Nezha: A Key-Value Separated Distributed Store with Optimized Raft Integration

2026-03-09

AI Summary

This work addresses redundant I/O in distributed key-value stores caused by overlapping persistence operations between consensus protocols and storage engines, which limits performance. The authors propose an operation-level persistence strategy that deeply integrates log-structured merge (LSM)-style key-value separation into the Raft consensus protocol, eliminating redundant writes while preserving strong consistency. Key innovations include a co-designed architecture aligning Raft logs with key-value separation and a hierarchical garbage collection mechanism that balances safety and efficiency. Experimental results demonstrate significant performance improvements, with throughput gains of 460.2%, 12.5%, and 72.6% for Put, Get, and Scan operations, respectively.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Blockchains and distributed ledgers
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Distributed key-value stores are widely adopted to support elastic big data applications, leveraging purpose-built consensus algorithms like Raft to ensure data consistency. However, through systematic analysis, we reveal a critical performance issue in such consistent stores, i.e., overlapping persistence operations between consensus protocols and underlying storage engines result in significant I/O overhead. To address this issue, we present Nezha, a prototype distributed storage system that innovatively integrates key-value separation with Raft to provide scalable throughput in a strong consistency guarantee. Nezha redesigns the persistence strategy at the operation level and incorporates leveled garbage collection, significantly improving read and write performance while preserving Raft's safety properties. Experimental results demonstrate that, on average, Nezha achieves throughput improvements of 460.2%, 12.5%, and 72.6% for put, get, and scan operations, respectively.

Problem

• distributed key-value store
• Raft
• persistence overhead
• I/O overhead
• data consistency

Innovation

• key-value separation
• Raft integration
• persistence optimization
• distributed storage
• garbage collection