Really enjoyed a recent Architecht Show podcast interview about co-design of hardware and software to improve task performance. Feels like computing is getting close to “software defined everything,” which provides lots of opportunity to build optimized cross-layer stacks.
In this episode of the ARCHITECHT AI Show, Hillery Hunter—IBM Fellow and director of the Accelerated Cognitive Infrastructure group at IBM Research—speaks about the state of the art in deep learning systems design. She discusses some record-breaking results that IBM recently achieved in distributed deep learning; ideal use cases for state-of-the-art image recognition; and the pros, cons and advancements in everything from GPUs to cloud-specific hardware such as Google’s TPUs.
Hillery Hunter comes through as a really sharp cookie, while cleanly and clearly expressing the impact of her team’s results.
“C’mon ya’ll, grab a hold, get off the wall…”
Definitely of a particular era, but I always loved this specific mix of Seduction’s, Two to Make it Right. Not quite House, not really good hip-hop, but worked like hell in the club. Michelle Visage’s attitude and that C&C Music Factory sensibility holds up halfway decently.
Wasn’t all that difficult, but I got my peyote substrate sketch working again. The only interesting thing I discovered is that the Python
cairocffi module doesn’t work particularly well with
cairocffi has this weird bit where it stashes data buffers in an internal cache to help finalize external data garbage collection. Unfortunately, this conflicts with
pygame’s locking of Surfaces before blitting to the screen. Switching back to
pycairo was the resolution.
Anyhoo, debugging the issue forced a reintroduction with peyote. The codebase is surprisingly although a bit hackish. It’ll be fun to clean it up, modernize it, and generate some new sketches.
Just taking a little breather. Planning to come back strong in September.
What we’re gonna do is go way back. Back into time.
Classic proto-Hip Hop via Malcolm McClaren.
We’re on a world tour with Mr. Malcom McLaren. Goin’ each and every place including Spain…
Ken Birman is a giant of Systems research.
I’ve really worked in Cloud Computing for most of my career, although it obviously wasn’t called cloud computing in the early days. As a result, our papers in this area date back to 1985. Some examples of mission-critical systems on which my software was used in the past include the New York Stock Exchange and Swiss Exchange, the French Air Traffic Control system, the AEGIS warship and a wide range of applications in settings like factory process control and telephony. In fact, every stock quote or trade on the NYSE from 1995 until early 2006 was reported to the overhead trading consoles through software I personally implemented – a cool (but also scary) image, for me at least! During the ten years this system was running, many computers crashed during the trading day, and many network problems have occurred – but the design we developed and implemented has managed to reconfigure itself automatically and kept the overall system up, without exception. They didn’t have a single trading disruption during the entire period. As far as I know, the other organizations listed above have similar stories to report.
So what mission critical system has your work ended up in?
Don’t know why I happened to be trawling his Cornell website recently, but it turns out he’s been publishing a series of essays on the Web. First off, Birman writes really well. This is advanced technical material but fairly accessible. Second, if he says RDMA is a big deal, I’ll just get on the bandwagon and buckle my seatbelt. Actually, the more of his essays I read the more I’m convinced of his position. If the trajectory is right, HPC grade network interconnects will be commoditized and made accessible to average programmers. Sort of like what Hadoop did for Map/Reduce.
If you are at all interested in Systems research, I strongly encourage you to work your way through Ken Birman’s “…Thoughts on Distributed Computing.”
Link parkin’: A Tale of Two Kafka Clients
We use and love Kafka at Data Syndrome. It enables us to move processing from batch to realtime with minimal pain and complexity. However, during a recent project we learned a hard lesson about the kafka-python package that has me thinking about how to make the choice between open source tools. In this post we reflect on the open source decision making process. We describe two Kafka clients for Python, the issues we encountered, and the solution we’ll be using going forward.
Slightly disappointed that there wasn’t mention of pykafka, which is not Confluent controlled but still pretty robust.
Damn if 2 out of 3 example sketches I wrote for peyote don’t still work in Python 2.7 using current versions of modules such as pygame and pycairo. One sketch seems to have suffered from bitrot relative to the numpy module. This seems relatively surmountable though.
Well if this code can survive seven years of neglect, it probably deserves for me to resurrect it with some modern Python tooling and my improved development skills. Maybe even throw in some Python 3 compatibility.
