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From Michael Armbrust <mich...@databricks.com>
Subject Re: Selecting Based on Nested Values using Language Integrated Query Syntax
Date Wed, 29 Oct 2014 19:07:15 GMT
We are working on more helpful error messages, but in the meantime let me
explain how to read this output.

org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved
attributes: 'p.name,'p.age, tree:

Project ['p.name,'p.age]
 Filter ('location.number = 2300)
  Join Inner, Some((location#110.number AS number#111 = 'ln.streetnumber))
   Generate explode(locations#10), true, false, Some(l)
    LowerCaseSchema
     Subquery p
      Subquery people
       SparkLogicalPlan (ExistingRdd [age#9,locations#10,name#11],
MappedRDD[28] at map at JsonRDD.scala:38)
   LowerCaseSchema
    Subquery ln
     Subquery locationNames
      SparkLogicalPlan (ExistingRdd [locationName#80,locationNumber#81],
MappedRDD[99] at map at JsonRDD.scala:38)

'tickedFields indicate a failure to resolve, where as numbered#10
attributes have been resolved. (The numbers are globally unique and can be
used to disambiguate where a column is coming from when the names are the
same)

Resolution happens bottom up.  So the first place that there is a problem
is 'ln.streetnumber, which prevents the rest of the query from resolving.
If you look at the subquery ln, it is only producing two columns:
locationName and locationNumber. So "streetnumber" is not valid.


On Tue, Oct 28, 2014 at 8:02 PM, Corey Nolet <cjnolet@gmail.com> wrote:

> scala> locations.queryExecution
>
> warning: there were 1 feature warning(s); re-run with -feature for details
>
> res28: _4.sqlContext.QueryExecution forSome { val _4:
> org.apache.spark.sql.SchemaRDD } =
>
> == Parsed Logical Plan ==
>
> SparkLogicalPlan (ExistingRdd [locationName#80,locationNumber#81],
> MappedRDD[99] at map at JsonRDD.scala:38)
>
>
> == Analyzed Logical Plan ==
>
> SparkLogicalPlan (ExistingRdd [locationName#80,locationNumber#81],
> MappedRDD[99] at map at JsonRDD.scala:38)
>
>
> == Optimized Logical Plan ==
>
> SparkLogicalPlan (ExistingRdd [locationName#80,locationNumber#81],
> MappedRDD[99] at map at JsonRDD.scala:38)
>
>
> == Physical Plan ==
>
> ExistingRdd [locationName#80,locationNumber#81], MappedRDD[99] at map at
> JsonRDD.scala:38
>
>
> Code Generation: false
>
> == RDD ==
>
>
> scala> people.queryExecution
>
> warning: there were 1 feature warning(s); re-run with -feature for details
>
> res29: _5.sqlContext.QueryExecution forSome { val _5:
> org.apache.spark.sql.SchemaRDD } =
>
> == Parsed Logical Plan ==
>
> SparkLogicalPlan (ExistingRdd [age#9,locations#10,name#11], MappedRDD[28]
> at map at JsonRDD.scala:38)
>
>
> == Analyzed Logical Plan ==
>
> SparkLogicalPlan (ExistingRdd [age#9,locations#10,name#11], MappedRDD[28]
> at map at JsonRDD.scala:38)
>
>
> == Optimized Logical Plan ==
>
> SparkLogicalPlan (ExistingRdd [age#9,locations#10,name#11], MappedRDD[28]
> at map at JsonRDD.scala:38)
>
>
> == Physical Plan ==
>
> ExistingRdd [age#9,locations#10,name#11], MappedRDD[28] at map at
> JsonRDD.scala:38
>
>
> Code Generation: false
>
> == RDD ==
>
>
>
> Here's when I try executing the join and the lateral view explode() :
>
>
> 14/10/28 23:05:35 INFO ParseDriver: Parse Completed
>
> org.apache.spark.sql.catalyst.errors.package$TreeNodeException: Unresolved
> attributes: 'p.name,'p.age, tree:
>
> Project ['p.name,'p.age]
>
>  Filter ('location.number = 2300)
>
>   Join Inner, Some((location#110.number AS number#111 = 'ln.streetnumber))
>
>    Generate explode(locations#10), true, false, Some(l)
>
>     LowerCaseSchema
>
>      Subquery p
>
>       Subquery people
>
>        SparkLogicalPlan (ExistingRdd [age#9,locations#10,name#11],
> MappedRDD[28] at map at JsonRDD.scala:38)
>
>    LowerCaseSchema
>
>     Subquery ln
>
>      Subquery locationNames
>
>       SparkLogicalPlan (ExistingRdd [locationName#80,locationNumber#81],
> MappedRDD[99] at map at JsonRDD.scala:38)
>
>
> at
> org.apache.spark.sql.catalyst.analysis.Analyzer$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)
>
> On Tue, Oct 28, 2014 at 10:48 PM, Michael Armbrust <michael@databricks.com
> > wrote:
>
>> Can you println the .queryExecution of the SchemaRDD?
>>
>> On Tue, Oct 28, 2014 at 7:43 PM, Corey Nolet <cjnolet@gmail.com> wrote:
>>
>>> So this appears to work just fine:
>>>
>>> hctx.sql("SELECT p.name, p.age  FROM people p LATERAL VIEW
>>> explode(locations) l AS location JOIN location5 lo ON l.number =
>>> lo.streetNumber WHERE location.number = '2300'").collect()
>>>
>>> But as soon as I try to join with another set based on a property from
>>> the exploded locations set, I get invalid attribute exceptions:
>>>
>>> hctx.sql("SELECT p.name, p.age, ln.locationName  FROM people as p
>>> LATERAL VIEW explode(locations) l AS location JOIN locationNames ln ON
>>> location.number = ln.streetNumber WHERE location.number = '2300'").collect()
>>>
>>>
>>> On Tue, Oct 28, 2014 at 10:19 PM, Michael Armbrust <
>>> michael@databricks.com> wrote:
>>>
>>>>
>>>>
>>>> On Tue, Oct 28, 2014 at 6:56 PM, Corey Nolet <cjnolet@gmail.com> wrote:
>>>>
>>>>> Am I able to do a join on an exploded field?
>>>>>
>>>>> Like if I have another object:
>>>>>
>>>>> { "streetNumber":"2300", "locationName":"The Big Building"} and I want
>>>>> to join with the previous json by the locations[].number field- is that
>>>>> possible?
>>>>>
>>>>
>>>> I'm not sure I fully understand the question, but once its exploded its
>>>> a normal tuple and you can do any operations on it.  The explode is just
>>>> producing a new row for each element in the array.
>>>>
>>>> Awesome, this is what I was looking for. So it's possible to use hive
>>>>>> dialect in a regular sql context? This is what was confusing to me-
the
>>>>>> docs kind of allude to it but don't directly point it out.
>>>>>>
>>>>>
>>>> No, you need a HiveContext as we use the actual hive parser (SQLContext
>>>> only exists as a separate entity so that people who don't want Hive's
>>>> dependencies in their app can still use a limited subset of Spark SQL).
>>>>
>>>
>>>
>>
>

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