Enrico Minack created SPARK-42168:
-------------------------------------
Summary: CoGroup with window function returns incorrect result
when partition keys differ in order
Key: SPARK-42168
URL: https://issues.apache.org/jira/browse/SPARK-42168
Project: Spark
Issue Type: Bug
Components: PySpark, SQL
Affects Versions: 3.2.3, 3.1.3, 3.0.3
Reporter: Enrico Minack
The following example returns an incorrect result:
{code:java}
import pandas as pd
from pyspark.sql import SparkSession, Window
from pyspark.sql.functions import col, lit, sum
spark = SparkSession \
.builder \
.getOrCreate()
ids = 1000
days = 1000
parts = 10
id_df = spark.range(ids)
day_df = spark.range(days).withColumnRenamed("id", "day")
id_day_df = id_df.join(day_df)
left_df = id_day_df.select(col("id").alias("id"), col("day").alias("day"),
lit("left").alias("side")).repartition(parts).cache()
right_df = id_day_df.select(col("id").alias("id"), col("day").alias("day"),
lit("right").alias("side")).repartition(parts).cache()
#.withColumnRenamed("id", "id2")
# note the column order is different to the groupBy("id", "day") column order
below
window = Window.partitionBy("day", "id")
left_grouped_df = left_df.groupBy("id", "day")
right_grouped_df = right_df.withColumn("day_sum",
sum(col("day")).over(window)).groupBy("id", "day")
def cogroup(left: pd.DataFrame, right: pd.DataFrame) -> pd.DataFrame:
return pd.DataFrame([{
"id": left["id"][0] if not left.empty else (right["id"][0] if not
right.empty else None),
"day": left["day"][0] if not left.empty else (right["day"][0] if not
right.empty else None),
"lefts": len(left.index),
"rights": len(right.index)
}])
df = left_grouped_df.cogroup(right_grouped_df) \
.applyInPandas(cogroup, schema="id long, day long, lefts integer,
rights integer")
df.explain()
df.show(5)
{code}
Output is
{code}
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- FlatMapCoGroupsInPandas [id#8L, day#9L], [id#29L, day#30L], cogroup(id#8L,
day#9L, side#10, id#29L, day#30L, side#31, day_sum#54L), [id#64L, day#65L,
lefts#66, rights#67]
:- Sort [id#8L ASC NULLS FIRST, day#9L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#8L, day#9L, 200), ENSURE_REQUIREMENTS,
[plan_id=117]
: +- InMemoryTableScan [id#8L, day#9L, id#8L, day#9L, side#10]
: +- InMemoryRelation [id#8L, day#9L, side#10], StorageLevel(disk,
memory, deserialized, 1 replicas)
: +- Exchange RoundRobinPartitioning(10),
REPARTITION_BY_NUM, [plan_id=33]
: +- *(2) Project [id#0L, day#4L, left AS side#10]
: +- *(2) BroadcastNestedLoopJoin BuildRight, Inner
: :- *(2) Range (0, 1000, step=1, splits=16)
: +- BroadcastExchange IdentityBroadcastMode,
[plan_id=28]
: +- *(1) Project [id#2L AS day#4L]
: +- *(1) Range (0, 1000, step=1, splits=16)
+- Sort [id#29L ASC NULLS FIRST, day#30L ASC NULLS FIRST], false, 0
+- Project [id#29L, day#30L, id#29L, day#30L, side#31, day_sum#54L]
+- Window [sum(day#30L) windowspecdefinition(day#30L, id#29L,
specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$()))
AS day_sum#54L], [day#30L, id#29L]
+- Sort [day#30L ASC NULLS FIRST, id#29L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(day#30L, id#29L, 200),
ENSURE_REQUIREMENTS, [plan_id=112]
+- InMemoryTableScan [id#29L, day#30L, side#31]
+- InMemoryRelation [id#29L, day#30L, side#31],
StorageLevel(disk, memory, deserialized, 1 replicas)
+- Exchange RoundRobinPartitioning(10),
REPARTITION_BY_NUM, [plan_id=79]
+- *(2) Project [id#0L, day#4L, right AS
side#31]
+- *(2) BroadcastNestedLoopJoin BuildRight,
Inner
:- *(2) Range (0, 1000, step=1,
splits=16)
+- BroadcastExchange
IdentityBroadcastMode, [plan_id=74]
+- *(1) Project [id#2L AS day#4L]
+- *(1) Range (0, 1000, step=1,
splits=16)
+---+---+-----+------+
| id|day|lefts|rights|
+---+---+-----+------+
| 0| 3| 0| 1|
| 0| 4| 0| 1|
| 0| 13| 1| 0|
| 0| 27| 0| 1|
| 0| 31| 0| 1|
+---+---+-----+------+
only showing top 5 rows
{code}
The first child is hash-partitioned by {{id}} and {{{}day{}}}, while the second
child is hash-partitioned by {{day}} and {{id}} (required by the window
function). Therefore, rows end up in different partitions.