Viva El Peyote!
Once upon a time, I posted 80 times in one month. Then followed it up with three straight months of 50 or more posts. Yowza!
Different era though. Link blogging was more in fashion and of course I had way more discretionary time. Plus, the task actually did support getting insights related to my research agenda of the moment.
Link blogging is sort of fun though. Between faving tweets and starring feed items, feels like I should be able to mash something up into my own hosted Tumblr style blog.
Somebody burn the tape of these last seven days.
Classic Chicago House music from Joe Smooth Inc., The Promised Land
Once upon a time, I had the pleasure of taking a graduate database course with ACM Turing Award Winner, Professor Michael Stonebraker. It was a hoot because Stonebraker was a character in a good way. He has strong opinions but expresses them with a very dry, non-confrontational, wit. Said approach exemplified by this interview, “How Hardware Drives The Shape Of Databases To Come,” at The Next Platform.
The issue is the changing storage hierarchy and what it has to do with databases. Let’s start with online transaction processing. In my opinion, this is a main memory system right now, and there are a bunch of NewSQL startups that are addressing this market. An OLTP database that is 1 TB in size is a really big one, and 1 TB of main memory is no big deal any more. So I think OLTP will entirely go to main memory for anybody who cares about performance. If you don’t care about performance, then run the database on your wristwatch or whatever.
Definitely give it a read, especially the thoughts on how accelerating innovation in the networking space will affect data management. Go Bears!
Link parkin’: “Announcing Streamlio”
We are excited to announce Streamlio, the world’s leading enterprise-grade, unified, end-to-end real-time solution. Founded by co-creators of best-of-breed open-source technologies proven at Twitter and Yahoo, Streamlio unifies Apache Pulsar (incubating), Heron, and Apache BookKeeper to deliver the next generation of real-time applications. Enterprises across all industries now need to adopt event-driven architecture, characterized by a new era of real-time action with no humans in the loop. That’s why we have founded Streamlio. Our mission is to deliver an easy-to-use, enterprise-grade, real-time solution to the masses.
As Ben Lorica points out, this looks like a play on vertical integration of key technologies for stream processing, machine learning, and I/O. TIL about Apache BookKeeper.
Gettin’ a little crowded in the production ready, stream processing platform/toolkit space.
Link parkin’. UniK: The Unikernel Compilation and Deployment Platform
From an introductory blog post on Unik:
UniK (pronounced you-neek) is a tool for compiling application sources into unikernels — lightweight bootable disk images — rather than binaries. UniK runs and manages instances of compiled images across a variety of cloud providers as well as locally on Virtualbox. UniK utilizes a simple docker-like command line interface, making building unikernels as easy as building containers. UniK is built to be easily extensible, allowing – and encouraging – adding support for unikernel compilers and cloud providers.
I found Julia Evans’ take on learning new tech interesting. The short story is, do it on the job!
Right now at work I’m working a bit in Go! That is interesting and I’m happy to be doing it. But it is not so fun that I feel like spending a lot of my personal time on it. And I don’t really think it’s necessary, I learn languages by writing them, reading other people’s code to learn about conventions, and having my code reviewed. I can just do all of those things at work!
To be clear, I don’t think it’s bad to learn programming languages outside of work. I just don’t really do it.
The real key is understanding that conducting mindful, motivated, learning is actually a responsible act that provides value to the business. Sure, boning up on skills as a form of moonlighting for your next startup gig is bad form. But taking reasonable time out to read up on and internalize technologies one doesn’t have depth on is perfectly reasonable. At least in any organization worth it’s salt.
My current gig is at the moment diametrically opposed to hers. I don’t have any dev tools on my work laptop, much less do any development. So it’s challenging to learn on the job. And as opportunity allows, I’m quite happy to learn on my off time. Even so, with the plethora of vibrant open source communities out there, there’s plenty of places to follow her advice on how to learn.
Big milestone today on Mass Programming Resistance. According to my WordPress console, this post is number 1500. Also, this blog’s first post was on August 11th, 2008, making today the close of 9 blogging years on MPR. Not to mention 52 straight days of posting.
