Nezha: A Key-Value Separated Distributed Store with Optimized Raft Integration
New prototype slashes I/O overhead by redesigning persistence, delivering massive performance gains for consistent data stores.
A team of researchers has unveiled Nezha, a prototype distributed storage system designed to solve a critical performance bottleneck in modern databases. The core innovation addresses a previously identified but unresolved issue: overlapping persistence operations between the Raft consensus protocol and the underlying storage engine create significant I/O overhead, throttling performance. Nezha tackles this by fundamentally redesigning the persistence strategy at the operation level and integrating key-value separation directly with Raft. This novel architecture allows the system to batch and streamline writes, dramatically reducing redundant disk activity.
Experimental validation, as detailed in the paper accepted to ICDE 2026, shows staggering results. On average, Nezha achieved a 460.2% throughput improvement for put (write) operations, a 12.5% gain for get (read) operations, and a 72.6% boost for scan operations compared to baseline systems. These gains are achieved without sacrificing the strong consistency guarantees provided by Raft, a non-negotiable requirement for reliable distributed databases. The system also incorporates a leveled garbage collection mechanism to manage the separated key-value data efficiently over time.
The work represents a significant step in optimizing the software stack for big data applications that demand both elasticity and strict data consistency, such as financial ledgers, real-time analytics platforms, and large-scale web services. By decoupling and optimizing the interaction between consensus and storage, Nezha points toward a new generation of high-performance distributed databases that can scale throughput without compromising on reliability.
- Eliminates overlapping I/O: Redesigned persistence strategy removes redundant disk writes between Raft and storage, the paper's identified core bottleneck.
- Massive performance gains: Demonstrates average throughput improvements of 460.2% for writes, 12.5% for reads, and 72.6% for scans in experiments.
- Maintains strong consistency: Achieves speed while preserving the safety properties of the Raft consensus protocol, crucial for reliable systems.
Why It Matters
Enables next-gen databases for finance and analytics to be significantly faster without sacrificing the consistency required for accurate, reliable operations.