Akka是一种消息驱动运算模式,它实现跨JVM程序运算的方式是通过能跨JVM的消息系统来调动分布在不同JVM上ActorSystem中的Actor进行运算,前题是Akka的地址系统可以支持跨JVM定位。Akka的消息系统最高境界可以实现所谓的Actor位置透明化,这样在Akka编程中就无须关注Actor具体在哪个JVM上运行,分布式Actor编程从方式上跟普通Actor编程就不会有什么区别了。Akka的Remoting是一种点对点的跨JVM消息通道,让一个JVM上ActorSystem中的某个Actor可以连接另一个JVM上ActorSystem中的另一个Actor。两个JVM上的ActorSystem之间只需具备TCP网络连接功能就可以实现Akka Remoting了。Akka-Remoting还没有实现完全的位置透明化,因为用户还必须在代码里或者配置文件里指明目标Actor的具体地址。

Akka-Remoting提供了两种Actor之间的沟通方法:

1、远程查找:通过路径Path查找在远程机上已经创建存在的Actor,获取ActorRef后进行沟通

2、远程创建:在远程机上直接创建Actor作为沟通对象

Akka-Remoting的主要应用应该是把一些任务部署到远程机上去运算。发起方(Local JVM)在这里面的主要作用是任务分配,有点像Akka-Router。我们可以用下面的例子来示范:模拟一个计算器,可以进行连续的加减乘除,保留累计结果。我们会把这个计算器部署到远程机上,然后从本机与之沟通分配运算任务及获取运算结果。这个计算器就是个简单的Actor:

import akka.actor._

object Calculator {
sealed trait MathOps
case class Num(dnum: Double) extends MathOps
case class Add(dnum: Double) extends MathOps
case class Sub(dnum: Double) extends MathOps
case class Mul(dnum: Double) extends MathOps
case class Div(dnum: Double) extends MathOps sealed trait CalcOps
case object Clear extends CalcOps
case object GetResult extends CalcOps } class Calcultor extends Actor {
import Calculator._
var result: Double = 0.0 //internal state override def receive: Receive = {
case Num(d) => result = d
case Add(d) => result += d
case Sub(d) => result -= d
case Mul(d) => result *= d
case Div(d) => result = result / d case Clear => result = 0.0
case GetResult =>
sender() ! s"Result of calculation is: $result"
} }

就是一个简单的Actor实现,跟Remoting没什么关系。

下面我们会在一个远程机上部署这个Calculator Actor。 先看看这个示范的项目结构:remoteLookup/build.sbt

lazy val commonSettings = seq (
name := "RemoteLookupDemo",
version := "1.0",
scalaVersion := "2.11.8",
libraryDependencies := Seq(
"com.typesafe.akka" %% "akka-actor" % "2.5.2",
"com.typesafe.akka" %% "akka-remote" % "2.5.2"
)
) lazy val local = (project in file("."))
.settings(commonSettings)
.settings(
name := "localSystem"
).aggregate(messages,remote).dependsOn(messages) lazy val messages = (project in file("messages"))
.settings(commonSettings)
.settings(
name := "commands"
) lazy val remote = (project in file("remote"))
.settings(commonSettings)
.settings(
name := "remoteSystem"
).aggregate(messages).dependsOn(messages)

在这里我们分了三个项目:local是主项目,messages和remote是分项目(subprojects)。messages里只有OpsMessages.scala一个源文件:

package remoteLookup.messages

object Messages {
sealed trait MathOps
case class Num(dnum: Double) extends MathOps
case class Add(dnum: Double) extends MathOps
case class Sub(dnum: Double) extends MathOps
case class Mul(dnum: Double) extends MathOps
case class Div(dnum: Double) extends MathOps sealed trait CalcOps
case object Clear extends CalcOps
case object GetResult extends CalcOps }

我们看到:这个文件是把上面的Calculator支持的消息拆了出来。这是因为Calculator Actor会在另一个JVM remote上部署,而我们会从local JVM里向Calculator发送操作消息,所以Messages必须是local和remote共享的。这个要求我们通过dependOn(messages)实现了。现在Calculator是在remote项目里定义的:remote/Calculator.scala

package remoteLookup.remote

import akka.actor._
import remoteLookup.messages.Messages._ object CalcProps {
def props = Props(new Calcultor)
} class Calcultor extends Actor with ActorLogging { var result: Double = 0.0 //internal state override def receive: Receive = {
case Num(d) => result = d
case Add(d) => result += d
case Sub(d) => result -= d
case Mul(d) => result *= d
case Div(d) =>
val _ = result.toInt / d.toInt //yield ArithmeticException
result /= d
case Clear => result = 0.0
case GetResult =>
sender() ! s"Result of calculation is: $result"
} override def preRestart(reason: Throwable, message: Option[Any]): Unit = {
log.info(s"Restarting calculator: ${reason.getMessage}")
super.preRestart(reason, message)
}
}

