OSM-tree：一种支持排序的索引
OSM-tree: A Sortedness-Aware Index
摘要：
当选择谓词位于索引键上时，索引有助于高效查询。因此，当加载数据时，如果我们预期将来会有选择性（点或范围）查询，我们通常会维护一个索引，随着新数据的摄入而逐渐填充。在这方面，索引可以被看作是向传入的、否则未排序的数据汇集添加结构的过程。添加结构的过程是有代价的，因为不是简单地附加传入数据，而是将每个新条目插入到索引中。如果数据摄取顺序与索引的属性顺序匹配，则摄取成本是完全冗余的并且可以避免（例如，经由B+树中的批量加载）。然而，当数据以接近排序但未完全排序的顺序被摄取时，最先进的索引设计没有益处。在本文中，我们研究了如何索引可以受益于部分数据排序或近排序，我们提出了一个集成的技术，结合批量加载，索引追加，可变节点填充/拆分因子，和缓冲，以优化摄入成本的树索引中存在的部分数据排序。我们使用必要的元数据结构进一步增强了拟议的设计，以确保具有竞争力的读取性能。我们应用所提出的设计范例上的一个国家的最先进的B+树，我们提出了有序排序合并树（OSM树）。OSM-tree在排序的情况下在摄取性能上超过最先进的8.8倍，而当数据被打乱时，它的摄取性能又回到了B+树。OSM-tree提供有竞争力的查询性能，对于混合读/写工作负载，性能优势在28%到5倍之间。
英文摘要：
Indexes facilitate efficient querying when the selection predicate is on an indexed key. As a result, when loading data, if we anticipate future selective (point or range) queries, we typically maintain an index that is gradually populated as new data is ingested. In that respect, indexing can be perceived as the process of adding structure to an incoming, otherwise unsorted, data collection. The process of adding structure comes at a cost, as instead of simply appending incoming data, every new entry is inserted into the index. If the data ingestion order matches the indexed attribute order, the ingestion cost is entirely redundant and can be avoided (e.g., via bulk loading in a B+-tree). However, state-of-the-art index designs do not benefit when data is ingested in an order that is close to being sorted but not fully sorted. In this paper, we study how indexes can benefit from partial data sortedness or near-sortedness, and we propose an ensemble of techniques that combine bulk loading, index appends, variable node fill/split factor, and buffering, to optimize the ingestion cost of a tree index in presence of partial data sortedness. We further augment the proposed design with necessary metadata structures to ensure competitive read performance. We apply the proposed design paradigm on a state-of-the-art B+-tree, and we propose the Ordered Sort-Merge tree (OSM-tree). OSM-tree outperforms the state of the art by up to 8.8x in ingestion performance in the presence of sortedness, while falling back to a B+-tree's ingestion performance when data is scrambled. OSM-tree offers competitive query performance, leading to performance benefits between 28% and 5x for mixed read/write workloads.
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