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//https://bitbucket.org/snippets/centaur/6K4kR

case class Point(x: Int, y: Int)
def isSquare(p1: Point, p2: Point, p3: Point, p4: Point): Boolean = {
  val l = List(p1, p2, p3, p4).combinations(2).map{
    case List(a, b) =>
      val delta_x = a.x - b.x
      val delta_y = a.y - b.y
      delta_x * delta_x + delta_y * delta_y
  }.toList.sorted

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trait FooComponent {
  def fooService: FooService

  trait FooService {
    def query: String
  }
}

trait ConcreteFooComponent extends FooComponent {
  val fooService = ???

binshuohu

import rx.lang.scala._
import rx.lang.scala.subjects.ReplaySubject

import scala.concurrent.Promise
import scala.concurrent.ExecutionContext.Implicits.global

val r = ReplaySubject[Int]()

r.onNext(1)
r.onNext(2)

binshuohu

package com.vpon.slickit

import java.util.concurrent.Executors
import scala.concurrent.duration.Duration
import scala.concurrent.{Await, ExecutionContext, Future}


sealed trait Task {
  def execute(parameter: String): String
}

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import spray.http._

object Foo {
  def collectQueryParameters(query: Uri.Query, headers: List[HttpHeader]) = {
    val contentType = headers.filter(_.name == "Content-Type")
    val referrer = headers.filter(_.name == "Referrer")
    (contentType.isEmpty, referrer.isEmpty) match {
      case (false, false) => {
        val cookie = headers.filter(_.name == "Cookie")
        val contentTypeHead = contentType.head

binshuohu

1. 输入url.
2. 浏览器尝试解析域名得到响应的ip. 浏览器会依次在浏览器, OS, 路由器上查询DNS缓存, 如果找到就可以停止继续查询. 如果没有找到对应域名的DNS缓存, 会发起一个对DNS查询的递归搜索.
3. 获得对应服务器的IP后, 浏览器尝试建立一个TCP连接.
4. 连接建立好之后, 浏览器发起一个HTTP GET请求, header中的Content-Type为'text/html'.
5. web server接到这个HTTP请求后, 会尝试将请求转换为响应, 这中间可能包含了其他业务逻辑. 5.1 如果web server配置了反向代理, web server会根据uri将此请求路由到响应的服务器, 等待其响应后再将对应的HTTP Response转发返回给浏览器.
6. 浏览器拿到HTTP响应后, 检查其Status Code, Content-type, encoding等包含在header中的信息, 然后尝试从body中解析html.
7. 浏览器解析完毕html的基本结构后, 根据情况会发送额外的HTTP请求去获取其它还需要的资源, 例如css, js以及图片.

从WireShark抓包来看, 访问www.vpon.com.tw时, 在第一条HTTP Response返回了200状态码后, 浏览器又通过额外的HTTP GET去获取了js, css以及图片等静态资源. 此外还发送了两条POST请求.

binshuohu

package sample.stream

import akka.actor.ActorSystem
import akka.stream.{ OverflowStrategy, ActorMaterializer }
import akka.stream.scaladsl._
import akka.actor.Status

import scala.util.{ Failure, Success }

object BasicTransformation {

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#include <memory>
#include <vector>
#include <functional>
#include <iostream>

template <class T>
class SimpleObjectPool
{
public:
    using DeleterType = std::function<void(T*)>;

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object Crap extends App {
  sealed abstract class <=<[From, +To] extends (From => To) {
  }

  implicit def conforms[A]: <=<[A, A] = new <=<[A, A] {
    def apply(from: A): A = ???
  }

  class A[T] {
    def foo[U](u: U)(implicit ev: <=<[U, T]) = ()

binshuohu

下面是如何使用 Akka Streams 过滤日志并汇总上传总量的代码示例