在HBase上级联映射端连接以实现可扩展的连接处理
Cascading map-side joins over HBase for scalable join processing
摘要：
使用MapReduce进行大规模数据处理的主要挑战之一是连接的智能计算。由于语义Web数据集发布的RDF在过去几年中迅速增加，可扩展的连接技术成为SPARQL查询处理的一个重要问题。在本文中，我们介绍了Map-Side Index Nested Loop Join（MAPSIN join），它结合了NoSQL存储系统（如HBase）的可扩展索引功能，这些系统具有不足的分布式处理层，MapReduce又不能为有效的大规模连接处理提供适当的存储结构。在保留常用reduce端连接的灵活性的同时，我们利用了map端连接的有效性，而无需对底层框架进行任何更改。我们证明了显着的好处MAPSIN加入处理SPARQL基本图形模式的大型RDF数据集的评估与LUBM和SP2Bench基准。对于大多数查询，基于MAPSIN连接的查询执行比基于reduce端连接的查询执行性能高一个数量级。
英文摘要：
One of the major challenges in large-scale data processing with MapReduce is the smart computation of joins. Since Semantic Web datasets published in RDF have increased rapidly over the last few years, scalable join techniques become an important issue for SPARQL query processing as well. In this paper, we introduce the Map-Side Index Nested Loop Join (MAPSIN join) which combines scalable indexing capabilities of NoSQL storage systems like HBase, that suffer from an insufficient distributed processing layer, with MapReduce, which in turn does not provide appropriate storage structures for efficient large-scale join processing. While retaining the flexibility of commonly used reduce-side joins, we leverage the effectiveness of map-side joins without any changes to the underlying framework. We demonstrate the significant benefits of MAPSIN joins for the processing of SPARQL basic graph patterns on large RDF datasets by an evaluation with the LUBM and SP2Bench benchmarks. For most queries, MAPSIN join based query execution outperforms reduce-side join based execution by an order of magnitude.
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