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Flash memory based solid state drives (SSDs) have emerged as a new alternative to replace magnetic disks due to their high performance and low power consumption. However, random writes on SSDs are much slower than SSD reads. Therefore, traditional index structures, which are designed based on the symmetrical I/O property of magnetic disks, cannot completely exert the high performance of SSDs. In this paper, we propose an SSD-optimized linear hashing index called Self-Adaptive Linear Hashing (SAL-hashing) to reduce small random-writes to SSDs that are caused by index operations. The contributions of our work are manifold. First, we propose to organize the buckets of a linear hashing index into groups and sets to facilitate coarse-grained writes and adaptivity to access patterns. A group consisting of a fixed number of buckets is proposed to transform small random writes to buckets into coarse-grained writes and in turn improve write performance of the index. A set consists of a number of groups, and we propose to employ different split strategies for each set. With this mechanism, SAL-hashing is able to adapt to the changes of access patterns. Second, we attach a log region to each set, and amortize the cost of reads and writes by committing updates to the log region in batch. Third, in order to reduce search cost, each log region is equipped with Bloom filters to index update logs. We devise a cost-based online algorithm to adaptively merge the log region with the corresponding set when the set becomes search-intensive. Fourth, we propose a new technique called virtual split to optimize the search performance of SAL-hashing. Finally, we propose a new scheme for the management of the log buffer. We conduct extensive experiments on real SSDs. The results suggest that our proposal is self-adaptive according to the change of access patterns, and outperforms several competitors under various workloads. [ABSTRACT FROM AUTHOR]

Recently, with the widely use of flash memory based solid state drives (SSDs), a lot of studies have been conducted on SSD-based data management, such as index structures, query processing, and buffer management schemes. This paper focuses on buffer schemes for SSD-based database systems. However, differing from previous studies, we concentrate on buffer schemes for tree indexes on SSDs. This work is motivated by the observation that access patterns on index pages are much different from those on data pages. Generally, in a typical tree index, e.g., B+-tree, the root and internal nodes have higher read frequencies than leaf nodes have. However, traditional SSD-oriented buffering methods do not consider this special feature of indexes, and thus is not efficient when used as index buffer management schemes. In this paper, we present a new buffering scheme for tree indexes on SSDs that is named Clean-First and Dirty-Redundant-Write (CFDRW) scheme. The contributions of CFDRW are manifold. First, it assigns priorities to index pages to reflect the differences of access patterns of the nodes in a tree index. Second, it uses priority and recency to detect the hotness of index pages and proposes a new replacement algorithm based on priority and recency of the buffered index pages. Third, it exploits the internal parallelism of SSDs and proposes to write out buffer pages in a coarse granularity, i.e., to write out several pages using one physical I/O operation. We compare our proposal on two commodity SSDs with many previous methods including LRU, LIRS, and six flash-memory-based buffering schemes, by using synthetic and real workloads. The results show that our proposal outperforms the competitors under all workloads and SSDs in terms of various metrics including hit ratio, read count, write count, and elapsed time. [ABSTRACT FROM AUTHOR]

R-tree has been widely used in spatial data management and data analysis to improve the performance of spatial data retrieval. However, the original R-tree is designed for magnetic disks, and has poor performance on flash memory, due to the special features of flash memory such as asymmetric read/write speeds (fast read, slow write) and the erase-before-write feature. Particularly, the original updating mechanism of R-tree usually has to update a few interior nodes when inserting an indexing item into or deleting an item from a leaf node, yielding many slow writes to flash memory. With the wide use of flash memory in many location-based fields, e.g., to store moving trajectories in intelligent transportation systems, how to optimize R-tree for flash memory has become a critical issue. In this paper, we propose a novel spatial index named Flash-Optimized R-tree that is optimized for flash memory. In particular, we propose to defer the node-splitting operations on R-tree by introducing overflow nodes, which results in an unbalanced tree structure. With this mechanism, we can reduce random writes to flash memory and improve the overall performance of R-tree. In addition, we present a new buffering scheme to efficiently cache the updates to the tree, which can further reduce random writes to flash memory. We conduct extensive experiments on real flash-memory storage devices as well as a flash memory simulation platform to evaluate the performance of our proposal, and the results suggest the efficiency of our proposal with respect to different metrics. [ABSTRACT FROM AUTHOR]