zhengruifeng edited a comment on pull request #34367:
URL: https://github.com/apache/spark/pull/34367#issuecomment-949516811
a simple example:
```
import org.apache.spark.sql.expressions.Window
val df1 = spark.range(0, 100000000, 1, 9).select(when('id < 90000000,
123).otherwise('id).as("key1"), 'id as "value1").withColumn("hash1",
abs(hash(col("key1"))).mod(1000))
df1.withColumn("rank",
row_number().over(Window.partitionBy("hash1").orderBy("value1"))).where(col("rank")
<= 1).write.mode("overwrite").parquet("/tmp/tmp1")
spark.conf.set("spark.sql.rankLimit.enabled", "true")
df1.withColumn("rank",
row_number().over(Window.partitionBy("hash1").orderBy("value1"))).where(col("rank")
<= 1).write.mode("overwrite").parquet("/tmp/tmp2")
```
existing plan took 33 sec, while the new plan with `RankLimit` took only 8
sec.
and the shuffle write was reduced from 544.9 MiB to 26.7 KiB
```
== Physical Plan ==
Execute InsertIntoHadoopFsRelationCommand (18)
+- AdaptiveSparkPlan (17)
+- == Final Plan ==
* Filter (11)
+- Window (10)
+- * Sort (9)
+- AQEShuffleRead (8)
+- ShuffleQueryStage (7)
+- Exchange (6)
+- RankLimit (5)
+- * Sort (4)
+- * Project (3)
+- * Project (2)
+- * Range (1)
```
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