advancedxy commented on code in PR #451:
URL: https://github.com/apache/datafusion-comet/pull/451#discussion_r1607709720
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spark/src/test/scala/org/apache/comet/DataGenerator.scala:
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@@ -95,4 +102,55 @@ 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 ArrayType(childType, nullable) =>
+ val data = if (f.nullable && r.nextFloat() <= PROBABILITY_OF_NULL) {
+ null
+ } else {
+ val arr = mutable.ArrayBuffer.empty[Any]
+ val n = 1 // rand.nextInt(10)
+ var i = 0
+ val generator = RandomDataGenerator.forType(childType, nullable, r)
+ assert(generator.isDefined, "Unsupported type")
+ val gen = generator.get
+ while (i < n) {
+ arr += gen()
+ i += 1
+ }
+ arr.toSeq
+ }
+ fields += data
+ case StructType(children) =>
+ fields += generateRow(StructType(children))
+ case StringType if stringGen.isDefined =>
+ val gen = stringGen.get
+ val data = if (f.nullable && r.nextFloat() <= PROBABILITY_OF_NULL) {
+ null
+ } else {
+ gen()
+ }
+ fields += data
+ case _ =>
+ val generator = RandomDataGenerator.forType(f.dataType, f.nullable,
r)
+ assert(generator.isDefined, "Unsupported type")
Review Comment:
> Hmmm should we do the same (create nulls in forType) for the rest of
types?
Let me think about that. Maybe we can remove the array type case. However I
think the user specified `stringGen` still have to access `PROBABILITY_OF_NULL`
to generate nullable strings. Otherwise, the `stringGen` method itself should
handle nullable by itself.
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