Hi Jacek & Gengliang,
let's take a look at the following query:
val pos = spark.read.parquet(prefix + "POSITION.parquet")
spark.sql("SELECT POSITION.POSITION_ID FROM POSITION POSITION JOIN
POSITION POSITION1 ON POSITION.POSITION_ID0 = POSITION1.POSITION_ID
This query is working for me right now using spark 2.2.
Now we can try implementing the same logic with DataFrame API:
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")
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"): _*)
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
On Mon, Jan 15, 2018 at 11:27 AM, Gengliang Wang
> Hi Michael,
> You can use `Explain` to see how your query is optimized.
> I believe your query is an actual cross join, which is usually very slow in
> 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 <email@example.com> 写道：
> Hi Michael,
> -dev +user
> What's the query? How do you "fool spark"?
> Jacek Laskowski
> Mastering Spark SQL https://bit.ly/mastering-
> Spark Structured Streaming https://bit.ly/spark-
> Mastering Kafka Streams https://bit.ly/mastering-
> Follow me at https://twitter.com/
> On Mon, Jan 15, 2018 at 10:23 AM, Michael Shtelma <firstname.lastname@example.org>
>> 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?
>> To unsubscribe e-mail: email@example.com.