In addition, this is also the anniversary of my starting employment with Schafer Corporation. That name is pretty mundane, and going away after acquisition, but the job is anything but. Being a Scientific, Engineering, and Technical Advisor (SETA) for the Defense Advanced Research Projects Agency (that DARPA) gives one a solid peek behind the curtain of a high impact, technology innovation organization. Can’t really give much detail but it’s been a good year.
What better punctuation than The Black Science Orchestra’s, New Jersey Deep.
Yeaaaah! Black Science got it goin’ on.
Link parkin’. Oldie, but goodie, on taking a bunch of git commits and mashing them into one:
But one thing occasionally bothers me, and that’s pull requests that come loaded with several temporary commits. I often find myself asking contributors to squash those into a single descriptive commit. Not everyone knows how to do this effectively, hence this quick writeup.
I can also attest that magit makes squashing commits, from within the Emacs editor, quite agreeable.
As is my wont, I sometimes prowl around old proceedings of systems conferences. Today I landed on the program for ACM CoNEXT 2012, with a paper interestingly titled, “Reproducible Network Experiments Using Container-Based Emulation” (PDF)
In an ideal world, all research papers would be runnable: simply click to replicate all results, using the same setup as the authors. One approach to enable runnable network systems papers is Container-Based Emulation (CBE), where an environment of virtual hosts, switches, and links runs on a modern multicore server, using real application and kernel code with software-emulated network elements. CBE combines many of the best features of software simulators and hardware testbeds, but its performance fidelity is unproven.
In this paper, we put CBE to the test, using our prototype, Mininet-HiFi, to reproduce key results from published network experiments such as DCTCP, Hedera, and router buffer sizing. We report lessons learned from a graduate networking class at Stanford, where 37 students used our platform to replicate 16 published results of their own choosing. Our experiences suggest that CBE makes research results easier to reproduce and build upon.
Obviously containers, even Linux containers, are fairly old, but I’m just amused that an impactful research paper involving containers was published 5 years ago, with the work essentially done 10 months before the uber-hyped Docker was open sourced. The future is already here, just unevenly distributed and all that jazz.
Ditto for all the computational, reproducible, publication hoohah around web-based scientific notebooks, such as Jupyter.
Bonus entertainment! Check out the accompanying presentation slides (PDF), especially slide 60. I always tell people, at some point in the process getting a PhD really sucks. No exceptions!
Link parkin’. Practice Your Python
No matter where you are in your Python journey, it can always help to practice and refine your skills. This site lists the best resources I’ve found for practicing Python — through exercises and challenges.
If you have found a good resource that helps people to practice and improve their Python skills, send me e-mail at email@example.com, and I’ll add it to this list.
Need to also mention Reuven M. Lerner’s Practice Makes Python
Lord knows I need to put in some practice time.
I have totally missed the boat on Game of Thrones, having watched only bits and pieces of episodes here and there. Maybe one day I’ll have the opportunity to binge my way through, but not in the foreseeable future.
An API of Ice And Fire is the world’s greatest source for quantified and structured data from the universe of Ice and Fire (and the HBO series Game of Thrones). We give you access to data about all the Books, Characters and Houses in an easy to use JSON format. …
Why did you build this?
I decided to build this API because I like to build stuff that other developers can use. A Song of Ice And Fire and Game of Thrones is also a great universe and I find it really interesting.
Besides, this was (and is) a great opportunity to try out new technologies.
This is exactly the type of project I’d like to take on with the Discogs.com Data Dumps. There seem to be lots of fun and interesting developer challenges embedded in to what seems like a straightforward task. Data cleaning, schema design, API design, app design, access control, performance, scalability, etc. etc. I just need to get off my ass and get to it.
Keeping with the Jupyter theme, you can use the nbtransom library to manipulate notebooks with code:
This is a Python 3 library to read/write cells programmatically in Jupyter notebooks which anticipates upcoming collaborative features in Jupyter.
