andygrove commented on code in PR #451:
URL: https://github.com/apache/datafusion-comet/pull/451#discussion_r1610414004
##########
spark/src/test/scala/org/apache/comet/DataGenerator.scala:
##########
@@ -95,4 +102,38 @@ class DataGenerator(r: Random) {
Range(0, n).map(_ => r.nextLong())
}
+ // Generate a random row according to the schema, the string filed in the
struct could be
+ // configured to generate strings by passing a stringGen function. Other
types are delegated
+ // to Spark's RandomDataGenerator.
+ def generateRow(schema: StructType, stringGen: Option[() => String] = None):
Row = {
+ val fields = mutable.ArrayBuffer.empty[Any]
+ schema.fields.foreach { f =>
+ f.dataType match {
+ case StructType(children) =>
+ fields += generateRow(StructType(children), stringGen)
Review Comment:
This could be a `map` operation to remove the mutable buffer and simplify
the code slightly. For example:
```scala
val fields = schema.fields.map { f =>
f.dataType match {
case StructType(children) =>
generateRow(StructType(children), stringGen)
case ...
```
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