Re: How to handle the UUID in Spark 1.3.1

[Ted Yu]

This is related:
SPARK-10501

On Fri, Oct 9, 2015 at 7:28 AM, java8964  wrote:

> Hi,  Sparkers:
>
> In this case, I want to use Spark as an ETL engine to load the data from
> Cassandra, and save it into HDFS.
>
> Here is the environment specified information:
>
> Spark 1.3.1
> Cassandra 2.1
> HDFS/Hadoop 2.2
>
> I am using the Cassandra Spark Connector 1.3.x, which I have no problem to
> query the C* data in the Spark. But I have a problem trying to save the
> data into HDFS, like below:
>
> val df = sqlContext.load("org.apache.spark.sql.cassandra", options = Map(
> "c_table" -> "table_name", "keyspace" -> "keyspace_name")
> df: org.apache.spark.sql.DataFrame = [account_id: bigint, campaign_id:
> uuid, business_info_ids: array, closed_date: timestamp,
> compliance_hold: boolean, contacts_list_id: uuid, contacts_list_seq:
> bigint, currency_type: string, deleted_date: timestamp, discount_info:
> map, end_date: timestamp, insert_by: string, insert_time:
> timestamp, last_update_by: string, last_update_time: timestamp, name:
> string, parent_id: uuid, publish_date: timestamp, share_incentive:
> map, start_date: timestamp, version: int]
>
> scala> df.count
> res12: Long = 757704
>
> I can also dump the data output suing df.first, without any problem.
>
> But when I try to save it:
>
> scala> df.save("hdfs://location", "parquet")
> java.lang.RuntimeException: Unsupported datatype UUIDType
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:372)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:316)
> at scala.Option.getOrElse(Option.scala:120)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$.fromDataType(ParquetTypes.scala:315)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:395)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:394)
> 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.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypes.scala:393)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$.writeMetaData(ParquetTypes.scala:440)
> at
> org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache.prepareMetadata(newParquet.scala:260)
> at
> org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:276)
> at
> org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:269)
> 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.parquet.ParquetRelation2$MetadataCache.refresh(newParquet.scala:269)
> at
> org.apache.spark.sql.parquet.ParquetRelation2.(newParquet.scala:391)
> at
> org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:98)
> at
> org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:128)
> at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:240)
> at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1196)
> at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1156)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:28)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:33)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:35)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:37)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:39)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:41)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:43)
> at $iwC$$iwC$$iwC$$iwC$$iwC.(:45)
> at $iwC$$iwC$$iwC$$iwC.(:47)
> at $iwC$$iwC$$iwC.(:49)
> at $iwC$$iwC.(:51)
> at $iwC.(:53)
> at (:55)
> at .(:59)
> at .()
> at .(:7)
> at .()
> at $print()
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at
> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
> at
> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
> at java.lang.reflect.Method.invoke(Method.java:606)
> at
> org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)
> at
> org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)
> at 

How to handle the UUID in Spark 1.3.1

[java8964]

