Re: Error in spark-xml
Can you try once by creating your own schema file and using it to read the XML.
I had similar issue but got that resolved by custom schema and by specifying
each attribute in that.
Pradeep
> On May 1, 2016, at 9:45 AM, Hyukjin Kwon wrote:
>
> To be more clear,
>
> If you set the rowTag as "book", then it will produces an exception which is
> an issue opened here, https://github.com/databricks/spark-xml/issues/92
>
> Currently it does not support to parse a single element with only a value as
> a row.
>
>
> If you set the rowTag as "bkval", then it should work. I tested the case
> below to double check.
>
> If it does not work as below, please open an issue with some information so
> that I can reproduce.
>
>
> I tested the case above with the data below
>
>
> bk_113
> bk_114
>
>
> bk_114
> bk_116
>
>
> bk_115
> bk_116
>
>
>
>
> I tested this with the codes below
>
> val path = "path-to-file"
> sqlContext.read
> .format("xml")
> .option("rowTag", "bkval")
> .load(path)
> .show()
>
> Thanks!
>
>
> 2016-05-01 15:11 GMT+09:00 Hyukjin Kwon :
>> Hi Sourav,
>>
>> I think it is an issue. XML will assume the element by the rowTag as object.
>>
>> Could you please open an issue in
>> https://github.com/databricks/spark-xml/issues please?
>>
>> Thanks!
>>
>>
>> 2016-05-01 5:08 GMT+09:00 Sourav Mazumder :
>>> Hi,
>>>
>>> Looks like there is a problem in spark-xml if the xml has multiple
>>> attributes with no child element.
>>>
>>> For example say the xml has a nested object as below
>>>
>>> bk_113
>>> bk_114
>>>
>>>
>>> Now if I create a dataframe starting with rowtag bkval and then I do a
>>> select on that data frame it gives following error.
>>>
>>>
>>> scala.MatchError: ENDDOCUMENT (of class
>>> com.sun.xml.internal.stream.events.EndDocumentEvent) at
>>> com.databricks.spark.xml.parsers.StaxXmlParser$.checkEndElement(StaxXmlParser.scala:94)
>>> at
>>> com.databricks.spark.xml.parsers.StaxXmlParser$.com$databricks$spark$xml$parsers$StaxXmlParser$$convertObject(StaxXmlParser.scala:295)
>>> at
>>> com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:58)
>>> at
>>> com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:46)
>>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at
>>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at
>>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at
>>> scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308) at
>>> scala.collection.Iterator$class.foreach(Iterator.scala:727) at
>>> scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at
>>> scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at
>>> scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103)
>>> at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47)
>>> at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) at
>>> scala.collection.AbstractIterator.to(Iterator.scala:1157) at
>>> scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265)
>>> at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at
>>> scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252)
>>> at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
>>> at
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
>>> at
>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
>>> at
>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
>>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at
>>> org.apache.spark.scheduler.Task.run(Task.scala:88) at
>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at
>>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>>> at
>>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>>> at java.lang.Thread.run(Thread.java:745)
>>>
>>> However if there is only one row like below, it works fine.
>>>
>>>
>>> bk_113
>>>
>>>
>>> Any workaround ?
>>>
>>> Regards,
>>> Sourav
>
Re: Error in spark-xml
To be more clear,
If you set the rowTag as "book", then it will produces an exception which
is an issue opened here, https://github.com/databricks/spark-xml/issues/92
Currently it does not support to parse a single element with only a value
as a row.
If you set the rowTag as "bkval", then it should work. I tested the case
below to double check.
If it does not work as below, please open an issue with some information so
that I can reproduce.
I tested the case above with the data below
bk_113
bk_114
bk_114
bk_116
bk_115
bk_116
I tested this with the codes below
val path = "path-to-file"
sqlContext.read
.format("xml")
.option("rowTag", "bkval")
.load(path)
.show()
Thanks!
2016-05-01 15:11 GMT+09:00 Hyukjin Kwon :
> Hi Sourav,
>
> I think it is an issue. XML will assume the element by the rowTag as
> object.
>
> Could you please open an issue in
> https://github.com/databricks/spark-xml/issues please?
>
> Thanks!
>
>
> 2016-05-01 5:08 GMT+09:00 Sourav Mazumder :
>
>> Hi,
>>
>> Looks like there is a problem in spark-xml if the xml has multiple
>> attributes with no child element.
>>
>> For example say the xml has a nested object as below
>>
>> bk_113
>> bk_114
>>
>>
>> Now if I create a dataframe starting with rowtag bkval and then I do a
>> select on that data frame it gives following error.
