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:
