Spark DataFrame explode map with key as member
I found an example of a map explosion in the databrick blog :
// input
{
"a": {
"b": 1,
"c": 2
}
}
Python: events.select(explode("a").alias("x", "y"))
Scala: events.select(explode('a) as Seq("x", "y"))
SQL: select explode(a) as (x, y) from events
// output
[{ "x": "b", "y": 1 }, { "x": "c", "y": 2 }]
However, I don't see the way that this leads me to change my map to an array, into which the key is flattened, which then explodes:
// input
{
"id": 0,
"a": {
"b": {"d": 1, "e": 2}
"c": {"d": 3, "e": 4}
}
}
// Schema
struct<id:bigint,a:map<string,struct<d:bigint,e:bigint>>>
root
|-- id: long (nullable = true)
|-- a: map (nullable = true)
| |-- key: string
| |-- value: struct (valueContainsNull = true)
| | |-- d: long (nullable = true)
| | |-- e: long (nullable = true)
// Imagined proces
Python: …
Scala: events.select('id, explode('a) as Seq("x", "*")) //? "*" ?
SQL: …
// Desired output
[{ "id": 0, "x": "b", "d": 1, "e": 2 }, { "id": 0, "x": "c", "d": 3, "e": 4 }]
Is there some obvious way to make an input like this to make a table like:
id | x | d | e
---|---|---|---
0 | b | 1 | 2
0 | c | 3 | 4
+3
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1 answer
While I don't know if it's possible to blow it up a map with one single one explode
, there is a way to it with a UDF. The trick is to use Row#schema.fields(i).name
to get the name of the "key"
def mapStructs = udf((r: Row) => {
r.schema.fields.map(f => (
f.name,
r.getAs[Row](f.name).getAs[Long]("d"),
r.getAs[Row](f.name).getAs[Long]("e"))
)
})
df
.withColumn("udfResult", explode(mapStructs($"a")))
.withColumn("x", $"udfResult._1")
.withColumn("d", $"udfResult._2")
.withColumn("e", $"udfResult._3")
.drop($"udfResult")
.drop($"a")
.show
gives
+---+---+---+---+
| id| x| d| e|
+---+---+---+---+
| 0| b| 1| 2|
| 0| c| 3| 4|
+---+---+---+---+
+1
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