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Methods for parallel execution of complex database queries

机译:并行执行复杂数据库查询的方法

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摘要

During the last decade, all commercial database systems have included features for parallel processing into their products. This development has been driven by the fact that databases grow in size at considerable rates. According to the results of the 1998 ‘very large database contest' the world's largest databases, which have reached a size of over 10TB, double in size every year. At that speed, they outgrow the increase in processor speed and memory size, so additional measures are required to accommodate the effects of rapidly growing volumes of data. Parallelism is one of those options. It helps to keep processing times constant, even if the size of the database increases. That effect, which is often referred to as ‘scaleup' is important for loading, index creation. all kinds of administrative operations on the database, and of course for long batch-type applications. Parallelism is also employed to speed-up queries that otherwise would take days or weeks to process and thus would be useless for the application. This type of requirement f fast results of complex queries on large data sets is characteristic of decision support applications. In this overview we will explain how parallelism in databases can help to solve such problems.
机译:在过去的十年中,所有商业数据库系统都将并行处理功能纳入其产品。事实是,数据库的规模以相当大的速度增长。根据1998年“超大型数据库竞赛”的结果,世界上最大的数据库已达到10TB以上的规模,每年的规模翻了一番。以这种速度,它们超过了处理器速度和内存大小的增长,因此需要采取其他措施来适应快速增长的数据量的影响。并行是这些选择之一。即使数据库大小增加,它也有助于保持处理时间恒定。这种效应(通常称为“放大”)对于加载,创建索引非常重要。数据库上的各种管理操作,当然也适用于长批处理类型的应用程序。并行还用于加速查询,否则将需要几天或几周的时间来处理查询,因此对应用程序无用。大型数据集上复杂查询的快速结果的这种需求类型是决策支持应用程序的特征。在本概述中,我们将解释数据库中的并行性如何帮助解决此类问题。

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