This has been fixed in Spark 3.3 by
[#32875|https://github.com/apache/spark/pull/32875/files#diff-e938569a4ca4eba8f7e10fe473d4f9c306ea253df151405bcaba880a601f075fR75-R76]:
{code}
== Physical Plan ==
AdaptiveSparkPlan isFinalPlan=false
+- FlatMapCoGroupsInPandas [id#8L, day#9L], [id#29L, day#30L], cogroup(id#8L,
day#9L, side#10, id#29L, day#30L, side#31, day_sum#54L)#63, [id#64L, day#65L,
lefts#66, rights#67]
:- Sort [id#8L ASC NULLS FIRST, day#9L ASC NULLS FIRST], false, 0
: +- Exchange hashpartitioning(id#8L, day#9L, 200), ENSURE_REQUIREMENTS,
[plan_id=117]
: +- InMemoryTableScan [id#8L, day#9L, id#8L, day#9L, side#10]
: +- InMemoryRelation [id#8L, day#9L, side#10], StorageLevel(disk,
memory, deserialized, 1 replicas)
: +- Exchange RoundRobinPartitioning(10),
REPARTITION_BY_NUM, [plan_id=33]
: +- *(2) Project [id#0L, day#4L, left AS side#10]
: +- *(2) BroadcastNestedLoopJoin BuildRight, Inner
: :- *(2) Range (0, 1000, step=1, splits=16)
: +- BroadcastExchange IdentityBroadcastMode,
[plan_id=28]
: +- *(1) Project [id#2L AS day#4L]
: +- *(1) Range (0, 1000, step=1, splits=16)
+- Sort [id#29L ASC NULLS FIRST, day#30L ASC NULLS FIRST], false, 0
+- Exchange hashpartitioning(id#29L, day#30L, 200), ENSURE_REQUIREMENTS,
[plan_id=118]
+- Project [id#29L, day#30L, id#29L, day#30L, side#31, day_sum#54L]
+- Window [sum(day#30L) windowspecdefinition(day#30L, id#29L,
specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$()))
AS day_sum#54L], [day#30L, id#29L]
+- Sort [day#30L ASC NULLS FIRST, id#29L ASC NULLS FIRST],
false, 0
+- Exchange hashpartitioning(day#30L, id#29L, 200),
ENSURE_REQUIREMENTS, [plan_id=112]
+- InMemoryTableScan [id#29L, day#30L, side#31]
+- InMemoryRelation [id#29L, day#30L, side#31],
StorageLevel(disk, memory, deserialized, 1 replicas)
+- Exchange RoundRobinPartitioning(10),
REPARTITION_BY_NUM, [plan_id=79]
+- *(2) Project [id#0L, day#4L, right AS
side#31]
+- *(2) BroadcastNestedLoopJoin
BuildRight, Inner
:- *(2) Range (0, 1000, step=1,
splits=16)
+- BroadcastExchange
IdentityBroadcastMode, [plan_id=74]
+- *(1) Project [id#2L AS day#4L]
+- *(1) Range (0, 1000, step=1,
splits=16)
+---+---+-----+------+
| id|day|lefts|rights|
+---+---+-----+------+
| 0| 13| 1| 1|
| 0| 63| 1| 1|
| 0| 89| 1| 1|
| 0| 95| 1| 1|
| 0| 96| 1| 1|
+---+---+-----+------+
only showing top 5 rows
{code}
Only PySpark seems to be affected.
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