CRDT

The simplest CRDT is a grow-only counter (G-Counter). Each replica keeps its own slot in a map and only ever increments its own slot. To read the total you sum every slot. To merge two replicas you take the max of each slot. Two nodes can increment at the same time and the sums still add up correctly because each only touches its own number.

Sets are harder because deletion is the enemy of merging. A grow-only set (G-Set) only allows adds. A two-phase set (2P-Set) keeps an add set and a tombstone set, so a removed item can never come back. The widely used OR-Set (Observed-Remove Set) tags every add with a unique id, so concurrent add and remove resolve cleanly: you only remove the exact adds you actually saw, and a fresh add survives.

Collaborative text, the famous Google Docs case, uses sequence CRDTs like RGA or the tree structure behind Yjs and Automerge. Each character gets a unique, densely ordered identifier so two people typing in the same spot interleave deterministically instead of overwriting each other. Causality is tracked with version vectors so every replica knows which updates it has already seen and can ignore duplicates.

Redis Enterprise ships active-active databases built directly on CRDTs (they call them CRDBs). You can run the same database in data centers in Mumbai, Frankfurt, and Virginia, let users write to whichever is closest, and Redis merges the replicas in the background. A counter incremented in two regions at once sums correctly; a string uses last-write-wins keyed by a hybrid clock; a set uses OR-Set semantics.

Concretely, suppose a user in India adds a phone to their cart while a flaky connection causes a retry that also lands in the EU region. Because the cart is a set, the phone still appears once, not twice: a set holds each item at most once. (The retried add gets its own tag, so a later remove has to cover both tags, which an OR-Set remove does for every tag it has seen.) If they remove the phone in one region while a recommendation service adds an accessory in another, both operations survive the merge and no write is silently lost.

This is the same pattern behind Riak's data types, Microsoft's Azure Cosmos DB multi-region writes, and figma-style design tools. The application gets fast local writes everywhere and a single converged truth a few hundred milliseconds later, without ever standing up a leader election or a global lock.