Reputation: 185
I exploded a nested schema but I am not getting what I want,
before exploded it looks like this:
df.show()
+----------+----------------------------------------------------------+
|CaseNumber| SourceId |
+----------+----------------------------------------------------------+
| 0 |[{"id":"1","type":"Sku"},{"id":"22","type":"ContractID"}] |
+----------|----------------------------------------------------------|
| 1 |[{"id":"3","type":"Sku"},{"id":"24","type":"ContractID"}] |
+---------------------------------------------------------------------+
I want it to be like this
+----------+-------------------+
| CaseNumber| Sku | ContractId |
+----------+-------------------+
| 0 | 1 | 22 |
+----------|------|------------|
| 1 | 3 | 24 |
+------------------------------|
Upvotes: 1
Views: 689
Reputation: 7316
Here is one way using the build-in get_json_object
function:
import org.apache.spark.sql.functions.get_json_object
val df = Seq(
(0, """[{"id":"1","type":"Sku"},{"id":"22","type":"ContractID"}]"""),
(1, """[{"id":"3","type":"Sku"},{"id":"24","type":"ContractID"}]"""))
.toDF("CaseNumber", "SourceId")
df.withColumn("sku", get_json_object($"SourceId", "$[0].id").cast("int"))
.withColumn("ContractId", get_json_object($"SourceId", "$[1].id").cast("int"))
.drop("SourceId")
.show
// +----------+---+----------+
// |CaseNumber|sku|ContractId|
// +----------+---+----------+
// | 0| 1| 22|
// | 1| 3| 24|
// +----------+---+----------+
UPDATE
After our discussion we realised that the mentioned data is of array<struct<id:string,type:string>>
type and not a simple string. Next is the solution for the new schema:
df.withColumn("sku", $"SourceIds".getItem(0).getField("id"))
.withColumn("ContractId", $"SourceIds".getItem(1).getField("id"))
Upvotes: 1