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From Jacek Laskowski <ja...@japila.pl>
Subject Re: Inner join with the table itself
Date Mon, 15 Jan 2018 21:33:01 GMT
Hi Michael,

scala> spark.version
res0: String = 2.4.0-SNAPSHOT

scala> val r1 = spark.range(1)
r1: org.apache.spark.sql.Dataset[Long] = [id: bigint]

scala> r1.as("left").join(r1.as("right")).filter($"left.id" === $"right.id
").show
+---+---+
| id| id|
+---+---+
|  0|  0|
+---+---+

Am I missing something? When aliasing a table, use the identifier in column
refs (inside).


Pozdrawiam,
Jacek Laskowski
----
https://about.me/JacekLaskowski
Mastering Spark SQL https://bit.ly/mastering-spark-sql
Spark Structured Streaming https://bit.ly/spark-structured-streaming
Mastering Kafka Streams https://bit.ly/mastering-kafka-streams
Follow me at https://twitter.com/jaceklaskowski

On Mon, Jan 15, 2018 at 3:26 PM, Michael Shtelma <mshtelma@gmail.com> wrote:

> Hi Jacek & Gengliang,
>
> let's take a look at the following query:
>
> val pos = spark.read.parquet(prefix + "POSITION.parquet")
> pos.createOrReplaceTempView("POSITION")
> spark.sql("SELECT  POSITION.POSITION_ID  FROM POSITION POSITION JOIN
> POSITION POSITION1 ON POSITION.POSITION_ID0 = POSITION1.POSITION_ID
> ").collect()
>
> This query is working for me right now using spark 2.2.
>
> Now we can try implementing the same logic with DataFrame API:
>
> pos.join(pos, pos("POSITION_ID0")===pos("POSITION_ID")).collect()
>
> I am getting the following error:
>
> "Join condition is missing or trivial.
>
> Use the CROSS JOIN syntax to allow cartesian products between these
> relations.;"
>
> I have tried using alias function, but without success:
>
> val pos2 = pos.alias("P2")
> pos.join(pos2, pos("POSITION_ID0")===pos2("POSITION_ID")).collect()
>
> This also leads us to the same error.
> Am  I missing smth about the usage of alias?
>
> Now let's rename the columns:
>
> val pos3 = pos.toDF(pos.columns.map(_ + "_2"): _*)
> pos.join(pos3, pos("POSITION_ID0")===pos3("POSITION_ID_2")).collect()
>
> It works!
>
> There is one more really odd thing about all this: a colleague of mine
> has managed to get the same exception ("Join condition is missing or
> trivial") also using original SQL query, but I think he has been using
> empty tables.
>
> Thanks,
> Michael
>
>
> On Mon, Jan 15, 2018 at 11:27 AM, Gengliang Wang
> <gengliang.wang@databricks.com> wrote:
> > Hi Michael,
> >
> > You can use `Explain` to see how your query is optimized.
> > https://docs.databricks.com/spark/latest/spark-sql/
> language-manual/explain.html
> > I believe your query is an actual cross join, which is usually very slow
> in
> > execution.
> >
> > To get rid of this, you can set `spark.sql.crossJoin.enabled` as true.
> >
> >
> > 在 2018年1月15日,下午6:09,Jacek Laskowski <jacek@japila.pl> 写道:
> >
> > Hi Michael,
> >
> > -dev +user
> >
> > What's the query? How do you "fool spark"?
> >
> > Pozdrawiam,
> > Jacek Laskowski
> > ----
> > https://about.me/JacekLaskowski
> > Mastering Spark SQL https://bit.ly/mastering-spark-sql
> > Spark Structured Streaming https://bit.ly/spark-structured-streaming
> > Mastering Kafka Streams https://bit.ly/mastering-kafka-streams
> > Follow me at https://twitter.com/jaceklaskowski
> >
> > On Mon, Jan 15, 2018 at 10:23 AM, Michael Shtelma <mshtelma@gmail.com>
> > wrote:
> >>
> >> Hi all,
> >>
> >> If I try joining the table with itself using join columns, I am
> >> getting the following error:
> >> "Join condition is missing or trivial. Use the CROSS JOIN syntax to
> >> allow cartesian products between these relations.;"
> >>
> >> This is not true, and my join is not trivial and is not a real cross
> >> join. I am providing join condition and expect to get maybe a couple
> >> of joined rows for each row in the original table.
> >>
> >> There is a workaround for this, which implies renaming all the columns
> >> in source data frame and only afterwards proceed with the join. This
> >> allows us to fool spark.
> >>
> >> Now I am wondering if there is a way to get rid of this problem in a
> >> better way? I do not like the idea of renaming the columns because
> >> this makes it really difficult to keep track of the names in the
> >> columns in result data frames.
> >> Is it possible to deactivate this check?
> >>
> >> Thanks,
> >> Michael
> >>
> >> ---------------------------------------------------------------------
> >> To unsubscribe e-mail: dev-unsubscribe@spark.apache.org
> >>
> >
> >
>

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