Indexing is a well-known database technique used to facilitate data access and speed up query processing. Nevertheless, the construction and modification of indexes are very expensive. In traditional approaches, all records in the database table are equally covered by the index. It is not effective, since some records may be queried very often and some never. To avoid this problem, adaptive merging has been introduced. The key idea is to create index adaptively and incrementally as a side-product of query processing. As a result, the database table is indexed partially depending on the query workload. This paper faces a problem of adaptive merging for phase change memory (PCM). The most important features of this memory type are: limited write endurance and high write latency. As a consequence, adaptive merging should be investigated from the scratch. We solve this problem in two steps. First, we apply several PCM optimization techniques to the traditional adaptive merging approach. We prove that the proposed method (eAM) outperforms a traditional approach by 60%. After that, we invent the framework for adaptive merging (PAM) and a new PCM-optimized index. It further improves the system performance by 20% for databases where search queries interleave with data modifications.
翻译:索引是一种众所周知的数据库技术,用于便利数据存取和加快查询处理。然而,索引的构建和修改费用非常昂贵。在传统方法中,数据库表格中的所有记录都同样包含在索引中。由于有些记录可能常常被查询,有些记录可能从未被查询过,这是无效的。为了避免这个问题,引入了适应性合并。关键的想法是以适应性和递增的方式创建索引,作为查询处理的副产品。结果,数据库表格部分地根据查询工作量而编制索引。本文面临着为阶段变化内存(PCM)调整合并(PCM)的问题。这一记忆类型的最重要特征是:有限的写耐久性和高写耐久性。因此,调整性合并应该从头研究。我们分两个步骤解决这个问题。首先,我们对传统的适应性合并方法应用了几种PCM优化技术。我们证明,拟议的方法(eAM)比传统方法高出60%。之后,我们发明了适应性合并框架(PAM)和新的PCM-optiminal索引。它进一步改进了系统运行状态,以便查询20%的数据间数据库。