Hi Jacek,
Thank you for the workaround.
It is really working in this way:
pos.as("p1").join(pos.as("p2")).filter($"p1.POSITION_ID0"===$"p2.POSITION_ID")
I have checked, that in this way I get the same execution plan as for
the join with renamed columns.
Best,
Michael
On Mon, Jan 15, 2018 at 10:33 PM, Jacek Laskowski <ja...@japila.pl> wrote:
> 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 <mshte...@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.w...@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 <ja...@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 <mshte...@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
>> >>
>> >> ---------------------------------------------------------------------
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>> >>
>> >
>> >
>
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