super interesting. On Wed, Mar 7, 2018 at 11:44 AM, kant kodali <kanth...@gmail.com> wrote:
> It looks to me that the StateStore described in this doc > <https://docs.google.com/document/d/1i528WI7KFica0Dg1LTQfdQMsW8ai3WDvHmUvkH1BKg4/edit> > Actually > has full outer join and every other join is a filter of that. Also the doc > talks about update mode but looks like Spark 2.3 ended up with append mode? > Anyways the moment it is in master I am ready to test so JIRA tickets on > this would help to keep track. please let me know. > > Thanks! > > On Tue, Mar 6, 2018 at 9:16 PM, kant kodali <kanth...@gmail.com> wrote: > >> Sorry I meant Spark 2.4 in my previous email >> >> On Tue, Mar 6, 2018 at 9:15 PM, kant kodali <kanth...@gmail.com> wrote: >> >>> Hi TD, >>> >>> I agree I think we are better off either with a full fix or no fix. I am >>> ok with the complete fix being available in master or some branch. I guess >>> the solution for me is to just build from the source. >>> >>> On a similar note, I am not finding any JIRA tickets related to full >>> outer joins and update mode for maybe say Spark 2.3. I wonder how hard is >>> it two implement both of these? It turns out the update mode and full outer >>> join is very useful and required in my case, therefore, I'm just asking. >>> >>> Thanks! >>> >>> On Tue, Mar 6, 2018 at 6:25 PM, Tathagata Das < >>> tathagata.das1...@gmail.com> wrote: >>> >>>> I thought about it. >>>> I am not 100% sure whether this fix should go into 2.3.1. >>>> >>>> There are two parts to this bug fix to enable self-joins. >>>> >>>> 1. Enabling deduping of leaf logical nodes by extending >>>> MultInstanceRelation >>>> - This is safe to be backported into the 2.3 branch as it does not >>>> touch production code paths. >>>> >>>> 2. Fixing attribute rewriting in MicroBatchExecution, when the >>>> micro-batch plan is spliced into the streaming plan. >>>> - This touches core production code paths and therefore, may not safe >>>> to backport. >>>> >>>> Part 1 enables self-joins in all but a small fraction of self-join >>>> queries. That small fraction can produce incorrect results, and part 2 >>>> avoids that. >>>> >>>> So for 2.3.1, we can enable self-joins by merging only part 1, but it >>>> can give wrong results in some cases. I think that is strictly worse than >>>> no fix. >>>> >>>> TD >>>> >>>> >>>> >>>> On Thu, Feb 22, 2018 at 2:32 PM, kant kodali <kanth...@gmail.com> >>>> wrote: >>>> >>>>> Hi TD, >>>>> >>>>> I pulled your commit that is listed on this ticket >>>>> https://issues.apache.org/jira/browse/SPARK-23406 specifically I did >>>>> the following steps and self joins work after I cherry-pick your commit! >>>>> Good Job! I was hoping it will be part of 2.3.0 but looks like it is >>>>> targeted for 2.3.1 :( >>>>> >>>>> git clone https://github.com/apache/spark.gitcd spark >>>>> git fetch >>>>> git checkout branch-2.3 >>>>> git cherry-pick 658d9d9d785a30857bf35d164e6cbbd9799d6959 >>>>> export MAVEN_OPTS="-Xmx2g -XX:ReservedCodeCacheSize=512m" >>>>> ./build/mvn -DskipTests compile >>>>> ./dev/make-distribution.sh --name custom-spark --pip --r --tgz -Psparkr >>>>> -Phadoop-2.7 -Phive -Phive-thriftserver -Pmesos -Pyarn >>>>> >>>>> >>>>> On Thu, Feb 22, 2018 at 11:25 AM, Tathagata Das < >>>>> tathagata.das1...@gmail.com> wrote: >>>>> >>>>>> Hey, >>>>>> >>>>>> Thanks for testing out stream-stream joins and reporting this issue. >>>>>> I am going to take a look at this. >>>>>> >>>>>> TD >>>>>> >>>>>> >>>>>> >>>>>> On Tue, Feb 20, 2018 at 8:20 PM, kant kodali <kanth...