andygrove commented on code in PR #451:
URL: https://github.com/apache/datafusion-comet/pull/451#discussion_r1610422808
##########
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)
+ 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")
+ val gen = generator.get
Review Comment:
Rather than use `isDefined` and `get`, it may be more idiomatic to use a
`match` statement.
```scala
val gen = RandomDataGenerator.forType(f.dataType, f.nullable, r)
match {
case Some(generator) => generator
case None => throw new IllegalStateException(s"No
RandomDataGenerator for type ${f.dataType}")
}
fields += gen()
```
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