The CAP theorem, also known as Brewer's theorem, states that distributed data systems can only provide two of the following three guarantees:
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.
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.
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.
No, the CAP theorem does not require abandoning both consistency and availability completely. It impliesWeakening consistency and availability guarantees to an acceptable level.
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.
In principle, but only by sacrificing performance, latency, scalability, and other practical engineering concerns. True CA and partition tolerance remains elusive at scale.
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.
Database partitioning refers to splitting large tables into smaller, independent pieces called partitions stored across different filegroups, drives or nodes.
Read more ->Collision resistance is the property of cryptographic hash functions to minimize chances of different inputs mapping to the same output hash, making it difficult to intentionally cause collisions.
Read more ->A time-series database (TSDB) is a database engineered and optimized for handling time-series data, where each data point contains a timestamp.
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