Flink单机版安装与wordCount

Flink为大数据处理工具，类似hadoop,spark.但它能够在大规模分布式系统中快速处理，与spark相似也是基于内存运算，并以低延迟性和高容错性主城，其核心特性是实时的处理流数据。从此大数据生态圈又再填一员。。。具体详解，还要等之后再分享，这里就先简要带过~

Flink的机制:

当Flink启动时，会拉起一个jobmanager和一个或多个taskManager,jobmanager作用就好比spark中的driver，taskManager的作用就好比spark中的worker.

flink源码：http://www.apache.org/dyn/closer.lua/flink/flink-0.10.1/flink-0.10.1-src.tgz

下载与hadoop2.6兼容版本:http://apache.dataguru.cn/flink/flink-0.10.1/flink-0.10.1-bin-hadoop26-scala_2.10.tgz

下载完毕后确定确定配置了jdk

java -version

执行 bin/start-local.sh 启动local模式 (conf下默认配置的是localhost 其他参数暂且不必配置)

 bin/start-local.sh
tail log/flink-*-jobmanager-*.log

随后可以导入idea 进行wordcount测试 ，这里用官网的example包，记得导入

package test

import org.apache.flink.api.scala._
import org.apache.flink.examples.java.wordcount.util.WordCountData

/**
 * Created by root on 12/15/15.
 */
object WordCount {
  def main(args: Array[String]) {
    if (!parseParameters(args)) {
      return
    }

    val env = ExecutionEnvironment.getExecutionEnvironment
    val text = getTextDataSet(env)

    val counts = text.flatMap { _.toLowerCase.split("\\W+") filter { _.nonEmpty } }
      .map { (_, 1) }
      .groupBy(0)
      .sum(1)

    if (fileOutput) {
      counts.writeAsCsv(outputPath, "\n", " ")
      env.execute("Scala WordCount Example")
    } else {
      counts.print()
    }

  }

  private def parseParameters(args: Array[String]): Boolean = {
    if (args.length > 0) {
      fileOutput = true
      if (args.length == 2) {
        textPath = args(0)
        outputPath = args(1)
        true
      } else {
        System.err.println("Usage: WordCount <text path> <result path>")
        false
      }
    } else {
      System.out.println("Executing WordCount example with built-in default data.")
      System.out.println("  Provide parameters to read input data from a file.")
      System.out.println("  Usage: WordCount <text path> <result path>")
      true
    }
  }

  private def getTextDataSet(env: ExecutionEnvironment): DataSet[String] = {
    if (fileOutput) {
      env.readTextFile(textPath)
    }
    else {
      env.fromCollection(WordCountData.WORDS)
    }

运行一下子: