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Posted to user@spark.apache.org by raghukiran <ra...@gmail.com> on 2016/01/20 15:07:42 UTC
Scala MatchError in Spark SQL
Hi,
I created a custom UserDefinedType in Java as follows:
SQLPoint = new UserDefinedType<JavaPoint>() {
//overriding serialize, deserialize, sqlType, userClass functions here
}
When creating a dataframe, I am following the manual mapping, I have a
constructor for JavaPoint - JavaPoint(double x, double y) and a Customer
record as follows:
public class CustomerRecord {
private int id;
private String name;
private Object location;
//setters and getters follow here
}
Following the example in Spark source, when I create a RDD as follows:
sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() {
//call method
CustomerRecord rec = new CustomerRecord();
rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
});
This results in a MatchError. The stack trace is as follows:
scala.MatchError: [B@45aa3dd5 (of class [B)
at
org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
at
org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
at
org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
at
org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
at
org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
at
org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
at
scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
at
org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
at
org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
at
org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at
org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
at org.apache.spark.scheduler.Task.run(Task.scala:89)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
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)
--
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Re: Scala MatchError in Spark SQL
Posted by Andy Grove <an...@agildata.com>.
I would walk through a Spark tutorial in Scala. It will be the best way to
learn this.
In brief though, a Scala case class is like a Java bean / pojo but has a
more concise syntax (no getters/setters).
case class Person(firstName: String, lastName: String, age: Int)
Thanks,
Andy.
--
Andy Grove
Chief Architect
AgilData - Simple Streaming SQL that Scales
www.agildata.com
On Wed, Jan 20, 2016 at 10:28 AM, Raghu Ganti <ra...@gmail.com> wrote:
> Ah, OK! I am a novice to Scala - will take a look at Scala case classes.
> It would be awesome if you can provide some pointers.
>
> Thanks,
> Raghu
>
> On Wed, Jan 20, 2016 at 12:25 PM, Andy Grove <an...@agildata.com>
> wrote:
>
>> I'm talking about implementing CustomerRecord as a scala case class,
>> rather than as a Java class. Scala case classes implement the scala.Product
>> trait, which Catalyst is looking for.
>>
>>
>> Thanks,
>>
>> Andy.
>>
>> --
>>
>> Andy Grove
>> Chief Architect
>> AgilData - Simple Streaming SQL that Scales
>> www.agildata.com
>>
>>
>> On Wed, Jan 20, 2016 at 10:21 AM, Raghu Ganti <ra...@gmail.com>
>> wrote:
>>
>>> Is it not internal to the Catalyst implementation? I should not be
>>> modifying the Spark source to get things to work, do I? :-)
>>>
>>> On Wed, Jan 20, 2016 at 12:21 PM, Raghu Ganti <ra...@gmail.com>
>>> wrote:
>>>
>>>> Case classes where?
>>>>
>>>> On Wed, Jan 20, 2016 at 12:21 PM, Andy Grove <an...@agildata.com>
>>>> wrote:
>>>>
>>>>> Honestly, moving to Scala and using case classes is the path of least
>>>>> resistance in the long term.
>>>>>
>>>>>
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Andy.
>>>>>
>>>>> --
>>>>>
>>>>> Andy Grove
>>>>> Chief Architect
>>>>> AgilData - Simple Streaming SQL that Scales
>>>>> www.agildata.com
>>>>>
>>>>>
>>>>> On Wed, Jan 20, 2016 at 10:19 AM, Raghu Ganti <ra...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Thanks for your reply, Andy.
>>>>>>
>>>>>> Yes, that is what I concluded based on the Stack trace. The problem
>>>>>> is stemming from Java implementation of generics, but I thought this will
>>>>>> go away if you compiled against Java 1.8, which solves the issues of proper
>>>>>> generic implementation.
>>>>>>
>>>>>> Any ideas?
>>>>>>
>>>>>> Also, are you saying that in order for my example to work, I would
>>>>>> need to move to Scala and have the UDT implemented in Scala?
