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https://issues.apache.org/jira/browse/SPARK-20169?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15972270#comment-15972270
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Hyukjin Kwon edited comment on SPARK-20169 at 4/18/17 7:50 AM:
---------------------------------------------------------------
Yea, I was confused too when I tried to reproduce this before. Probably, let me
repharse the steps to reproduce as below:
*source A*
{code}
from pyspark.sql.functions import *
e=sc.parallelize([(1,2),(1,3),(1,4),(2,1),(3,1),(4,1)]).toDF(["src","dst"])
e.show()
{code}
{code}
+---+---+
|src|dst|
+---+---+
| 1| 2|
| 1| 3|
| 1| 4|
| 2| 1|
| 3| 1|
| 4| 1|
+---+---+
{code}
*source B*
with a {{graph.csv}} file as below:
{code}
src,dst
1,2
1,3
1,4
2,1
3,1
4,1
{code}
{code}
from pyspark.sql.functions import *
e = spark.read.csv("graph.csv", header=True)
e.show()
{code}
{code}
+---+---+
|src|dst|
+---+---+
| 1| 2|
| 1| 3|
| 1| 4|
| 2| 1|
| 3| 1|
| 4| 1|
+---+---+
{code}
*Reproducer*
{code}
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')
r2 = jr.groupBy('dst').count()
r2.show()
{code}
*Results from source A and the reproducer*
{code}
+---+-----+
|dst|count|
+---+-----+
| 1| 3|
| 3| 1|
| 2| 1|
| 4| 1|
+---+-----+
{code}
*Results from source B and the reproducer*
{code}
+---+-----+
|dst|count|
+---+-----+
| 1| 1|
| 4| 1|
| 3| 1|
| 2| 1|
| 1| 1|
| 1| 1|
+---+-----+
{code}
was (Author: hyukjin.kwon):
Yea, I was confused too when I tried to reproduce this before. Probably, let me
repharse the steps to reproduce as below:
*source A*
with a {{graph.csv}} file as below:
{code}
src,dst
1,2
1,3
1,4
2,1
3,1
4,1
{code}
{code}
from pyspark.sql.functions import *
e=sc.parallelize([(1,2),(1,3),(1,4),(2,1),(3,1),(4,1)]).toDF(["src","dst"])
e.show()
{code}
{code}
+---+---+
|src|dst|
+---+---+
| 1| 2|
| 1| 3|
| 1| 4|
| 2| 1|
| 3| 1|
| 4| 1|
+---+---+
{code}
*source B*
{code}
from pyspark.sql.functions import *
e = spark.read.csv("graph.csv", header=True)
e.show()
{code}
{code}
+---+---+
|src|dst|
+---+---+
| 1| 2|
| 1| 3|
| 1| 4|
| 2| 1|
| 3| 1|
| 4| 1|
+---+---+
{code}
*Reproducer*
{code}
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')
r2 = jr.groupBy('dst').count()
r2.show()
{code}
*Results from source A and the reproducer*
{code}
+---+-----+
|dst|count|
+---+-----+
| 1| 3|
| 3| 1|
| 2| 1|
| 4| 1|
+---+-----+
{code}
*Results from source B and the reproducer*
{code}
+---+-----+
|dst|count|
+---+-----+
| 1| 1|
| 4| 1|
| 3| 1|
| 2| 1|
| 1| 1|
| 1| 1|
+---+-----+
{code}
> 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 in Catalyst optimizer which cannot correctly
> process "groupby". You can reproduce it 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 (commented line), 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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