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.

翻译：最近,许多大有希望的结果显示,不断提高的学习指数比传统的指数效果更好,记忆空间消耗量要低得多。但是,目前还不知道这些学习的指数如何相互比较,如何与数据分布和同值变化、数据分配和货币水平变化等现实工作量下的传统指数进行比较。这使从业者仍然对这些新指数在实践中的实际表现感到谨慎。为填补这一空白,本文件对不断提高的学习指数进行第一次全面评估。我们的评估利用十个真实数据集和各种工作量在三个方面挑战已经学习的指数:业绩、记忆空间效率和稳健性。根据结果,我们提供了一系列能够指导今后编制和部署学到的指数的外卖。