基于闪存的数据库性能评测与优化分析

随着闪存容量的不断提升,技术的不断成熟,闪存数据库成为数据库的热点研究领域之一.了解闪存的IO特性,以及现有数据库产品在闪存上的性能瓶颈所在是闪存数据库设计改进的首要一步.目前广泛使用的是由闪存制造的固态硬盘,而固态硬盘与闪存芯片的特性又有很大不同.首先测试了固态硬盘的基本IO特性,接着使用TPC-B标准对部署在固态硬盘上的通用数据库产品进行了评估.分析了缓冲区大小、CPU处理能力、用户并发数等对基于闪存数据库性能的影响.最后,结合测试结果,从数据组织、数据库资源利用等多方面给出优化建议.     

闪存数据库管理系统的分析与设计

固态硬盘采用闪存作为存储介质,它的随机读取速度是磁盘的大约20倍,此外它还具有低功耗、抗震性强,体积小的特点,被认为是取代磁盘的新一代数据存储设备。但是传统的基于磁盘的数据库管理系统并不适用于固态硬盘,这使得固态硬盘并不能充分发挥它快速读取的优良性能。在近两年来,对闪存数据库管理系统的研究成为新兴的热门领域。本文对用于固态硬盘的闪存数据库管理系统作了简要的设计与分析。     

基于SSD高速缓存的桌面虚拟机交互性能优化方法

针对在桌面虚拟机办公环境下应用软件响应时间过长的问题,提出一种利用固态硬盘高速缓存来改善虚拟机响应时间和交互性的方法。在传统磁盘和内存之间添加固态硬盘高速缓存,将影响系统交互性的磁盘内容保存到固态硬盘高速缓存中。通过利用固态硬盘读速度快、随机寻道时间短的特征,提高虚拟机读磁盘性能,进而缩短应用软件的响应时间并改善系统的交互性。针对桌面虚拟机办公环境下磁盘I/O的特征,设计固态硬盘高速缓存的替换算法和缓存策略。实验显示,固态硬盘高速缓存是缩短虚拟机响应时间、改善虚拟机交互性的一个好方法。     

基于固态硬盘数据库在OLTP应用中的优化研究

基于磁盘数据库系统的瓶颈主要在于磁盘速度。固态硬盘的出现,使磁盘的速度得到了极大的提高。传统的数据库底层设计主要面向磁盘I／O性能极低的传统机械硬盘。文章通过比较和分析固态硬盘的优势,提出了基于固态硬盘数据库的底层性能优化的方法。通过实验比较了基于固态硬盘数据库性能优化前后的差异。     

Sub-Join:面向闪存数据库的查询优化算法

固态硬盘具有高速的随机读取速度、低功耗、体积小等特点,被认为将取代磁盘成为新一代的数据存储设备。但是闪存数据库的查询性能的提高却远小于固态硬盘相比于磁盘I/O性能的提高,其原因在于现有的数据库是基于磁盘设计的,不能充分发挥固态硬盘的高速性能。提出一种名为子连接(Sub-Join)的连接算法。首先将数据表的连接列和主键投影为新的子表,然后对子表进行接连操作,最后根据子表的连接结果再从原始数据表中回取查询结果。通过和开源数据库Oracle Berkeley DB的比较实验,结果表明子连接算法比原有算法的性能提高了40%～100%,充分说明了它的优越性。     

运行速度最快的企业级硬盘——希捷推出企业级固态混合硬盘

7月24日,希捷科技宣布推出全球运行速度最快的硬盘——希捷企业级Turbo固态混合型硬盘。作为业界首款企业级固态混合型硬盘(SSHD),希捷Turbo固态混合型硬盘融合了固态硬盘的容量以及固态闪存技术,能够在处理任务关键型数据业务时提供高速的性能。希捷企业级Turbo固态混合型硬盘在处理关键任务性能方面设定了新的标准,与目前15K-RPM硬盘相比,随机读取性能提升了3倍,存储容量高达600GB。希捷Turbo固态混合型硬盘统一了固态存储和磁记录技术的优势,提升了分层存储性能,实现了提速与传统硬盘(HDD)大容量的统一。希捷公司执行副总裁、首席营销官RockyPimentel     

一步一步优化SSD

固态硬盘(SSD)越来越普及,由于现有操作系统根据传统机械硬盘设计而优化的,因此在使用时。固态硬盘还必须进行一些优化,才能充分发挥固态硬盘的优势和延长使用寿命,不过一般用户对SSD的优化容易忽视,导致容量被浪费或者不能发挥固态硬盘的性能。下面我们从拿到     

固态硬盘在InnoDB存储引擎中的应用研究

固态硬盘的出现解决了长期以来困扰存储引擎的IO瓶颈问题,根据固态硬盘的物理特性对InnoDB的r/w模块做适当的改进必将大幅提高数据库的性能.     

