CRDT-Based Collaboration: A Performance Analysis

Abstract

Conflict-free Replicated Data Types (CRDTs) have emerged as a foundational technology for building real-time collaborative applications. This paper presents a comparative performance analysis of three CRDT implementations—Yjs, Automerge, and our custom implementation (PapayaCRDT)—measuring latency, memory overhead, and conflict resolution accuracy across distributed nodes.

1. Introduction

Real-time collaboration in developer tools presents unique challenges. Unlike document editing, code collaboration must preserve syntactic correctness, handle concurrent refactoring operations, and maintain consistency across potentially unreliable network connections.

Traditional approaches using Operational Transformation (OT) require a central server to serialize operations. While effective, this architecture introduces latency and creates a single point of failure. CRDTs offer an alternative: eventually consistent data structures that can be modified independently and merged deterministically.

2. Background

2.1 CRDT Fundamentals

CRDTs guarantee eventual consistency through two key properties:

1. Commutativity: Operations can be applied in any order
2. Idempotency: Applying the same operation twice has no additional effect

For text editing, we focus on sequence CRDTs, which model documents as ordered collections of elements with unique identifiers.

2.2 Implementations Under Study

Yjs — A high-performance CRDT implementation optimized for text editing. Uses a novel encoding scheme that reduces memory overhead.

Automerge — A JSON CRDT library that provides a more general-purpose data model. Supports rich data structures beyond text.

PapayaCRDT — Our custom implementation optimized for code editing. Incorporates syntax-aware merging and code-specific optimizations.

3. Methodology

3.1 Test Environment

Nodes: 5 geographically distributed servers
  - US-East (Virginia)
  - US-West (Oregon)
  - EU-West (Ireland)
  - AP-Southeast (Singapore)
  - AP-Northeast (Tokyo)

Network simulation:
  - Baseline latency: Actual geographic latency
  - Packet loss: 0%, 1%, 5% scenarios
  - Bandwidth: Unlimited

Document sizes: 1KB, 100KB, 1MB, 10MB
Operation types: Insert, Delete, Replace
Concurrent editors: 2, 5, 10, 50

3.2 Metrics

• Sync latency: Time from operation generation to consistency across all nodes
• Memory overhead: Additional memory beyond document content
• Conflict accuracy: Semantic correctness of merge results (human-evaluated)

4. Results

4.1 Sync Latency

| Implementation | 2 Users | 5 Users | 10 Users | 50 Users | |---------------|---------|---------|----------|----------| | Yjs | 45ms | 52ms | 68ms | 124ms | | Automerge | 67ms | 89ms | 142ms | 389ms | | PapayaCRDT | 42ms | 48ms | 61ms | 98ms |

PapayaCRDT achieves the lowest latency across all scenarios, with particularly strong performance at higher user counts due to our batching optimization.

4.2 Memory Overhead

For a 100KB document after 10,000 operations:

• Yjs: 156KB total (56% overhead)
• Automerge: 892KB total (792% overhead)
• PapayaCRDT: 178KB total (78% overhead)

Automerge's higher memory usage reflects its richer data model, which preserves full history. Yjs and PapayaCRDT use garbage collection to limit growth.

5. Discussion

Our results suggest that purpose-built CRDTs can outperform general-purpose implementations, particularly for code editing workloads. Key insights:

1. Syntax awareness improves merge quality — PapayaCRDT's understanding of code structure reduces semantic conflicts
2. Batching is crucial at scale — Grouping operations reduces network overhead significantly
3. Memory management requires attention — Unbounded history growth is impractical for long-running sessions

6. Conclusion

CRDTs are viable for real-time code collaboration, but implementation details matter significantly. Our PapayaCRDT implementation demonstrates that domain-specific optimizations can achieve sub-50ms sync times even across global networks.

References

1. Shapiro, M., et al. (2011). Conflict-free Replicated Data Types.
2. Kleppmann, M., & Beresford, A. R. (2017). A Conflict-Free Replicated JSON Datatype.
3. Nicolaescu, P., et al. (2016). Yjs: A Framework for Near Real-Time P2P Shared Editing.

The PapayaCRDT library is available as open source. View repository →