Hi
Have you tried the joins without the where clause? When you use them you
are filtering all the rows with null columns in those fields. In other
words you are doing a inner join in all your queries.
On Tue, May 3, 2016 at 11:37 AM, Gourav Sengupta <gourav.sengu...@gmail.com>
wrote:
> Hi Kevin,
>
> Having given it a first look I do think that you have hit something here
> and this does not look quite fine. I have to work on the multiple AND
> conditions in ON and see whether that is causing any issues.
>
> Regards,
> Gourav Sengupta
>
> On Tue, May 3, 2016 at 8:28 AM, Kevin Peng <kpe...@gmail.com> wrote:
>
>> Davies,
>>
>> Here is the code that I am typing into the spark-shell along with the
>> results (my question is at the bottom):
>>
>> val dps =
>> sqlContext.read.format("com.databricks.spark.csv").option("header",
>> "true").load("file:///home/ltu/dps_csv/")
>> val swig =
>> sqlContext.read.format("com.databricks.spark.csv").option("header",
>> "true").load("file:///home/ltu/swig_csv/")
>>
>> dps.count
>> res0: Long = 42694
>>
>> swig.count
>> res1: Long = 42034
>>
>>
>> dps.registerTempTable("dps_pin_promo_lt")
>> swig.registerTempTable("swig_pin_promo_lt")
>>
>> sqlContext.sql("select * from dps_pin_promo_lt where date >
>> '2016-01-03'").count
>> res4: Long = 42666
>>
>> sqlContext.sql("select * from swig_pin_promo_lt where date >
>> '2016-01-03'").count
>> res5: Long = 34131
>>
>> sqlContext.sql("select distinct date, account, ad from dps_pin_promo_lt
>> where date > '2016-01-03'").count
>> res6: Long = 42533
>>
>> sqlContext.sql("select distinct date, account, ad from swig_pin_promo_lt
>> where date > '2016-01-03'").count
>> res7: Long = 34131
>>
>>
>> sqlContext.sql("SELECT s.date AS edate , s.account AS s_acc , d.account
>> AS d_acc , s.ad as s_ad , d.ad as d_ad , s.spend AS s_spend ,
>> d.spend_in_dollar AS d_spend FROM swig_pin_promo_lt s INNER JOIN
>> dps_pin_promo_lt d ON (s.date = d.date AND s.account = d.account AND
>> s.ad = d.ad) WHERE s.date >= '2016-01-03' AND d.date >=
>> '2016-01-03'").count()
>> res9: Long = 23809
>>
>>
>> sqlContext.sql("SELECT s.date AS edate , s.account AS s_acc , d.account
>> AS d_acc , s.ad as s_ad , d.ad as d_ad , s.spend AS s_spend ,
>> d.spend_in_dollar AS d_spend FROM swig_pin_promo_lt s FULL OUTER JOIN
>> dps_pin_promo_lt d ON (s.date = d.date AND s.account = d.account AND
>> s.ad = d.ad) WHERE s.date >= '2016-01-03' AND d.date >=
>> '2016-01-03'").count()
>> res10: Long = 23809
>>
>>
>> sqlContext.sql("SELECT s.date AS edate , s.account AS s_acc , d.account
>> AS d_acc , s.ad as s_ad , d.ad as d_ad , s.spend AS s_spend ,
>> d.spend_in_dollar AS d_spend FROM swig_pin_promo_lt s LEFT OUTER JOIN
>> dps_pin_promo_lt d ON (s.date = d.date AND s.account = d.account AND
>> s.ad = d.ad) WHERE s.date >= '2016-01-03' AND d.date >=
>> '2016-01-03'").count()
>> res11: Long = 23809
>>
>>
>> sqlContext.sql("SELECT s.date AS edate , s.account AS s_acc , d.account
>> AS d_acc , s.ad as s_ad , d.ad as d_ad , s.spend AS s_spend ,
>> d.spend_in_dollar AS d_spend FROM swig_pin_promo_lt s RIGHT OUTER JOIN
>> dps_pin_promo_lt d ON (s.date = d.date AND s.account = d.account AND
>> s.ad = d.ad) WHERE s.date >= '2016-01-03' AND d.date >=
>> '2016-01-03'").count()
>> res12: Long = 23809
>>
>>
>>
>> From my results above, we notice that the counts of distinct values based
>> on the join criteria and filter criteria for each individual table is
>> located at res6 and res7. My question is why is the outer join producing
>> less rows than the smallest table; if there are no matches it should still
>> bring in that row as part of the outer join. For the full and right outer
>> join I am expecting to see a minimum of res6 rows, but I get less, is there
>> something specific that I am missing here? I am expecting that the full
>> outer join would give me the union of the two table sets so I am expecting
>> at least 42533 rows not 23809.
>>
>>
>> Gourav,
>>
>> I just ran this result set on a new session with slightly newer data...
>> still seeing those results.
>>
>>
>>
>> Thanks,
>>
>> KP
>>
>>
>> On Mon, May 2, 2016 at 11:16 PM, Davies Liu <dav...@databricks.com>
>> wrote:
>>
>>> as @Gourav said, all the join with different join type show the same
>>> results,
>>> which meant that all the rows from left could match at least one row
>>> from right,
>>> all the rows from right could match at least one row from left, even
>>> the number of row from left does not equal that of right.
>>>
>>> This is correct result.