由于ArithmeticException默认的处理策略SupervisorStrategy是Restart,一旦输入Div(0.0)时会重启将result清零。我们可以在remote上加一个Supervisor来把异常处理策略改为Resume。

下面我们先在remote项目本地对Calculator的功能进行测试:remote/CalculatorRunner.scala

package remoteLookup.remote
import akka.actor._
import akka.pattern._
import remoteLookup.messages.Messages._ import scala.concurrent.duration._ class SupervisorActor extends Actor {
def decider: PartialFunction[Throwable,SupervisorStrategy.Directive] = {
case _: ArithmeticException => SupervisorStrategy.Resume
} override def supervisorStrategy: SupervisorStrategy =
OneForOneStrategy(maxNrOfRetries = , withinTimeRange = seconds){
decider.orElse(SupervisorStrategy.defaultDecider)
} val calcActor = context.actorOf(CalcProps.props,"calculator") override def receive: Receive = {
case msg@ _ => calcActor.forward(msg)
} } object CalculatorRunner extends App { val remoteSystem = ActorSystem("remoteSystem")
val calcActor = remoteSystem.actorOf(Props[SupervisorActor],"supervisorActor") import remoteSystem.dispatcher calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5) implicit val timeout = akka.util.Timeout( second) ((calcActor ? GetResult).mapTo[String]) foreach println
scala.io.StdIn.readLine() calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println
scala.io.StdIn.readLine() remoteSystem.terminate() }

测试运算得出以下结果:

Result of calculation is: 19.5

Result of calculation is: 113.0
[WARN] [// ::10.720] [remoteSystem-akka.actor.default-dispatcher-] [akka://remoteSystem/user/parentActor/calculator] / by zero

supervisorActor实现了它应有的功能。

下面进行远程查找示范:首先,remote需要把Calculator向外发布。这可以通过配置文件设置实现:remote/src/main/resources/application.conf

akka {
actor {
provider = remote
}
remote {
enabled-transports = ["akka.remote.netty.tcp"]
netty.tcp {
hostname = "127.0.0.1"
port =
}
log-sent-messages = on
log-received-messages = on
}
}

上面这段的意思是:所有向外公开Actor的地址前缀为:akka.tcp://remoteSystem@127.0.0.1:2552/user/???

那么Calculator的完整地址path应该就是:akka.tcp://remoteSystem@127.0.0.1:2552/user/supervisorActor/calculator

Akka-Remoting提供了两种远程查找方式:actorSelection.resolveOne方法和Identify消息确认。无论如何,local都需要进行Remoting配置: local/src/main/resources/application.conf

akka {
actor {
provider = remote
}
remote {
enabled-transports = ["akka.remote.netty.tcp"]
netty.tcp {
hostname = "127.0.0.1"
port =
}
}
}

port=0的意思是由系统自动选择任何可用的端口。现在我们完成了Remoting设置,也得到了在远程机上Calculator的具体地址,应该足够进行远程Actor沟通了。我们先用actorSelection.resolveOne示范。resolveOne源代码如下:

  /**
* Resolve the [[ActorRef]] matching this selection.
* The result is returned as a Future that is completed with the [[ActorRef]]
* if such an actor exists. It is completed with failure [[ActorNotFound]] if
* no such actor exists or the identification didn't complete within the
* supplied `timeout`.
*
* Under the hood it talks to the actor to verify its existence and acquire its
* [[ActorRef]].
*/
def resolveOne()(implicit timeout: Timeout): Future[ActorRef] = {
implicit val ec = ExecutionContexts.sameThreadExecutionContext
val p = Promise[ActorRef]()
this.ask(Identify(None)) onComplete {
case Success(ActorIdentity(_, Some(ref))) ⇒ p.success(ref)
case _ ⇒ p.failure(ActorNotFound(this))
}
p.future
}

resolveOne返回Future[ActorRef],我们可以用Future的函数组件(combinator)来操作:localAccessDemo.scala

import akka.actor._
import akka.util.Timeout
import scala.concurrent.duration._
import akka.pattern._
import remoteLookup.messages.Messages._ object LocalSelectionDemo extends App { val localSystem = ActorSystem("localSystem")
import localSystem.dispatcher val path = "akka.tcp://remoteSystem@127.0.0.1:2552/user/supervisorActor/calculator" implicit val timeout = Timeout( seconds)
for (calcActor : ActorRef <- localSystem.actorSelection(path).resolveOne()) { calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5)
((calcActor ? GetResult).mapTo[String]) foreach println calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println } scala.io.StdIn.readLine()
localSystem.terminate() }

