Explode (transpose?) multiple columns in Spark SQL table

I am using Spark SQL (I mention that it is in Spark in case that affects the SQL syntax - I'm not familiar enough to be sure yet) and I have a table that I am trying to re-structure, but I'm getting stuck trying to transpose multiple columns at the same time.

Basically I have data that looks like:

userId    someString      varA     varB
   1      "example1"    [0,2,5]   [1,2,9]
   2      "example2"    [1,20,5]  [9,null,6]

and I'd like to explode both varA and varB simultaneously (the length will always be consistent) - so that the final output looks like this:

userId    someString      varA     varB
   1      "example1"       0         1
   1      "example1"       2         2
   1      "example1"       5         9
   2      "example2"       1         9
   2      "example2"       20       null
   2      "example2"       5         6

but I can only seem to get a single explode(var) statement to work in one command, and if I try to chain them (ie create a temp table after the first explode command) then I obviously get a huge number of duplicate, unnecessary rows.

Many thanks!

Answers


Spark >= 2.4

You can skip zip udf and use arrays_zip function:

df.withColumn("vars", explode(arrays_zip($"varA", $"varB"))).select(
  $"userId", $"someString",
  $"vars.varA", $"vars.varB").show

Spark < 2.4

What you want is not possible without a custom UDF. In Scala you could do something like this:

val data = sc.parallelize(Seq(
    """{"userId": 1, "someString": "example1",
        "varA": [0, 2, 5], "varB": [1, 2, 9]}""",
    """{"userId": 2, "someString": "example2",
        "varA": [1, 20, 5], "varB": [9, null, 6]}"""
))

val df = spark.read.json(data)

df.printSchema
// root
//  |-- someString: string (nullable = true)
//  |-- userId: long (nullable = true)
//  |-- varA: array (nullable = true)
//  |    |-- element: long (containsNull = true)
//  |-- varB: array (nullable = true)
//  |    |-- element: long (containsNull = true)

Now we can define zip udf:

import org.apache.spark.sql.functions.{udf, explode}

val zip = udf((xs: Seq[Long], ys: Seq[Long]) => xs.zip(ys))

df.withColumn("vars", explode(zip($"varA", $"varB"))).select(
   $"userId", $"someString",
   $"vars._1".alias("varA"), $"vars._2".alias("varB")).show

// +------+----------+----+----+
// |userId|someString|varA|varB|
// +------+----------+----+----+
// |     1|  example1|   0|   1|
// |     1|  example1|   2|   2|
// |     1|  example1|   5|   9|
// |     2|  example2|   1|   9|
// |     2|  example2|  20|null|
// |     2|  example2|   5|   6|
// +------+----------+----+----+

With raw SQL:

sqlContext.udf.register("zip", (xs: Seq[Long], ys: Seq[Long]) => xs.zip(ys))
df.registerTempTable("df")

sqlContext.sql(
  """SELECT userId, someString, explode(zip(varA, varB)) AS vars FROM df""")

You could also try

case class Input(
 userId: Integer,
 someString: String,
 varA: Array[Integer],
 varB: Array[Integer])

case class Result(
 userId: Integer,
 someString: String,
 varA: Integer,
 varB: Integer)

def getResult(row : Input) : Iterable[Result] = {
 val user_id = row.user_id
 val someString = row.someString
 val varA = row.varA
 val varB = row.varB
 val seq = for( i <- 0 until varA.size) yield {Result(user_id,someString,varA(i),varB(i))}
 seq
 }

val obj1 = Input(1, "string1", Array(0, 2, 5), Array(1, 2, 9))
val obj2 = Input(2, "string2", Array(1, 3, 6), Array(2, 3, 10))
val input_df = sc.parallelize(Seq(obj1, obj2)).toDS

val res = input_df.flatMap{ row => getResult(row) }
res.show
// +------+----------+----+-----+
// |userId|someString|varA|varB |
// +------+----------+----+-----+
// |     1|  string1 |   0|   1 |
// |     1|  string1 |   2|   2 |
// |     1|  string1 |   5|   9 |
// |     2|  string2 |   1|   2 |
// |     2|  string2 |   3|   3 |
// |     2|  string2 |   6|   10|
// +------+----------+----+-----+

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