>>>>>>
>>>>>>
>>>>>> On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <andy.grove@agildata.com
>>>>>> > wrote:
>>>>>>
>>>>>>> Catalyst is expecting a class that implements scala.Row or
>>>>>>> scala.Product and is instead finding a Java class. I've run into this issue
>>>>>>> a number of times. Dataframe doesn't work so well with Java. Here's a blog
>>>>>>> post with more information on this:
>>>>>>>
>>>>>>> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>>>>>>>
>>>>>>>
>>>>>>> Thanks,
>>>>>>>
>>>>>>> Andy.
>>>>>>>
>>>>>>> --
>>>>>>>
>>>>>>> Andy Grove
>>>>>>> Chief Architect
>>>>>>> AgilData - Simple Streaming SQL that Scales
>>>>>>> www.agildata.com
>>>>>>>
>>>>>>>
>>>>>>> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>> Hi,
>>>>>>>>
>>>>>>>> I created a custom UserDefinedType in Java as follows:
>>>>>>>>
>>>>>>>> SQLPoint = new UserDefinedType<JavaPoint>() {
>>>>>>>> //overriding serialize, deserialize, sqlType, userClass functions
>>>>>>>> here
>>>>>>>> }
>>>>>>>>
>>>>>>>> When creating a dataframe, I am following the manual mapping, I
>>>>>>>> have a
>>>>>>>> constructor for JavaPoint - JavaPoint(double x, double y) and a
>>>>>>>> Customer
>>>>>>>> record as follows:
>>>>>>>>
>>>>>>>> public class CustomerRecord {
>>>>>>>> private int id;
>>>>>>>> private String name;
>>>>>>>> private Object location;
>>>>>>>>
>>>>>>>> //setters and getters follow here
>>>>>>>> }
>>>>>>>>
>>>>>>>> Following the example in Spark source, when I create a RDD as
>>>>>>>> follows:
>>>>>>>>
>>>>>>>> sc.textFile(inputFileName).map(new Function<String,
>>>>>>>> CustomerRecord>() {
>>>>>>>> //call method
>>>>>>>> CustomerRecord rec = new CustomerRecord();
>>>>>>>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>>>>>>>> });
>>>>>>>>
>>>>>>>> This results in a MatchError. The stack trace is as follows:
>>>>>>>>
>>>>>>>> scala.MatchError: [B@45aa3dd5 (of class [B)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>>>>> at
>>>>>>>>
>>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>>>>> at
>>>>>>>>
>>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>>>>> at
>>>>>>>>
>>>>>>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>>>>>>> at
>>>>>>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>>>>>>> at
>>>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>>>>>> at
>>>>>>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>>>>>>>> at
>>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>>> at
>>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>>> at
>>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>>> at
>>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>>> at
>>>>>>>> scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>>>>>>> at
>>>>>>>> scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>>>> at
>>>>>>>>
>>>>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>>>> at
>>>>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>>>> at
>>>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>>>>>>> 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)
>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>> --
>>>>>>>> View this message in context:
>>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>>>>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>>>>> Nabble.com.
>>>>>>>>
>>>>>>>>
>>>>>>>> ---------------------------------------------------------------------
>>>>>>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>>>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>>>>>
>>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Raghu Ganti <ra...@gmail.com>.
Ah, OK! I am a novice to Scala - will take a look at Scala case classes. It
would be awesome if you can provide some pointers.
Thanks,
Raghu
On Wed, Jan 20, 2016 at 12:25 PM, Andy Grove <an...@agildata.com>
wrote:
> I'm talking about implementing CustomerRecord as a scala case class,
> rather than as a Java class. Scala case classes implement the scala.Product
> trait, which Catalyst is looking for.
>
>
> Thanks,
>
> Andy.
>
> --
>
> Andy Grove
> Chief Architect
> AgilData - Simple Streaming SQL that Scales
> www.agildata.com
>
>
> On Wed, Jan 20, 2016 at 10:21 AM, Raghu Ganti <ra...@gmail.com>
> wrote:
>
>> Is it not internal to the Catalyst implementation? I should not be
>> modifying the Spark source to get things to work, do I? :-)
>>
>> On Wed, Jan 20, 2016 at 12:21 PM, Raghu Ganti <ra...@gmail.com>
>> wrote:
>>
>>> Case classes where?
