Have you tried using withColumn? You can add a boolean column based on whether the age exists or not and then drop the older age column. You wouldn't need union of dataframes then

On Tue, Dec 18, 2018 at 8:58 AM Devender Yadav <devender.yadav@impetus.co.in> wrote:
Hi All,

useful code:

public class EmployeeBean implements Serializable {

    private Long id;

    private String name;

    private Long salary;

    private Integer age;

    // getters and setters


Relevant spark code:

SparkSession spark = SparkSession.builder().master("local[2]").appName("play-with-spark").getOrCreate();
List<EmployeeBean> employees1 = populateEmployees(1, 10);

Dataset<EmployeeBean> ds1 = spark.createDataset(employees1, Encoders.bean(EmployeeBean.class));

Dataset<Row> ds2 = ds1.where("age is null").withColumn("is_age_null", lit(true));
Dataset<Row> ds3 = ds1.where("age is not null").withColumn("is_age_null", lit(false));

Dataset<Row> ds4 = ds2.union(ds3);

Relevant Output:


| age| id|name|salary|
|null|  1|dev1| 11000|
|   2|  2|dev2| 12000|
|null|  3|dev3| 13000|
|   4|  4|dev4| 14000|
|null|  5|dev5| 15000|


| age| id|name|salary|is_age_null|
|null|  1|dev1| 11000|       true|
|null|  3|dev3| 13000|       true|
|null|  5|dev5| 15000|       true|
|   2|  2|dev2| 12000|      false|
|   4|  4|dev4| 14000|      false|

Is there any better solution to add this column in the dataset rather than creating two datasets and performing union?




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