CAP Choices: Pick two and hope for the best

Databases and system design

The „unsolvable Rubik’s cube of distributed systems” emerged at a time when the tech world was wrestling with the complexities of scaling systems across unreliable networks. It was the dawn of the 21st century, and the internet’s rapid growth demanded architectures that could handle global traffic without skipping a beat. In this landscape of ambition and innovation, a provocative idea raised that would reshape how we think about distributed systems. It wasn’t just a technical challenge - it was a reality check, one that forced architects to confront the limits of what distributed systems could achieve in a messy, unpredictable world.

Computer scientist Eric Brewer postulated that distributed systems can only guarantee two of three properties: Consistency, Availability, and Partition tolerance. This was the birth of the CAP theorem.

The CAP theorem

The CAP theorem introduced by computer scientist Eric Brewer in 2000, is a foundational principle in distributed systems. It addresses the inherent trade-offs in distributed databases, especially those that prioritize scalability, reliability, and fault tolerance across multiple nodes.

The CAP theorem asserts that it is impossible for a distributed system to simultaneously guarantee all three of these properties: Consistency, Availability, and Partition tolerance.

Instead, distributed systems can provide at most two out of the three properties, with each combination having unique advantages and limitations.

The CAP theorem has significant implications for the design and architecture of distributed databases, guiding system architects in choosing which properties to prioritize based on application needs. Let’s dive into the details of each property, understand the trade-offs, and examine how different types of systems implement CAP in practice.

Understanding the Three Properties of CAP

The CAP theorem defines the three core properties that distributed systems strive to achieve.

Consistency

Consistency means that every read request returns the most recent write for a given piece of data across all nodes. In other words, if a system is consistent, all clients, no matter which node they connect to, see the same, most up-to-date data at any given moment. In a consistent system, any update to data on one node is immediately propagated to all other nodes, ensuring a single, synchronized view of data across the system.

In a banking application, consistency ensures that if a withdrawal occurs in one part of the system, the new balance reflects immediately across all nodes, preventing scenarios where another part of the system might show an outdated balance.

Achieving consistency often involves techniques like synchronous replication or two-phase commit protocols, which enforce that updates are propagated across all nodes before confirming the write. This ensures that all nodes have an identical view of the data, but it can introduce latency, as nodes must wait for each other to confirm the update.

Maintaining consistency in a distributed system requires global coordination, which can reduce availability and increase response time, particularly when network latency or failures are present.

Availability

Availability guarantees that every request (read or write) receives a response, even if some nodes are down or unreachable. In other words, the system should remain responsive and serve requests even in the presence of partial failures. An available system is always operational, but it may provide outdated data if the system prioritizes availability over consistency.

In a social media application, users may see a post count or comment section that doesn’t immediately reflect recent activity, as long as the system remains responsive and able to serve user requests, ensuring uninterrupted experience.

Availability is often achieved through replication and partitioning (sharding) of data, so requests can be routed to operational nodes even if some nodes are offline. Distributed systems like Cassandra or DynamoDB are designed to favor availability by replicating data across multiple nodes and enabling clients to access any replica for reads and writes.

Prioritizing availability means the system may serve slightly outdated data, as updates are not immediately reflected across all nodes. Additionally, ensuring availability can lead to inconsistency in cases where network partitions or failures prevent nodes from synchronizing.

Partition Tolerance

Partition tolerance means that the system continues to operate even if there are communication breakdowns or network partitions between nodes. In a partition-tolerant system, the network can break into multiple isolated parts (partitions), and each part can continue processing requests independently. This property is crucial in distributed systems that span multiple data centers or geographic locations, where network disruptions can occur.

Consider a global application where users in Asia and Europe access data stored across various data centers. If network issues prevent the data centers from syncing, a partition-tolerant system would still allow users in each region to access their local data, maintaining functionality despite the network partition.

Partition tolerance is inherent in distributed systems by design, as networks are unpredictable, and nodes may become temporarily unreachable. Distributed databases achieve this by replicating data across nodes so each partition can continue processing requests independently, even if disconnected.