Hi,  Sparkers:
In this case, I want to use Spark as an ETL engine to load the data from 
Cassandra, and save it into HDFS.
Here is the environment specified information:
Spark 1.3.1Cassandra 2.1HDFS/Hadoop 2.2
I am using the Cassandra Spark Connector 1.3.x, which I have no problem to 
query the C* data in the Spark. But I have a problem trying to save the data 
into HDFS, like below:
val df = sqlContext.load("org.apache.spark.sql.cassandra", options = Map( 
"c_table" -> "table_name", "keyspace" -> "keyspace_name")df: 
org.apache.spark.sql.DataFrame = [account_id: bigint, campaign_id: uuid, 
business_info_ids: array, closed_date: timestamp, compliance_hold: 
boolean, contacts_list_id: uuid, contacts_list_seq: bigint, currency_type: 
string, deleted_date: timestamp, discount_info: map, end_date: 
timestamp, insert_by: string, insert_time: timestamp, last_update_by: string, 
last_update_time: timestamp, name: string, parent_id: uuid, publish_date: 
timestamp, share_incentive: map, start_date: timestamp, version: 
int]
scala> df.countres12: Long = 757704
I can also dump the data output suing df.first, without any problem.
But when I try to save it:
scala> df.save("hdfs://location", "parquet")java.lang.RuntimeException: 
Unsupported datatype UUIDType   at scala.sys.package$.error(package.scala:27)   
at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:372)
 at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:316)
 at scala.Option.getOrElse(Option.scala:120) at 
org.apache.spark.sql.parquet.ParquetTypesConverter$.fromDataType(ParquetTypes.scala:315)
 at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:395)
  at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:394)
  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.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypes.scala:393)
at 
org.apache.spark.sql.parquet.ParquetTypesConverter$.writeMetaData(ParquetTypes.scala:440)
at 
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache.prepareMetadata(newParquet.scala:260)
at 
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:276)
   at 
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:269)
   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.parquet.ParquetRelation2$MetadataCache.refresh(newParquet.scala:269)
at 
org.apache.spark.sql.parquet.ParquetRelation2.(newParquet.scala:391)   at 
org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:98)  
 at 
org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:128) 
 at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:240)   
 at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1196)at 
org.apache.spark.sql.DataFrame.save(DataFrame.scala:1156)at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:28)
 at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:33)  at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:35)   at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:37)at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:39) at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:41)  at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:43)   at 
$iwC$$iwC$$iwC$$iwC$$iwC.(:45)at 
$iwC$$iwC$$iwC$$iwC.(:47) at 
$iwC$$iwC$$iwC.(:49)  at $iwC$$iwC.(:51)   at 
$iwC.(:53)at (:55) at .(:59)   
 at .() at .(:7) at .() at 
$print()at sun.reflect.NativeMethodAccessorImpl.invoke0(Native 
Method)  at 
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)   
 at 
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:606) at 
org.apache.spark.repl.SparkIMain$ReadEvalPrint.call(SparkIMain.scala:1065)   at 
org.apache.spark.repl.SparkIMain$Request.loadAndRun(SparkIMain.scala:1338)   at 
org.apache.spark.repl.SparkIMain.loadAndRunReq$1(SparkIMain.scala:840)   at 

RE: How to handle the UUID in Spark 1.3.1

[java8964]

Thanks, Ted.
Does this mean I am out of luck for now? If I use HiveContext, and cast the 
UUID as string, will it work?
Yong

Date: Fri, 9 Oct 2015 09:09:38 -0700
Subject: Re: How to handle the UUID in Spark 1.3.1
From: yuzhih...@gmail.com
To: java8...@hotmail.com
CC: user@spark.apache.org

This is related:SPARK-10501

On Fri, Oct 9, 2015 at 7:28 AM, java8964  wrote:



Hi,  Sparkers:
In this case, I want to use Spark as an ETL engine to load the data from 
Cassandra, and save it into HDFS.
Here is the environment specified information:
Spark 1.3.1Cassandra 2.1HDFS/Hadoop 2.2
I am using the Cassandra Spark Connector 1.3.x, which I have no problem to 
query the C* data in the Spark. But I have a problem trying to save the data 
into HDFS, like below:
val df = sqlContext.load("org.apache.spark.sql.cassandra", options = Map( 
"c_table" -> "table_name", "keyspace" -> "keyspace_name")df: 
org.apache.spark.sql.DataFrame = [account_id: bigint, campaign_id: uuid, 
business_info_ids: array, closed_date: timestamp, compliance_hold: 
boolean, contacts_list_id: uuid, contacts_list_seq: bigint, currency_type: 
string, deleted_date: timestamp, discount_info: map, end_date: 
timestamp, insert_by: string, insert_time: timestamp, last_update_by: string, 
last_update_time: timestamp, name: string, parent_id: uuid, publish_date: 
timestamp, share_incentive: map, start_date: timestamp, version: 
int]
scala> df.countres12: Long = 757704
I can also dump the data output suing df.first, without any problem.
But when I try to save it:
scala> df.save("hdfs://location", "parquet")java.lang.RuntimeException: 
Unsupported datatype UUIDType   at scala.sys.package$.error(package.scala:27)   
at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:372)
 at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:316)
 at scala.Option.getOrElse(Option.scala:120) at 
org.apache.spark.sql.parquet.ParquetTypesConverter$.fromDataType(ParquetTypes.scala:315)
 at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:395)
  at 
org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:394)
  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.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypes.scala:393)
at 
org.apache.spark.sql.parquet.ParquetTypesConverter$.writeMetaData(ParquetTypes.scala:440)
at 
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache.prepareMetadata(newParquet.scala:260)
at 
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:276)
   at 
org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:269)
   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.parquet.ParquetRelation2$MetadataCache.refresh(newParquet.scala:269)
at 
org.apache.spark.sql.parquet.ParquetRelation2.(newParquet.scala:391)   at 
org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:98)  
 at 
org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:128) 
 at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:240)   
 at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1196)at 
org.apache.spark.sql.DataFrame.save(DataFrame.scala:1156)at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:28)
 at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:33)  at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:35)   at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:37)at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:39) at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:41)  at 
$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:43)   at 
$iwC$$iwC$$iwC$$iwC$$iwC.(:45)at 
$iwC$$iwC$$iwC$$iwC.(:47) at 
$iwC$$iwC$$iwC.(:49)  at $iwC$$iwC.(:51)   at 
$iwC.(:53)at (:55) at .(:59)   
 at .() at .(:7) at .() at 
$print()at sun.reflect.NativeMethodAccessorImpl.invoke0(Native 
Method)  at 
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)   
 at 

