I'm working around it like this:
val testMapped2 = test1.rdd.map(t => t.copy(id = t.id + 1)).toDF.as[Test]
testMapped2.as("t1").joinWith(testMapped2.as("t2"), $"t1.id" === $"t2.id
").show
Switching from RDD, then mapping, then going back to DS seemed to avoid the
issue.
On Fri, May 27, 2016 at 3:10 PM Koert Kuipers <ko...@tresata.com> wrote:
> i am glad to see this, i think we can into this as well (in
> 2.0.0-SNAPSHOT) but i couldn't reproduce it nicely.
>
> my observation was that joins of 2 datasets that were derived from the
> same datasource gave this kind of trouble. i changed my datasource from val
> to def (so it got created twice) as a workaround. the error did not occur
> with datasets created in unit test with sc.parallelize.
>
> On Fri, May 27, 2016 at 1:26 PM, Ted Yu <yuzhih...@gmail.com> wrote:
>
>> I tried master branch :
>>
>> scala> val testMapped = test.map(t => t.copy(id = t.id + 1))
>> testMapped: org.apache.spark.sql.Dataset[Test] = [id: int]
>>
>> scala> testMapped.as("t1").joinWith(testMapped.as("t2"), $"t1.id" === $"
>> t2.id").show
>> org.apache.spark.sql.AnalysisException: cannot resolve '`t1.id`' given
>> input columns: [id];
>> at
>> org.apache.spark.sql.catalyst.analysis.package$AnalysisErrorAt.failAnalysis(package.scala:42)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:62)
>> at
>> org.apache.spark.sql.catalyst.analysis.CheckAnalysis$$anonfun$checkAnalysis$1$$anonfun$apply$2.applyOrElse(CheckAnalysis.scala:59)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:287)
>> at
>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:287)
>> at
>> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:68)
>>
>>
>> Suggest logging a JIRA if there is none logged.
>>
>> On Fri, May 27, 2016 at 10:19 AM, Tim Gautier <tim.gaut...@gmail.com>
>> wrote:
>>
>>> Oops, screwed up my example. This is what it should be:
>>>
>>> case class Test(id: Int)
>>> val test = Seq(
>>> Test(1),
>>> Test(2),
>>> Test(3)
>>> ).toDS
>>> test.as("t1").joinWith(test.as("t2"), $"t1.id" === $"t2.id").show
>>> val testMapped = test.map(t => t.copy(id = t.id + 1))
>>> testMapped.as("t1").joinWith(testMapped.as("t2"), $"t1.id" === $"
>>> t2.id").show
>>>
>>>
>>> On Fri, May 27, 2016 at 11:16 AM Tim Gautier <tim.gaut...@gmail.com>
>>> wrote:
>>>
>>>> I figured it out the trigger. Turns out it wasn't because I loaded it
>>>> from the database, it was because the first thing I do after loading is to
>>>> lower case all the strings. After a Dataset has been mapped, the resulting
>>>> Dataset can't be self joined. Here's a test case that illustrates the
>>>> issue:
>>>>
>>>> case class Test(id: Int)
>>>> val test = Seq(
>>>> Test(1),
>>>> Test(2),
>>>> Test(3)
>>>> ).toDS
>>>> test.as("t1").joinWith(test.as("t2"), $"t1.id" === $"t2.id").show
>>>> // <-- works fine
>>>> val testMapped = test.map(_.id + 1) // add 1 to each
>>>> testMapped.as("t1").joinWith(testMapped.as("t2"), $"t1.id" === $"
>>>> t2.id").show // <-- error
>>>>
>>>>
>>>> On Fri, May 27, 2016 at 10:44 AM Tim Gautier <tim.gaut...@gmail.com>
>>>> wrote:
>>>>
>>>>> I stand corrected. I just created a test table with a single int field
>>>>> to test with and the Dataset loaded from that works with no issues. I'll
>>>>> see if I can track down exactly what the difference might be.
>>>>>
>>>>> On Fri, May 27, 2016 at 10:29 AM Tim Gautier <tim.gaut...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> I'm using 1.6.1.
>>>>>>
>>>>>> I'm not sure what good fake data would do since it doesn't seem to
>>>>>> have anything to do with the data itself. It has to do with how the
>>>>>> Dataset
>>>>>> was created. Both datasets have exactly the same data in them, but the
>>>>>> one
>>>>>> created from a sql query fails where the one created from a Seq works.
>>>>>> The
>>>>>> case class is just a few Option[Int] and Option[String] fields, nothing
>>>>>> special.
>>>>>>
>>>>>> Obviously there's some sort of difference between the two datasets,
>>>>>> but Spark tells me they're exactly the same type with exactly the same
>>>>>> data, so I couldn't create a test case without actually accessing a sql
>>>>>> database.
>>>>>>
>>>>>> On Fri, May 27, 2016 at 10:15 AM Ted Yu <yuzhih...@gmail.com> wrote:
>>>>>>
>>>>>>> Which release of Spark are you using ?
>>>>>>>
>>>>>>> Is it possible to come up with fake data that shows what you
>>>>>>> described ?
>>>>>>>
>>>>>>> Thanks
>>>>>>>
>>>>>>> On Fri, May 27, 2016 at 8:24 AM, Tim Gautier <tim.gaut...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Unfortunately I can't show exactly the data I'm using, but this is
>>>>>>>> what I'm seeing:
>>>>>>>>
>>>>>>>> I have a case class 'Product' that represents a table in our
>>>>>>>> database. I load that data via
>>>>>>>> sqlContext.read.format("jdbc").options(...).
>>>>>>>> load.as[Product] and register it in a temp table 'product'.
>>>>>>>>
>>>>>>>> For testing, I created a new Dataset that has only 3 records in it:
>>>>>>>>
>>>>>>>> val ts = sqlContext.sql("select * from product where
>>>>>>>> product_catalog_id in (1, 2, 3)").as[Product]
>>>>>>>>
>>>>>>>> I also created another one using the same case class and data, but
>>>>>>>> from a sequence instead.
>>>>>>>>
>>>>>>>> val ds: Dataset[Product] = Seq(
>>>>>>>> Product(Some(1), ...),
>>>>>>>> Product(Some(2), ...),
>>>>>>>> Product(Some(3), ...)
>>>>>>>> ).toDS
>>>>>>>>
>>>>>>>> The spark shell tells me these are exactly the same type at this
>>>>>>>> point, but they don't behave the same.
>>>>>>>>
>>>>>>>> ts.as("ts1").joinWith(ts.as("ts2"), $"ts1.product_catalog_id" ===
>>>>>>>> $"ts2.product_catalog_id")
>>>>>>>> ds.as("ds1").joinWith(ds.as("ds2"), $"ds1.product_catalog_id" ===
>>>>>>>> $"ds2.product_catalog_id")
>>>>>>>>
>>>>>>>> Again, spark tells me these self joins return exactly the same
>>>>>>>> type, but when I do a .show on them, only the one created from a Seq
>>>>>>>> works.
>>>>>>>> The one created by reading from the database throws this error:
>>>>>>>>
>>>>>>>> org.apache.spark.sql.AnalysisException: cannot resolve
>>>>>>>> 'ts1.product_catalog_id' given input columns: [..., product_catalog_id,
>>>>>>>> ...];
>>>>>>>>
>>>>>>>> Is this a bug? Is there anyway to make the Dataset loaded from a
>>>>>>>> table behave like the one created from a sequence?
>>>>>>>>
>>>>>>>> Thanks,
>>>>>>>> Tim
>>>>>>>>
>>>>>>>
>>>>>>>
>>
>
