What is the CAP Theorem?

The CAP theorem, also known as Brewer's theorem, states that distributed data systems can only provide two of the following three guarantees:

• Consistency - All nodes see the same data at the same time. Consistency ensures reads return the latest write or an error.
• Availability - Every request receives a response on success/failure. Availability guarantees every node will respond to queries even if some data is stale.
• Partition tolerance - The system works despite arbitrary partitioning failures. Partition tolerance means the system can sustain network failures that isolate nodes.

This fundamental tradeoff informs design of distributed systems. For example, partitioned databases optimize for partition tolerance and availability over strong consistency. Time series databases may leverage collision resistant hashes for efficient distributed concurrency control.

The theorem implies it is impossible to build distributed systems that are always consistent, available and partition tolerant. Systems have to make design tradeoffs based on application requirements around consistency, availability and resilience.

What does it mean? How does it work?

The CAP theorem implies that in the presence of a network partition, one has to choose between consistency and availability. Consistency implies reduced availability in partitions. Availability implies potentially stale data being visible to some nodes.

Most modern distributed systems opt for partition tolerance and availability. Sacrificing consistency allows the system to continue working despite failures. Weaker consistency models are employed.

Why is it important? Where is it used?

The CAP theorem is fundamental to the design of distributed data systems. It highlights the difficulty of maintaining ACID properties at scale.

Understanding the inherent CAP tradeoffs helps architects pick appropriate data models, consistency levels, replication factors, and failure handling strategies for highly available and scalable distributed systems like cloud databases and data lakes.

FAQ

Does CAP prohibit both consistency and availability?

No, the CAP theorem does not require abandoning both consistency and availability completely. It impliesWeakening consistency and availability guarantees to an acceptable level.

Are all three CAP properties binary choices?

Not exactly. Availability and partition tolerance tend to be more discrete, but there is a spectrum of consistency models between strong and weak or eventual consistency.

Can't modern systems achieve all three CAP properties?

In principle, but only by sacrificing performance, latency, scalability, and other practical engineering concerns. True CA and partition tolerance remains elusive at scale.

What are some examples of CAP tradeoffs?

• CP systems like Bigtable and Spanner sacrifice availability.
• AP systems like Cassandra and DynamoDB sacrifice consistency.
• Multi-master and leaderless designs improve availability over single-master.

Are there ways to work around CAP limitations?

Strategies like conflict-free replicated data types, quorum reads/writes, consensus protocols, and multi-regional deployments can help maximize both consistency and availability. But fundamental CAP tradeoffs remain.

References:

• [Book] What Is Distributed SQL? by O'Reilly Media, Inc.
• [Article] CAP twelve years later: How the "rules" have changed
• [Post] How to beat the CAP theorem
• [Post] CAP Theorem and Distributed Database Management Systems