[
https://issues.apache.org/jira/browse/SPARK-21034?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Abhijit Bhole updated SPARK-21034:
--
Description:
If the column is involved in aggregation / join then pushing down filter should
not change the results.
Here is a sample code -
{code:java}
from pyspark.sql import functions as F
df = spark.createDataFrame([{ "a": 1, "b" : 2, "c":7}, { "a": 3, "b" : 4, "c" :
8},
{ "a": 1, "b" : 5, "c":7}, { "a": 1, "b" : 6, "c":7}
])
df.groupBy(["a"]).agg(F.sum("b")).where("a = 1").explain()
df.groupBy(["a"]).agg(F.sum("b"), F.first("c")).where("a = 1").explain()
== Physical Plan ==
*HashAggregate(keys=[a#15L], functions=[sum(b#16L)])
+- Exchange hashpartitioning(a#15L, 4)
+- *HashAggregate(keys=[a#15L], functions=[partial_sum(b#16L)])
+- *Project [a#15L, b#16L]
+- *Filter (isnotnull(a#15L) && (a#15L = 1))
+- Scan ExistingRDD[a#15L,b#16L,c#17L]
>>>
>>> df.groupBy(["a"]).agg(F.sum("b"), F.first("c")).where("a = 1").explain()
== Physical Plan ==
*Filter (isnotnull(a#15L) && (a#15L = 1))
+- *HashAggregate(keys=[a#15L], functions=[sum(b#16L), first(c#17L, false)])
+- Exchange hashpartitioning(a#15L, 4)
+- *HashAggregate(keys=[a#15L], functions=[partial_sum(b#16L),
partial_first(c#17L, false)])
+- Scan ExistingRDD[a#15L,b#16L,c#17L]
{code}
As you can see, the filter is not pushed down when F.first aggregate function
is used.
was:
Here is a sample code -
{code:java}
from pyspark.sql import functions as F
df = spark.createDataFrame([{ "a": 1, "b" : 2, "c":7}, { "a": 3, "b" : 4, "c" :
8},
{ "a": 1, "b" : 5, "c":7}, { "a": 1, "b" : 6, "c":7}
])
df.groupBy(["a"]).agg(F.sum("b")).where("a = 1").explain()
df.groupBy(["a"]).agg(F.sum("b"), F.first("c")).where("a = 1").explain()
== Physical Plan ==
*HashAggregate(keys=[a#15L], functions=[sum(b#16L)])
+- Exchange hashpartitioning(a#15L, 4)
+- *HashAggregate(keys=[a#15L], functions=[partial_sum(b#16L)])
+- *Project [a#15L, b#16L]
+- *Filter (isnotnull(a#15L) && (a#15L = 1))
+- Scan ExistingRDD[a#15L,b#16L,c#17L]
>>>
>>> df.groupBy(["a"]).agg(F.sum("b"), F.first("c")).where("a = 1").explain()
== Physical Plan ==
*Filter (isnotnull(a#15L) && (a#15L = 1))
+- *HashAggregate(keys=[a#15L], functions=[sum(b#16L), first(c#17L, false)])
+- Exchange hashpartitioning(a#15L, 4)
+- *HashAggregate(keys=[a#15L], functions=[partial_sum(b#16L),
partial_first(c#17L, false)])
+- Scan ExistingRDD[a#15L,b#16L,c#17L]
{code}
As you can see, the filter is not pushed down when F.first aggregate function
is used.
> Allow filter pushdown filters through non deterministic functions for columns
> involved in groupby / join
>
>
> Key: SPARK-21034
> URL: https://issues.apache.org/jira/browse/SPARK-21034
> Project: Spark
> Issue Type: Bug
> Components: Optimizer, SQL
>Affects Versions: 2.1.1, 2.2.0
>Reporter: Abhijit Bhole
>
> If the column is involved in aggregation / join then pushing down filter
> should not change the results.
> Here is a sample code -
> {code:java}
> from pyspark.sql import functions as F
> df = spark.createDataFrame([{ "a": 1, "b" : 2, "c":7}, { "a": 3, "b" : 4, "c"
> : 8},
>{ "a": 1, "b" : 5, "c":7}, { "a": 1, "b" : 6,
> "c":7} ])
> df.groupBy(["a"]).agg(F.sum("b")).where("a = 1").explain()
> df.groupBy(["a"]).agg(F.sum("b"), F.first("c")).where("a = 1").explain()
> == Physical Plan ==
> *HashAggregate(keys=[a#15L], functions=[sum(b#16L)])
> +- Exchange hashpartitioning(a#15L, 4)
>+- *HashAggregate(keys=[a#15L], functions=[partial_sum(b#16L)])
> +- *Project [a#15L, b#16L]
> +- *Filter (isnotnull(a#15L) && (a#15L = 1))
> +- Scan ExistingRDD[a#15L,b#16L,c#17L]
> >>>
> >>> df.groupBy(["a"]).agg(F.sum("b"), F.first("c")).where("a = 1").explain()
> == Physical Plan ==
> *Filter (isnotnull(a#15L) && (a#15L = 1))
> +- *HashAggregate(keys=[a#15L], functions=[sum(b#16L), first(c#17L, false)])
>+- Exchange hashpartitioning(a#15L, 4)
> +- *HashAggregate(keys=[a#15L], functions=[partial_sum(b#16L),
> partial_first(c#17L, false)])
> +- Scan ExistingRDD[a#15L,b#16L,c#17L]
> {code}
> As you can see, the filter is not pushed down when F.first aggregate function
> is used.
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