We use this at O’Reilly Media for notebooks used to manage machine learning pipelines. That is to say, machines and people collaborate on documents, implementing a “human-in-the-loop” design pattern: …
nbtransom seems to be a key element for this upcoming JupyterCon talk from Paco Nathan:
Paco Nathan reviews use cases where Jupyter provides a frontend to AI as the means for keeping humans in the loop (and shares the code used). Jupyter gets used in two ways. First, people responsible for managing ML pipelines use notebooks to set the necessary hyperparameters. In that sense, the notebooks serve in place of configuration scripts. Second, the ML pipelines update those notebooks with telemetry, summary analytics, etc., in lieu of merely sending that data out to log files. Analysis is kept contextualized, making it simple for a person to review. This process enhances the feedback loop between people and machines: humans-in-the-loop use Jupyter notebooks to inspect ML pipelines remotely, adjusting them at any point and inserting additional analysis, data visualization, plus their notes into the notebooks; the machine component is mostly automated but available interactively for troubleshooting and adjustment.
I’ll have to wait until the talk gets released to Safari, but I’m interested to see if there’s discussion of how notebooks are organized at a higher level to support these types of manipulations.
Link parkin’. The Jupyter team has been making it easier to deploy Jupiter notebook servers in cloud environments. Andrew Odewahn provides some helpful notes.
JupyterHub, a “multi-user server for Jupyter Notebooks,” is an essential tool for teaching and training at scale with Jupyter. …
Their new project, a Helm package for JupyterHub and an accompanying article called Zero to JupyterHub on how to use it, describes the relatively straightforward steps needed to install and run JupyterHub on Google cloud.
In this article, I’ve followed along with the tutorial, adding additional detail on setting up gcloud, preparing a docker image with the content project you want to deploy in it, and provided more background on some of the tools used.
Not trivial at this moment, but expect the edges to get smoothed out over time.
Over at HackerNews, Today I Learned about a potentially interesting newsletter on API development “GET PUT POST”.
Welcome to GET PUT POST, a newsletter all about APIs. Each edition features an interview with a startup about their API and ideas for developers to build on their platform.
I have a fascination with image collections, which are great fodder for generative art and wallpaper / screensavers. Shoutout Minneapolis College of Art and Design for making a collection of classic lithograph posters available as a complete Zip collection (via Flickr).
Today, more and more cultural institutions have started to offer free art resources to the public through online archives. From high-quality reproductions of antique photographs to museum-approved art books, these complimentary materials promote and exhibit the endless possibilities of Internet learning. Recently, the Minneapolis College of Art and Design (MCAD) has opted to contribute to this downloadable, digitized phenomenon with Art of the Poster 1880-1918, a curated collection of posters from the “Golden Age” of graphic design.
Featuring over 200 printed works, Art of the Poster 1880-1918 presents a look at lithography’s rise in popularity during La Belle Époque. It was during this time that artists like Alphonse Mucha, Jules Chéret, and Henri de Toulouse-Lautrec popularized the art form, which gained public prominence thanks to new methods of production.
The only downer is that the licensing seems a little inconsistent, although most I’ve looked at are Creative Commons 2.0, which is pretty permissive.
What if I told you a blog author could rise from the dead and become a better poster than ever?
Not to be overly dramatic but the month of July 2017 was my most prolific posting month on Mass Programming Resistance. I logged 51 posts in 31 days besting the previous high of 50 in the month of March 2013. 51 posts is more than my total output for the calendar year 2016, the total output for the calendar year 2015, and the total output for the calendar year 2014. In June and July of 2017 there were a total of 84 entries, more than the combined total for the years of 2016, 2015, and 2014.
I’m not going to declare it my best month though. There were a lot of link outs to interesting technical content, but I’m still leaning a bit heavily on pull quote style posts. A next goal should be to expand these out with some more technical thinking or more work to weave themes together. Not quite sure how I feel about the YouTube and Twitter embeds as convenient as they are. On the positive side, July 2017 featured no sports related content. None of my old style, consumerist, “what piece of gadget junk am I going to buy,” threads either.
Looking at past content, the best eras of this here site typically involved some sort of side software project where I was narrating the development effort. A focus of August will be getting some sort of side hacking going to write about. Also, as a former New Media Hack, I’d like to get back into ocasional media+technology commentary, which is tricky since that seems to inevitably get sucked into the political sphere. Not that I don’t have serious political interests and thoughts, but I’m a firm believer that in the current environment leaving that engagement offline preserves mental health, personal safety, and positive energy.
One thing that has taken a big hit recently is my book reading page count. Consistent book reading also generates a lot of great posting fodder. Need to get back in the groove on that front.
