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Posted to issues@spark.apache.org by "Neville Kadwa (JIRA)" <ji...@apache.org> on 2016/05/04 14:53:12 UTC
[jira] [Comment Edited] (SPARK-15063) filtering and joining back
doesn't work
[ https://issues.apache.org/jira/browse/SPARK-15063?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15270735#comment-15270735 ]
Neville Kadwa edited comment on SPARK-15063 at 5/4/16 2:52 PM:
---------------------------------------------------------------
Ahh yes. That explains it.
Thank you very much!
The key is that the join type I started with results in multiple uids being present in the result:
{code}
scala> t1.join(t2c, t1("uid") <=> t2c("uid"), "left")
res6: org.apache.spark.sql.DataFrame = [uid: int, name: string, uid: int, type: string, amount: double]
{code}
Which then needs to be dealt with:
{code}
t1.join(t2c, $"uid", "left")
org.apache.spark.sql.AnalysisException: Reference 'uid' is ambiguous, could be: uid#2, uid#7.
{code}
However, wouldn't {{t1("uid")}} retain the reference {{uid#2}}?
Does that explain why it doesn't error but executes incorrectly? I think it should either error or execute correctly since the query is the same.
was (Author: nkadwa):
Ahh yes. That explains it.
Thank you very much!
The key is that the join type I started with results in multiple uids being present in the result:
{code}
scala> t1.join(t2c, t1("uid") <=> t2c("uid"), "left")
res6: org.apache.spark.sql.DataFrame = [uid: int, name: string, uid: int, type: string, amount: double]
{code}
Which then needs to be dealt with:
{code}
t1.join(t2c, $"uid", "left")
org.apache.spark.sql.AnalysisException: Reference 'uid' is ambiguous, could be: uid#2, uid#7.
{code}
However, wouldn't {{t1("uid")}} retain the reference {{uid#2}}?
It would make code look much more elegant to allow that to work.
> filtering and joining back doesn't work
> ---------------------------------------
>
> Key: SPARK-15063
> URL: https://issues.apache.org/jira/browse/SPARK-15063
> Project: Spark
> Issue Type: Bug
> Affects Versions: 1.6.1
> Reporter: Neville Kadwa
>
> I'm trying to filter and join to do a simple pivot but getting very odd results.
> {quote} {noformat}
> val sqlContext = new org.apache.spark.sql.SQLContext(sc)
> import sqlContext.implicits._
> val people = Array((1, "sam"), (2, "joe"), (3, "sally"), (4, "joanna"))
> val accounts = Array(
> (1, "checking", 100.0),
> (1, "savings", 300.0),
> (2, "savings", 1000.0),
> (3, "carloan", 12000.0),
> (3, "checking", 400.0)
> )
> val t1 = sc.makeRDD(people).toDF("uid", "name")
> val t2 = sc.makeRDD(accounts).toDF("uid", "type", "amount")
> val t2c = t2.filter(t2("type") <=> "checking")
> val t2s = t2.filter(t2("type") <=> "savings")
> t1.
> join(t2c, t1("uid") <=> t2c("uid"), "left").
> join(t2s, t1("uid") <=> t2s("uid"), "left").
> take(10)
> {noformat} {quote}
> The results are wrong:
> {quote} {noformat}
> Array(
> [1,sam,1,checking,100.0,1,savings,300.0],
> [1,sam,1,checking,100.0,2,savings,1000.0],
> [2,joe,null,null,null,null,null,null],
> [3,sally,3,checking,400.0,1,savings,300.0],
> [3,sally,3,checking,400.0,2,savings,1000.0],
> [4,joanna,null,null,null,null,null,null]
> )
> {noformat} {quote}
> The way I can force it to work properly is to create a new df for each filter:
> {quote} {noformat}
> val t2a = sc.makeRDD(accounts).toDF("uid", "type", "amount")
> val t2s = t2a.filter(t2a("type") <=> "savings")
> t1.
> join(t2c, t1("uid") <=> t2c("uid"), "left").
> join(t2s, t1("uid") <=> t2s("uid"), "left").
> take(10)
> {noformat} {quote}
> The results are right:
> {quote} {noformat}
> Array(
> [1,sam,1,checking,100.0,1,savings,300.0],
> [2,joe,null,null,null,2,savings,1000.0],
> [3,sally,3,checking,400.0,null,null,null],
> [4,joanna,null,null,null,null,null,null]
> )
> {noformat} {quote}
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