To maintain partition tolerance, systems must often compromise on consistency or availability during network partitions. If consistency is prioritized, requests may be denied until the partition is resolved, reducing availability. If availability is prioritized, inconsistent data may be served across partitions.

The CAP Theorem’s Trade-offs

According to the CAP theorem, it is impossible to achieve Consistency, Availability, and Partition tolerance all at the same time in a distributed system. Thus, systems must make trade-offs, choosing two of the three properties.

Consistency and Partition Tolerance (CP)

CP systems prioritize consistency and partition tolerance, sacrificing availability during network partitions. In a CP system, nodes must synchronize or reach consensus before a transaction can be completed, which ensures consistency but can delay responses if nodes are disconnected.

HBase and MongoDB (in some configurations) prioritize CP. If there is a network partition, these systems may refuse reads or writes to avoid serving outdated data, ensuring a consistent view across nodes when partitions are resolved.

Use Case: CP systems are suitable for applications where data accuracy is critical, such as financial systems or inventory management, where serving inconsistent data could lead to significant errors.

Availability and Partition Tolerance (AP)

AP systems prioritize availability and partition tolerance, sacrificing consistency. They remain operational and responsive even during network partitions but may serve inconsistent data. In an AP system, each partition can continue to serve requests independently, allowing for faster response times but at the cost of data consistency across nodes.

Cassandra and DynamoDB are AP-focused systems that provide high availability and can handle network partitions, even if data consistency is temporarily compromised.

Use Case: AP systems are suitable for applications that can tolerate some inconsistency, like social media, where minor delays in data synchronization (e.g., in likes or comments) won’t significantly impact user experience.

Consistency and Availability (CA)

CA systems provide consistency and availability but cannot tolerate partitions. These systems work well in single-site deployments or environments with low risk of network failures. In CA systems, all nodes are synchronized to ensure consistency, and the system remains available, but any partition (network failure) could bring down the entire system or halt operations.

Traditional relational databases like MySQL and PostgreSQL in a single-node or single-site deployment emphasize CA, as they provide both consistency and availability in a controlled environment without partition tolerance.

Use Case: CA is typically suitable for applications in controlled environments, such as within a single data center, where network partitions are unlikely. Examples include small-scale business applications or environments where consistency and availability are more important than partition tolerance.

Practical Implications of CAP

The CAP theorem provides essential guidance in designing distributed systems by forcing engineers to prioritize the properties most relevant to their use case. Here’s how CAP influences system design in real-world applications:

• System Design Decisions: CAP helps architects decide which type of database or distributed system model best fits their requirements. For example, e-commerce platforms may favor availability and partition tolerance (AP) to ensure users can always access the platform, even during high traffic or partial network failures.

• Choosing Between Consistency Models: Systems that need strict data accuracy, like banking or transactional systems, may choose CP, sacrificing availability temporarily to ensure that only consistent data is served. In contrast, a social media platform may accept eventual consistency, choosing AP to keep the platform responsive and available.

• Handling Network Partitions: CAP guides how systems should behave during network partitions. For instance, an AP system may continue serving requests, tolerating some inconsistency, while a CP system may go offline, waiting until partitions are resolved to restore consistent data access.

• Flexibility with Multi-Data Center Applications: The CAP theorem is highly relevant for global applications running across multiple data centers, where network partitions are likely. Systems that prioritize partition tolerance (CP or AP) can remain functional even during network disruptions, whereas CA systems might struggle in such environments.

Summary

The CAP Theorem states that in a distributed system, it is impossible to simultaneously guarantee all three of the following properties: Consistency, Availability, and Partition tolerance. Consistency ensures that all nodes see the same data at the same time, availability guarantees that every request receives a response (even if it’s not the latest data), and partition tolerance allows the system to continue operating despite network failures. The theorem doesn’t suggest choosing only two but rather highlights the trade-offs necessary when designing systems. In practice, distributed architectures prioritize different combinations based on the specific needs of the application, making the CAP Theorem a guiding principle for understanding and managing these trade-offs.