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Description:Something Similar Something Similar About Jeff Hodges Atom Feed Archive A couple of phone calls to Congresspeople January 23, 2017 22:59:13 PM PST I’ve just heard that one of my senators, Senator Fein


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Something Similar Something Similar About Jeff Hodges Atom Feed Archive Code to Read When Learning Go December 26, 2013 20:15:00 PM PST When folks ask me how they should learn Go, I usually say: Run through the activities in A Tour of Go, then read Effective Go (and maybe check out Go By Example), and then read some code. This seems to work well for them. But the last part could use some examples of good, clear, idiomatic code to read. Let me do my part there. Now, I’m holding these up as someone who already knows Go, didn’t use all of these to learn Go, and, perhaps worse, came to Go already knowing how to program. But even with these caveats, these are codebases that are useful to learn from. pat (godoc) - Pat is a cute, small library that provides Sinatra-like HTTP handling in Go on top of the already fairly easy to use net/http Handlers. It’s a breeze to read. Feature-ful Go projects often have just a handful of files in them, gathering power from they way they combine the standard library in a particular convenient and narrower fashion than the original API. Pat is a great example of that (and Go’s stdlib being good enough for production is to be commended). It integrates well with other code you’ll use simply by relying on the interfaces in the net/http library. codesearch (godoc) - To see how a larger project can fit together, I recommend the codesearch project. It’s use of the regexp package’s abstract syntax tree API to implement regexp search is wonderful. For maximum understanding, pair this code with Russ Cox’s articles on how to implement searching with regular expressions in queries. I don’t want to undersell that article. It describes how Google’s (now defunct) Code Search solved that problem and how codesearch, the Go implementation Russ wrote, works. The whole series of articles on regular expressions is good, but this one article is especially fun. My workplace shipped this project internally and it’s pretty much Solved? our code search problems. groupcache (godoc) - Groupcache is another larger codebase for those interested in that, and contains plenty of goodies within. It’s part of the, Blogger and Google Code architecture, and is, in many ways, a “smarter memcached” as a library. The README details its design. From its ownership model designed to avoid thundering herds, to the singleflight implementation of request collapsing, to the interface for extracting stats, groupcache is a maturely designed piece of software. Not just good code, but wise distributed system design is inside. net/http - The net/http library is very Go-y, and used everywhere in the Go community. It’s been hardened for production use at scale and HTTP 1.x is a very hard protocol to get right, so it’s not always the easiest read. That said, it’s worth learning how such a common building block was designed. It also provides capabilities like net/http/pprof that takes Go’s built-in profiler and makes it available over the web. (Reading Profiling Go Programs is really nice, by the by. Be sure to check out the goroutine blocking profiler.) There are so many more projects I could talk about. Especially ones using the Go AST tooling (like in’s gddo codebase), or the surprisingly clear crypto libraries (like crypto/subtle) but time and space won’t permit it. Poke your nose around, search for projects on Go Search, and write some code. One of Go’s major selling points is it’s readability, even to those inexperienced in it, and just digging in is worth your time. (And, if you do nothing else, read Effective Go.) A Story About a Photograph December 09, 2013 08:15:00 AM PST Jacob and I shipped Forward Secrecy at Twitter. A little while later, the New York Times asked to take some photos of the two of us for the couple of pieces they were writing about what Twitter and other companies were doing in response to the Snowden revelations. When the photographer came, he asked us to work next to each other in a way that “didn’t look too fake”. Alright, authenticity is important, so Jacob and I started poking at this DH parameter thing we’d been meaning to investigate. Ten minutes later, the photographer says “alright, we’re done over here. Let’s go to the next spot.” But we don’t look up, and Jacob says “wait, one more run” while I give a distracted “one minute” wave. I think it was real enough for him. I kind of like the shot better in greyscale. Credit to Noah Berger for the shot. Notes on Distributed Systems for Young Bloods January 14, 2013 08:15:00 AM PST I’ve been thinking about the lessons distributed systems engineers learn on the job. A great deal of our instruction is through scars made by mistakes made in production traffic. These scars are useful reminders, sure, but it’d be better to have more engineers with the full count of their fingers. New systems engineers will find the Fallacies of Distributed Computing and the CAP theorem as part of their self-education. But these are abstract pieces without the direct, actionable advice the inexperienced engineer needs to start moving1. It’s surprising how little context new engineers are given when they start out. Below is a list of some lessons I’ve learned as a distributed systems engineer that are worth being told to a new engineer. Some are subtle, and some are surprising, but none are controversial. This list is for the new distributed systems engineer to guide their thinking about the field they are taking on. It’s not comprehensive, but it’s a good beginning. The worst characteristic of this list is that it focuses on technical problems with little discussion of social problems an engineer may run into. Since distributed systems require more machines and more capital, their engineers tend to work with more teams and larger organizations. The social stuff is usually the hardest part of any software developer’s job, and, perhaps, especially so with distributed systems development. Our background, education, and experience bias us towards a technical solution even when a social solution would be more efficient, and more pleasing. Let’s try to correct for that. People are less finicky than computers, even if their interface is a little less standardized. Alright, here we go. # Distributed systems are different because they fail often. When asked what separates distributed systems from other fields of software engineering, the new engineer often cites latency, believing that’s what makes distributed computation hard. But they’re wrong. What sets distributed systems engineering apart is the probability of failure and, worse, the probability of partial failure. If a well-formed mutex unlock fails with an error, we can assume the process is unstable and crash it. But the failure of a distributed mutex’s unlock must be built into the lock protocol. Systems engineers that haven’t worked in distributed computation will come up with ideas like “well, it’ll just send the write to both machines” or “it’ll just keep retryi... Similar Website

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