基于固态硬盘的在线全文索引管理策略研究

为解决基于固态硬盘的全文检索系统的在线索引管理问题,首先分析了现有的基于磁盘的索引管理策略,指出传统索引方法会造成固态硬盘的过分损耗,并不适用于固态硬盘.然后提出了一种针对固态硬盘的在线索引管理方法:基于分块的部分合并.该方法避免了对固态硬盘有害的索引原地更新,并以固态硬盘高速随机读取的特点为基础,通过减少不必要索引合并减轻固态硬盘的负担.实验结果表明,该策略不仅具有优越的索引与检索性能,还大幅减少了固态硬盘的损耗.     

电脑固态硬盘和普通硬盘的区别

硬盘分为固态硬盘(SSD)和机械硬盘(HDD);SSD采用闪存颗粒来存储,HDD采用磁性碟片来存储。固态硬盘启动快,不用磁头,快速随机读取,读延迟极小。根据相关测试:两台电脑在同样配置的电脑下,搭载固态硬盘的笔记本从开机到出现桌面一共只用了18秒,而搭载传统硬盘的笔记本总共用了31秒,两者几乎有将近一半的差距:相对固定的读取时间。由于寻址时间与数据存储位置无关,因此磁盘碎片不会影响读取时间:无噪音。因为没有机械马达和风     

Read/write-optimized tree indexing for solid-state drives

Flash-memory-based solid-state drives (SSDs) are used widely for secondary storage. To be effective for SSDs, traditional indices have to be redesigned to cope with the special properties of flash memory, such as asymmetric read/write latencies (fast reads and slow writes) and out-of-place updates. Previous flash-optimized indices focus mainly on reducing random writes to SSDs, which is typically accomplished at the expense of a substantial number of extra reads. However, modern SSDs show a narrowing gap between read and write speeds, and read operations on SSDs increasingly affect the overall performance of indices on SSDs. As a consequence, how to optimize SSD-aware indices by reducing both write and read costs is a pertinent and open challenge. We propose a new tree index for SSDs that is able to reduce both writes and extra reads. In particular, we use an update buffer and overflow pages to reduce random writes, and we further exploit Bloom filters to reduce the extra reads to the overflow nodes in the tree. With this mechanism, we construct a read/write-optimized index that is capable of offering better overall performance than previous flash-aware indices. In addition, we present an analysis of the proposed index and show that the read and write costs of the operations on the index can be balanced by only tuning the false-positive rate of the Bloom filters. Our experimental results suggest that our proposal is efficient and represents an improvement over existing methods.     

Exploiting flash memory characteristics to improve performance of RAIS storage systems

Redundant array of independent SSDs (RAIS) is generally based on the traditional RAID design and implementation. The random small write problem is a serious challenge of RAIS. Random small writes in parity-based RAIS systems generate significantly more pre-reads and writes which can degrade RAIS performance and shorten SSD lifetime. In order to overcome the well-known write-penalty problem in the parity-based RAID5 storage systems, several logging techniques such as Parity Logging and Data Logging have been put forward. However, these techniques are originally based on mechanical characteristics of the HDDs, which ignore the properties of the flash memory. In this article, we firstly propose RAISL, a flash-aware logging method that improves the small write performance of RAIS storage systems. RAISL writes new data instead of new data and pre-read data to the log SSD by making full use of the invalid pages on the SSD of RAIS. RAISL does not need to perform the pre-read operations so that the original characteristics of workloads are kept. Secondly, we propose AGCRL on the basis of RAISL to further boost performance. AGCRL combines RAISL with access characteristic to guide read and write cost regulation to improve the performance of RAIS storage systems. Our experiments demonstrate that the RAISL significantly improves write performance and AGCRL improves both of write performance and read performance. AGCRL on average outperforms RAIS5 and RAISL by 39.15% and 16.59% respectively.     

