Can you take a look at SPARK-5278 where ambiguity is shown between field
names which differ only by case ?
Cheers
On Sat, Sep 12, 2015 at 3:40 AM, Fengdong Yu <fengdo...@everstring.com>
wrote:
> Hi Ted,
> I checked the JSON, there aren't duplicated key in JSON.
>
>
> Azuryy Yu
> Sr. Infrastructure Engineer
>
> cel: 158-0164-9103
> wetchat: azuryy
>
>
> On Sat, Sep 12, 2015 at 5:52 PM, Ted Yu <yuzhih...@gmail.com> wrote:
>
>> Is it possible that Canonical_URL occurs more than once in your json ?
>>
>> Can you check your json input ?
>>
>> Thanks
>>
>> On Sat, Sep 12, 2015 at 2:05 AM, Fengdong Yu <fengdo...@everstring.com>
>> wrote:
>>
>>> Hi,
>>>
>>> I am using spark1.4.1 data frame, read JSON data, then save it to orc.
>>> the code is very simple:
>>>
>>> DataFrame json = sqlContext.read().json(input);
>>>
>>> json.write().format("orc").save(output);
>>>
>>> the job failed. what's wrong with this exception? Thanks.
>>>
>>> Exception in thread "main" org.apache.spark.sql.AnalysisException:
>>> Reference 'Canonical_URL' is ambiguous, could be: Canonical_URL#960,
>>> Canonical_URL#1010.; at
>>> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolve(LogicalPlan.scala:279)
>>> at
>>> org.apache.spark.sql.catalyst.plans.logical.LogicalPlan.resolveChildren(LogicalPlan.scala:116)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4$$anonfun$16.apply(Analyzer.scala:350)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4$$anonfun$16.apply(Analyzer.scala:350)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.package$.withPosition(package.scala:48)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4.applyOrElse(Analyzer.scala:350)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8$$anonfun$applyOrElse$4.applyOrElse(Analyzer.scala:341)
>>> at
>>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
>>> at
>>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
>>> at
>>> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
>>> at
>>> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:285)
>>> at
>>> org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$transformExpressionUp$1(QueryPlan.scala:108)
>>> at
>>> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2$$anonfun$apply$2.apply(QueryPlan.scala:123)
>>> at
>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>> at
>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>> at scala.collection.immutable.List.foreach(List.scala:318) at
>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244) at
>>> scala.collection.AbstractTraversable.map(Traversable.scala:105) at
>>> org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:122)
>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328) at
>>> scala.collection.Iterator$class.foreach(Iterator.scala:727) at
>>> scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at
>>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at
>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273)
>>> at scala.collection.AbstractIterator.to(Iterator.scala:1157) at
>>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at
>>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at
>>> org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsUp(QueryPlan.scala:127)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8.applyOrElse(Analyzer.scala:341)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$$anonfun$apply$8.applyOrElse(Analyzer.scala:243)
>>> at
>>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
>>> at
>>> org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:286)
>>> at
>>> org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:51)
>>> at
>>> org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:285)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:243)
>>> at
>>> org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveReferences$.apply(Analyzer.scala:242)
>>> at
>>> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:61)
>>> at
>>> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:59)
>>> at
>>> scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:111)
>>> at scala.collection.immutable.List.foldLeft(List.scala:84) at
>>> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:59)
>>> at
>>> org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:51)
>>> at scala.collection.immutable.List.foreach(List.scala:318) at
>>> org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:51)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.analyzed$lzycompute(SQLContext.scala:933)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.analyzed(SQLContext.scala:933)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.assertAnalyzed(SQLContext.scala:931)
>>> at org.apache.spark.sql.DataFrame.(DataFrame.scala:131) at
>>> org.apache.spark.sql.DataFrame$.apply(DataFrame.scala:51) at
>>> org.apache.spark.sql.sources.InsertIntoHadoopFsRelation.run(commands.scala:132)
>>> at
>>> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult$lzycompute(commands.scala:57)
>>> at
>>> org.apache.spark.sql.execution.ExecutedCommand.sideEffectResult(commands.scala:57)
>>> at
>>> org.apache.spark.sql.execution.ExecutedCommand.doExecute(commands.scala:68)
>>> at
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:88)
>>> at
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:88)
>>> at
>>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:147)
>>> at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:87) at
>>> org.apache.spark.sql.SQLContext$QueryExecution.toRdd$lzycompute(SQLContext.scala:950)
>>> at
>>> org.apache.spark.sql.SQLContext$QueryExecution.toRdd(SQLContext.scala:950)
>>> at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:336) at
>>> org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:144) at
>>> org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:135) at
>>> com.es.infrastructure.spark.orc.transformer.JsonTransformer.run(JsonTransformer.java:22)
>>> at Main.main(Main.java:70) at
>>> sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) at
>>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
>>> at
>>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
>>> at java.lang.reflect.Method.invoke(Method.java:606) at
>>> org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:665)
>>> at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:170)
>>> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:193) at
>>> org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:112) at
>>> org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
>>>
>>>
>>
>
