3 principles however, are mutually exclusive. There all 3 cannot be achieved simultaneously. We have to select any two when designing the system.
The system can be Consistent and Available, but in case of network failure, It has to sacrifice any one of the two to ensure Partition Tolerance
Let's Look at a simple example
Consistency and Availability
Consistency ensures that all nodes see the same data at the same time. For example, in a banking system, if you deposit money to an ATM, consistency guarantees that all systems reflect this transaction immediately.
Availability ensures that every request receives a response, regardless of the current state of the system. This means that the system remains operational and continues to process requests even if some nodes are down or unreachable.
Let's look at the figure below:
1) User 1 was connected to A, user performed write operation to update x to add 5.
2) User 2 was connected to B, user performed read operation cand x was 5 greater then it's last recent value even though user 2 did not update the value.
Partition Tolerance and Availability
Partition tolerance means that the system continues to function even if there are communication breakdowns between nodes. Distributed systems must be designed to handle partitions because network failures are inevitable in real-world scenarios. Prioritizing partition tolerance often involves trade-offs with consistency or availability.
If the system prioritizes availability,
1) User 1 performed write operation to A, adding 5 to x.
2) User 2 performed read operation and found that X is still it's last recent value, 10.
For these type of scenarios, The system must have a mechanism to handle consistency when the network is back up as X cannot be 15 and 10 at the same time.
Partition Tolerance ad Consistency
If the system prioritizes consistency,
The feature to perform write operations might be made temporarily unavailable when the network is down while read operations might be still available.
Caption: The CAP Theorem illustrates that a distributed system can only achieve two of the three guarantees.
Trade-offs in High-Level System Design
When designing a distributed system, understanding the CAP Theorem helps in making informed decisions about which properties to prioritize based on the application's requirements. Here are a few scenarios to consider:
Scenario 1: Financial Transactions
In a system handling financial transactions, consistency is paramount. Users need to be confident that their transactions are accurately recorded and reflected across all nodes. Therefore, such systems often prioritize Consistency and Partition Tolerance (CP), potentially sacrificing availability during network issues.
Scenario 2: Social Media Platforms
For a social media platform where user experience and responsiveness are critical, availability and partition tolerance might be prioritized. Users expect quick responses and continuous access, even if some data might be slightly out of date. These systems typically favor Availability and Partition Tolerance (AP).
Scenario 3: E-commerce Platforms
E-commerce platforms often require a balance between consistency and availability. While it's crucial to reflect accurate product stock levels (consistency), the platform must also remain available to handle user requests, especially during peak times like sales events. These systems might lean towards Consistency and Availability (CA), managing partitions through other means.
Caption: High-level system design involves making trade-offs based on application requirements and CAP Theorem considerations.
Conclusion
The CAP Theorem is a critical concept in the design of distributed systems, providing a framework to understand the inevitable trade-offs between consistency, availability, and partition tolerance. By grasping these principles, system designers can make informed decisions to build robust, efficient, and reliable systems tailored to their specific needs.
As you delve deeper into system design, remember that no system can fully achieve all three guarantees simultaneously. Embrace the trade-offs, prioritize based on your application’s requirements, and leverage the CAP Theorem to guide your design choices.
Caption: The growth of distributed systems continues to shape the future of technology, requiring thoughtful design and a deep understanding of foundational principles like the CAP Theorem.
By keeping these considerations in mind, you can design distributed systems that meet the demands of modern applications, providing a balance that aligns with your strategic goals.
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