如何处理 spark rdd 生成的 CSV 文件列？

Question

这是我的 Java 火花代码。这是 Spark CSV 数据格式。

"f_name","l_name","job","gender","age","salary"
"Elsdon","Jaycob","Java programmer","male",43,2000
"Tamsen","Brittany","Java programmer","female",23,1500
"Floyd","Donny","Java programmer","male",33,1800

我生成了包含上述数据的 Person 类

public class Person implements Serializable {

    private String firstName;
    private String lastName;
    private String job;
    private String gender;
    private int salary;
    private int age;

    public Person(String firstName, String lastName, String job, String gender, int age, int salary) {

        this.firstName = firstName;
        this.lastName = lastName;
        this.job = job;
        this.gender = gender;
        this.age = age;
        this.salary = salary;
        }
... getter and setter method.

以下代码尝试使用 spark java 客户端生成 Java RDD。

SparkConf sc = new SparkConf().setAppName("SparkTest").setMaster("local[*]");
JavaSparkContext jsc = new JavaSparkContext(sc);
JavaRDD<String> rdd_text = jsc.textFile("file:///" + srcDir + srcFile);


String[] header = rdd_text.map(line -> line.split(",")).first();
System.out.println(header[4]); // "age" is printed
JavaRDD<Person> persons = rdd_text.filter(line -> line.split(",")[4] != header[4]).map(
   line -> {
      String[] info = line.split(",");

      System.out.println(info[4]); //43,23,33,"age" are printed

      Person p = new Person(info[0], info[1], info[2], info[3], 
                                    Integer.parseInt(info[4]), Integer.parseInt(info[5]));

     return p;
});

System.out.println(persons.collect());

System.out.println(info[4]) 代码行打印：

43
23
33
"age"

然后它会抛出以下异常，

java.lang.NumberFormatException: For input string: ""age""
    at java.lang.NumberFormatException.forInputString(NumberFormatException.java:65)
    at java.lang.Integer.parseInt(Integer.java:569)
    at java.lang.Integer.parseInt(Integer.java:615)
    at com.aaa.spark.JavaClient.lambda$2(JavaClient.java:33)
    at org.apache.spark.api.java.JavaPairRDD$$anonfun$toScalaFunction$1.apply(JavaPairRDD.scala:1040)
    at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
    at scala.collection.Iterator$class.foreach(Iterator.scala:893)
    at scala.collection.AbstractIterator.foreach(Iterator.scala:1336)
    at scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:59)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:104)
    at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:48)
    at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:310)
    at scala.collection.AbstractIterator.to(Iterator.scala:1336)
    at scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:302)
    at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1336)
    at scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:289)
    at scala.collection.AbstractIterator.toArray(Iterator.scala:1336)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:936)
    at org.apache.spark.rdd.RDD$$anonfun$collect$1$$anonfun$13.apply(RDD.scala:936)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
    at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:2062)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
    at org.apache.spark.scheduler.Task.run(Task.scala:108)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
    at java.lang.Thread.run(Thread.java:748)

我不知道哪一行有错误代码以及为什么。 System.out.println(info[4]) 打印“年龄”字符串值。

Answer 1

您像往常一样读取文件，而不是 CSV：

 jsc.textFile("file:///" + srcDir + srcFile);

带有标题的文件第一行（带有“age”值）也由 Integer.parseInt(info[4]) 处理，这就是错误的原因。

Spark 有解析 CSV 的特定方法，你可以使用它们：

https://github.com/databricks/spark-csv

最新的 Spark 版本已从包装盒中解析 CSV，请查看文档。