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  • Writer's pictureVolodymyr Tsiluiko

Decoding Distributed Systems: Navigating CAP and PACELC Principles for Modern Architectures

Today, let's delve into the significance of CAP (Consistency, Availability, Partition Tolerance) and PACELC (Partition Tolerance, Availability, Consistency, Else Latency, and Complexity) in the realm of distributed systems.

Understanding these principles is vital for making informed decisions in designing resilient and efficient systems.

We'll explore how these concepts shape the landscape of distributed architecture, influencing choices related to data consistency, system availability, and handling network partitions.





The CAP theorem is crucial in distributed systems, as it outlines that in such systems, it's possible to achieve only two out of three properties: consistency, availability, and partition tolerance.


Understanding this theorem is essential when selecting the optimal database.

To grasp its implications, two articles are recommended.

The first article uses a fictional service and simple scenarios to illustrate how the theorem operates in the context of human communication, making it accessible to a broader audience.


The second article delves into the technical aspects, providing a detailed description of each property with visuals and exploring various applications of the theorem.

This comprehensive technical text caters to a more specialized audience seeking in-depth insights into distributed systems and database choices.




In the realm of distributed systems, the PACELC theorem extends the insights of the CAP theorem by introducing the consideration of latency when partitioning is absent.

While CAP highlights the trade-offs between partitioning, availability, and consistency, PACELC brings a nuanced perspective.

The theorem consists of two components:

  • PAC (partitioning, availability, consistency) inherited from CAP

  • ELC (else, latency, consistency)


First articulated in 2010, PACELC aids in assessing databases.

For instance, systems like Dynamo, Cassandra, and Riak fall under the PA/EL category, prioritizing availability over consistency in the presence of partitioning and favoring latency over consistency without partitioning.


On the flip side, MongoDB follows the PA/EC model, emphasizing consistency over latency when partitioning and maintaining the same behavior in its absence.

Understanding these principles is essential for informed decision-making in database selection, especially in the dynamic landscape of software architecture.

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