Anyway, too much meta is murder, but I feel reenergized and looking forward to a good, long continued run.
As software engineers, we need to build applications that are reliable, scalable and maintainable in the long run. We need to understand the range of available tools and their trade-offs. For that, we have to dig deeper than buzzwords.
This book will help you navigate the diverse and fast-changing landscape of technologies for storing and processing data. We compare a broad variety of tools and approaches, so that you can see the strengths and weaknesses of each, and decide what’s best for your application.
I read the first couple of chapters from the early release edition, but haven’t been tracking the book to its completion. Based upon the quality of his blog posts (really enjoyed the post on techniques for real-time text search), I’m looking forward to catching up with the final product.
Also interesting to see Kleppmann joined the University of Cambridge as a Research Associate, working on the TRVE DATA project.
I’ve mentioned before the fine work that Mark Litwintschik does putting data management systems through their paces using a dataset of 1.1 billion taxi rides. He’s back with another post on BrytlytDB.
BrytlytDB is an in-GPU-memory database built on top of PostgreSQL. It’s operated using many of PostgreSQL’s command line utilities, it’s wire protocol compatible so third-party PostgreSQL clients can connect to BrytlytDB and queries are even parsed, planned and optimised by PostgreSQL’s regular codebase before the execution plan is passed off to GPU-optimised portions of code BrytlytDB offer.
There have been quite a few posts by Litwintschik since I noted his efforts. What caught my eye this time is the mention of the, new to me, BrytlytDB. BrytlytDB apparently leverages a lot of the core capabilities of the PostgreSQL code base and presents a lot of API compatibility. To quote from the homepage, “Brytlyt combines the power of GPUs with patent pending IP and integrates with PostgreSQL.”
I probably have a bit of myopia, but it feels like PostgreSQL essentially defines the baseline for commercial DBMS functionality these days.
And once again, I have to commend Litwintschik on the thoroughness of his reporting on these posts. One of the few technical bloggers who provides enough detail to actually approach “reproducibility.”
In this episode of the ARCHITECHT Show, In this episode of the ARCHITECHT Show, Ion Stoica talks about the promise of real-time data and machine learning he’s pursuing with the new RISELab project he directs at UC-Berkeley, along with some other big names in big data. Stoica previously was director of the university’s AMPLab, which created and helped to mature technologies such as Apache Spark, Apache Mesos and Alluxio. Stoica is also co-founder and executive chairman of Apache Spark startup Databricks, and he shares some insights into that company’s business and the evolution of the big data ecosystem.
eBPF/bcc enables us to write a new range of tools to deeply troubleshoot, trace and track issues in places previously unreachable without patching the kernel. Tracepoints are also quite handy as they give a good hint on interesting places, removing the need to tediously read the kernel code and can be placed in portions of the code that would otherwise be unreachable from kprobes, like inline or static functions.
Also, I learned about the
Link parkin’. A free(ish) e-book comparing and contrasting the current leading frameworks for messaging. Free as in “give us contact info first” free. Haven’t read yet, YMMV.
Author and consultant Jakub Korab describes use cases and design choices that lead developers to very different approaches for developing message-based systems. You’ll come away with a high-level understanding of both ActiveMQ and Kafka, including how they should and should not be used, how they handle concerns such as throughput and high-availability, and what to look out for when considering other messaging technologies in future.
I’ll probably grab it out of message nerd curiosity. Also wondering if the book touches on somewhat divergent frameworks like NATS.
I promised to revisit the topic of Kafka’s new “exactly once processing.” A while ago, Tyler Treat generated a relatively popular post entitled “You Cannot Have Exactly Once Delivery”. Treat came back and recontextualized the original argument in the face of Confluent’s recent work.
First, let me say what Confluent has accomplished with Kafka is an impressive achievement and one worth celebrating. They made a monumental effort to implement these semantics, and it paid off. The intention of this post is not to minimize any of that work but to try to clarify a few key points and hopefully cut down on some of the misinformation and noise.
The gist is that the Kafka Streams approach is a fairly closed framework that works with the messaging system to ensure a particular semantics correctly with reasonable performance. That’s a good thing. Definitely worth a read if you’re a messaging junkie.
