论文标题
可以更新的学习索引准备好了吗？
Are Updatable Learned Indexes Ready?
论文作者
论文摘要
最近，许多有希望的结果表明，可更新的学习索引的性能比传统索引的表现更好，而记忆空间消耗却要低得多。但是，尚不清楚这些学识渊博的索引如何相互比较，而在现实的工作负载下，数据分布和并发级别与传统索引相比。这使从业者仍然对这些新索引在实践中的实际行为保持警惕。为了填补这一空白，本文对可更新的索引进行了首次全面评估。我们的评估使用十个真正的数据集和各种工作负载来挑战三个方面的学习指数：性能，内存空间效率和鲁棒性。根据结果，我们提供了一系列的收获，可以指导未来的学习索引的发展和部署。
Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against the traditional ones under realistic workloads with changing data distributions and concurrency levels. This makes practitioners still wary about how these new indexes would actually behave in practice. To fill this gap, this paper conducts the first comprehensive evaluation on updatable learned indexes. Our evaluation uses ten real datasets and various workloads to challenge learned indexes in three aspects: performance, memory space efficiency and robustness. Based on the results, we give a series of takeaways that can guide the future development and deployment of learned indexes.