>>>
>>> On Wed, Jan 20, 2016 at 12:21 PM, Andy Grove <an...@agildata.com>
>>> wrote:
>>>
>>>> Honestly, moving to Scala and using case classes is the path of least
>>>> resistance in the long term.
>>>>
>>>>
>>>>
>>>> Thanks,
>>>>
>>>> Andy.
>>>>
>>>> --
>>>>
>>>> Andy Grove
>>>> Chief Architect
>>>> AgilData - Simple Streaming SQL that Scales
>>>> www.agildata.com
>>>>
>>>>
>>>> On Wed, Jan 20, 2016 at 10:19 AM, Raghu Ganti <ra...@gmail.com>
>>>> wrote:
>>>>
>>>>> Thanks for your reply, Andy.
>>>>>
>>>>> Yes, that is what I concluded based on the Stack trace. The problem is
>>>>> stemming from Java implementation of generics, but I thought this will go
>>>>> away if you compiled against Java 1.8, which solves the issues of proper
>>>>> generic implementation.
>>>>>
>>>>> Any ideas?
>>>>>
>>>>> Also, are you saying that in order for my example to work, I would
>>>>> need to move to Scala and have the UDT implemented in Scala?
>>>>>
>>>>>
>>>>> On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <an...@agildata.com>
>>>>> wrote:
>>>>>
>>>>>> Catalyst is expecting a class that implements scala.Row or
>>>>>> scala.Product and is instead finding a Java class. I've run into this issue
>>>>>> a number of times. Dataframe doesn't work so well with Java. Here's a blog
>>>>>> post with more information on this:
>>>>>>
>>>>>> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>>>>>>
>>>>>>
>>>>>> Thanks,
>>>>>>
>>>>>> Andy.
>>>>>>
>>>>>> --
>>>>>>
>>>>>> Andy Grove
>>>>>> Chief Architect
>>>>>> AgilData - Simple Streaming SQL that Scales
>>>>>> www.agildata.com
>>>>>>
>>>>>>
>>>>>> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com>
>>>>>> wrote:
>>>>>>
>>>>>>> Hi,
>>>>>>>
>>>>>>> I created a custom UserDefinedType in Java as follows:
>>>>>>>
>>>>>>> SQLPoint = new UserDefinedType<JavaPoint>() {
>>>>>>> //overriding serialize, deserialize, sqlType, userClass functions
>>>>>>> here
>>>>>>> }
>>>>>>>
>>>>>>> When creating a dataframe, I am following the manual mapping, I have
>>>>>>> a
>>>>>>> constructor for JavaPoint - JavaPoint(double x, double y) and a
>>>>>>> Customer
>>>>>>> record as follows:
>>>>>>>
>>>>>>> public class CustomerRecord {
>>>>>>> private int id;
>>>>>>> private String name;
>>>>>>> private Object location;
>>>>>>>
>>>>>>> //setters and getters follow here
>>>>>>> }
>>>>>>>
>>>>>>> Following the example in Spark source, when I create a RDD as
>>>>>>> follows:
>>>>>>>
>>>>>>> sc.textFile(inputFileName).map(new Function<String,
>>>>>>> CustomerRecord>() {
>>>>>>> //call method
>>>>>>> CustomerRecord rec = new CustomerRecord();
>>>>>>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>>>>>>> });
>>>>>>>
>>>>>>> This results in a MatchError. The stack trace is as follows:
>>>>>>>
>>>>>>> scala.MatchError: [B@45aa3dd5 (of class [B)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>>>> at
>>>>>>>
>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>>>> at
>>>>>>>
>>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>>>> at
>>>>>>>
>>>>>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>>>>>> at
>>>>>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>>>>>> at
>>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>>>>> at
>>>>>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>>>>>>> at
>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>> at
>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>> at
>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>> at
>>>>>>> scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>>> at
>>>>>>> scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>>>>>> at
>>>>>>> scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>>> at
>>>>>>>
>>>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>>> at
>>>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>>> at
>>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>>>>>> 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)
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>> --
>>>>>>> View this message in context:
>>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>>>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>>>> Nabble.com.
>>>>>>>
>>>>>>> ---------------------------------------------------------------------
>>>>>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>>>>
>>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Andy Grove <an...@agildata.com>.
