When I joined Amazon in 1998, the company had a single U.S.-based website selling only books and running a monolithic C application on five servers, a handful of Berkeley DBs for key/value data, and a relational database. That database was called "ACB" which stood for "Amazon.Com Books," a name that failed to reflect the range of our ambition. In 2006, acmqueue published a conversation between Jim Gray and Werner Vogels, Amazon's CTO, in which Vogel explained that Amazon should be viewed not just as an online bookstore but as a technology company. In the intervening 14 years, Amazon's distributed systems, and the patterns used to build and operate them, have grown in influence. In this follow-up conversation, Vogel and I pay particular attention to the lessons to be learned from the evolution of a single distributed system--Simple Storage Service (S3)--that was publicly launched close to the time of that 2006 conversation. TOM KILLALEA: In your keynote at the AWS re:Invent conference in December 2019, you said that in March 2006 when it launched, S3 was made up of eight services, and by 2019 it was up to 262 services. As I sat there I thought that's a breathtaking number, and it struck me that very little has been written about how a large-scale, always-on service evolves over a very extended period of time. That is a journey that would be of great interest to our software practitioner community. This is evolution at a scale that is unseen and certainly hasn't been broadly discussed. WERNER VOGELS: I absolutely agree that this is unparalleled scale. Even today, even though there are Internet services these days that have reached incredible scale--I mean look at Zoom, for example [this interview took place over Zoom]--I think S3 is still two or three generations ahead of that. Because we started earlier; it's just a matter of time, and at the same time having a strict feedback loop with your customers that continuously evolves the service. Believe me, when we were designing it, when we were building it, I don't think that anyone anticipated the complexity of it eventually. I think what we did realize is that we would not be running the same architecture six months later, or a year later. So, I think one of the tenets up front was don't lock yourself into your architecture, because two or three orders of magnitude of scale and you will have to rethink it.
It makes it easier to get correct results and reason about what happens if the machine fails in the middle of processing something. Um, but you do trade off, you know, a little bit of flexibility in, in how you, how you write that versus the low-level read and write. And then one level up from that, uh, I think is, is ksqlDB. So the analogy you can use is, you know, uh, if you've ever used one of these key value interfaces like rocks DB itself, you know, it's kind of very flexible and allowing you to work with data at a low level, um, probably more so than a SQL interface, but it's actually a lot more work for kind of simple stuff, uh, that you might want to do that then using a SQL database.
Eight technologies developed by MIT Lincoln Laboratory researchers, either wholly or in collaboration with researchers from other organizations, were among the winners of the 2020 R&D 100 Awards. Annually since 1963, these international R&D awards recognize 100 technologies that a panel of expert judges selects as the most revolutionary of the past year. Six of the laboratory's winning technologies are software systems, a number of which take advantage of artificial intelligence techniques. The software technologies are solutions to difficulties inherent in analyzing large volumes of data and to problems in maintaining cybersecurity. Another technology is a process designed to assure secure fabrication of integrated circuits, and the eighth winner is an optical communications technology that may enable future space missions to transmit error-free data to Earth at significantly higher rates than currently possible.
In this episode of the Data Exchange I speak with Viral Shah, co-founder and CEO, Julia Computing. Along with his Julia language co-creators, Viral was awarded the 2019 Wilkinson prize, for outstanding contributions in the field of numerical software. I first tweeted about Julia at the beginning of March 2012 after seeing Jeff Bezanson give a talk in Stanford. I've dabbled with it here and there, but have never used it for a major project. Over the past few years, Julia continued to add packages at a steady pace and the package manager is really quite impressive and solid.
Dr. Sander kindly agreed to give us this interview at the Idorsia headquarters in Basel, Switzerland. Asking the questions from CDD are Neil Chapman and Mariana Vaschetto. By education I am organic chemist. During my seventh year at school we started to have chemistry classes and soon I had made up my mind to study chemistry. Four years later while still at school I had an opportunity to access the local University's Tectronix graphics computers.
Frances E. Allen, an American computer scientist, ACM Fellow, and the first female recipient of the ACM A.M. Turing Award (2006), passed away on Aug. 4, 2020--her 88th birthday--from complications of Alzheimer's disease. Allen was raised on a dairy farm in Peru, NY, without running water or electricity. She received a BS degree in mathematics from the New York State College for Teachers (now the State University of New York at Albany). Inspired by a beloved math teacher, and by the example of her mother, who had also been a grade-school teacher, Allen started teaching high school math. She needed a master's degree to be certified, so she enrolled in a mathematics master's program at the University of Michigan.
Welcome to Signals & Threads, in-depth conversations about every layer of the tech stack, from Jane Street. Today, I'm going to have a conversation with Andrey Mokhov about build systems. Build systems are an important but I think poorly understood and often unloved part of programming. Developers often end up with only a hazy understanding of what's going on with their build system learning just enough to figure out what arcane invocation they need to get the damn thing working and then stop thinking about it at that point, and that's a shame because build systems matter a lot to our experience as developers. A lot of what underlies a good developer experience really comes out of the build system that you use and also there's a lot of beautiful ideas and structure inside of build systems. Sadly, a lot of that beauty is obscured by a complex thicket of messy systems of different kinds and a complicated ecosystem of different build systems for different purposes, and I'm hoping that ...
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Notebooks are the data scientist best friend and can also be a nightmare to work with. For someone accustomed to work with modern integrated develop environments(IDEs), working with notebooks feels like going back decades.
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
Moritz Lipp is a Ph.D. candidate at Graz University of Technology, Flanders, Austria. Michael Schwarz is a postdoctoral researcher at Graz University of Technology, Flanders, Austria. Daniel Gruss is an assistant professor at Graz University of Technology, Flanders, Austria. Thomas Prescher is a chief architect at Cyberus Technology GmbH, Dresden, Germany. Werner Haas is the Chief Technology Officer at Cyberus Technology GmbH, Dresden, Germany.