浓缩数据立方高效实化和快速查询方法研究
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摘要
联机分析处理(OLAP)服务器中以数据立方作为基本的数据模型。为了提高OLAP查询效率,数据立方的构建成为许多研究的焦点。除了可以利用浓缩数据立方来减少数据立方的尺寸,从而大幅减少数据立方的计算时间与存储开销外,在应用实践中,往往还可以通过预先将数据立方进行实化的方法提高OLAP的查询响应速度。因此,进一步研究复杂数据立方的快速计算方法、浓缩数据立方在不同存储介质中的高效实化方法、以及如何利用实化数据快速响应查询等具有重要意义。
为了解决层次结构引入到数据立方的构建中带来的问题,提出了层次前缀立方的结构。层次结构带来了两个主要问题：一是立方格上的节点急剧增加,它的模型更加复杂,为了有效对其计算,需要开发新的立方格遍历方法；二是数据立方中需要实化的元组数急剧增加,为有效利用空间,需要研究新的存储模式,以消除各种形式的冗余。结合基本单元组浓缩与小方内前缀共享这两种方法,就得到了一种新的数据立方结构：前缀立方,但是,前缀立方不能直接支持维层次。为此,对前缀立方组织结构进行扩展,使之能够计算层次数据立方,并提出了一种新的结构：层次前缀立方(HierPrefixCube),将层次数据立方组织成一组共享前缀簇树,从而在数据立方尺寸压缩、数据立方元组恢复以及数据立方查询这几个方面求得了平衡。试验结果表明,层次前缀立方在实现了基于维层次查询的同时,其计算时间代价较低,对数据立方尺寸压缩的效果也很明显。
预先计算并实化数据立方,可大大缩短OLAP查询响应时间。但在外存存储实化数据,仍会带来大量的I/O操作。随着内存价格逐渐地降低,将数据立方的一个子集在内存实化,将特别适用于有时间约束的联机分析处理环境。为此,在现有技术的基础上,以元组为实化单元构建适用于浓缩数据立方的内存实化数据选择模型。以内存空间至少能容纳最细粒度数据小方为前提,在内存中构造两级元组存储结构,达到避免数据立方重新计算,快速准确响应查询的目的。并进一步对查询进行优化,构造性能更好的选择模型。由于最细粒度小方元组和其它一些粗粒度元组都在内存中,避免了费时的外存存取,数据立方更新和维护代价也得以降低。试验证明,在内存实化数据立方可有效降低查询响应时间,浓缩数据立方优先小尺寸是内存实化元组几种不同的选择模型中时间最优的。
通过在内存实化数据立方可以缩短查询响应时间,但易受内存空间的限制,很难满足尺寸较大数据立方的实化要求。随着闪存技术的快速发展,基于NAND闪存的固态硬盘具有了读取速度快、功耗低等优点,且其成本要比内存低得多,访问速度比传统硬盘要快得多。为此,结合浓缩数据立方的元组存储特征,提出了在内存实化粗粒度的小方,在闪存实化细粒度的元组,在硬盘存放事实表的三级存储结构。由于闪存具有读、写、擦除的时间延迟不均衡、非本地更新和擦除次数受限等特性,对于闪存中存放的实化数据立方元组采用了多级动态完美哈希索引,并把实化过程中的写操作转变为串行化的操作序列,以逐一追加的方式解决了由数据插入引发的闪存“频繁写”问题。实验结果表明,基于该索引结构的数据立方存储方法,既能提供高于磁盘存储的查询响应速度,又能避免内存空间不足的问题。
使用实化视图加速查询是一种常用的查询优化方法,在多维聚集应用中,其本质也是利用实化的数据立方来快速响应查询。含有SPREADSHEET子句的SQL语句增强了多维计算能力。研究了含有SPREADSHEET子句的实化视图匹配,利用实化数据加快SPREADSHEET查询的响应速度。提出了含有SPREADSHEET子句的视图匹配算法。实验结果表明,含有SPREADSHEET子句的视图匹配方法,具有良好的查询响应能力和良好的可扩展性。
Data cube is the basic data model for online analytical processing server. In order to improve the efficiency of OLAP queries, data cube's building become the focus of many studies. In addition to the condensed data cube can be used to reduce the dimensions of the cube data, which can significantly reduce the computation time of data cube and storage overhead. In practice, to speed up the query response time of OLAP, data cube is often materialized in advance. Therefore, it is of great significance for further research on method of complex data cube computation, efficient materialization method of condensed data cube in different storage medium and how to use materialized data to respond to queries quickly.
HierPrefixCube was proposed to solve the issues caused by the introduction of hierarchy to the data cube's construction. Hierarchy brings two major problems:First, the nodes in Cube Lattice increased dramatically, and its model becomes more complex, so a new Cube Lattice traversal algorithm to make the calculate effectively is need to be developed; Second, the tuples of data cube which is needed to be materialized increased rapidly, then, a new storage model should be studied to eliminate all forms of redundancy for using the space effectively. PrefixCube was proposed to be an efficient cube structure by augmenting BU-BST Condensing with intra-cuboid prefix-sharing, however, it does not support dimension hierarchies directly. Therefore, we extend the PrefixCube architecture for incorporating hierarchical data cubes, which can calculate hierarchy data cube, and hence get HierPrefixCube. HierPrefixCube has not only got efficient cube compression ratio but also made a good compromise among data cube compression, restoring and query. Experimental results show that while realizing a query based on dimension hierarchy, HierPrefixCube has a lower calculation time, and its compression effect for data cube size is also apparent.