I'm talking about implementing CustomerRecord as a scala case class, rather
than as a Java class. Scala case classes implement the scala.Product trait,
which Catalyst is looking for.
Thanks,
Andy.
--
Andy Grove
Chief Architect
AgilData - Simple Streaming SQL that Scales
www.agildata.com
On Wed, Jan 20, 2016 at 10:21 AM, Raghu Ganti <ra...@gmail.com> wrote:
> Is it not internal to the Catalyst implementation? I should not be
> modifying the Spark source to get things to work, do I? :-)
>
> On Wed, Jan 20, 2016 at 12:21 PM, Raghu Ganti <ra...@gmail.com>
> wrote:
>
>> Case classes where?
>>
>> On Wed, Jan 20, 2016 at 12:21 PM, Andy Grove <an...@agildata.com>
>> wrote:
>>
>>> Honestly, moving to Scala and using case classes is the path of least
>>> resistance in the long term.
>>>
>>>
>>>
>>> Thanks,
>>>
>>> Andy.
>>>
>>> --
>>>
>>> Andy Grove
>>> Chief Architect
>>> AgilData - Simple Streaming SQL that Scales
>>> www.agildata.com
>>>
>>>
>>> On Wed, Jan 20, 2016 at 10:19 AM, Raghu Ganti <ra...@gmail.com>
>>> wrote:
>>>
>>>> Thanks for your reply, Andy.
>>>>
>>>> Yes, that is what I concluded based on the Stack trace. The problem is
>>>> stemming from Java implementation of generics, but I thought this will go
>>>> away if you compiled against Java 1.8, which solves the issues of proper
>>>> generic implementation.
>>>>
>>>> Any ideas?
>>>>
>>>> Also, are you saying that in order for my example to work, I would need
>>>> to move to Scala and have the UDT implemented in Scala?
>>>>
>>>>
>>>> On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <an...@agildata.com>
>>>> wrote:
>>>>
>>>>> Catalyst is expecting a class that implements scala.Row or
>>>>> scala.Product and is instead finding a Java class. I've run into this issue
>>>>> a number of times. Dataframe doesn't work so well with Java. Here's a blog
>>>>> post with more information on this:
>>>>>
>>>>> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>>>>>
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Andy.
>>>>>
>>>>> --
>>>>>
>>>>> Andy Grove
>>>>> Chief Architect
>>>>> AgilData - Simple Streaming SQL that Scales
>>>>> www.agildata.com
>>>>>
>>>>>
>>>>> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com>
>>>>> wrote:
>>>>>
>>>>>> Hi,
>>>>>>
>>>>>> I created a custom UserDefinedType in Java as follows:
>>>>>>
>>>>>> SQLPoint = new UserDefinedType<JavaPoint>() {
>>>>>> //overriding serialize, deserialize, sqlType, userClass functions here
>>>>>> }
>>>>>>
>>>>>> When creating a dataframe, I am following the manual mapping, I have a
>>>>>> constructor for JavaPoint - JavaPoint(double x, double y) and a
>>>>>> Customer
>>>>>> record as follows:
>>>>>>
>>>>>> public class CustomerRecord {
>>>>>> private int id;
>>>>>> private String name;
>>>>>> private Object location;
>>>>>>
>>>>>> //setters and getters follow here
>>>>>> }
>>>>>>
>>>>>> Following the example in Spark source, when I create a RDD as follows:
>>>>>>
>>>>>> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>()
>>>>>> {
>>>>>> //call method
>>>>>> CustomerRecord rec = new CustomerRecord();
>>>>>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>>>>>> });
>>>>>>
>>>>>> This results in a MatchError. The stack trace is as follows:
>>>>>>
>>>>>> scala.MatchError: [B@45aa3dd5 (of class [B)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>>> at
>>>>>>
>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>>> at
>>>>>>
>>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>>> at
>>>>>>
>>>>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>>>>> at
>>>>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>>>>> at
>>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>>>> at
>>>>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>>>>> at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>> at
>>>>>>
>>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>>> at
>>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>>> at
>>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>>>>> 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)
>>>>>>
>>>>>>
>>>>>>
>>>>>> --
>>>>>> View this message in context:
>>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>>> Nabble.com.
