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! >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>> >> >
