Coordination-free Collaborative Replication based on Operational Transformation
Masato Takeichi
TL;DR
Coordination-free Collaborative Replication (CCR) tackles the problem of maintaining consistent replicas in distributed, server-less environments without coordination messages. It blends Operational Transformation with a confluence-centric framework, defining a data state $D$, a monoid of operations $O$, and a transform $T_D$ to ensure $D \odot p \odot q' = D \odot q \odot p'$ across arbitrary message orderings, formalized through TP1/TP2 concepts and the confluence relation $\#_D$. The work outlines a complete asynchronous replication protocol, contrasts it with traditional CRDTs and CmRDTs, and demonstrates practical CCR with a CCR Agent and a variety of basic and composite replica data types (e.g., Counter, LWW Register, ESet, MRQ, tuples, maps). The results indicate CCR can provide intuitive conflict resolution, reduce messaging inefficiency, and enable server-less, real-time collaboration, with future work focusing on formal proofs and cost benchmarking to validate practical viability and scalability. Overall, CCR offers a theoretically grounded, coordination-free pathway to robust, flexible replication in P2P and local-first applications, potentially broadening the applicability of OT-based replication beyond centralized-server paradigms.
Abstract
We introduce Coordination-free Collaborative Replication (CCR), a new method for maintaining consistency across replicas in distributed systems without requiring explicit coordination messages. CCR automates conflict resolution, contrasting with traditional Data-sharing systems that typically involve centralized update management or predefined consistency rules. Operational Transformation (OT), commonly used in collaborative editing, ensures consistency by transforming operations while maintaining document integrity across replicas. However, OT assumes server-based coordination, which is unsuitable for modern, decentralized Peer-to-Peer (P2P) systems. Conflict-free Replicated Data Type (CRDT), like Two-Phase Sets (2P-Sets), guarantees eventual consistency by allowing commutative and associative operations but often result in counterintuitive behaviors, such as failing to re-add an item to a shopping cart once removed. In contrast, CCR employs a more intuitive approach to replication. It allows for straightforward updates and conflict resolution based on the current data state, enhancing clarity and usability compared to CRDTs. Furthermore, CCR addresses inefficiencies in messaging by developing a versatile protocol based on data stream confluence, thus providing a more efficient and practical solution for collaborative data sharing in distributed systems.