火花斯卡拉运行

Question

您好，我是 spark 和 scala 的新手。我在 spark scala 提示符下运行 scala 代码。该程序很好，它显示“定义的模块 MLlib”，但它没有在屏幕上打印任何内容。我做错了什么？有没有其他方法可以在 scala shell 中运行这个程序 spark 并获得输出？

 import org.apache.spark.{SparkConf, SparkContext}
    import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
    import org.apache.spark.mllib.feature.HashingTF
    import org.apache.spark.mllib.regression.LabeledPoint

object MLlib {

  def main(args: Array[String]) {
    val conf = new SparkConf().setAppName(s"Book example: Scala")
    val sc = new SparkContext(conf)

    // Load 2 types of emails from text files: spam and ham (non-spam).
    // Each line has text from one email.
    val spam = sc.textFile("/home/training/Spam.txt")
    val ham = sc.textFile("/home/training/Ham.txt")

    // Create a HashingTF instance to map email text to vectors of 100 features.
    val tf = new HashingTF(numFeatures = 100)
    // Each email is split into words, and each word is mapped to one feature.
    val spamFeatures = spam.map(email => tf.transform(email.split(" ")))
    val hamFeatures = ham.map(email => tf.transform(email.split(" ")))

    // Create LabeledPoint datasets for positive (spam) and negative (ham) examples.
    val positiveExamples = spamFeatures.map(features => LabeledPoint(1, features))
    val negativeExamples = hamFeatures.map(features => LabeledPoint(0, features))
    val trainingData = positiveExamples ++ negativeExamples
    trainingData.cache() // Cache data since Logistic Regression is an iterative algorithm.

    // Create a Logistic Regression learner which uses the LBFGS optimizer.
    val lrLearner = new LogisticRegressionWithSGD()
    // Run the actual learning algorithm on the training data.
    val model = lrLearner.run(trainingData)

    // Test on a positive example (spam) and a negative one (ham).
    // First apply the same HashingTF feature transformation used on the training data.
    val posTestExample = tf.transform("O M G GET cheap stuff by sending money to ...".split(" "))
    val negTestExample = tf.transform("Hi Dad, I started studying Spark the other ...".split(" "))
    // Now use the learned model to predict spam/ham for new emails.
    println(s"Prediction for positive test example: ${model.predict(posTestExample)}")
    println(s"Prediction for negative test example: ${model.predict(negTestExample)}")

    sc.stop()
  }
}

Answer 1

有几点：

您在 Spark shell 中定义了 object，因此不会立即调用 main 类。定义 object 后，您必须显式调用它：

MLlib.main(Array())

事实上，如果您继续在 shell/REPL 上工作，您可以完全取消该对象；你可以直接定义函数。例如：

import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.mllib.classification.LogisticRegressionWithSGD
import org.apache.spark.mllib.feature.HashingTF
import org.apache.spark.mllib.regression.LabeledPoint

def MLlib {
    //the rest of your code
}

但是，您不应该在 shell 中初始化 SparkContext 它。来自documentation：

在 Spark shell 中，一个特殊的解释器感知 SparkContext 是 已经在名为 sc 的变量中为您创建了。制作你自己的 SparkContext 将不起作用

因此，您必须从代码中删除该位，或者将其编译到 jar 中并使用 spark-submit 运行它