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I worked at Stripe for about seven years, from 2012 to 2019. Over that time, I used and contributed to many generations of Stripe’s developer environment – the tools that engineers used daily to write and test code. I think Stripe did a pretty good job designing and building that developer experience, and since leaving, I’ve found myself repeatedly describing features of that environment to friend

At this point in my career, I’ve worked on at least three projects where performance was a defining characteristic: Livegrep, Taktician, and Sorbet (I discussed sorbet in particular last time, and livegrep in an earlier post). I’ve also done a lot of other performance work on the tools I use, some of which ended up on my other blog, Accidentally Quadratic. In this post, I want to reflect on some o

This is the second in an indefinite series of posts about things that I think went well in the Sorbet project. The previous one covered our testing approach. Sorbet is fast. Numerous of our early users commented specifically on how fast it was, and how much they appreciated this speed. Our informal benchmarks on Stripe’s codebase clocked it as typechecking around 100,000 lines of code per second p

If you’re a regular user of the terminal on a UNIX system, there are probably a large number of behaviors you take mostly for granted without really thinking about them. If you press ^C or ^Z it kills or stops the foreground program – unless it’s something like emacs or vim, in which case it gets handled like a normal keystroke. When you ssh to a remote host, though, they go to the processes on th

The longer I spend as a software engineer, the more obsessive I get about testing. I fully subscribe to the definition of legacy code as “code without an automated test suite.” I’m convinced that the best thing you can do to encourage fast progress in a test suite is to design for testing and have a fast, reliable, comprehensive test suite. But for all that, I’ve never really subscribed to any of

If you program in Python, you’re probably familiar with the pickle serialization library, which provides for efficient binary serialization and loading of Python datatypes. Hopefully, you’re also familiar with the warning printed prominently near the start of pickle’s documentation: Warning: The pickle module is not intended to be secure against erroneous or maliciously constructed data. Never unp

But because the suffix array is sorted, we can find those entries efficiently by binary searching in the suffix array. The indexes then give us the location of the search string in the larger text. Towards Regular Expression Search  🔗︎ We begin with a large corpus of source code we want to do regular expression search over. During indexing, livegrep reads all of the source and flattens them toget

reptyr (announced recently on this blog) takes a process that is currently running in one terminal, and transplants it to a new terminal. reptyr comes from a proud family of similar hacks, and works in the same basic way: We use ptrace(2) to attach to a target process and force it to execute code of our own choosing, in order to open the new terminal, and dup2(2) it over stdout and stderr. The mai

At Stripe, we rely heavily on ruby and EventMachine to power various internal and external services. Over the last several months, we’ve known that one such service suffered from a gradual memory leak, that would cause its memory usage to gradually balloon from a normal ~50MB to multiple gigabytes. It was easy enough to work around the leak by adding monitoring and restarting the process whenever

Over the last week, I’ve written a nifty tool that I call reptyr. reptyr is a utility for taking an existing running program and attaching it to a new terminal. Started a long-running process over ssh, but have to leave and don’t want to interrupt it? Just start a screen, use reptyr to grab it, and then kill the ssh session and head on home. You can grab the source, or read on for some more detail