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：索引是一种广为人知的数据库技术，用于加速数据访问和查询处理。然而，索引的构建和修改代价高昂。传统方法中，数据库表中的所有记录均被索引等量覆盖，但这种方式效率不高，因为某些记录可能被频繁查询，而另一些则从未被查询。为避免该问题，研究人员提出了自适应合并技术。其核心思想是将索引作为查询处理的副产品，以自适应和增量方式创建。因此，数据库表的索引部分取决于查询工作负载。本文针对相变存储器（PCM）面临的自适应合并问题展开研究。此类存储器最重要的特性包括：有限的写入耐久性和较高的写入延迟。因此，需从零开始重新研究自适应合并技术。我们分两步解决该问题：首先，将多种PCM优化技术应用于传统自适应合并方法，证明所提方法（eAM）相比传统方法性能提升60%；其次，我们发明了自适应合并框架（PAM）及一种新型PCM优化索引。在查询与数据修改交替执行的数据库中，该框架进一步将系统性能提升了20%。