ESBT: A Scalable and Deterministic Sequence CRDT for Distributed Collaborative Editing

2026-07-30

AI Summary

This work addresses the scalability limitations of existing sequence CRDTs, which suffer from identifier bloat and excessive memory consumption under high concurrency and prolonged collaborative editing. To overcome these challenges, we introduce the Extended Stern-Brocot Tree (ESBT) into sequence CRDT design, proposing a mathematically grounded mechanism for dense, deterministic, and compact identifier allocation. Our approach ensures strong eventual consistency and convergence independent of operation order while effectively curbing identifier growth. Experimental results demonstrate that, under 100,000 concurrent operations, our method achieves 28%–88% faster response times and reduces identifier memory usage by 50%–92.81% compared to state-of-the-art alternatives such as Logoot and LSEQ, with particularly pronounced advantages in adversarial scenarios involving frequent mid-sequence insertions.

Abstract

Modern collaborative editing systems require efficient mechanisms for managing concurrent updates across distributed replicas. Sequence Conflict-free Replicated Data Types (CRDTs) have become the de facto standard for supporting decentralized collaboration; they enable decentralized replicas to apply operations in arbitrary order while converging to a common state. Although existing sequence CRDTs guarantee Strong Eventual Consistency (SEC), they often suffer from uncontrolled identifier growth and increasing memory consumption during long-running, highly concurrent editing sessions, which severely limits their scalability and performance. This paper presents the Extended Stern-Brocot Tree (ESBT), a mathematically grounded identifier allocation scheme for distributed collaborative text editing, providing a dense, deterministic, and compact identifier space. The proposed allocation strategy bounds identifier growth while preserving deterministic ordering and Strong Eventual Consistency. Experimental evaluation using workloads of up to 100,000 concurrent operations generated across 50 collaborating sites shows that ESBT improves responsiveness by 28 to 88% under pure insertions and 59 to 74% under mixed insertion/deletion workloads, while reducing identifier memory consumption by 50 to 75% in beginning and random insertion patterns compared with the best-performing baseline sequence CRDTs (Logoot and LSEQ). Under the adversarial middle-insertion workload (10,000 operations), ESBT further improves responsiveness by 86.53% and reduces identifier size by 92.81%. These results demonstrate that ESBT effectively addresses the principal scalability limitations of existing sequence CRDTs and provides an efficient and scalable foundation for large-scale collaborative editing systems.

Problem

• Sequence CRDT
• identifier growth
• memory consumption
• scalability
• distributed collaborative editing

Innovation

• Sequence CRDT
• Identifier Allocation
• Strong Eventual Consistency
• Scalability
• Distributed Collaborative Editing