Many seasoned engineers working in JVM based languages have seen errors like this: [error] (run-main-0) java.lang.OutOfMemoryError: unable to create native thread: [error] java.lang.OutOfMemoryError: unable to create native thread: [error] at java.base/java.lang.Thread.start0(Native Method) [error] at java.base/java.lang.Thread.start(Thread.java:813) ... [error] at java.base/java.lang.Thread.run(Thread.java:844) OutOfMemory…err…out of threads. On my laptop running Linux, this happens after a paltry 11500 threads. If you try the same thing in Go by starting Goroutines that sleep indefinitely, you get a very different result.

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.