因为resolveOne返回的是个Future[x],我们可以用for来对嵌在Future内的x进行操作。现在remoteSystem只需要构建Calculator待用就行了:remote/CalculatorRunner.scala

package remoteLookup.remote
import akka.actor._
import akka.pattern._
import remoteLookup.messages.Messages._ import scala.concurrent.duration._ class SupervisorActor extends Actor {
def decider: PartialFunction[Throwable,SupervisorStrategy.Directive] = {
case _: ArithmeticException => SupervisorStrategy.Resume
} override def supervisorStrategy: SupervisorStrategy =
OneForOneStrategy(maxNrOfRetries = , withinTimeRange = seconds){
decider.orElse(SupervisorStrategy.defaultDecider)
} val calcActor = context.actorOf(CalcProps.props,"calculator") override def receive: Receive = {
case msg@ _ => calcActor.forward(msg)
} } object CalculatorRunner extends App { val remoteSystem = ActorSystem("remoteSystem")
val calcActor = remoteSystem.actorOf(Props[SupervisorActor],"supervisorActor")
/*
import remoteSystem.dispatcher calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5) implicit val timeout = akka.util.Timeout(1 second) ((calcActor ? GetResult).mapTo[String]) foreach println
scala.io.StdIn.readLine() calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println
*/
scala.io.StdIn.readLine()
remoteSystem.terminate() }

注意:注销的操作转移到了localSelectionDemo里。

先运行remote项目:

INFO] [// ::37.955] [main] [akka.remote.Remoting] Starting remoting
[INFO] [// ::38.091] [main] [akka.remote.Remoting] Remoting started; listening on addresses :[akka.tcp://remoteSystem@127.0.0.1:2552]
[INFO] [// ::38.092] [main] [akka.remote.Remoting] Remoting now listens on addresses: [akka.tcp://remoteSystem@127.0.0.1:2552]

remoteSystem开始监视配置的公开地址。

用sbt run 运行local:

Result of calculation is: 19.5
Result of calculation is: 113.0

结果正确。supervisorActor的SupervisorStrategy起到了应有的作用。

remote项目输出显示也能证明:

[INFO] [// ::37.955] [main] [akka.remote.Remoting] Starting remoting
[INFO] [// ::38.091] [main] [akka.remote.Remoting] Remoting started; listening on addresses :[akka.tcp://remoteSystem@127.0.0.1:2552]
[INFO] [// ::38.092] [main] [akka.remote.Remoting] Remoting now listens on addresses: [akka.tcp://remoteSystem@127.0.0.1:2552]
[WARN] [// ::06.330] [remoteSystem-akka.actor.default-dispatcher-] [akka://remoteSystem/user/supervisorActor/calculator] / by zero
[ERROR] [// ::34.176] [remoteSystem-akka.remote.default-remote-dispatcher-] [akka.tcp://remoteSystem@127.0.0.1:2552/system/endpointManager/reliableEndpointWriter-akka.tcp%3A%2F%2FlocalSystem%40127.0.0.1%3A60601-0/endpointWriter] AssociationError [akka.tcp://remoteSystem@127.0.0.1:2552] <- [akka.tcp://localSystem@127.0.0.1:60601]: Error [Shut down address: akka.tcp://localSystem@127.0.0.1:60601] [
akka.remote.ShutDownAssociation: Shut down address: akka.tcp://localSystem@127.0.0.1:60601
Caused by: akka.remote.transport.Transport$InvalidAssociationException: The remote system terminated the association because it is shutting down.