Precomputed and materialized data cubes, can greatly shorten the OLAP query response time. However, materialized data stored in external storage will still bring a lot of I/O operation. As memory prices decreases, the materialization of a subset of cube data in memory turns out to be particularly applicable to the OLAP with time constraints. So, on the basis of existing technologies, the tuple as materializing unit is used to build materialized data selection model which applies to condensed data cube in memory. Under the precondition that there is enough main memory space to hold the finest granularity cuboid at least, two-level Hash structure is adopted in memory, to achieve the purpose of avoiding to recalculate date cube and responsing query rapidly and correctly. And further to optimize queries, build better choice model. Because of the finest granularity tuples and other coarser granularity tuples are in main memory, the time-consuming accessing from disk is avoided, the update and maintenance cost is also reduced. Experimental results show that the materialized data cubes in memory can reduce the query response time effectively, and prioritising the smaller size cuboids based on condensed data cube is time optimal among several different selection models of materialized tuples in main memory.
Query response time can be reduced by materializing data cubes in memory, but vulnerable to memory space limitations, materialized requirement of the larger cube is difficult to meet. With the rapid development of flash memory technology, NAND flash based SSD has advantages such as higher access speed, low power consumption and lower costs. Combined with the tuple storage characteristics of condensed data cube, the three level storage structure of "Disk-Memory-NAND flash memory" is proposed. Because of the unbalanced time delays of read, write, and erase, as well as the restricted features of non-local update and erase times, the Multi Level Dynamic Perfect Hash index structure is used to index the materialized tuples of data cube stored in flash memory. In the process of materialization, the write operation is transformed into serialized operation series, and data are inserted by adding on without causing the problem of "frequent write" operation. Final experimental results show that: the data cube storage method based on the Dynamic Perfect Hash index structure not only provides higher disk storage query response time, but also avoid the problem of insufficient memory space.
Query by using materialized view speed-up is a common method for optimization, and its real essence in multi-dimensional aggregation applications is also to use the materialized data cube to speed-up response time. The capability of multi-dimensional computation of traditional SQL can be strengthened by the SPREADSHEET clause. This paper studies the SPREADSHEET clause with materialized-view match, improves the response time of spreadsheet-queries by using materialized data, and gives the algorithm of materialized view matching with SPREADSHEET clause. Experimental results show that materialized view matching with SPREADSHEET clause can speed up queries effectively and has outstanding scalability.
为了解决层次结构引入到数据立方的构建中带来的问题,提出了层次前缀立方的结构。层次结构带来了两个主要问题：一是立方格上的节点急剧增加,它的模型更加复杂,为了有效对其计算,需要开发新的立方格遍历方法；二是数据立方中需要实化的元组数急剧增加,为有效利用空间,需要研究新的存储模式,以消除各种形式的冗余。结合基本单元组浓缩与小方内前缀共享这两种方法,就得到了一种新的数据立方结构：前缀立方,但是,前缀立方不能直接支持维层次。为此,对前缀立方组织结构进行扩展,使之能够计算层次数据立方,并提出了一种新的结构：层次前缀立方(HierPrefixCube),将层次数据立方组织成一组共享前缀簇树,从而在数据立方尺寸压缩、数据立方元组恢复以及数据立方查询这几个方面求得了平衡。试验结果表明,层次前缀立方在实现了基于维层次查询的同时,其计算时间代价较低,对数据立方尺寸压缩的效果也很明显。
预先计算并实化数据立方,可大大缩短OLAP查询响应时间。但在外存存储实化数据,仍会带来大量的I/O操作。随着内存价格逐渐地降低,将数据立方的一个子集在内存实化,将特别适用于有时间约束的联机分析处理环境。为此,在现有技术的基础上,以元组为实化单元构建适用于浓缩数据立方的内存实化数据选择模型。以内存空间至少能容纳最细粒度数据小方为前提,在内存中构造两级元组存储结构,达到避免数据立方重新计算,快速准确响应查询的目的。并进一步对查询进行优化,构造性能更好的选择模型。由于最细粒度小方元组和其它一些粗粒度元组都在内存中,避免了费时的外存存取,数据立方更新和维护代价也得以降低。试验证明,在内存实化数据立方可有效降低查询响应时间,浓缩数据立方优先小尺寸是内存实化元组几种不同的选择模型中时间最优的。
通过在内存实化数据立方可以缩短查询响应时间,但易受内存空间的限制,很难满足尺寸较大数据立方的实化要求。随着闪存技术的快速发展,基于NAND闪存的固态硬盘具有了读取速度快、功耗低等优点,且其成本要比内存低得多,访问速度比传统硬盘要快得多。为此,结合浓缩数据立方的元组存储特征,提出了在内存实化粗粒度的小方,在闪存实化细粒度的元组,在硬盘存放事实表的三级存储结构。由于闪存具有读、写、擦除的时间延迟不均衡、非本地更新和擦除次数受限等特性,对于闪存中存放的实化数据立方元组采用了多级动态完美哈希索引,并把实化过程中的写操作转变为串行化的操作序列,以逐一追加的方式解决了由数据插入引发的闪存“频繁写”问题。实验结果表明,基于该索引结构的数据立方存储方法,既能提供高于磁盘存储的查询响应速度,又能避免内存空间不足的问题。
使用实化视图加速查询是一种常用的查询优化方法,在多维聚集应用中,其本质也是利用实化的数据立方来快速响应查询。含有SPREADSHEET子句的SQL语句增强了多维计算能力。研究了含有SPREADSHEET子句的实化视图匹配,利用实化数据加快SPREADSHEET查询的响应速度。提出了含有SPREADSHEET子句的视图匹配算法。实验结果表明,含有SPREADSHEET子句的视图匹配方法,具有良好的查询响应能力和良好的可扩展性。