基于SSD SMR混合存储的LSM树键值存储系统的性能优化

大数据对存储系统的可扩展性、性能和成本等方面提出了更高的要求。瓦记录(Shingled Magnetic Recor-ding,SMR)硬盘由于存储密度高、价格便宜,正逐步被广泛应用于大数据存储系统。但是,SMR硬盘的随机写性能较差,与快速的基于闪存的固态硬盘(Solid State Drive,SSD)一起构成混合存储时可以显著提升性能。同时,基于写优化的日志结构合并(Log-Structured Merge,LSM)树的键值存储已被广泛应用于许多NoSQL系统,如BigTable,Cassandra和HBase等。因此,如何基于新型的SSD-SMR混合存储构建出高性能的LSM树键值存储系统是一个具有很大研究价值的问题。首先建立基于SSD-SMR混合存储的LSM树键值系统的性能模型,然后针对SSD和SMR的硬件特征以及LSM树键值存储的软件特点,设计了一套面向SSD-SMR混合存储进行性能优化的LSM树键值存储系统,并基于LevelDB实现了该系统。在仅仅使用0.4%～2%空间的SSD的情况下,所提方法可以使SSD-SMR混合存储方案比普通磁盘方案的随机写性能提高20%,随机读性能提高5倍。     

利用页面重构与数据温度识别的闪存缓存算法

基于闪存的固态盘(SSD)具有比磁盘更加优越的性能,并且在桌面系统中逐渐替代磁盘。但是,尽管在SSD中嵌入了DRAM作为缓存,闪存在不断写入的过程中也可能产生不稳定的写性能,主要是因为逻辑页写入时会频繁引发非覆盖写和垃圾回收操作。针对此问题,提出了一种叫作PRLRU的新型闪存缓存管理方法,通过页面重构机制以及数据温度识别机制来管理缓存区。页面重构机制把即将回写的有效数据未满一个整页大小的页与多个其他有效数据不足一个页大小的页进行数据重组后再回写至闪存,通过尽可能减少非覆盖写操作来达到减少实际写操作的目的。数据温度识别机制通过对缓存页进行温度等级标记,按预定优先级顺序回写缓存页。对真实负载进行测试,实验结果表明,PRLRU能够有效提高SSD性能并延长SSD使用寿命,与LRU、BPLRU和2QW-Clock三种算法相比,写性能平均分别提高了34.5%、22.8%和28.8%,读性能平均分别提高了12.5%、10.6%和8.3%,垃圾回收数量平均分别降低了10.5%、8.7%和6.3%。     

Cascaded Write Amplification of LSM-tree-based Key-Value Stores underlying Solid-State Disks

Log-structured merge tree (i.e., LSM-tree)-based key–value stores (i.e., KV stores) are widely used in big-data applications and provide high performance. NAND Flash-based Solid-state disks (i.e., SSDs) have become a popular storage device alternative to hard disk drives (i.e., HDDs) because of their high performance and low power consumption. LSM-tree KV stores with SSDs are deployed in large-scale storage systems, which aims to achieve high performance in the cloud. Write amplification in LSM-tree KV stores and NAND Flash memory in SSDs are defined as WA1 and WA2 in this paper. The former, which is attributed to compaction operations in LSM-tree-based KV stores, is a burden on I/O bandwidth between the host and the device. The latter, which results from out-place updates in NAND Flash memory, blocks user I/O requests between the host and NAND Flash memory, thereby degrading the SSD performance. Write amplification impairs the overall system performance. In this study, we explored the two-level cascaded write amplification in LSM-tree KV stores with SSDs. The cascaded write amplification is represented as WA. Our primary goal is to comprehensively study two-level cascaded write amplification on the host-side LSM-tree KV stores and the device-side SSDs. We quantitatively analyze the impact of two-level write amplification on overall performance. The cascaded write amplification is 16.44 (WA1 is 16.55; WA2 is 0.99) and 35.51 (WA1 is 16.6; WA2 is 2.14) for SSD-I and SSD-S with LevelDB’s default setting under DB_bench. The larger cascaded write amplification of KV stores has a bad impact on SSD performance and lifetime. The throughput of SSD-S and SSD-I under an 80%-write workload is approximately 0.28x and 0.31x of that under a 100%-write workload. Therefore, it is important to design a novel approach to balance the cost of an SSD lifetime caused by cascaded write amplification and its high performance under the read-write-mixed workloads. We attempt to reveal details of cascaded write amplification and hope that this study is useful for developers of LSM-tree-based KV stores and SSD software stacks.     

