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From Cheng Lian <lian.cs....@gmail.com>
Subject Re: Using one sql query's result inside another sql query
Date Mon, 29 Sep 2014 03:41:20 GMT
This workaround looks good to me. In this way, all queries are still 
executed lazily within a single DAG, and Spark SQL is capable to 
optimize the query plan as a whole.

On 9/29/14 11:26 AM, twinkle sachdeva wrote:
> Thanks Cheng.
>
> For the time being , As a work around, I had applied the schema 
> to Queryresult1, and then registered the result as temp table.  
> Although that works, but I was not sure of performance impact, as that 
> might block some optimisation in some scenarios.
>
> This flow (on spark 1.1 ) works:
>
> registerTempTable(cachedSchema)
> Queryresult1 = Query1 using cachedSchema  [ works ]
>
> *queryResult1withSchema = hiveContext.applySchema( Queryresult1, 
> Queryresult1.schema )*
> registerTempTable(*queryResult1withSchema*)
>
> Queryresult2 = Query2 using *queryResult1withSchema* [ *works* ]
>
>
> On Fri, Sep 26, 2014 at 5:13 PM, Cheng Lian <lian.cs.zju@gmail.com 
> <mailto:lian.cs.zju@gmail.com>> wrote:
>
>     H Twinkle,
>
>     The failure is caused by case sensitivity. The temp table actually
>     stores the original un-analyzed logical plan, thus field names
>     remain capital (F1, F2, etc.). I believe this issue has already
>     been fixed by PR #2382
>     <https://github.com/apache/spark/pull/2382>. As a workaround, you
>     can use lowercase letters in field names instead.
>
>     Cheng
>
>     On 9/25/14 1:18 PM, twinkle sachdeva wrote:
>
>>     Hi,
>>
>>     I am using Hive Context to fire the sql queries inside spark. I
>>     have created a schemaRDD( Let's call it cachedSchema ) inside my
>>     code.
>>     If i fire a sql query ( Query 1 ) on top of it, then it works.
>>
>>     But if I refer to Query1's result inside another sql, that fails.
>>     Note that I have already registered Query1's result as temp table.
>>
>>     registerTempTable(cachedSchema)
>>     Queryresult1 = Query1 using cachedSchema  [ works ]
>>     registerTempTable(Queryresult1)
>>
>>     Queryresult2 = Query2 using Queryresult1  [ FAILS ]
>>
>>     Is it expected?? Any known work around?
>>
>>     Following is the exception I am receiving :
>>
>>
>>     *org.apache.spark.sql.catalyst.errors.package$TreeNodeException:
>>     Unresolved attributes: 'f1,'f2,'f3,'f4, tree:*
>>
>>     *Project ['f1,'f2,'f3,'f4]*
>>
>>     * Filter ('count > 3)*
>>
>>     *  LowerCaseSchema *
>>
>>     *   Subquery x*
>>
>>     *    Project ['F1,'F2,'F3,'F4,'F6,'Count]*
>>
>>     *     LowerCaseSchema *
>>
>>     *      Subquery src*
>>
>>     *       SparkLogicalPlan (ExistingRdd
>>     [F1#0,F2#1,F3#2,F4#3,F5#4,F6#5,Count#6], MappedRDD[4] at map at
>>     SQLBlock.scala:64)*
>>
>>
>>     *at
>>     org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$anonfun$apply$1.applyOrElse(Analyzer.scala:72)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$anonfun$apply$1.applyOrElse(Analyzer.scala:70)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:165)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.trees.TreeNode.transform(TreeNode.scala:156)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:70)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.analysis.Analyzer$CheckResolution$.apply(Analyzer.scala:68)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.rules.RuleExecutor$anonfun$apply$1$anonfun$apply$2.apply(RuleExecutor.scala:61)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.rules.RuleExecutor$anonfun$apply$1$anonfun$apply$2.apply(RuleExecutor.scala:59)*
>>
>>     *at
>>     scala.collection.IndexedSeqOptimized$class.foldl(IndexedSeqOptimized.scala:51)*
>>
>>     *at
>>     scala.collection.IndexedSeqOptimized$class.foldLeft(IndexedSeqOptimized.scala:60)*
>>
>>     *at
>>     scala.collection.mutable.WrappedArray.foldLeft(WrappedArray.scala:34)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.rules.RuleExecutor$anonfun$apply$1.apply(RuleExecutor.scala:59)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.rules.RuleExecutor$anonfun$apply$1.apply(RuleExecutor.scala:51)*
>>
>>     *at scala.collection.immutable.List.foreach(List.scala:318)*
>>
>>     *at
>>     org.apache.spark.sql.catalyst.rules.RuleExecutor.apply(RuleExecutor.scala:51)*
>>
>>     *at
>>     org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:397)*
>>
>>     *at
>>     org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:397)*
>>
>>     *at
>>     org.apache.spark.sql.hive.HiveContext$QueryExecution.optimizedPlan$lzycompute(HiveContext.scala:358)*
>>
>>     *at
>>     org.apache.spark.sql.hive.HiveContext$QueryExecution.optimizedPlan(HiveContext.scala:357)*
>>
>>     *at
>>     org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan$lzycompute(SQLContext.scala:402)*
>>
>>     *at
>>     org.apache.spark.sql.SQLContext$QueryExecution.sparkPlan(SQLContext.scala:400)*
>>
>>     *at
>>     org.apache.spark.sql.SQLContext$QueryExecution.executedPlan$lzycompute(SQLContext.scala:406)*
>>
>>     *at
>>     org.apache.spark.sql.SQLContext$QueryExecution.executedPlan(SQLContext.scala:406)*
>>
>>     *at
>>     org.apache.spark.sql.hive.HiveContext$QueryExecution.toRdd$lzycompute(HiveContext.scala:360)*
>>
>>     *at
>>     org.apache.spark.sql.hive.HiveContext$QueryExecution.toRdd(HiveContext.scala:360)*
>>
>>     *at
>>     org.apache.spark.sql.SchemaRDD.getDependencies(SchemaRDD.scala:120)*
>>
>>     *at
>>     org.apache.spark.rdd.RDD$anonfun$dependencies$2.apply(RDD.scala:191)*
>>
>>     *at
>>     org.apache.spark.rdd.RDD$anonfun$dependencies$2.apply(RDD.scala:189)*
>>
>     ‚Äč
>
>


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