>>>>>>
>>>>>> ---------------------------------------------------------------------
>>>>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>>>
>>>>>>
>>>>>
>>>>
>>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Raghu Ganti <ra...@gmail.com>.
Is it not internal to the Catalyst implementation? I should not be
modifying the Spark source to get things to work, do I? :-)
On Wed, Jan 20, 2016 at 12:21 PM, Raghu Ganti <ra...@gmail.com> wrote:
> Case classes where?
>
> On Wed, Jan 20, 2016 at 12:21 PM, Andy Grove <an...@agildata.com>
> wrote:
>
>> Honestly, moving to Scala and using case classes is the path of least
>> resistance in the long term.
>>
>>
>>
>> Thanks,
>>
>> Andy.
>>
>> --
>>
>> Andy Grove
>> Chief Architect
>> AgilData - Simple Streaming SQL that Scales
>> www.agildata.com
>>
>>
>> On Wed, Jan 20, 2016 at 10:19 AM, Raghu Ganti <ra...@gmail.com>
>> wrote:
>>
>>> Thanks for your reply, Andy.
>>>
>>> Yes, that is what I concluded based on the Stack trace. The problem is
>>> stemming from Java implementation of generics, but I thought this will go
>>> away if you compiled against Java 1.8, which solves the issues of proper
>>> generic implementation.
>>>
>>> Any ideas?
>>>
>>> Also, are you saying that in order for my example to work, I would need
>>> to move to Scala and have the UDT implemented in Scala?
>>>
>>>
>>> On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <an...@agildata.com>
>>> wrote:
>>>
>>>> Catalyst is expecting a class that implements scala.Row or
>>>> scala.Product and is instead finding a Java class. I've run into this issue
>>>> a number of times. Dataframe doesn't work so well with Java. Here's a blog
>>>> post with more information on this:
>>>>
>>>> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>>>>
>>>>
>>>> Thanks,
>>>>
>>>> Andy.
>>>>
>>>> --
>>>>
>>>> Andy Grove
>>>> Chief Architect
>>>> AgilData - Simple Streaming SQL that Scales
>>>> www.agildata.com
>>>>
>>>>
>>>> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com>
>>>> wrote:
>>>>
>>>>> Hi,
>>>>>
>>>>> I created a custom UserDefinedType in Java as follows:
>>>>>
>>>>> SQLPoint = new UserDefinedType<JavaPoint>() {
>>>>> //overriding serialize, deserialize, sqlType, userClass functions here
>>>>> }
>>>>>
>>>>> When creating a dataframe, I am following the manual mapping, I have a
>>>>> constructor for JavaPoint - JavaPoint(double x, double y) and a
>>>>> Customer
>>>>> record as follows:
>>>>>
>>>>> public class CustomerRecord {
>>>>> private int id;
>>>>> private String name;
>>>>> private Object location;
>>>>>
>>>>> //setters and getters follow here
>>>>> }
>>>>>
>>>>> Following the example in Spark source, when I create a RDD as follows:
>>>>>
>>>>> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() {
>>>>> //call method
>>>>> CustomerRecord rec = new CustomerRecord();
>>>>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>>>>> });
>>>>>
>>>>> This results in a MatchError. The stack trace is as follows:
>>>>>
>>>>> scala.MatchError: [B@45aa3dd5 (of class [B)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>>> at
>>>>>
>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>> at
>>>>>
>>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>>> at
>>>>>
>>>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>>>> at
>>>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>>>> at
>>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>>> at
>>>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>>>> at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>>>>> at
>>>>>
>>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>>>>> at
>>>>>
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>> at
>>>>>
>>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>>> at
>>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>>> at
>>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>>>> 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)
>>>>>
>>>>>
>>>>>
>>>>> --
>>>>> View this message in context:
>>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>>>>> Sent from the Apache Spark User List mailing list archive at
>>>>> Nabble.com.
>>>>>
>>>>> ---------------------------------------------------------------------
>>>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>>
>>>>>
>>>>
>>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Raghu Ganti <ra...@gmail.com>.
Case classes where?
On Wed, Jan 20, 2016 at 12:21 PM, Andy Grove <an...@agildata.com>
wrote:
> Honestly, moving to Scala and using case classes is the path of least
> resistance in the long term.
>
>
>
> Thanks,
>
> Andy.
