[
https://issues.apache.org/jira/browse/SPARK-20169?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Bin Wu updated SPARK-20169:
---------------------------
Description:
We find a potential bug int Catalyst optimizer which will cannot correctly
process "groupby" command.
You can reproduce the bug by following simple example:
=========================
from pyspark.sql.functions import *
#e=sc.parallelize([(1,2),(1,3),(1,4),(2,1),(3,1),(4,1)]).toDF(["src","dst"])
e = spark.read.csv("graph.csv", header=True)
r = sc.parallelize([(1,),(2,),(3,),(4,)]).toDF(['src'])
r1 = e.join(r, 'src').groupBy('dst').count().withColumnRenamed('dst','src')
jr = e.join(r1, 'src')
jr.show()
r2 = jr.groupBy('dst').count()
r2.show()
=========================
FYI, "graph.csv" contains exactly the same data as the commented line.
You can find that jr is:
|src|dst|count|
| 3| 1| 1|
| 1| 4| 3|
| 1| 3| 3|
| 1| 2| 3|
| 4| 1| 1|
| 2| 1| 1|
But, after the last groupBy, the 3 rows with dst = 1 are not grouped together:
|dst|count|
| 1| 1|
| 4| 1|
| 3| 1|
| 2| 1|
| 1| 1|
| 1| 1|
If we build jr directly from raw data, this error will not show up. So we
suspect
that there is a bug in the Catalyst optimizer when multiple joins and groupBy's
are being optimized.
was:
We find a potential bug int Catalyst optimizer which will cannot correctly
process "groupby" command.
You can reproduce the bug by following simple example:
=========================
from pyspark.sql.functions import *
#e=sc.parallelize([(1,2),(1,3),(1,4),(2,1),(3,1),(4,1)]).toDF(["src","dst"])
e = spark.read.csv("graph.csv", header=True)
r = sc.parallelize([(1,),(2,),(3,),(4,)]).toDF(['src'])
r1 = e.join(r, 'src').groupBy('dst').count().withColumnRenamed('dst','src')
jr = e.join(r1, 'src')
jr.show()
r2 = jr.groupBy('dst').count()
r2.show()
=========================
FYI, "graph.csv" contains exactly the same data as the commented line.
You can find that jr is:
|src|dst|count|
| 3| 1| 1|
| 1| 4| 3|
| 1| 3| 3|
| 1| 2| 3|
| 4| 1| 1|
| 2| 1| 1|
But, after the last groupBy, the 3 rows with dst = 1 are not grouped together:
|dst|count|
| 1| 1|
| 4| 1|
| 3| 1|
| 2| 1|
| 1| 1|
| 1| 1|
If we build jr directly from raw data, this error will not show up. So we
suspect that there is a bug in the Catalyst optimizer when multiple joins and
groupBy's are being optimized.
> Groupby Bug with Sparksql
> -------------------------
>
> Key: SPARK-20169
> URL: https://issues.apache.org/jira/browse/SPARK-20169
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 2.0.0, 2.1.0
> Reporter: Bin Wu
>
> We find a potential bug int Catalyst optimizer which will cannot correctly
> process "groupby" command.
> You can reproduce the bug by following simple example:
> =========================
> from pyspark.sql.functions import *
> #e=sc.parallelize([(1,2),(1,3),(1,4),(2,1),(3,1),(4,1)]).toDF(["src","dst"])
> e = spark.read.csv("graph.csv", header=True)
> r = sc.parallelize([(1,),(2,),(3,),(4,)]).toDF(['src'])
> r1 = e.join(r, 'src').groupBy('dst').count().withColumnRenamed('dst','src')
> jr = e.join(r1, 'src')
> jr.show()
> r2 = jr.groupBy('dst').count()
> r2.show()
> =========================
> FYI, "graph.csv" contains exactly the same data as the commented line.
> You can find that jr is:
> |src|dst|count|
> | 3| 1| 1|
> | 1| 4| 3|
> | 1| 3| 3|
> | 1| 2| 3|
> | 4| 1| 1|
> | 2| 1| 1|
> But, after the last groupBy, the 3 rows with dst = 1 are not grouped together:
> |dst|count|
> | 1| 1|
> | 4| 1|
> | 3| 1|
> | 2| 1|
> | 1| 1|
> | 1| 1|
> If we build jr directly from raw data, this error will not show up. So we
> suspect
> that there is a bug in the Catalyst optimizer when multiple joins and
> groupBy's
> are being optimized.
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