Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines. Using four real-world datasets, we demonstrate that learned index structures can indeed outperform non-learned indexes in read-only in-memory workloads over a dense array. We also investigate the impact of caching, pipelining, dataset size, and key size. We study the performance profile of learned index structures, and build an explanation for why learned models achieve such good performance. Finally, we investigate other important properties of learned index structures, such as their performance in multi-threaded systems and their build times.

中文翻译：

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基准学习索引

学习索引结构的最新进展建议用近似学习模型替换现有的索引结构，如 B 树。在这项工作中，我们提出了一个统一的基准，将三个学习的索引结构的调整良好的实现与几个最先进的“传统”基线进行比较。使用四个真实世界的数据集，我们证明学习索引结构确实可以在只读内存工作负载中优于非学习索引。我们还调查了缓存、流水线、数据集大小和密钥大小的影响。我们研究了学习索引结构的性能概况，并解释了为什么学习模型能取得如此好的性能。最后，我们研究了学习索引结构的其他重要属性，