旧技术用于新连接算法：RDF处理中的案例研究
Old Techniques for New Join Algorithms: A Case Study in RDF Processing
摘要：
最近，人们对设计专门的RDF引擎产生了浓厚的兴趣，因为传统的查询处理机制在许多RDF工作负载上产生了数量级的性能差距。与此同时，研究人员已经发布了新的最坏情况下的最优连接算法，可以渐近优于传统引擎中的连接算法。在本文中，我们应用最坏情况下的最佳连接算法的标准RDF工作负载，LUBM基准，第一次。我们使用两个最坏情况下的最优引擎：（1）LogicBlox，一个商业数据库引擎，和（2）EmptyHeaded，我们的原型研究引擎，具有增强的最坏情况下的最优连接算法。我们表明，没有任何增加的优化LogicBlox和EmptyHeaded优于两个国家的最先进的专门的RDF引擎，RDF-3X和TripleBit，高达6倍的循环连接查询，查询传统的优化器是次优的。在剩下的，不太复杂的查询在LUBM基准测试，我们表明，三个经典的查询优化技术，使EmptyHeaded与RDF引擎竞争，即使没有渐近优势的最坏情况下的最佳方法。我们验证了我们的设计具有优点，因为EmptyHeaded的性能优于MonetDB三个数量级，LogicBlox两个数量级，同时保持在RDF-3X和TripleBit的数量级内。
英文摘要：
Recently there has been significant interest around designing specialized RDF engines, as traditional query processing mechanisms incur orders of magnitude performance gaps on many RDF workloads. At the same time researchers have released new worst-case optimal join algorithms which can be asymptotically better than the join algorithms in traditional engines. In this paper we apply worst-case optimal join algorithms to a standard RDF workload, the LUBM benchmark, for the first time. We do so using two worst-case optimal engines: (1) LogicBlox, a commercial database engine, and (2) EmptyHeaded, our prototype research engine with enhanced worst-case optimal join algorithms. We show that without any added optimizations both LogicBlox and EmptyHeaded outperform two state-of-the-art specialized RDF engines, RDF-3X and TripleBit, by up to 6x on cyclic join queries-the queries where traditional optimizers are suboptimal. On the remaining, less complex queries in the LUBM benchmark, we show that three classic query optimization techniques enable EmptyHeaded to compete with RDF engines, even when there is no asymptotic advantage to the worst-case optimal approach. We validate that our design has merit as EmptyHeaded outperforms MonetDB by three orders of magnitude and LogicBlox by two orders of magnitude, while remaining within an order of magnitude of RDF-3X and TripleBit.
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