viirya commented on pull request #29645:
URL: https://github.com/apache/spark/pull/29645#issuecomment-687486287
An example looks like:
```scala
private lazy val arrayType = ArrayType(
StructType(Seq(
StructField("a", IntegerType, nullable = false),
StructField("b", IntegerType, nullable = true),
StructField("c", IntegerType, nullable = false))),
containsNull = true)
private lazy val arrayStructArrayLevel1: DataFrame = spark.createDataFrame(
sparkContext.parallelize(Row(Array(Row(Array(Row(1, null, 3)), null,
3))) :: Nil),
StructType(
Seq(StructField("a", ArrayType(
StructType(Seq(
StructField("a", arrayType, nullable = false),
StructField("b", IntegerType, nullable = true),
StructField("c", IntegerType, nullable = false))),
containsNull = false)))))
```
The data looks like:
```
+---------------------------+
|a |
+---------------------------+
|[{[{1, null, 3}], null, 3}]|
+---------------------------+
```
In order to replace deeply nested `b` column, like:
```
+------------------------+
|a |
+------------------------+
|[{[{1, 2, 3}], null, 3}]|
+------------------------+
```
Currently by using `transform` + `withField`, we probably need:
```scala
arrayStructArrayLevel1.withColumn("a",
transform($"a", _.withField("a",
flatten(transform($"a.a", transform(_, _.withField("b", lit(2))))))))
```
Using modified `withField`, we can do it like:
```scala
arrayStructArrayLevel1.withColumn("a", $"a".withField("a.b", lit(2)))
```
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