One SQL to rule them all: an efficient and syntactically idiomatic approach to management of streams and tables Begoli et al., SIGMOD'19 In data processing it seems, all roads eventually lead back to SQL! Today’s paper choice is authored by a collection of experts from the Apache Beam, Apache Calcite, and Apache Flink projects, outlining … Continue reading One SQL to rule them all: an efficient and syntactically idiomatic approach to management of streams and tables
The convoy phenomenon Blasgen et al., IBM Research Report 1977 (revised 1979) Today we’re jumping from HotOS topics of 2019, to hot topics of 1977! With thanks to Pat Helland for the recommendation, and with Jim Gray as one of the authors, we have a combination that’s very hard to ignore :). Here’s the set-up … Continue reading The convoy phenomenon
Machine learning systems are stuck in a rut Barham & Isard, HotOS'19 In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year old benchmarks, but gradually making it harder to explore innovative machine … Continue reading Machine learning systems are stuck in a rut
Designing far memory data structures: think outside the box Aguilera et al., HotOS'19 Last time out we looked at some of the trade-offs between RInKs and LInKs, and the advantages of local in-memory data structures. There’s another emerging option that we didn’t talk about there: the use of far-memory, memory attached to the network that … Continue reading Designing far memory data structures: think outside the box
Fast key-value stores: an idea whose time has come and gone Adya et al., HotOS'19 No controversy here! Adya et al. would like you to stop using Memcached and Redis, and start building 11-factor apps. Factor VI in the 12-factor app manifesto, "Execute the app as one or more stateless processes," to be dropped and … Continue reading Fast key-value stores: an idea whose time has come and gone
What bugs cause production cloud incidents? Liu et al., HotOS'19 Last time out we looked at SLOs for cloud platforms, today we're looking at what causes them to be broken! This is a study of every high severity production incident at Microsoft Azure services over a span of six months, where the root cause of … Continue reading What bugs cause cloud production incidents?
Nines are not enough: meaningful metrics for clouds Mogul & Wilkes, HotOS'19 It’s hard to define good SLOs, especially when outcomes aren’t fully under the control of any single party. The authors of today’s paper should know a thing or two about that: Jeffrey Mogul and John Wilkes at Google1! John Wilkes was also one … Continue reading Nines are not enough: meaningful metrics for clouds