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Thread Pools

Thread pools on the JVM should usually be divided into the following three categories:

CPU-bound
Blocking IO
Non-blocking IO polling

Each of these categories has a different optimal configuration and usage pattern.

Applied Functional Programming with Scala - Notes

1. Mastering Functions

A function is a mapping from one set, called a domain, to another set, called the codomain. A function associates every element in the domain with exactly one element in the codomain. In Scala, both domain and codomain are types.

val square : Int => Int = x => x * x

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

Clarify and agree on the scope of the system

User cases (description of sequences of events that, taken together, lead to a system doing something useful)
- Who is going to use it?
- How are they going to use it?

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

	Latency Comparison Numbers (~2012)
	----------------------------------
	L1 cache reference 0.5 ns
	Branch mispredict 5 ns
	L2 cache reference 7 ns 14x L1 cache
	Mutex lock/unlock 25 ns
	Main memory reference 100 ns 20x L2 cache, 200x L1 cache
	Compress 1K bytes with Zippy 3,000 ns 3 us
	Send 1K bytes over 1 Gbps network 10,000 ns 10 us
	Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD

	case class ReaderWriterStateT[R, W, S, F[_], A](
	run: (R, S) => F[(W, A, S)]
	) {
	def map[B](f: A => B)(implicit F: Functor[F])
	: ReaderWriterStateT[R, W, S, F, B] =
	ReaderWriterStateT {
	case (r, s) => F.map(run(r, s)) {
	case (w, a, s) => (w, f(a), s)
	}
	}

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