Details
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Bug
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Status: Resolved
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Critical
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Resolution: Fixed
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2.4.4, 3.0.1, 3.1.0
Description
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:
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()
output for 2.4.4:
>>> 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]
output for 3.0.1:
// 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]
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:
+-------+-------+-----+-----+----+ |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| +-------+-------+-----+-----+----+