Data cube is the basic data model for online analytical processing server. In order to improve the efficiency of OLAP queries, data cube's building become the focus of many studies. In addition to the condensed data cube can be used to reduce the dimensions of the cube data, which can significantly reduce the computation time of data cube and storage overhead. In practice, to speed up the query response time of OLAP, data cube is often materialized in advance. Therefore, it is of great significance for further research on method of complex data cube computation, efficient materialization method of condensed data cube in different storage medium and how to use materialized data to respond to queries quickly.
HierPrefixCube was proposed to solve the issues caused by the introduction of hierarchy to the data cube's construction. Hierarchy brings two major problems:First, the nodes in Cube Lattice increased dramatically, and its model becomes more complex, so a new Cube Lattice traversal algorithm to make the calculate effectively is need to be developed; Second, the tuples of data cube which is needed to be materialized increased rapidly, then, a new storage model should be studied to eliminate all forms of redundancy for using the space effectively. PrefixCube was proposed to be an efficient cube structure by augmenting BU-BST Condensing with intra-cuboid prefix-sharing, however, it does not support dimension hierarchies directly. Therefore, we extend the PrefixCube architecture for incorporating hierarchical data cubes, which can calculate hierarchy data cube, and hence get HierPrefixCube. HierPrefixCube has not only got efficient cube compression ratio but also made a good compromise among data cube compression, restoring and query. Experimental results show that while realizing a query based on dimension hierarchy, HierPrefixCube has a lower calculation time, and its compression effect for data cube size is also apparent.
Precomputed and materialized data cubes, can greatly shorten the OLAP query response time. However, materialized data stored in external storage will still bring a lot of I/O operation. As memory prices decreases, the materialization of a subset of cube data in memory turns out to be particularly applicable to the OLAP with time constraints. So, on the basis of existing technologies, the tuple as materializing unit is used to build materialized data selection model which applies to condensed data cube in memory. Under the precondition that there is enough main memory space to hold the finest granularity cuboid at least, two-level Hash structure is adopted in memory, to achieve the purpose of avoiding to recalculate date cube and responsing query rapidly and correctly. And further to optimize queries, build better choice model. Because of the finest granularity tuples and other coarser granularity tuples are in main memory, the time-consuming accessing from disk is avoided, the update and maintenance cost is also reduced. Experimental results show that the materialized data cubes in memory can reduce the query response time effectively, and prioritising the smaller size cuboids based on condensed data cube is time optimal among several different selection models of materialized tuples in main memory.
Query response time can be reduced by materializing data cubes in memory, but vulnerable to memory space limitations, materialized requirement of the larger cube is difficult to meet. With the rapid development of flash memory technology, NAND flash based SSD has advantages such as higher access speed, low power consumption and lower costs. Combined with the tuple storage characteristics of condensed data cube, the three level storage structure of "Disk-Memory-NAND flash memory" is proposed. Because of the unbalanced time delays of read, write, and erase, as well as the restricted features of non-local update and erase times, the Multi Level Dynamic Perfect Hash index structure is used to index the materialized tuples of data cube stored in flash memory. In the process of materialization, the write operation is transformed into serialized operation series, and data are inserted by adding on without causing the problem of "frequent write" operation. Final experimental results show that: the data cube storage method based on the Dynamic Perfect Hash index structure not only provides higher disk storage query response time, but also avoid the problem of insufficient memory space.
Query by using materialized view speed-up is a common method for optimization, and its real essence in multi-dimensional aggregation applications is also to use the materialized data cube to speed-up response time. The capability of multi-dimensional computation of traditional SQL can be strengthened by the SPREADSHEET clause. This paper studies the SPREADSHEET clause with materialized-view match, improves the response time of spreadsheet-queries by using materialized data, and gives the algorithm of materialized view matching with SPREADSHEET clause. Experimental results show that materialized view matching with SPREADSHEET clause can speed up queries effectively and has outstanding scalability.
引文
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