>>>
>>> On Mon, May 2, 2016 at 2:13 PM, Kevin Peng <kpe...@gmail.com> wrote:
>>> > Yong,
>>> >
>>> > Sorry, let explain my deduction; it is going be difficult to get a
>>> sample
>>> > data out since the dataset I am using is proprietary.
>>> >
>>> > From the above set queries (ones mentioned in above comments) both
>>> inner and
>>> > outer join are producing the same counts. They are basically pulling
>>> out
>>> > selected columns from the query, but there is no roll up happening or
>>> > anything that would possible make it suspicious that there is any
>>> difference
>>> > besides the type of joins. The tables are matched based on three keys
>>> that
>>> > are present in both tables (ad, account, and date), on top of this
>>> they are
>>> > filtered by date being above 2016-01-03. Since all the joins are
>>> producing
>>> > the same counts, the natural suspicions is that the tables are
>>> identical,
>>> > but I when I run the following two queries:
>>> >
>>> > scala> sqlContext.sql("select * from swig_pin_promo_lt where date
>>> >>='2016-01-03'").count
>>> >
>>> > res14: Long = 34158
>>> >
>>> > scala> sqlContext.sql("select * from dps_pin_promo_lt where date
>>> >>='2016-01-03'").count
>>> >
>>> > res15: Long = 42693
>>> >
>>> >
>>> > The above two queries filter out the data based on date used by the
>>> joins of
>>> > 2016-01-03 and you can see the row count between the two tables are
>>> > different, which is why I am suspecting something is wrong with the
>>> outer
>>> > joins in spark sql, because in this situation the right and outer
>>> joins may
>>> > produce the same results, but it should not be equal to the left join
>>> and
>>> > definitely not the inner join; unless I am missing something.
>>> >
>>> >
>>> > Side note: In my haste response above I posted the wrong counts for
>>> > dps.count, the real value is res16: Long = 42694
>>> >
>>> >
>>> > Thanks,
>>> >
>>> >
>>> > KP
>>> >
>>> >
>>> >
>>> >
>>> > On Mon, May 2, 2016 at 12:50 PM, Yong Zhang <java8...@hotmail.com>
>>> wrote:
>>> >>
>>> >> We are still not sure what is the problem, if you cannot show us with
>>> some
>>> >> example data.
>>> >>
>>> >> For dps with 42632 rows, and swig with 42034 rows, if dps full outer
>>> join
>>> >> with swig on 3 columns; with additional filters, get the same
>>> resultSet row
>>> >> count as dps lefter outer join with swig on 3 columns, with additional
>>> >> filters, again get the the same resultSet row count as dps right
>>> outer join
>>> >> with swig on 3 columns, with same additional filters.
>>> >>
>>> >> Without knowing your data, I cannot see the reason that has to be a
>>> bug in
>>> >> the spark.
>>> >>
>>> >> Am I misunderstanding your bug?
>>> >>
>>> >> Yong
>>> >>
>>> >> ________________________________
>>> >> From: kpe...@gmail.com
>>> >> Date: Mon, 2 May 2016 12:11:18 -0700
>>> >> Subject: Re: Weird results with Spark SQL Outer joins
>>> >> To: gourav.sengu...@gmail.com
>>> >> CC: user@spark.apache.org
>>> >>
>>> >>
>>> >> Gourav,
>>> >>
>>> >> I wish that was case, but I have done a select count on each of the
>>> two
>>> >> tables individually and they return back different number of rows:
>>> >>
>>> >>
>>> >> dps.registerTempTable("dps_pin_promo_lt")
>>> >> swig.registerTempTable("swig_pin_promo_lt")
>>> >>
>>> >>
>>> >> dps.count()
>>> >> RESULT: 42632
>>> >>
>>> >>
>>> >> swig.count()
>>> >> RESULT: 42034
>>> >>
>>> >> On Mon, May 2, 2016 at 11:55 AM, Gourav Sengupta
>>> >> <gourav.sengu...@gmail.com> wrote:
>>> >>
>>> >> This shows that both the tables have matching records and no
>>> mismatches.
>>> >> Therefore obviously you have the same results irrespective of whether
>>> you
>>> >> use right or left join.
>>> >>
>>> >> I think that there is no problem here, unless I am missing something.
>>> >>
>>> >> Regards,
>>> >> Gourav
>>> >>
>>> >> On Mon, May 2, 2016 at 7:48 PM, kpeng1 <kpe...@gmail.com> wrote:
>>> >>
>>> >> Also, the results of the inner query produced the same results:
>>> >> sqlContext.sql("SELECT s.date AS edate , s.account AS s_acc ,
>>> d.account
>>> >> AS
>>> >> d_acc , s.ad as s_ad , d.ad as d_ad , s.spend AS s_spend ,
>>> >> d.spend_in_dollar AS d_spend FROM swig_pin_promo_lt s INNER JOIN
>>> >> dps_pin_promo_lt d ON (s.date = d.date AND s.account = d.account AND
>>> s.ad
>>> >> =
>>> >> d.ad) WHERE s.date >= '2016-01-03' AND d.date >=
>>> '2016-01-03'").count()
>>> >> RESULT:23747
>>> >>
>>> >>
>>> >>
>>> >> --
>>> >> View this message in context:
>>> >>
>>> http://apache-spark-user-list.1001560.n3.nabble.com/Weird-results-with-Spark-SQL-Outer-joins-tp26861p26863.html
>>> >> Sent from the Apache Spark User List mailing list archive at
>>> Nabble.com.
>>> >>
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>>> >>
>>> >>
>>> >>
>>> >
>>>
>>
>>
>
--
Cesar Flores