It’s been over 7 years since MarsEdit 3 was released. Typically I would like to maintain a schedule of releasing major upgrades every two to three years. This time, a variety of unexpected challenges led to a longer and longer delay.
The good news? MarsEdit 4 is finally shaping up. I plan to release the update later this year.
Over seven years ago, I hypothesized about ESPN falling from, what looked like at the time, an unassailable perch. All my speculation turned out to be off base, but ESPN has been taking it in the shorts recently. Witness The Athletic preparing to swoop on newly available talent, according to Bloomberg.
2017 is shaping up to be a rough year for sports journalism. ESPN, Fox Sports, Sports Illustrated, Bleacher Report, and Yahoo Sports have all cut staff positions in the last several months, showing the deep cracks in the predominant business model of online sports news. The founders of the Athletic, an 18-month-old online sports publication, see opportunity in the struggles of the biggest companies. As the news of the cuts kept coming, co-founders Alex Mather and Adam Hansmann, who have no previous journalism experience, hastily pulled together $5.8 million in new capital from investors, in a round they closed last week. The plan is to scoop up laid-off writers, and put them to work building a new kind of sports news operation as the traditional industry leaders are in retreat.
From what I can tell, a combo of mobile / digital (cannibalizing cable subscriptions on volume and price) and demographics (younger folks interested in “edgier” not SportsCenter) are the trends causing fits in Bristol, Conn.
These four companies underscore the unbroken link between on-demand computing, big data, and machine learning. While the ‘90s and “oughties” were about building up the front-end user interface—and in the process, making powerful technology simple enough to find billions of users—more recent years have been about laying the groundwork for adaptive, always-aware organizations.
I wasn’t there to view the startup sales pitchers or do any voting. However, Allistair Croll’s assertion that these are AI startups seems a bit off. The short descriptions make three seem to be more data wrangling / harnessing companies. The fourth is a media analytics platform for the e-sports era. There might be some AI hidden in there, and maybe that’s the point, but they sure don’t feel like Bradford Cross’ vertical AI startups.
I’ve actually been enjoying Podcast.__init__ for a bit now. (The Podcast About Python and the People Who Make It Great .) Recently, Tobias Macey, the host, had an interview with Tim Abbott. Abbott’s the lead developer of the open source Zulip project, which is a “modern group chat” application.
As good as Podcast.__init__ has been, this was a really interesting interview. First, Abbott had successfully exited two startups and spent some time deeply embedded in the Dropbox engineering team. So there was some interesting technical organization discussion. Second, Abbott had some very cogent thoughts on how to create a vibrant open source project. A couple of key things that stood out to me were making onboarding of new contributors as frictionless as possible and systematically externalizing his knowledge into visible documentation, as opposed to invisible e-mails.
I’m also sort of curious if Zephyr, which inspired Zulip, is still used at M.I.T. The community of Zephyr users must be vanishingly small, so I was surprised to hear of Abbott’s fondness for it. He strikes me as a true Engineer.
At my last gig, we routinely had breakouts of bikeshedding arguments regarding the mandatory, organizational discussion, group chat application. A year or two ago, as a gag, I had half a mind to propose Zulip to the company, but wisely thought better of it.
Just wanted to mark the fact that I’m a fan of Overcast for subscribing to and playing podcasts. As a technonerd, I might do with a few more bells and whistles, but the overall simplicity makes it a compelling app on the iPhone.
Overcast is good enough that I paid for an annual subscription. YMMV.
Diggin’ in the starred items crates and fell into this post from Camille Fournier about some field-earned wisdom on microservices:
This article is going to have two examples. The first is the rough way “microservices” was deployed in my last gig, and why I made the decisions I made in the architecture. The second is an example of an architecture that is much closer to the “beautiful dream” microservices as I have heard it preached, for architectures that are stream-focused.
Not too deep into the weeds but enough technical insight to be useful. Key takeaway is to not get hyper-aggressive about decentralizing data management.
If you want to say “my database is better than your database” then you really also need to specify “for what?”. And if you want to evaluate whether graph databases really do earn their keep as compared to relational databases, you really want to do the comparison on the home turf of the graph databases – the use cases they claim to be good at.
The final outcome is that traditional RDBMS engines, using straight SQL instead of a specialized graph query language, have much better performance. Gremlin takes it on the chin a bit.