Period Index: A Learned 2D Hash Index for Range and Duration Queries

Today, most commercial database systems provide some support for the management of temporal data, but the index support for efficiently accessing such data is rather limited. Existing access paths neglect the fact that time intervals are located on the timeline and have a duration, two important pieces of information for querying temporal data. In this paper, we tackle this problem and introduce a novel index structure, termed Period Index, for efficiently accessing temporal data based on these two pieces of information. The index supports temporal queries that constrain the position of an interval on the timeline (range queries), its interval duration (duration queries), or both (range-duration queries). The key idea of the new index is to split the timeline into fixedlength buckets, each of which is divided into a set of cells that are organized in levels. The cells encode the position of intervals on the timeline, whereas the levels encode their duration. This grid-based index is well-suited for parallelization and non-uniform memory access (NUMA) architectures as it is common for modern hardware with large main-memories and multi-core servers. The Period Index is independent of the physical order of the data and has predictable performance due to the underlying hashing approach. We also propose an enhanced version of our index structure, termed Period Index., which continuously adapts the optimal bucket length to the distribution of the data. Our experiments show that Period Index. significantly beats other indexes for the class of queries that constrain both the position and the length of the time intervals, and it is competitive for queries that involve solely one temporal dimension.