For a write request, today's flash storage cannot distinguish the logical object it comes from ( e.g. , SSTables in RocksDB). In such object-oblivious flash devices, concurrent writes from different objects are simply packed in their arrival order to flash memory blocks; hence data pages from multiple objects with different lifetimes are multiplexed onto the same flash blocks. This multiplexing incurs write amplification, worsening the performance. Tackling the multiplexing problem, we propose a novel interface for flash storage, FlashAlloc. It is used to pass the logical address ranges of objects to the underlying flash device and thus to enlighten the device to stream writes by objects. The object-aware flash storage can now de-multiplex concurrent writes from multiple objects with distinct deathtimes into per-object dedicated flash blocks. In essence, the interface enables the per-object fine-grained write streaming. Given that popular data stores tend to separate writes by logical objects, we can achieve, compared to the existing solutions, transparent streaming just by calling FlashAlloc upon object creation. Also, FlashAlloc is adaptive to workload changes, and liberates the stream conflicts in the multi-tenant environment. Our experimental results using an open-source SSD prototype demonstrate that FlashAlloc can reduce the device-level write amplification factor (WAF) under RocksDB, F2FS, and MySQL by 1.5, 2.5, and 0.3, respectively and improve their throughput by 2.7x, 1.8x, and 1.2x, respectively. Also, FlashAlloc can mitigate the WAF interference among tenants: when running RocksDB and MySQL together on the same SSD, FlashAlloc reduced WAF from 2.5 to 1.6 and doubled their throughputs.

Every database engine runs on top of an operating system in the host, strictly separated with the storage. This more-than-half-century-old IHDE (In-Host-Database-Engine) architecture, however, reveals its limitations when run on fast flash memory SSDs. In particular, the IO stacks incur significant run-time overhead and also hinder vertical optimizations between database engines and SSDs. In this paper, we envisage a new database architecture, called SaS (SSD as SQL database engine), where a full-blown SQL database engine runs inside SSD, tightly integrated with SSD architecture without intervening kernel stacks. As IO stacks are removed, SaS is free from their run-time overhead and further can explore numerous vertical optimizations between database engine and SSD. SaS evolves SSD from dummy block device to database server with SQL as its primary interface. The benefit of SaS will be more outstanding in the data centers where the distance between database engine and the storage is ever widening because of virtualization, storage disaggregation, and open software stacks. The advent of computational SSDs with more compute resource will enable SaS to be more viable and attractive database architecture.

short-paper Share on Freezing Frozen Pages with Multi-Stream SSDs Authors: Hyun-Woo Park View Profile , Soyee Choi View Profile , Mijin An View Profile , Sang-Won Lee View Profile Authors Info & Claims DaMoN'19: Proceedings of the 15th International Workshop on Data Management on New HardwareJuly 2019 Article No.: 16Pages 1–3https://doi.org/10.1145/3329785.3329935Published:01 July 2019Publication History 3citation246DownloadsMetricsTotal Citations3Total Downloads246Last 12 Months31Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

SSD as SQLite Engine Share on Author: Soyee Choi SungKyunKwan University, Suwon, Rebublic of Korea SungKyunKwan University, Suwon, Rebublic of KoreaView Profile Authors Info & Affiliations SIGMOD '18: Proceedings of the 2018 International Conference on Management of DataMay 2018 Pages 1829–1831https://doi.org/10.1145/3183713.3183720Published:27 May 2018 0citation252DownloadsMetricsTotal Citations0Total Downloads252Last 12 Months30Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access