FlintKV: A Fast Durable Storage Engine for Modern Databases
2026-07-02 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors introduce FlintKV, a new storage engine designed for fast, durable memory called byte-addressable non-volatile memory (NVM). Unlike other systems, FlintKV supports important database features like snapshots, consistent iteration, and atomic batch writes, which are needed for reliable transactions. It uses a special concurrency control method called flat-combining and multi-versioning to handle many operations efficiently and correctly. Tests show FlintKV can be up to 75% faster than previous systems in handling data operations.
byte-addressable non-volatile memoryskiplistkey-value storeatomic batch writessnapshot consistencylinearizabilityconcurrency controlmulti-versioningflat combiningthroughput
Authors
Sergey Egorov, Gregory Chockler, Brijesh Dongol, Dan O'Keeffe, Sadegh Keshavarzi
Abstract
Byte-addressable non-volatile memory (NVM) offers an opportunity to rethink storage engine architectures. While recent NVM key-value stores achieve high throughput for ingestion and point lookups, they omit or under-specify the support for the richer interface guarantees required by modern databases. Production key-value engines (e.g., RocksDB) provide point-in-time snapshots, consistent iterators, and atomic batches-features essential for implementing transactions and concurrency control. We present FlintKV, an NVM-optimized skiplist-based storage engine that natively supports the full API of production key-value stores. FlintKV supports both atomic batch writes and snapshot-consistent iteration efficiently while guaranteeing durable linearizability. FlintKV can be deployed standalone or its durable skiplist can be integrated into existing NVM stores to enhance their capabilities. Central to FlintKV is a novel flat-combining based concurrency control algorithm that leverages multi-versioning and carefully co-designed persistence mechanisms to ensure high performance and scalability. Our empirical evaluation shows that FlintKV can achieve up to a 75% improvement in end-to-end throughput over prior work.