采用jar提交集群模式流程為：

本地完成代碼開發(fā) –> 本地編譯打包 -> 提交集群執(zhí)行

創(chuàng)建三層包

需要先創(chuàng)建三層package（eg:cn.nokia.bigdata）吊说，然后在package下創(chuàng)建object论咏，如下圖

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稍微修改了下官方例子

package cn.nokia.bigdata

import org.apache.spark.{SparkConf, SparkContext}
// $example on$
import org.apache.spark.mllib.classification.{LogisticRegressionModel, LogisticRegressionWithLBFGS}
import org.apache.spark.mllib.evaluation.MulticlassMetrics
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.util.MLUtils
// $example off$

object Test {
  
  def main(args: Array[String]): Unit = {
    // val conf = new SparkConf().setAppName("LogisticRegressionWithLBFGSExample")

    val conf = new SparkConf().setAppName("LogisticRegressionWithLBFGSExample").setMaster("local[*]")
    val sc = new SparkContext(conf)

    // $example on$
    // Load training data in LIBSVM format.
    //val data = MLUtils.loadLibSVMFile(sc, "file:///usr/local/spark-2.1.0/data/mllib/sample_libsvm_data.txt")

    val data = MLUtils.loadLibSVMFile(sc, "D:\\spark\\data\\mllib\\sample_libsvm_data.txt")
    // Split data into training (60%) and test (40%).
    val splits = data.randomSplit(Array(0.6, 0.4), seed = 11L)
    val training = splits(0).cache()
    val test = splits(1)

    // Run training algorithm to build the model
    val model = new LogisticRegressionWithLBFGS()
      .setNumClasses(10)
      .run(training)

    // Compute raw scores on the test set.
    val predictionAndLabels = test.map { case LabeledPoint(label, features) =>
      val prediction = model.predict(features)
      (prediction, label)
    }

    // Get evaluation metrics.
    val metrics = new MulticlassMetrics(predictionAndLabels)
    val accuracy = metrics.accuracy
    println(s"Accuracy = $accuracy")

    // Save and load model
    model.save(sc, "target/tmp/scalaLogisticRegressionWithLBFGSModl")
    val sameModel = LogisticRegressionModel.load(sc,
      "target/tmp/scalaLogisticRegressionWithLBFGSModel")
    // $example off$

    sc.stop()
  }
}
// scalastyle:on println

當(dāng)前項(xiàng)目結(jié)構(gòu)

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打開項(xiàng)目結(jié)構(gòu)

File -> Project Structure：

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快捷按鈕

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artifact => + => jar

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選擇主類：

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輸出設(shè)置

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編譯

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• build（首次打包）
• rebuild（重新打包）
• clean（清理當(dāng)前內(nèi)容）

打包完后，可以在如下目錄中找到對應(yīng)jar包：

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本地提交

D:\spark\bin>spark-submit --class cn.nokia.bigdata.Test spark.jar local

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