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When to use a CRDT-based database

Bending the consistency and availability as described by the CAP theorem has been a great challenge for the architects of geo-distributed applications. Network partition is unavoidable. The high latency between data centers always results in some disconnect between the data centers for a short period of time. Thus traditional architectures for geo-distributed applications are designed to either give up data consistency or take a hit on availability.

Unfortunately, you cannot afford to sacrifice availability for interactive user applications. In recent times, the architects have taken a shot at consistency and embraced the eventual consistency model. In this model, the applications depend on the database management system to merge all the local copies of the data to make them eventually consistent.

Everything looks good with the eventual consistency model until there are data conflicts. A few eventual consistency models promise best effort to fix the conflicts, but fall short of guaranteeing strong consistency. The good news is, the models built around conflict-free replicated data types (CRDTs) deliver strong eventual consistency.

CRDTs achieve strong eventual consistency through a predetermined set of conflict resolution rules and semantics. Applications built on top of CRDT-based databases must be designed to accommodate the conflict resolution semantics. In this article we will explore how to design, develop, and test geo-distributed applications using a CRDT-based database. We will also examine four sample use cases: counters, distributed caching, shared sessions, and multi-region data ingest.

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