>
> --
>
> Andy Grove
> Chief Architect
> AgilData - Simple Streaming SQL that Scales
> www.agildata.com
>
>
> On Wed, Jan 20, 2016 at 10:19 AM, Raghu Ganti <ra...@gmail.com>
> wrote:
>
>> Thanks for your reply, Andy.
>>
>> Yes, that is what I concluded based on the Stack trace. The problem is
>> stemming from Java implementation of generics, but I thought this will go
>> away if you compiled against Java 1.8, which solves the issues of proper
>> generic implementation.
>>
>> Any ideas?
>>
>> Also, are you saying that in order for my example to work, I would need
>> to move to Scala and have the UDT implemented in Scala?
>>
>>
>> On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <an...@agildata.com>
>> wrote:
>>
>>> Catalyst is expecting a class that implements scala.Row or scala.Product
>>> and is instead finding a Java class. I've run into this issue a number of
>>> times. Dataframe doesn't work so well with Java. Here's a blog post with
>>> more information on this:
>>>
>>> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>>>
>>>
>>> Thanks,
>>>
>>> Andy.
>>>
>>> --
>>>
>>> Andy Grove
>>> Chief Architect
>>> AgilData - Simple Streaming SQL that Scales
>>> www.agildata.com
>>>
>>>
>>> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com>
>>> wrote:
>>>
>>>> Hi,
>>>>
>>>> I created a custom UserDefinedType in Java as follows:
>>>>
>>>> SQLPoint = new UserDefinedType<JavaPoint>() {
>>>> //overriding serialize, deserialize, sqlType, userClass functions here
>>>> }
>>>>
>>>> When creating a dataframe, I am following the manual mapping, I have a
>>>> constructor for JavaPoint - JavaPoint(double x, double y) and a Customer
>>>> record as follows:
>>>>
>>>> public class CustomerRecord {
>>>> private int id;
>>>> private String name;
>>>> private Object location;
>>>>
>>>> //setters and getters follow here
>>>> }
>>>>
>>>> Following the example in Spark source, when I create a RDD as follows:
>>>>
>>>> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() {
>>>> //call method
>>>> CustomerRecord rec = new CustomerRecord();
>>>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>>>> });
>>>>
>>>> This results in a MatchError. The stack trace is as follows:
>>>>
>>>> scala.MatchError: [B@45aa3dd5 (of class [B)
>>>> at
>>>>
>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>>>> at
>>>>
>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>>>> at
>>>>
>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>>>> at
>>>>
>>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>>>> at
>>>>
>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>> at
>>>>
>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>>> at
>>>>
>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>> at
>>>>
>>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>>> at
>>>>
>>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>>> at
>>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>>> at
>>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>>> at
>>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>>> at
>>>>
>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>>>> at
>>>>
>>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>>> at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>>>> at
>>>>
>>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>>>> at
>>>>
>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>> at
>>>>
>>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>>> at
>>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>>> at
>>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>>> 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)
>>>>
>>>>
>>>>
>>>> --
>>>> View this message in context:
>>>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>>>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>>>
>>>> ---------------------------------------------------------------------
>>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>>> For additional commands, e-mail: user-help@spark.apache.org
>>>>
>>>>
>>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Andy Grove <an...@agildata.com>.
Honestly, moving to Scala and using case classes is the path of least
resistance in the long term.
Thanks,
Andy.
--
Andy Grove
Chief Architect
AgilData - Simple Streaming SQL that Scales
www.agildata.com
On Wed, Jan 20, 2016 at 10:19 AM, Raghu Ganti <ra...@gmail.com> wrote:
> Thanks for your reply, Andy.
>
> Yes, that is what I concluded based on the Stack trace. The problem is
> stemming from Java implementation of generics, but I thought this will go
> away if you compiled against Java 1.8, which solves the issues of proper
> generic implementation.
>
> Any ideas?
>
> Also, are you saying that in order for my example to work, I would need to
> move to Scala and have the UDT implemented in Scala?
>
>
> On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <an...@agildata.com>
> wrote:
>
>> Catalyst is expecting a class that implements scala.Row or scala.Product
>> and is instead finding a Java class. I've run into this issue a number of
>> times. Dataframe doesn't work so well with Java. Here's a blog post with
>> more information on this:
>>
>> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>>
>>
>> Thanks,
>>
>> Andy.
