[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84647-en":3,"doc-seo-84647-105":29,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84647,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","FlintKV: A Fast Durable Storage Engine for Modern Databases","Byte-addressable non-volatile memory (NVM) enables storage engines to move beyond traditional SSD-oriented designs, but many NVM key-value stores lack the richer interface guarantees required by modern transactional databases. FlintKV is an NVM-optimized skiplist-based engine that natively supports production-style APIs, including atomic batch writes, snapshot-consistent iterators, and durable linearizability. It uses multiversioning plus a flat-combining-based concurrency control approach with co-designed persistence mechanisms, achieving up to 75% higher end-to-end throughput than prior work.","FlintKV: A Fast Durable Storage Engine for Modern Databases  \nSergey Egorov 12 , Gregory Chockler2 , Brijesh Dongol2  \nDan O’Keeffe 1 , Sadegh Keshavarzi2  \n1 Royal Holloway, University of London, Egham, UK 2 University of Surrey, Guildford, UK  \narXiv :2607 .0240 1v 1 [ cs .DC] 2 Jul 2026  \nABSTRACT  \nByte-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 underspecify 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.  \nWe present FlintKV, an NVM-optimised 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-toend throughput over prior work.  \n1 INTRODUCTION  \nModern transactional database systems increasingly rely on persistent key-value (KV) stores as their core storage engines [22 , 42 , 47] . To support advanced consistency conditions (such as snapshot isolation), these engines have a rich API that offers, in addition to basic put/get operations, highlevel operations such as atomic multi-key write batches and consistent snapshot iterators. While several SSD-based KV stores such as RocksDB [1], PebbleDB [34], and LevelDB [18] offer this functionality, most existing NVM-based KV stores do not, making it harder for transactional databases to benefit from the performance of NVM over SSDs.  \nOne exception to this lack of API support is PMemRocksDB, a version of RocksDB adapted for NVM. Although PMemRocksDB inherits RocksDB’s rich API, it attempts to retrofit its concurrency control and persistence mechanisms to an SSD-based design. As a result its performance suffers in comparison to clean slate NVM designs with more restricted APIs [9 , 31 , 41] . In particular, PMemRocksDB relies on askiplist-based index to balance the need for fast updates with support for efficient range queries [24] . To the best of our knowledge, there does not exist a durable skiplist (or comparable data structure) that both enables RocksDB’s rich API and fully exploits the potential performance gains of NVM.  \nWe propose FlintKV, an NVM-based storage engine that offers high performance and an API suited for use in modern databases. At the heart of FlintKV is a novel persistent skiplist data structure with carefully designed concurrency control and persistence mechanisms that is optimised for performance (see §3.3) . To support consistent snapshots while minimising interference between long running scansand update operations, FlintKV relies on multiversioning. To facilitate atomic write batches, FlintKV employs flatcombining [14 , 15 , 20], which naturally supports batching while also reducing synchronisation overheads under high contention between update operations.  \nFlintKV further enhances performance through a novel four phase execution framework for update operations. A key design goal is to minimise synchronisation and persistence delays in the flat-combiner’s critical section. To achieve this, FlintKV performs index traversals for update and read operations, bulk persistence of key-value data, and pointer updates to non-leaf index nodes in a lock-free manner. Only the minimal structural","cbCaikWXEV7V1Fm1","https://ap.wps.com/l/cbCaikWXEV7V1Fm1","pdf",1520793,1,16,"English","en",105,"# Introduction\n## Modern KV store requirements\n## Limitations of existing NVM engines\n## FlintKV approach and design highlights\n# Concurrency control and persistence mechanisms\n## Multiversioning for snapshots\n## Flat-combining for atomic batches\n# Four-phase execution and performance optimizations\n## Minimizing critical-section delays\n## Crash-consistency and persistence strategy\n# Evaluation and integration\n## Throughput improvements over prior systems\n## Modular integration into existing KV stores\n# Correctness and paper structure","[{\"question\":\"What problem does FlintKV address for NVM-based key-value stores?\",\"answer\":\"FlintKV targets the gap between high-throughput NVM KV stores and the richer API guarantees needed by modern databases, such as snapshot-consistent iteration, atomic batches, and durability safety.\"},{\"question\":\"What core data structure and interface does FlintKV provide?\",\"answer\":\"FlintKV uses an NVM-optimized skiplist and natively supports a production-grade KV API, including atomic batch writes and snapshot-consistent iterators.\"},{\"question\":\"How does FlintKV ensure both consistency and high performance?\",\"answer\":\"It combines multiversioning for consistent snapshots with a flat-combining-based concurrency control algorithm, alongside carefully co-designed persistence mechanisms to maintain durable linearizability.\"},{\"question\":\"What performance results and integration options are reported?\",\"answer\":\"Empirical evaluation shows up to 73% improvement over PMemRocksDB and 75% over ListDB, and the durable skiplist can be integrated into existing NVM stores like PMemRocksDB and ListDB.\"}]",1784197465,40,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":27},"flintkv-a-fast-durable-storage-engine-for-modern-databases","",{"@graph":35,"@context":89},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/flintkv-a-fast-durable-storage-engine-for-modern-databases/84647/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does FlintKV address for NVM-based key-value stores?","Question",{"text":75,"@type":76},"FlintKV targets the gap between high-throughput NVM KV stores and the richer API guarantees needed by modern databases, such as snapshot-consistent iteration, atomic batches, and durability safety.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What core data structure and interface does FlintKV provide?",{"text":80,"@type":76},"FlintKV uses an NVM-optimized skiplist and natively supports a production-grade KV API, including atomic batch writes and snapshot-consistent iterators.",{"name":82,"@type":73,"acceptedAnswer":83},"How does FlintKV ensure both consistency and high performance?",{"text":84,"@type":76},"It combines multiversioning for consistent snapshots with a flat-combining-based concurrency control algorithm, alongside carefully co-designed persistence mechanisms to maintain durable linearizability.",{"name":86,"@type":73,"acceptedAnswer":87},"What performance results and integration options are reported?",{"text":88,"@type":76},"Empirical evaluation shows up to 73% improvement over PMemRocksDB and 75% over ListDB, and the durable skiplist can be integrated into existing NVM stores like PMemRocksDB and ListDB.","https://schema.org",{"og:url":51,"og:type":91,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":93,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,123,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Story & 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