>>
>>
>> scala.MatchError: ENDDOCUMENT (of class
>> com.sun.xml.internal.stream.events.EndDocumentEvent) at
>> com.databricks.spark.xml.parsers.StaxXmlParser$.checkEndElement(StaxXmlParser.scala:94)
>> at
>> com.databricks.spark.xml.parsers.StaxXmlParser$.com$databricks$spark$xml$parsers$StaxXmlParser$$convertObject(StaxXmlParser.scala:295)
>> at
>> com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:58)
>> at
>> com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:46)
>> at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at
>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at
>> scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at
>> scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308) at
>> scala.collection.Iterator$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.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
>> at
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215)
>> at
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
>> at
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850)
>> at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at
>> org.apache.spark.scheduler.Task.run(Task.scala:88) at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at
>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
>> at
>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
>> at java.lang.Thread.run(Thread.java:745)
>>
>> However if there is only one row like below, it works fine.
>>
>>
>> bk_113
>>
>>
>> Any workaround ?
>>
>> Regards,
>> Sourav
>>
>>
>
Re: Error in spark-xml
Hi Sourav, I think it is an issue. XML will assume the element by the rowTag as object. Could you please open an issue in https://github.com/databricks/spark-xml/issues please? Thanks! 2016-05-01 5:08 GMT+09:00 Sourav Mazumder : > Hi, > > Looks like there is a problem in spark-xml if the xml has multiple > attributes with no child element. > > For example say the xml has a nested object as below > > bk_113 > bk_114 > > > Now if I create a dataframe starting with rowtag bkval and then I do a > select on that data frame it gives following error. > > > scala.MatchError: ENDDOCUMENT (of class > com.sun.xml.internal.stream.events.EndDocumentEvent) at > com.databricks.spark.xml.parsers.StaxXmlParser$.checkEndElement(StaxXmlParser.scala:94) > at > com.databricks.spark.xml.parsers.StaxXmlParser$.com$databricks$spark$xml$parsers$StaxXmlParser$$convertObject(StaxXmlParser.scala:295) > at > com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:58) > at > com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:46) > at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at > scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at > scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at > scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308) at > scala.collection.Iterator$class.foreach(Iterator.scala:727) at > scala.collection.AbstractIterator.foreach(Iterator.scala:1157) at > scala.collection.generic.Growable$class.$plus$plus$eq(Growable.scala:48) at > scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:103) > at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:47) > at scala.collection.TraversableOnce$class.to(TraversableOnce.scala:273) > at scala.collection.AbstractIterator.to(Iterator.scala:1157) at > scala.collection.TraversableOnce$class.toBuffer(TraversableOnce.scala:265) > at scala.collection.AbstractIterator.toBuffer(Iterator.scala:1157) at > scala.collection.TraversableOnce$class.toArray(TraversableOnce.scala:252) > at scala.collection.AbstractIterator.toArray(Iterator.scala:1157) at > org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215) > at > org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215) > at > org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850) > at > org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850) > at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at > org.apache.spark.scheduler.Task.run(Task.scala:88) at > org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at > java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) > at > java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) > at java.lang.Thread.run(Thread.java:745) > > However if there is only one row like below, it works fine. > > > bk_113 > > > Any workaround ? > > Regards, > Sourav > >
Error in spark-xml
Hi, Looks like there is a problem in spark-xml if the xml has multiple attributes with no child element. For example say the xml has a nested object as below bk_113 bk_114 Now if I create a dataframe starting with rowtag bkval and then I do a select on that data frame it gives following error. scala.MatchError: ENDDOCUMENT (of class com.sun.xml.internal.stream.events.EndDocumentEvent) at com.databricks.spark.xml.parsers.StaxXmlParser$.checkEndElement(StaxXmlParser.scala:94) at com.databricks.spark.xml.parsers.StaxXmlParser$.com$databricks$spark$xml$parsers$StaxXmlParser$$convertObject(StaxXmlParser.scala:295) at com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:58) at com.databricks.spark.xml.parsers.StaxXmlParser$$anonfun$parse$1$$anonfun$apply$4.apply(StaxXmlParser.scala:46) at scala.collection.Iterator$$anon$13.hasNext(Iterator.scala:371) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:327) at scala.collection.Iterator$$anon$10.hasNext(Iterator.scala:308) at scala.collection.Iterator$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.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215) at org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:215) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850) at org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1850) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66) at org.apache.spark.scheduler.Task.run(Task.scala:88) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:214) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) However if there is only one row like below, it works fine. bk_113 Any workaround ? Regards, Sourav