下面我们试着用Identify消息确认方式来复演上述例子。Akka是如下这样定义有关Identify消息确认的:

/**
* A message all Actors will understand, that when processed will reply with
* [[akka.actor.ActorIdentity]] containing the `ActorRef`. The `messageId`
* is returned in the `ActorIdentity` message as `correlationId`.
*/
@SerialVersionUID(1L)
final case class Identify(messageId: Any) extends AutoReceivedMessage with NotInfluenceReceiveTimeout /**
* Reply to [[akka.actor.Identify]]. Contains
* `Some(ref)` with the `ActorRef` of the actor replying to the request or
* `None` if no actor matched the request.
* The `correlationId` is taken from the `messageId` in
* the `Identify` message.
*/
@SerialVersionUID(1L)
final case class ActorIdentity(correlationId: Any, ref: Option[ActorRef]) {
if (ref.isDefined && ref.get == null) {
throw new IllegalArgumentException("ActorIdentity created with ref = Some(null) is not allowed, " +
"this could happen when serializing with Scala 2.12 and deserializing with Scala 2.11 which is not supported.")
} /**
* Java API: `ActorRef` of the actor replying to the request or
* null if no actor matched the request.
*/
@deprecated("Use getActorRef instead", "2.5.0")
def getRef: ActorRef = ref.orNull /**
* Java API: `ActorRef` of the actor replying to the request or
* not defined if no actor matched the request.
*/
def getActorRef: Optional[ActorRef] = {
import scala.compat.java8.OptionConverters._
ref.asJava
}
}

如果拿上面的例子,我们就会向远程机上的Calculator地址发送Identify(path),而Calculator返回ActorIdentity消息,参数包括correlationId = path, ref = Calculator的ActorRef。 下面是使用示范代码:

object LocalIdentifyDemo extends App {

  class RemoteCalc extends Actor with ActorLogging {

    val path = "akka.tcp://remoteSystem@127.0.0.1:2552/user/supervisorActor/calculator"

    context.actorSelection(path) ! Identify(path)  //send req for ActorRef

    import context.dispatcher
implicit val timeout = Timeout( seconds) override def receive: Receive = {
case ActorIdentity(p,someRef) if p.equals(path) =>
someRef foreach { calcActor => calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5)
((calcActor ? GetResult).mapTo[String]) foreach println calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println }
} } val localSystem = ActorSystem("localSystem")
val localActor = localSystem.actorOf(Props[RemoteCalc],"localActor") scala.io.StdIn.readLine()
localSystem.terminate() }

Identify消息确认机制是一种Actor沟通模式,所以我们需要构建一个RemoteCalc Actor,把程序包嵌在这个Actor里面。当receive收到确认消息ActorIdentity后获取ActorRef运算程序。

查看运算结果,正确。

下面是这次示范的完整源代码:

build.sbt

lazy val commonSettings = seq (
name := "RemoteLookupDemo",
version := "1.0",
scalaVersion := "2.11.8",
libraryDependencies := Seq(
"com.typesafe.akka" %% "akka-actor" % "2.5.2",
"com.typesafe.akka" %% "akka-remote" % "2.5.2"
)
) lazy val local = (project in file("."))
.settings(commonSettings)
.settings(
name := "remoteLookupDemo"
).aggregate(messages,remote).dependsOn(messages) lazy val messages = (project in file("messages"))
.settings(commonSettings)
.settings(
name := "commands"
) lazy val remote = (project in file("remote"))
.settings(commonSettings)
.settings(
name := "remoteSystem"
).aggregate(messages).dependsOn(messages)

messages/OpsMessages.scala

package remoteLookup.messages

object Messages {
sealed trait MathOps
case class Num(dnum: Double) extends MathOps
case class Add(dnum: Double) extends MathOps
case class Sub(dnum: Double) extends MathOps
case class Mul(dnum: Double) extends MathOps
case class Div(dnum: Double) extends MathOps sealed trait CalcOps
case object Clear extends CalcOps
case object GetResult extends CalcOps }

remote/src/main/resources/application.conf

akka {
actor {
provider = remote
}
remote {
enabled-transports = ["akka.remote.netty.tcp"]
netty.tcp {
hostname = "127.0.0.1"
port =
}
log-sent-messages = on
log-received-messages = on
}
}

remote/Calculator.scala

package remoteLookup.remote

import akka.actor._
import remoteLookup.messages.Messages._ object CalcProps {
def props = Props(new Calcultor)
} class Calcultor extends Actor with ActorLogging { var result: Double = 0.0 //internal state override def receive: Receive = {
case Num(d) => result = d
case Add(d) => result += d
case Sub(d) => result -= d
case Mul(d) => result *= d
case Div(d) =>
val _ = result.toInt / d.toInt //yield ArithmeticException
result /= d
case Clear => result = 0.0
case GetResult =>
sender() ! s"Result of calculation is: $result"
} override def preRestart(reason: Throwable, message: Option[Any]): Unit = {
log.info(s"Restarting calculator: ${reason.getMessage}")
super.preRestart(reason, message)
}
}

