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Parallel Processing of Very Large Databases Using Distributed Column Indexes

机译:使用分布式列索引并行处理超大型数据库

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The development and investigation of efficient methods of parallel processing of very large databases using the columnar data representation designed for computer cluster is discussed. An approach that combines the advantages of relational and column-oriented DBMSs is proposed. A new type of distributed column indexes fragmented based on the domain-interval principle is introduced. The column indexes are auxiliary structures that are constantly stored in the distributed main memory of a computer cluster. To match the elements of a column index to the tuples of the original relation, surrogate keys are used. Resource hungry relational operations are performed on the corresponding column indexes rather than on the original relations of the database. As a result, a precomputation table is obtained. Using this table, the DBMS reconstructs the resulting relation. For basic relational operations on column indexes, methods for their parallel decomposition that do not require massive data exchanges between the processor nodes are proposed. This approach improves the class OLAP query performance by hundreds of times.
机译:讨论了使用为计算机集群设计的列式数据表示方法对大型数据库进行并行处理的有效方法的开发和研究。提出了一种结合了关系型和面向列的DBMS优点的方法。介绍了一种基于域间隔原理的新型分布式列索引碎片。列索引是辅助结构,它们始终存储在计算机集群的分布式主存储器中。为了使列索引的元素与原始关系的元组匹配,使用了替代键。资源匮乏的关系操作在相应的列索引上执行,而不是在数据库的原始关系上执行。结果,获得了预计算表。使用此表,DBMS重建结果关系。对于列索引的基本关系操作,提出了不需要处理器节点之间大量数据交换的并行分解方法。这种方法将类OLAP查询性能提高了数百倍。

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