To say my first foray into Rust was a frustrating struggle would be an understatement. I picked a terrible first project that left me neck deep in Rust’s trickiest areas right off the bat. I was excited to try again. A few years ago I wrote Sumoshell, a CLI App for log analysis. I’d wanted to improve it for a while, so porting it to Rust seemed like a nice way to kill two birds with one stone.

Python 2 and Python 3 provide arbitrary precision integers. This means that you don’t need to worry about overflowing or underflowing the integer datatypes, ever. 2**10000? No problem. It makes it pretty convenient to write code. Floats, however, are not arbitrary precision: >>> a = 2 ** 10000 >>> a * .1 Traceback (most recent call last): File "<stdin>", line 1, in <module> OverflowError: int too large to convert to float Luckily, float overflow throws an exception to alert you to the problem.

Finding the median in a list seems like a trivial problem, but doing so in linear time turns out to be tricky. In this post I’m going to walk through one of my favorite algorithms, the median-of-medians approach to find the median of a list in deterministic linear time. Although proving that this algorithm runs in linear time is a bit tricky, this post is targeted at readers with only a basic level of algorithmic analysis.

In the last 2 months, at two separate clients, I ran into the same type of deadlock, so I figured it was worth writing about to save someone else from the same fate. In short: When you have a pool of resources, the same thread must not acquire a second resource while already holding a resource. Otherwise, multiple threads can deadlock when they have all acquired their first resource but none can acquire their second resource.

I always wondered how Google Authenticator style 2-factor codes worked. The process of going from QR code to rotating 6-digit pin seemed a bit magical. A few days ago, my curiosity found itself coupled with some free time. Here’s what I found: What’s in the QR Code I scanned the QR code from Github with a barcode scanning app. Here’s what’s inside: otpauth://totp/Github:rcoh?secret=onswg4tforrw6zdf&issuer=Github Not too surprising. It tells us the protocol, TOTP, who is issuing this OTP code (Github), and most importantly the secret:1

Few data-structures are more ubiquitous in real-world development than the hash table. Nearly every major programming features an implementation in its standard library or built into the runtime. Yet, there is no conclusive best strategy to implement one and the major programming languages diverge widely in their implementations! I did a survey of the Hash map implementations in Go, Python, Ruby, Java, C#, C++, and Scala to compare and contrast how they were implemented.

As I was learning to program, Python lists seemed totally magical to me. I imagined them as being implemented by some sort of magical datastructure that was part linked-list, part array that was perfect for everything. As I grew as an engineer, it occurred that this was unlikely. I guessed (correctly) that rather than some sort of magical implementation, it was just backed by a resizable array. I decided to read the code and find out.