@gmail.com> >>>>>> wrote: >>>>>> >>>>>>> if I change it to the below code it works. However, I don't believe >>>>>>> it is the solution I am looking for. I want to be able to do it in raw >>>>>>> SQL and moreover, If a user gives a big chained raw spark SQL join >>>>>>> query I >>>>>>> am not even sure how to make copies of the dataframe to achieve the >>>>>>> self-join. Is there any other way here? >>>>>>> >>>>>>> >>>>>>> >>>>>>> import org.apache.spark.sql.streaming.Trigger >>>>>>> >>>>>>> val jdf = >>>>>>> spark.readStream.format("kafka").option("kafka.bootstrap.servers", >>>>>>> "localhost:9092").option("subscribe", >>>>>>> "join_test").option("startingOffsets", "earliest").load(); >>>>>>> val jdf1 = >>>>>>> spark.readStream.format("kafka").option("kafka.bootstrap.servers", >>>>>>> "localhost:9092").option("subscribe", >>>>>>> "join_test").option("startingOffsets", "earliest").load(); >>>>>>> >>>>>>> jdf.createOrReplaceTempView("table") >>>>>>> jdf1.createOrReplaceTempView("table") >>>>>>> >>>>>>> val resultdf = spark.sql("select * from table inner join table1 on >>>>>>> table.offset=table1.offset") >>>>>>> >>>>>>> resultdf.writeStream.outputMode("append").format("console").option("truncate", >>>>>>> false).trigger(Trigger.ProcessingTime(1000)).start() >>>>>>> >>>>>>> >>>>>>> On Tue, Feb 20, 2018 at 8:16 PM, kant kodali <kanth...@gmail.com> >>>>>>> wrote: >>>>>>> >>>>>>>> If I change it to this >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On Tue, Feb 20, 2018 at 7:52 PM, kant kodali <kanth...@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>>> Hi All, >>>>>>>>> >>>>>>>>> I have the following code >>>>>>>>> >>>>>>>>> import org.apache.spark.sql.streaming.Trigger >>>>>>>>> >>>>>>>>> val jdf = >>>>>>>>> spark.readStream.format("kafka").option("kafka.bootstrap.servers", >>>>>>>>> "localhost:9092").option("subscribe", >>>>>>>>> "join_test").option("startingOffsets", "earliest").load(); >>>>>>>>> >>>>>>>>> jdf.createOrReplaceTempView("table") >>>>>>>>> >>>>>>>>> val resultdf = spark.sql("select * from table as x inner join table >>>>>>>>> as y on x.offset=y.offset") >>>>>>>>> >>>>>>>>> resultdf.writeStream.outputMode("update").format("console").option("truncate", >>>>>>>>> false).trigger(Trigger.ProcessingTime(1000)).start() >>>>>>>>> >>>>>>>>> and I get the following exception. >>>>>>>>> >>>>>>>>> org.apache.spark.sql.AnalysisException: cannot resolve '`x.offset`' >>>>>>>>> given input columns: [x.value, x.offset, x.key, x.timestampType, >>>>>>>>> x.topic, x.timestamp, x.partition]; line 1 pos 50; >>>>>>>>> 'Project [*] >>>>>>>>> +- 'Join Inner, ('x.offset = 'y.offset) >>>>>>>>> :- SubqueryAlias x >>>>>>>>> : +- SubqueryAlias table >>>>>>>>> : +- StreamingRelation >>>>>>>>> DataSource(org.apache.spark.sql.SparkSession@15f3f9cf,kafka,List(),None,List(),None,Map(startingOffsets >>>>>>>>> -> earliest, subscribe -> join_test, kafka.bootstrap.servers -> >>>>>>>>> localhost:9092),None), kafka, [key#28, value#29, topic#30, >>>>>>>>> partition#31, offset#32L, timestamp#33, timestampType#34] >>>>>>>>> +- SubqueryAlias y >>>>>>>>> +- SubqueryAlias table >>>>>>>>> +- StreamingRelation >>>>>>>>> DataSource(org.apache.spark.sql.SparkSession@15f3f9cf,kafka,List(),None,List(),None,Map(startingOffsets >>>>>>>>> -> earliest, subscribe -> join_test, kafka.bootstrap.servers -> >>>>>>>>> localhost:9092),None), kafka, [key#28, value#29, topic#30, >>>>>>>>> partition#31, offset#32L, timestamp#33, timestampType#34] >>>>>>>>> >>>>>>>>> any idea whats wrong here? >>>>>>>>> >>>>>>>>> Thanks! >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
