TLDR; this is the last edition of The Morning Paper for now. Plus: one strand of research you won’t want to miss! I was listening to a BBC Radio 4 podcast recently (More or Less: Behind the Stats - Ants and Algorithms) in which the host Tim Harford is interviewing David Sumpter about his recent ... Continue Reading

Elle: inferring isolation anomalies from experimental observations, Kingsbury & Alvaro, VLDB’20 Is there anything more terrifying, and at the same time more useful, to a database vendor than Kyle Kingsbury’s Jepsen? As the abstract to today’s paper choice wryly puts it, “experience shows that many databases do not provide the isolation guarantees they claim.” Jepsen ... Continue Reading

Helios: hyperscale indexing for the cloud & edge, Potharaju et al., PVLDB'20 Last time out we looked at the motivations for a new reference blueprint for large-scale data processing, as embodied by Helios. Today we're going to dive into the details of Helios itself. As a reminder: Helios is a distributed, highly-scalable system used at ... Continue Reading

The case for a learned sorting algorithm, Kristo, Vaidya, et al., SIGMOD’20 With thanks to Babur Muradov of pngset, a Russian translation of this post is now available at https://pngset.com/ru-learned-sorting-algorithm We’ve watched machine learning thoroughly pervade the web giants, make serious headway in large consumer companies, and begin its push into the traditional enterprise. ML, ... Continue Reading