comphead commented on PR #2258:
URL:
https://github.com/apache/datafusion-comet/pull/2258#issuecomment-3316131986
For Spark it can be something like
```
val schema = StructType(Seq(
StructField("region", StringType, true),
StructField("user_id", IntegerType, true),
StructField("age", IntegerType, true)
))
// Data with duplicates
val data = Seq(
Row("us", 1: Integer, 30: Integer),
Row("us", 2: Integer, 30: Integer),
Row("us", 3: Integer, 30: Integer),
Row("eu", 4: Integer, 20: Integer),
Row("eu", 5: Integer, 20: Integer),
Row("apac", 6: Integer, 25: Integer),
Row("apac", 7: Integer, 25: Integer),
Row("apac", 8: Integer, 25: Integer),
Row("apac", 9: Integer, 25: Integer)
)
val df = spark.createDataFrame(
spark.sparkContext.parallelize(data),
schema
)
// Range repartition into 3 partitions
val repartitioned = df.repartitionByRange(
3,
df("region").asc,
df("age").asc
)
// Print partition contents
repartitioned.rdd.mapPartitionsWithIndex { case (i, iter) =>
Iterator(s"--- Partition $i ---\n" + iter.mkString("\n"))
}.collect().foreach(println) <---- here we can assert
```
To check data distribution including duplicates
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