remote/CalculatorRunner.scala

package remoteLookup.remote
import akka.actor._
import akka.pattern._
import remoteLookup.messages.Messages._ import scala.concurrent.duration._ class SupervisorActor extends Actor {
def decider: PartialFunction[Throwable,SupervisorStrategy.Directive] = {
case _: ArithmeticException => SupervisorStrategy.Resume
} override def supervisorStrategy: SupervisorStrategy =
OneForOneStrategy(maxNrOfRetries = , withinTimeRange = seconds){
decider.orElse(SupervisorStrategy.defaultDecider)
} val calcActor = context.actorOf(CalcProps.props,"calculator") override def receive: Receive = {
case msg@ _ => calcActor.forward(msg)
} } object CalculatorRunner extends App { val remoteSystem = ActorSystem("remoteSystem")
val calcActor = remoteSystem.actorOf(Props[SupervisorActor],"supervisorActor")
/*
import remoteSystem.dispatcher calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5) implicit val timeout = akka.util.Timeout(1 second) ((calcActor ? GetResult).mapTo[String]) foreach println
scala.io.StdIn.readLine() calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println
*/
scala.io.StdIn.readLine()
remoteSystem.terminate() }

local/src/main/resources/application.conf

akka {
actor {
provider = remote
}
remote {
enabled-transports = ["akka.remote.netty.tcp"]
netty.tcp {
hostname = "127.0.0.1"
port =
}
}
}

local/localAccessDemo.scala

import akka.actor._
import akka.util.Timeout
import scala.concurrent.duration._
import akka.pattern._
import remoteLookup.messages.Messages._ object LocalSelectionDemo extends App { val localSystem = ActorSystem("localSystem")
import localSystem.dispatcher val path = "akka.tcp://remoteSystem@127.0.0.1:2552/user/supervisorActor/calculator" implicit val timeout = Timeout( seconds)
for (calcActor : ActorRef <- localSystem.actorSelection(path).resolveOne()) { calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5)
((calcActor ? GetResult).mapTo[String]) foreach println calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println } scala.io.StdIn.readLine()
localSystem.terminate() } object LocalIdentifyDemo extends App { class RemoteCalc extends Actor with ActorLogging { val path = "akka.tcp://remoteSystem@127.0.0.1:2552/user/supervisorActor/calculator" context.actorSelection(path) ! Identify(path) //semd req for ActorRef import context.dispatcher
implicit val timeout = Timeout( seconds) override def receive: Receive = {
case ActorIdentity(p,someRef) if p.equals(path) =>
someRef foreach { calcActor => calcActor ! Clear
calcActor ! Num(13.0)
calcActor ! Mul(1.5)
((calcActor ? GetResult).mapTo[String]) foreach println calcActor ! Div(0.0)
calcActor ! Div(1.5)
calcActor ! Add(100.0)
((calcActor ? GetResult).mapTo[String]) foreach println }
} } val localSystem = ActorSystem("localSystem")
val localActor = localSystem.actorOf(Props[RemoteCalc],"localActor") scala.io.StdIn.readLine()
localSystem.terminate() }

Akka(8): 分布式运算:Remoting-远程查找式的更多相关文章

  1. Akka(9): 分布式运算:Remoting-远程构建式

    上篇我们讨论了Akka-Remoting.我们说Akka-Remoting是一种点对点的通讯方式,能使两个不同JVM上Akka-ActorSystem上的两个Actor之间可以相互沟通.Akka-Re ...

  2. Akka(10): 分布式运算:集群-Cluster

    Akka-Cluster可以在一部物理机或一组网络连接的服务器上搭建部署.用Akka开发同一版本的分布式程序可以在任何硬件环境中运行,这样我们就可以确定以Akka分布式程序作为标准的编程方式了. 在上 ...

  3. Akka(13): 分布式运算:Cluster-Sharding-运算的集群分片

    通过上篇关于Cluster-Singleton的介绍,我们了解了Akka为分布式程序提供的编程支持:基于消息驱动的运算模式特别适合分布式程序编程,我们不需要特别的努力,只需要按照普通的Actor编程方 ...

