Seeing is believing: a client-centric specification of database isolation

Seeing is believing: a client-centric specification of database isolation, Crooks et al., PODC’17. Last week we looked at Elle, which detects isolation anomalies by setting things up so that the inner workings of the database, in the form of the direct serialization graph (DSG), can be externally recovered. Today’s paper choice, ‘Seeing is believing’ also ... Continue Reading

Elle: inferring isolation anomalies from experimental observations

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 – part 1

Helios: hyperscale indexing for the cloud & edge, Potharaju et al., PVLDB'20 On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. As a production system within Microsoft capturing around a quadrillion events and indexing 16 trillion search keys per day it would be interesting ... Continue Reading

The case for a learned sorting algorithm

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