How to unnest array with keys to join on afterwards?

Question

I have two tables, namely table1 and table2. table1 is big, whereas table2 is small. Also, I have a UDF function whose interface is defined as below:

--table1--
id
1
2
3

--table2--
category
a
b
c
d
e
f
g

UDF: foo(id: Int): List[String]

I intend to call UDF firstly to get the corresponding categories: foo(table1.id), which will return a WrappedArray, then I want to join every category in table2 to do some more manipulation. The expected result should look like this:

--view--

id,category
1,a
1,c
1,d
2,b
2,c
3,e
3,f
3,g

I try to find a unnest method in Hive, but with no luck, could anyone help me out? Thanks!

Answer 1

I believe that you want to use explode function or Dataset's flatMap operator.

explode function creates a new row for each element in the given array or map column.

flatMap operator returns a new Dataset by first applying a function to all elements of this Dataset, and then flattening the results.

After you execute your UDF foo(id: Int): List[String] you'll end up with a Dataset with the column of type array.

val fooUDF = udf { id: Int => ('a' to ('a'.toInt + id).toChar).map(_.toString) }

// table1 with fooUDF applied
val table1 = spark.range(3).withColumn("foo", fooUDF('id))

scala> table1.show
+---+---------+
| id|      foo|
+---+---------+
|  0|      [a]|
|  1|   [a, b]|
|  2|[a, b, c]|
+---+---------+

scala> table1.printSchema
root
 |-- id: long (nullable = false)
 |-- foo: array (nullable = true)
 |    |-- element: string (containsNull = true)

scala> table1.withColumn("fooExploded", explode($"foo")).show
+---+---------+-----------+
| id|      foo|fooExploded|
+---+---------+-----------+
|  0|      [a]|          a|
|  1|   [a, b]|          a|
|  1|   [a, b]|          b|
|  2|[a, b, c]|          a|
|  2|[a, b, c]|          b|
|  2|[a, b, c]|          c|
+---+---------+-----------+

With that, join should be quite easy.