CAP Theorem Revisited: PACELC

Connecting PACELC to Real-World Distributed Systems

Understanding how different distributed systems fit into the PACELC framework can help us appreciate their design choices and trade-offs. Let's map some popular distributed databases and systems to the PACELC categories:

1. Apache Cassandra

   • When Partitioned (P): Cassandra prioritizes high availability over consistency, so it opts for Evidence during partitions.
     • Example: If a partition occurs, writes can still be accepted by any node, but they might not be immediately visible to other nodes until the partition is healed.
   • In the "Else" Case: In normal operation without partitions, Cassandra offers tunable consistency levels. However, its default configuration often leans towards eventual consistency (E), though it supports stronger consistency options for critical reads and writes.

2. MongoDB

   • When Partitioned (P): MongoDB replica sets prioritize availability over strong consistency during a partition.
     • Example: Writes can be accepted by the primary node, but they might not propagate to secondaries until the network is restored.
   • In the "Else" Case: In normal operation without partitions, MongoDB offers various consistency levels. By default,

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