>>
>> --
>>
>> Andy Grove
>> Chief Architect
>> AgilData - Simple Streaming SQL that Scales
>> www.agildata.com
>>
>>
>> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com> wrote:
>>
>>> Hi,
>>>
>>> I created a custom UserDefinedType in Java as follows:
>>>
>>> SQLPoint = new UserDefinedType<JavaPoint>() {
>>> //overriding serialize, deserialize, sqlType, userClass functions here
>>> }
>>>
>>> When creating a dataframe, I am following the manual mapping, I have a
>>> constructor for JavaPoint - JavaPoint(double x, double y) and a Customer
>>> record as follows:
>>>
>>> public class CustomerRecord {
>>> private int id;
>>> private String name;
>>> private Object location;
>>>
>>> //setters and getters follow here
>>> }
>>>
>>> Following the example in Spark source, when I create a RDD as follows:
>>>
>>> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() {
>>> //call method
>>> CustomerRecord rec = new CustomerRecord();
>>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>>> });
>>>
>>> This results in a MatchError. The stack trace is as follows:
>>>
>>> scala.MatchError: [B@45aa3dd5 (of class [B)
>>> at
>>>
>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>>> at
>>>
>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>>> at
>>>
>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>>> at
>>>
>>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>>> at
>>>
>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>> at
>>>
>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>>> at
>>>
>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>> at
>>>
>>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>>> at
>>>
>>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>>> at
>>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>>> at
>>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>>> at
>>> scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>>> at
>>>
>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>>> at
>>>
>>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>>> at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>>> at
>>>
>>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>>> at
>>>
>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>> at
>>>
>>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>>> at
>>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>>> at
>>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>>> 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)
>>>
>>>
>>>
>>> --
>>> View this message in context:
>>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>>
>>> ---------------------------------------------------------------------
>>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>>> For additional commands, e-mail: user-help@spark.apache.org
>>>
>>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Raghu Ganti <ra...@gmail.com>.
Thanks for your reply, Andy.
Yes, that is what I concluded based on the Stack trace. The problem is
stemming from Java implementation of generics, but I thought this will go
away if you compiled against Java 1.8, which solves the issues of proper
generic implementation.
Any ideas?
Also, are you saying that in order for my example to work, I would need to
move to Scala and have the UDT implemented in Scala?
On Wed, Jan 20, 2016 at 10:27 AM, Andy Grove <an...@agildata.com>
wrote:
> Catalyst is expecting a class that implements scala.Row or scala.Product
> and is instead finding a Java class. I've run into this issue a number of
> times. Dataframe doesn't work so well with Java. Here's a blog post with
> more information on this:
>
> http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
>
>
> Thanks,
>
> Andy.
>
> --
>
> Andy Grove
> Chief Architect
> AgilData - Simple Streaming SQL that Scales
> www.agildata.com
>
>
> On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com> wrote:
>
>> Hi,
>>
>> I created a custom UserDefinedType in Java as follows:
>>
>> SQLPoint = new UserDefinedType<JavaPoint>() {
>> //overriding serialize, deserialize, sqlType, userClass functions here
>> }
>>
>> When creating a dataframe, I am following the manual mapping, I have a
>> constructor for JavaPoint - JavaPoint(double x, double y) and a Customer
>> record as follows:
>>
>> public class CustomerRecord {
>> private int id;
>> private String name;
>> private Object location;
>>
>> //setters and getters follow here
>> }
>>
>> Following the example in Spark source, when I create a RDD as follows:
>>
>> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() {
>> //call method
>> CustomerRecord rec = new CustomerRecord();
>> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
>> });
>>
>> This results in a MatchError. The stack trace is as follows:
>>
>> scala.MatchError: [B@45aa3dd5 (of class [B)
>> at
>>
>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
>> at
>>
>> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
>> at
>>
>> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
>> at
>>
>> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
>> at
>>
>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>> at
>>
>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
>> at
>>
>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>> at
>>
>> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
>> at
>>
>> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
>> at
>> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
>> at
>> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
>> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
>> at
>>
>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
>> at
>>
>> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
>> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
>> at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
>> at
>>
>> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
>> at
>>
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>> at
>>
>> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
>> at
>> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
>> at org.apache.spark.scheduler.Task.run(Task.scala:89)
>> at
>> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
>> 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)
>>
>>
>>
>> --
>> View this message in context:
>> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
>> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>>
>> ---------------------------------------------------------------------
>> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
>> For additional commands, e-mail: user-help@spark.apache.org
>>
>>
>
Re: Scala MatchError in Spark SQL
Posted by Andy Grove <an...@agildata.com>.
