George created SPARK-33071:
------------------------------
Summary: Join with ambiguous column succeeding but giving wrong
output
Key: SPARK-33071
URL: https://issues.apache.org/jira/browse/SPARK-33071
Project: Spark
Issue Type: Bug
Components: SQL
Affects Versions: 3.0.1, 2.4.4
Reporter: George
When joining two datasets where one column in each dataset is sourced from the
same input dataset, the join successfully runs, but does not select the correct
columns, leading to incorrect output.
Repro using pyspark:
{code:java}
sc.version
import pyspark.sql.functions as F
d = [{'key': 'a', 'sales': 1, 'units' : 2}, {'key': 'a', 'sales': 2, 'units' :
4}, {'key': 'b', 'sales': 5, 'units' : 10}, {'key': 'c', 'sales': 1, 'units' :
2}, {'key': 'd', 'sales': 3, 'units' : 6}]
input_df = spark.createDataFrame(d)
df1 = input_df.groupBy("key").agg(F.sum('sales').alias('sales'))
df2 = input_df.groupBy("key").agg(F.sum('units').alias('units'))
df1 = df1.filter(F.col("key") != F.lit("c"))
df2 = df2.filter(F.col("key") != F.lit("d"))
ret = df1.join(df2, df1.key == df2.key, "full").select(
df1["key"].alias("df1_key"),
df2["key"].alias("df2_key"),
df1["sales"],
df2["units"],
F.coalesce(df1["key"], df2["key"]).alias("key"))
ret.show()
ret.explain(){code}
output for 2.4.4:
{code:java}
>>> sc.version
u'2.4.4'
>>> import pyspark.sql.functions as F
>>> d = [{'key': 'a', 'sales': 1, 'units' : 2}, {'key': 'a', 'sales': 2,
>>> 'units' : 4}, {'key': 'b', 'sales': 5, 'units' : 10}, {'key': 'c', 'sales':
>>> 1, 'units' : 2}, {'key': 'd', 'sales': 3, 'units' : 6}]
>>> input_df = spark.createDataFrame(d)
>>> df1 = input_df.groupBy("key").agg(F.sum('sales').alias('sales'))
>>> df2 = input_df.groupBy("key").agg(F.sum('units').alias('units'))
>>> df1 = df1.filter(F.col("key") != F.lit("c"))
>>> df2 = df2.filter(F.col("key") != F.lit("d"))
>>> ret = df1.join(df2, df1.key == df2.key, "full").select(
... df1["key"].alias("df1_key"),
... df2["key"].alias("df2_key"),
... df1["sales"],
... df2["units"],
... F.coalesce(df1["key"], df2["key"]).alias("key"))
20/10/05 15:46:14 WARN Column: Constructing trivially true equals predicate,
'key#213 = key#213'. Perhaps you need to use aliases.
>>> ret.show()
+-------+-------+-----+-----+----+
|df1_key|df2_key|sales|units| key|
+-------+-------+-----+-----+----+
| d| d| 3| null| d|
| null| null| null| 2|null|
| b| b| 5| 10| b|
| a| a| 3| 6| a|
+-------+-------+-----+-----+----+>>> ret.explain()
== Physical Plan ==
*(5) Project [key#213 AS df1_key#258, key#213 AS df2_key#259, sales#223L,
units#230L, coalesce(key#213, key#213) AS key#260]
+- SortMergeJoin [key#213], [key#237], FullOuter
:- *(2) Sort [key#213 ASC NULLS FIRST], false, 0
: +- *(2) HashAggregate(keys=[key#213], functions=[sum(sales#214L)])
: +- Exchange hashpartitioning(key#213, 200)
: +- *(1) HashAggregate(keys=[key#213],
functions=[partial_sum(sales#214L)])
: +- *(1) Project [key#213, sales#214L]
: +- *(1) Filter (isnotnull(key#213) && NOT (key#213 = c))
: +- Scan ExistingRDD[key#213,sales#214L,units#215L]
+- *(4) Sort [key#237 ASC NULLS FIRST], false, 0
