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L1 cache reference                  0.5 ns
Branch mispredict                   5 ns
L2 cache reference                  7 ns
Mutex lock/unlock                   25 ns
Main memory reference               100 ns
Compress 1K bytes with Zippy        3,000 ns
Send 2K bytes over 1 Gbps network   20,000 ns
Read 1 MB sequentially from memory  250,000 ns
Round trip within same datacenter   500,000 ns
Disk seek                           10,000,000 ns

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// Reduction: We can sum all the elements
// with the usual recursive schema
def sum(xs: List[Int]): Int = xs match {
  case Nil     => 0
  case y :: ys => y + sum(ys)  
}

// But this can be abstracted out using 'reduceLeft'
// That will perform these operations LINKING to the left
def sum(xs: List[Int]) = (0 :: xs) reduceLeft ((x, y) = x + y)

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class A
class A2 extends A
class B

trait M[X]

//
// Upper Type Bound
//
def upperTypeBound[AA <: A](x: AA): A = x

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##Reactive System Design Links

#Articles and Papers

• The Reactive Manifesto: http://www.reactivemanifesto.org/
• https://en.wikipedia.org/wiki/Reactive_programming
• https://en.wikipedia.org/wiki/Functional_reactive_programming
• Design Methods for Reactive Systems book slides: http://booksite.elsevier.com/9781558607552/slides/slides.pdf
• Programming without a callstack Event Driven Architectures By G Hohpe: http://www.eaipatterns.com/docs/EDA.pdf
• On Distributed Memory Systems: http://blog.paralleluniverse.co/2012/07/10/on-distributed-memory/
• Disruptor source code, papers and articles: https://lmax-exchange.github.io/disruptor/

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About Cake Pattern

Below are the sources I took notes and hope to learn about Cake Pattern from.

Videos:

• SF Scala, Peter Potts: Cake Pattern in Practice
• Cake Pattern: The Bakery from the Black Lagoon
• Dead-Simple Dependency Injection

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CvRDTs are as general as they can be

What are you talking about, and why should I care?

Now that we live in the Big Data, Web 3.14159 era, lots of people want to build databases that are too big to fit on a single machine. But there's a problem in the form of the CAP theorem, which states that if your network ever partitions (a machine goes down, or part of the network loses its connection to the rest) then you can keep consistency (all machines return the same answer to

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trait %>[From, To] {
  def apply(x: From): To
}

def view[From, To](f: From => To): From %> To = 
  new %>[From, To] { 
    def apply(x: From): To = f(x) 
  }

implicit i: Int %> String = view(_.toString)

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import scalaz._
import scalaz.std.list._
import scalaz.syntax.monad._
import scalaz.syntax.monoid._
import scalaz.syntax.traverse.{ToFunctorOps => _, _}

class Foo[F[+_] : Monad, A, B](val execute: Foo.Request[A] => F[B], val joins: Foo.Request[A] => B => List[Foo.Request[A]])(implicit J: Foo.Join[A, B]) {

def bar: Foo[({type l[+a]=WriterT[F, Log[A, B], a]})#l, A, B] = {
    type TraceW[FF[+_], +AA] = WriterT[FF, Log[A, B], AA]

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The Indexed State Monad in Haskell, Scala, and C#

Have you ever had to write code that made a complex series of succesive modifications to a single piece of mutable state? (Almost certainly yes.)

Did you ever wish you could make the compiler tell you if a particular operation on the state was illegal at a given point in the modifications? (If you're a fan of static typing, probably yes.)

If that's the case, the indexed state monad can help!

Motivation

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The Indexed Continuation Monad in Haskell, Scala, and C#

The indexed state monad is not the only indexed monad out there; it's not even the only useful one. In this tutorial, we will explore another indexed monad, this time one that encapsulates the full power of delimited continuations: the indexed continuation monad.

Motivation

The relationship between the indexed and regular state monads holds true as well for the indexed and regular continuation monads, but while the indexed state monad allows us to keep a state while changing its type in a type-safe way, the indexed continuation monad allows us to manipulate delimited continuations while the return type of the continuation block changes arbitrarily. This, unlike the regular continuation monad, allows us the full power of delimited continuations in a dynamic language like Scheme while still remaining completely statically typed.