Semi-indexing semi-structured data in tiny space

G. Ottaviano, R. Grossi
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引用次数: 12

Abstract

Semi-structured textual formats are gaining increasing popularity for the storage of document collections and rich logs. Their flexibility comes at the cost of having to load and parse a document entirely even if just a small part of it needs to be accessed. For instance, in data analytics massive collections are usually scanned sequentially, selecting a small number of attributes from each document. We propose a technique to attach to a raw, unparsed document (even in compressed form) a "semi-index": a succinct data structure that supports operations on the document tree at speed comparable with an in-memory deserialized object, thus bridging textual formats with binary formats. After describing the general technique, we focus on the JSON format: our experiments show that avoiding the full loading and parsing step can give speedups of up to 12 times for on-disk documents using a small space overhead.
小空间中的半索引半结构化数据
半结构化文本格式在存储文档集合和丰富日志方面越来越受欢迎。它们的灵活性是以必须加载和解析整个文档为代价的,即使只需要访问文档的一小部分。例如,在数据分析中,通常顺序扫描大量集合,从每个文档中选择少量属性。我们提出了一种技术,将“半索引”附加到原始的、未解析的文档(即使是压缩形式)上:这是一种简洁的数据结构,支持对文档树的操作,其速度与内存中反序列化对象相当,从而将文本格式与二进制格式连接起来。在描述了一般技术之后,我们将重点关注JSON格式:我们的实验表明,避免完全加载和解析步骤可以使用很小的空间开销为磁盘上的文档提供高达12倍的速度提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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