A high-performance and endurable SSD cache for parity-based RAID

Solid-state drives (SSDs) have been widely used as caching tier for disk-based RAID systems to speed up dataintensive applications. However, traditional cache schemes fail to effectively boost the parity-based RAID storage systems (e.g., RAID-5/6), which have poor random write performance due to the small-write problem. What’s worse, intensive cache writes can wear out the SSD quickly, which causes performance degradation and cost increment. In this article, we present the design and implementation of KDD, an efficient SSD-based caching system which Keeps Data and Deltas in SSD. When write requests hit in the cache, KDD dispatches the data to the RAID storage without updating the parity blocks to mitigate the small write penalty, and compactly stores the compressed deltas in SSD to reduce the cache write traffic while guaranteeing reliability in case of disk failures. In addition, KDD organizes the metadata partition on SSD as a circular log to make the cache persistent with low overhead.We evaluate the performance of KDD via both simulations and prototype implementations. Experimental results show that KDD effectively reduces the small write penalty while extending the lifetime of the SSD-based cache by up to 6.85 times.     

Distorted mapping techniques to achieve high performance in mirrored disk systems

Mirrored disk systems provide high reliability by multiplexing disks. Performance is improved with parallel reads and shorter read seeks. However, writes must be performed by both disks, limiting performance. We introducedistorted mirrors, a mirroring system which combineswrite anywhere semantics with traditional database-specified block locations. This technique radically reduces the cost of small writes, making it attractive for random access applications such as OLTP, while retaining the ability to efficiently perform large sequential accesses. Distorted mirrors also scale better than traditional mirrors in terms of both disk caching and large mirrored sets. We show the effectiveness of distorted mirrors on the TP1 benchmark.     

A generic and efficient framework for flash-aware spatial indexing

Spatial indexing on flash-based Solid State Drives (SSDs) has become a core aspect in spatial database applications, and has been carried out by flash-aware spatial indices. Although there are some flash-aware spatial indices proposed in the literature, they do not exploit all the benefits of SSDs, leading to loss of efficiency and durability. In this article, we propose eFIND, a new generic and efficient framework for flash-aware spatial indexing. eFIND takes into account the intrinsic characteristics of SSDs by employing (i) a write buffer to avoid expensive random writes, (ii) a flushing algorithm that smartly picks modifications to be flushed in batch to the SSD, (iii) a read buffer to decrease the overhead of random reads, (iv) a temporal control to avoid interleaved reads and writes, and (v) a log-structured approach to provide data durability. Performance tests showed the efficiency of eFIND. Compared to the state of the art, eFIND improved the construction of spatial indices from 43% to 77%, and the spatial query processing from 4% to 23%.     

一种固态盘的读写性能优化调度方法

相比于传统机械硬盘,基于NAND Flash的固态盘由于具有非易失性、高性能、低功耗等优点,被广泛应用于数据中心、云计算、在线事务交易等场景。然而,由于NAND Flash中的读操作速度远远快于写操作速度,当读写请求并发执行时,读请求可能被写请求阻塞,从而表现出极大的读延时。在许多以读请求为主的场合,尤其是在线事物交易中(读请求占总请求的比例超过90%),读延时的急剧增加严重影响了系统的整体性能。提出一种读写性能优化调度的策略,通过在闪存转换层之下动态调整读写请求的优先序列,使读性能获得显著的提升。实验中,通过对固态盘仿真器的设计与实现,对读写调度策略的有效性进行了系统的评估。实验结果表明,在该调度策略下,系统中读延时的最大值和平均值均得到了显著的减少,且降幅分别达到了72%和41%。     

Buffer flush and address mapping scheme for flash memory solid-state disk

The flash memory solid-state disk (SSD) is emerging as a killer application for NAND flash memory due to its high performance and low power consumption. To attain high write performance, recent SSDs use an internal SDRAM write buffer and parallel architecture that uses interleaving techniques. In such architecture, coarse-grained address mapping called superblock mapping is inevitably used to exploit the parallel architecture. However, superblock mapping shows poor performance for random write requests. In this paper, we propose a novel victim block selection policy for the write buffer considering the parallel architecture of SSD. We also propose a multi-level address mapping scheme that supports small-sized write requests while utilizing the parallel architecture. Experimental results show that the proposed scheme improves the I/O performance of SSD by up to 64% compared to the existing technique.