Split struct type column in dataframe into multiple columns

Question

I have created the below data frame from an rdd using reducebyKey. I want to split the first column (originally the key) into 2 new columns which are split by the comma.

scala> result_winr_table.schema
res10: org.apache.spark.sql.types.StructType = StructType(StructField(_1,StructType(StructField(_1,IntegerType,false), StructField(_2,IntegerType,false)),true), StructField(_2,DoubleType,false))

scala> result_winr_table
    res5: org.apache.spark.sql.DataFrame = [_1: struct<_1:int,_2:int>, _2: double]

    scala> result_winr_table.show
    +--------+-------------------+
    |      _1|                 _2|
    +--------+-------------------+
    | [31,88]|              0.475|
    | [18,91]| 0.5833333333333334|
    | [56,95]|0.37142857142857144|
    | [70,61]| 0.6266666666666667|
    |[104,11]| 0.4527911784975879|
    | [42,58]| 0.6857142857142857|
    | [13,82]| 0.3333333333333333|
    | [30,18]|0.49310344827586206|
    | [99,18]|0.44285714285714284|
    | [53,31]| 0.2981366459627329|
    | [52,84]| 0.4444444444444444|
    | [60,38]|               0.38|
    |  [79,9]|0.36666666666666664|
    | [20,85]| 0.4389312977099237|
    | [61,87]| 0.4807692307692308|
    |  [3,67]| 0.4245810055865922|
    | [62,84]|0.47796610169491527|
    |  [9,32]| 0.4727272727272727|
    | [94,44]| 0.5698324022346368|
    | [50,67]|0.45083487940630795|
    +--------+-------------------+

I tried using split method on the column directly but it didnt work because of a type mis-match.

What would be the best way to achieve this?

Anahcolus · Accepted Answer · 2017-08-02 04:41:39Z

5

Given that the schema is

root
 |-- _1: struct (nullable = true)
 |    |-- _1: integer (nullable = false)
 |    |-- _2: integer (nullable = false)
 |-- _2: double (nullable = false)

You can use withColumn api as following

result_winr_table.withColumn("first", $"_1._1")
  .withColumn("second", $"_1._2")

and if you don't want the original column, you can use .drop("_1")

answered Aug 2, 2017 at 4:41

Anahcolus

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Anahcolus Over a year ago

Thanks @Adheip for accepting :) you can upvote too if the answer is helpful

Jeremy · Accepted Answer · 2018-10-17 02:33:06Z

1

If you have a complex struct that you don't know the property names for at compile time you can do this:

case class Foo(a: Int, b: String, c: Boolean)
val df = Seq( (1, Foo(2, "three", false)), (2, Foo(4, "five", true)) ).toDF("id", "foo")

df.show
+---+-----------------+
| id|              foo|
+---+-----------------+
|  1|[2, three, false]|
|  2|  [4, five, true]|
+---+-----------------+

df.select($"*", $"foo.*").show
+---+-----------------+---+-----+-----+
| id|              foo|  a|    b|    c|
+---+-----------------+---+-----+-----+
|  1|[2, three, false]|  2|three|false|
|  2|  [4, five, true]|  4| five| true|
+---+-----------------+---+-----+-----+

answered Oct 17, 2018 at 2:33

Jeremy

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Comments

Abhimanu Kumar · Accepted Answer · 2018-06-27 22:37:45Z

0

Folks looking to solve this using Spark sql, as I was, the following sql query will work to flatten your dataframe/table in spark 1.6+:

sqlContext.sql(s""" select _1["_1"] as col1, _1["_2"] as col2, _2 as col3 from result_winr_table """)

answered Jun 27, 2018 at 22:37

Abhimanu Kumar

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