Catalyst is expecting a class that implements scala.Row or scala.Product
and is instead finding a Java class. I've run into this issue a number of
times. Dataframe doesn't work so well with Java. Here's a blog post with
more information on this:
http://www.agildata.com/apache-spark-rdd-vs-dataframe-vs-dataset/
Thanks,
Andy.
--
Andy Grove
Chief Architect
AgilData - Simple Streaming SQL that Scales
www.agildata.com
On Wed, Jan 20, 2016 at 7:07 AM, raghukiran <ra...@gmail.com> wrote:
> Hi,
>
> I created a custom UserDefinedType in Java as follows:
>
> SQLPoint = new UserDefinedType<JavaPoint>() {
> //overriding serialize, deserialize, sqlType, userClass functions here
> }
>
> When creating a dataframe, I am following the manual mapping, I have a
> constructor for JavaPoint - JavaPoint(double x, double y) and a Customer
> record as follows:
>
> public class CustomerRecord {
> private int id;
> private String name;
> private Object location;
>
> //setters and getters follow here
> }
>
> Following the example in Spark source, when I create a RDD as follows:
>
> sc.textFile(inputFileName).map(new Function<String, CustomerRecord>() {
> //call method
> CustomerRecord rec = new CustomerRecord();
> rec.setLocation(SQLPoint.serialize(new JavaPoint(x, y)));
> });
>
> This results in a MatchError. The stack trace is as follows:
>
> scala.MatchError: [B@45aa3dd5 (of class [B)
> at
>
> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:255)
> at
>
> org.apache.spark.sql.catalyst.CatalystTypeConverters$StructConverter.toCatalystImpl(CatalystTypeConverters.scala:250)
> at
>
> org.apache.spark.sql.catalyst.CatalystTypeConverters$CatalystTypeConverter.toCatalyst(CatalystTypeConverters.scala:102)
> at
>
> org.apache.spark.sql.catalyst.CatalystTypeConverters$$anonfun$createToCatalystConverter$2.apply(CatalystTypeConverters.scala:401)
> at
>
> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
> at
>
> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1$$anonfun$apply$1.apply(SQLContext.scala:1358)
> at
>
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at
>
> scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:244)
> at
>
> scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at
> scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> at
> scala.collection.TraversableLike$class.map(TraversableLike.scala:244)
> at scala.collection.mutable.ArrayOps$ofRef.map(ArrayOps.scala:108)
> at
>
> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1358)
> at
>
> org.apache.spark.sql.SQLContext$$anonfun$org$apache$spark$sql$SQLContext$$beansToRows$1.apply(SQLContext.scala:1356)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$11.next(Iterator.scala:328)
> at scala.collection.Iterator$$anon$10.next(Iterator.scala:312)
> at scala.collection.Iterator$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$$anonfun$5.apply(SparkPlan.scala:212)
> at
>
> org.apache.spark.sql.execution.SparkPlan$$anonfun$5.apply(SparkPlan.scala:212)
> at
>
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
> at
>
> org.apache.spark.SparkContext$$anonfun$runJob$5.apply(SparkContext.scala:1858)
> at
> org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:66)
> at org.apache.spark.scheduler.Task.run(Task.scala:89)
> at
> org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:213)
> 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)
>
>
>
> --
> View this message in context:
> http://apache-spark-user-list.1001560.n3.nabble.com/Scala-MatchError-in-Spark-SQL-tp26021.html
> Sent from the Apache Spark User List mailing list archive at Nabble.com.
>
> ---------------------------------------------------------------------
> To unsubscribe, e-mail: user-unsubscribe@spark.apache.org
> For additional commands, e-mail: user-help@spark.apache.org
>
>