+- *(4) HashAggregate(keys=[key#237], functions=[sum(units#239L)])
+- Exchange hashpartitioning(key#237, 200)
+- *(3) HashAggregate(keys=[key#237],
functions=[partial_sum(units#239L)])
+- *(3) Project [key#237, units#239L]
+- *(3) Filter (isnotnull(key#237) && NOT (key#237 = d))
+- Scan ExistingRDD[key#237,sales#238L,units#239L]
{code}
output for 3.0.1:
{code:java}
// code placeholder
>>> sc.version
u'3.0.1'
>>> import pyspark.sql.functions as F
>>> d = [{'key': 'a', 'sales': 1, 'units' : 2}, {'key': 'a', 'sales': 2,
>>> 'units' : 4}, {'key': 'b', 'sales': 5, 'units' : 10}, {'key': 'c', 'sales':
>>> 1, 'units' : 2}, {'key': 'd', 'sales': 3, 'units' : 6}]
>>> input_df = spark.createDataFrame(d)
/usr/local/lib/python2.7/site-packages/pyspark/sql/session.py:381: UserWarning:
inferring schema from dict is deprecated,please use pyspark.sql.Row instead
warnings.warn("inferring schema from dict is deprecated,"
>>> df1 = input_df.groupBy("key").agg(F.sum('sales').alias('sales'))
>>> df2 = input_df.groupBy("key").agg(F.sum('units').alias('units'))
>>> df1 = df1.filter(F.col("key") != F.lit("c"))
>>> df2 = df2.filter(F.col("key") != F.lit("d"))
>>> ret = df1.join(df2, df1.key == df2.key, "full").select(
... df1["key"].alias("df1_key"),
... df2["key"].alias("df2_key"),
... df1["sales"],
... df2["units"],
... F.coalesce(df1["key"], df2["key"]).alias("key"))
>>> ret.show()
+-------+-------+-----+-----+----+
|df1_key|df2_key|sales|units| key|
+-------+-------+-----+-----+----+
| d| d| 3| null| d|
| null| null| null| 2|null|
| b| b| 5| 10| b|
| a| a| 3| 6| a|
+-------+-------+-----+-----+----+>>> ret.explain()
== Physical Plan ==
*(5) Project [key#0 AS df1_key#45, key#0 AS df2_key#46, sales#10L, units#17L,
coalesce(key#0, key#0) AS key#47]
+- SortMergeJoin [key#0], [key#24], FullOuter
:- *(2) Sort [key#0 ASC NULLS FIRST], false, 0
: +- *(2) HashAggregate(keys=[key#0], functions=[sum(sales#1L)])
: +- Exchange hashpartitioning(key#0, 200), true, [id=#152]
: +- *(1) HashAggregate(keys=[key#0],
functions=[partial_sum(sales#1L)])
: +- *(1) Project [key#0, sales#1L]
: +- *(1) Filter (isnotnull(key#0) AND NOT (key#0 = c))
: +- *(1) Scan ExistingRDD[key#0,sales#1L,units#2L]
+- *(4) Sort [key#24 ASC NULLS FIRST], false, 0
+- *(4) HashAggregate(keys=[key#24], functions=[sum(units#26L)])
+- Exchange hashpartitioning(key#24, 200), true, [id=#158]
+- *(3) HashAggregate(keys=[key#24],
functions=[partial_sum(units#26L)])
+- *(3) Project [key#24, units#26L]
+- *(3) Filter (isnotnull(key#24) AND NOT (key#24 = d))
+- *(3) Scan ExistingRDD[key#24,sales#25L,units#26L]{code}
key#0 is the reference used for both alias operations and both sides of the
coalesce, despite the query plan projecting key#24 for the right side of the
join.
Concretely, I believe the output of the join should be this:
{code:java}
+-------+-------+-----+-----+----+
|df1_key|df2_key|sales|units| key|
+-------+-------+-----+-----+----+
| d| null| 3| null| d|
| null| c| null| 2| c|
| b| b| 5| 10| b|
| a| a| 3| 6| a|
+-------+-------+-----+-----+----+
{code}
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