  4. alpakka-kafka(9)-kafka在分布式运算中的应用

    kafka具备的分布式.高吞吐.高可用特性,以及所提供的各种消息消费模式可以保证在一个多节点集群环境里消息被消费的安全性:即防止每条消息遗漏处理或重复消费.特别是exactly-once消费策略:可以 ...

  5. 大数据学习day18----第三阶段spark01--------0.前言(分布式运算框架的核心思想,MR与Spark的比较,spark可以怎么运行,spark提交到spark集群的方式)1. spark(standalone模式)的安装 2. Spark各个角色的功能 3.SparkShell的使用,spark编程入门(wordcount案例)

    0.前言 0.1  分布式运算框架的核心思想(此处以MR运行在yarn上为例)  提交job时,resourcemanager(图中写成了master)会根据数据的量以及工作的复杂度,解析工作量,从而 ...

  6. Akka(11): 分布式运算:集群-均衡负载

    在上篇讨论里我们主要介绍了Akka-Cluster的基本原理.同时我们也确认了几个使用Akka-Cluster的重点:首先,Akka-Cluster集群构建与Actor编程没有直接的关联.集群构建是A ...

  7. Akka(12): 分布式运算:Cluster-Singleton-让运算在集群节点中自动转移

    在很多应用场景中都会出现在系统中需要某类Actor的唯一实例(only instance).这个实例在集群环境中可能在任何一个节点上,但保证它是唯一的.Akka的Cluster-Singleton提供 ...

  8. 使用AKKA做分布式爬虫的思路

    上周公司其它小组在讨论做分布式爬虫,我也思考了一下.提了一个方案,就是使用akka分布式rpc框架来做,自己写master和worker程序,client向master提交begin任务或者其它爬虫需 ...

  9. storm drpc分布式本地和远程调用模式讲解

    一.drpc 的介绍 1.rpc RPC(Remote Procedure Call)—远程过程调用,它是一种通过网络从远程计算机程序上请求服务,而不需要了解底层网络技术的协议. 2.drpc drp ...

随机推荐

  1. java入门基础

    什么是java? java是一门编程语言  编程语言有很多种 你比如 C语言 等等 为什么学习java呢! 因为你要和计算机交互  当然了你用汉语跟她说她听不懂 所以你要学习编程语言 那么额咱们的ja ...

  2. 走进javascript——类型

    ECMAScript语言类型对应于使用ECMAScript语言的ECMAScript程序员直接操作的值.ECMAScript语言类型有以下几种Undefined,Null,Boolean,String ...

  3. PHP自定义函数

    啊哈

  4. SOM网络聚类完整示例(利用python和java)

    下面是几个城市的GDP等信息,根据这些信息,写一个SOM网络,使之对下面城市进行聚类.并且,将结果画在一个二维平面上. //表1中,X.为人均GDP(元):X2为工业总产值(亿元):X.为社会消费品零 ...

  5. [js笔记整理]正则篇

    一.正则基本概念 1.一种规则.模式 2.强大的字符串匹配工具 3.在js中常与字符串函数配合使用 二.js正则写法 正则在js中以正则对象存在: (1)var re=new RegExp(正则表达式 ...

  6. Java之路 ——初识Eclipse

    零.大纲 一.前言 二.获取Eclipse 三.运行Eclipse 四.创建及运行第一个Java Project 五.界面介绍 六.如何调试 七.获取插件 八.Eclipse 快捷键 九.总结 一.前 ...

  7. Vmware虚拟机中安装cnetOS7详细图解步骤

    1.安装VMware 下载一个软件安装: .新建一个虚拟机 .引用安装包 4.启动新建的虚拟机 .安装CentOS7的步骤 配置系统语言: 配置系统时间: 配置系统键盘: 配置键盘切换的快捷键: 配置 ...

  8. struts-config.xml的配置

    1.<struts-config>  元素 <struts-cofnig> 元素是 Struts 配置文件的根元素.<struts-config> 元素有 8 个子 ...

  9. 深入学习webpack(一)

    深入学习webpack(一) 模块化的相关库和工具已经很多了,包括require.js.sea.js和一些工程化工具webpack.gulp.grant.那么我们该如何选择呢? 其实,我们只需要掌握了 ...

  10. .Net程序猿需掌握的知识

    作为一个.Net的技术人员需要掌握的技术,当然,理解这些知识点并不能让你学会.NET开发. 但能够衡量你是否有着走出校门的能力,也算是给自己留一个知识的储备库吧. 共勉! 基础知识: 数据类型 变量 ...