How to handle CSV file column on spark rdd generation?

Question

Here is my Java spark code. This is the Spark CSV data format.

"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

And I generate the Person class which contains the above data

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.

And the below codes try to generate Java RDD with spark java client.

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]) code line prints:

43
23
33
"age"

And then It throws the following exception,

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)

I have no idea which line has the error codes and why. System.out.println(info[4]) prints the "age" String value.

pasha701 · Accepted Answer · 2017-11-04 18:41:22Z

1

You read file as usual text file, not CSV:

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

File first line with headers (with "age" value) also processed by Integer.parseInt(info[4]) and this the reason for error.

Spark has specific methods for parse CSV, you can use them:

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

Latest Spark versions has CSV parsing from the box, please check the documentation.

answered Nov 4, 2017 at 18:41

pasha701

7,2171 gold badge17 silver badges22 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

How to handle CSV file column on spark rdd generation?

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related