FLIRT: A Fast Learned Index for Rolling Time frames

Guang Yang, Liang Liang, A. Hadian, T. Heinis
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引用次数: 3

Abstract

Efficiently managing and querying sliding windows is a key com-ponent in stream processing systems. Conventional index structures such as the B+Tree are not efficient for handling a stream of time-series data, where the data is very dynamic, and the indexes must be updated on a continuous basis. Stream processing structures such as queues can accommodate large volumes of updates (enqueue and dequeue); however, they are not efficient for fast retrieval. This paper proposes FLIRT, a parameter-free index structure that manages a sliding window over a high-velocity stream of data and simultaneously supports efficient range queries on the sliding window. FLIRT uses learned indexing to reduce the lookup time. This is enabled by organising the incoming stream of time-series data into linearly predictable segments, allowing fast queue operations such as enqueue, dequeue, and search. We further boost the search performance by introducing two multithreaded versions of FLIRT for different query workloads. Experimental results show up to 7 × speedup over conventional indexes, 8 × speedup over queues, and up to 109 × speedup over learned indexes.
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调情:滚动时间框架的快速学习索引
有效地管理和查询滑动窗口是流处理系统的关键组成部分。传统的索引结构(如B+Tree)对于处理时间序列数据流来说效率不高,因为数据是非常动态的,索引必须连续更新。流处理结构,如队列,可以容纳大量的更新(enqueue和dequeue);然而,对于快速检索来说,它们的效率不高。本文提出了一种无参数索引结构FLIRT,它可以管理高速数据流上的滑动窗口,同时支持对滑动窗口的有效范围查询。FLIRT使用学习索引来减少查找时间。这是通过将传入的时间序列数据流组织成线性可预测的段来实现的,允许快速队列操作,如排队、脱队列和搜索。我们通过为不同的查询工作负载引入两个多线程版本的FLIRT来进一步提高搜索性能。实验结果表明,与传统索引相比,该方法的速度提高了7倍,与队列相比,速度提高了8倍,与学习索引相比,速度提高了109倍。
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