PLI+: efficient clustering of cloud databases
PLI+: efficient clustering of cloud databases
复制标题
PLI：云数据库的高效集群
DOI：
10.1007/s10619-018-7252-2
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发表时间：
2018
影响因子：
1.2
通讯作者：
Malik, Tanu
中科院分区：
文献类型：
--
作者：
Ton That, Dai Hai;Wagner, James;Rasin, Alexander;Malik, Tanu
商业云数据库服务提高了数据的可用性，并提供了对数据的可靠访问。然而，诸如集群之类的常规数据库维护任务增加了在商业云实例上托管数据的成本。集群会导致I/O爆发;一次性集群会耗尽实例积累的I/O信用，并增加托管数据的成本。非集群数据库会扫描大量数据，逐渐消耗I/O信用，从而降低查询性能。在本文中，我们介绍了物理位置索引+（PLI^+ PLI+），这是一种针对商业云上托管的数据库的索引方法。PLI^+ PLI+依赖于数据布局的内部知识，构建物理位置索引，该索引将一系列物理协同位置与一系列属性值映射以创建近似排序的桶。随着新数据的插入，写入将根据传入数据的分布在内存中进行分区。数据被写入磁盘上基于块的分区中的物理位置，以支持大粒度I/O。对索引属性值的传入SQL查询将根据物理位置范围重写。因此，PLI + PLI+不会降低未集群化的云数据库实例上的查询性能，DBA可以在具有足够大的I/O信用可用于集群化时选择集群化实例，从而延迟集群化的需要。我们通过在真实的数据集上将PLI^+ PLI+与聚集索引、非聚集（二级）索引和日志结构的合并树进行比较来评估其查询性能。实验表明，PLI^+ PLI+显着延迟聚类，但不会降低查询性能，从而实现更高级别的排序比非聚集索引和日志结构的合并树。我们还通过引入区间排序的度量和索引的大小来评估聚类的质量。
Commercial cloud database services increase availability of data and provide reliable access to data. Routine database maintenance tasks such as clustering, however, increase the costs of hosting data on commercial cloud instances. Clustering causes an I/O burst; clustering in one-shot depletes I/O credit accumulated by an instance and increases the cost of hosting data. An unclustered database decreases query performance by scanning large amounts of data, gradually depleting I/O credits. In this paper, we introduce Physical Location Index Plus (PLI^+ PLI+), an indexing method for databases hosted on commercial cloud. PLI^+ PLI+ relies on internal knowledge of data layout, building a physical location index, which maps a range of physical co-locations with a range of attribute values to create approximately sorted buckets. As new data is inserted, writes are partitioned in memory based on incoming data distribution. The data is written to physical locations on disk in block-based partitions to favor large granularity I/O. Incoming SQL queries on indexed attribute values are rewritten in terms of the physical location ranges. As a result, PLI^+ PLI+ does not decrease query performance on an unclustered cloud database instance, DBAs may choose to cluster the instance when they have sufficiently large I/O credit available for clustering thus delaying the need for clustering. We evaluate query performance over PLI^+ PLI+ by comparing it with clustered, unclustered (secondary) indexes, and log-structured merge trees on real datasets. Experiments show that PLI^+ PLI+ significantly delays clustering, and yet does not degrade query performance—thus achieving higher level of sortedness than unclustered indexes and log-structured merge trees. We also evaluate the quality of clustering by introducing a measure of interval sortedness, and the size of index.