The PACELC Theorem

The PACELC Theorem


The PACELC theorem,  is an extension of the CAP theorem that introduces the concept of latency (L) and provides a more nuanced view of the trade-offs in distributed systems. 

It considers the choices available to system designers when dealing with the challenges of consistency, availability, partition tolerance, and latency.

Latency (L) in Normal Operation



The PACELC theorem introduces the concept of latency (L) during normal operation 


  1. When the system is available and not experiencing any network partitions. 

In this situation, the system can prioritize either consistency (C) or latency (L). This trade-off is represented by the "PACEL" part of the theorem.




  • Prioritizing Consistency (C): If the system prioritizes consistency during normal operation, it ensures that all nodes always see the same data, even if this means higher latency for some operations. Consistency is maintained at the cost of increased latency.
  • Prioritizing Latency (L): If the system prioritizes latency during normal operation, it responds to requests quickly, even if this means that some nodes may have slightly outdated data. Low latency is achieved by sacrificing strict consistency.


  1. During a network partition (P), the system must choose between availability (A) and consistency (C). This is the "PACE" part of the theorem.


  • Maintaining Availability (A): If the system chooses to maintain availability during a partition, it continues to serve requests, even if it cannot guarantee consistency across all nodes. Availability is prioritized over consistency.
  • Maintaining Consistency (C): If the system chooses to maintain consistency during a partition, it blocks or cancels some operations until the partition is resolved, sacrificing availability. Consistency is prioritized over availability.





Implications and Examples of Different PACELC Configurations



The PACELC theorem describes four possible configurations for a distributed system:

  • PA/EL:  Prioritize availability over consistency during partitions, and prioritize latency over consistency during normal operation.
    • Examples: Cassandra, Amazon DynamoDB
    • Suitable for use cases that require high availability and low latency, and can tolerate eventual consistency.
  • PC/EC:  Prioritize consistency over availability during partitions, and prioritize consistency over latency during normal operation.
    • Examples: Google Spanner, Apache HBase, MongoDB (with strong consistency)
    • Suitable for use cases that require strong consistency and can tolerate higher latency and reduced availability during partitions.
  • PA/EC:  Prioritize availability over consistency during partitions, and prioritize consistency over latency during normal operation.
    • Examples: Apache Cassandra (with tunable consistency), Apache CouchDB
    • Suitable for use cases that require a balance between availability and consistency, depending on the specific requirements.
  • PC/EL:  Prioritize consistency over availability during partitions, and prioritize latency over consistency during normal operation.
    • Examples: Rare in practice, as it is an unusual combination of priorities.

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