Re: How to handle the UUID in Spark 1.3.1

[Ted Yu]

I guess that should work :-)

On Fri, Oct 9, 2015 at 10:46 AM, java8964  wrote:

> Thanks, Ted.
>
> Does this mean I am out of luck for now? If I use HiveContext, and cast
> the UUID as string, will it work?
>
> Yong
>
> --
> Date: Fri, 9 Oct 2015 09:09:38 -0700
> Subject: Re: How to handle the UUID in Spark 1.3.1
> From: yuzhih...@gmail.com
> To: java8...@hotmail.com
> CC: user@spark.apache.org
>
>
> This is related:
> SPARK-10501
>
> On Fri, Oct 9, 2015 at 7:28 AM, java8964  wrote:
>
> Hi,  Sparkers:
>
> In this case, I want to use Spark as an ETL engine to load the data from
> Cassandra, and save it into HDFS.
>
> Here is the environment specified information:
>
> Spark 1.3.1
> Cassandra 2.1
> HDFS/Hadoop 2.2
>
> I am using the Cassandra Spark Connector 1.3.x, which I have no problem to
> query the C* data in the Spark. But I have a problem trying to save the
> data into HDFS, like below:
>
> val df = sqlContext.load("org.apache.spark.sql.cassandra", options = Map(
> "c_table" -> "table_name", "keyspace" -> "keyspace_name")
> df: org.apache.spark.sql.DataFrame = [account_id: bigint, campaign_id:
> uuid, business_info_ids: array, closed_date: timestamp,
> compliance_hold: boolean, contacts_list_id: uuid, contacts_list_seq:
> bigint, currency_type: string, deleted_date: timestamp, discount_info:
> map, end_date: timestamp, insert_by: string, insert_time:
> timestamp, last_update_by: string, last_update_time: timestamp, name:
> string, parent_id: uuid, publish_date: timestamp, share_incentive:
> map, start_date: timestamp, version: int]
>
> scala> df.count
> res12: Long = 757704
>
> I can also dump the data output suing df.first, without any problem.
>
> But when I try to save it:
>
> scala> df.save("hdfs://location", "parquet")
> java.lang.RuntimeException: Unsupported datatype UUIDType
> at scala.sys.package$.error(package.scala:27)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:372)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$fromDataType$2.apply(ParquetTypes.scala:316)
> at scala.Option.getOrElse(Option.scala:120)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$.fromDataType(ParquetTypes.scala:315)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:395)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$$anonfun$4.apply(ParquetTypes.scala:394)
> 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.parquet.ParquetTypesConverter$.convertFromAttributes(ParquetTypes.scala:393)
> at
> org.apache.spark.sql.parquet.ParquetTypesConverter$.writeMetaData(ParquetTypes.scala:440)
> at
> org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache.prepareMetadata(newParquet.scala:260)
> at
> org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:276)
> at
> org.apache.spark.sql.parquet.ParquetRelation2$MetadataCache$$anonfun$6.apply(newParquet.scala:269)
> 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.parquet.ParquetRelation2$MetadataCache.refresh(newParquet.scala:269)
> at
> org.apache.spark.sql.parquet.ParquetRelation2.(newParquet.scala:391)
> at
> org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:98)
> at
> org.apache.spark.sql.parquet.DefaultSource.createRelation(newParquet.scala:128)
> at org.apache.spark.sql.sources.ResolvedDataSource$.apply(ddl.scala:240)
> at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1196)
> at org.apache.spark.sql.DataFrame.save(DataFrame.scala:1156)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:28)
> at
> $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:33)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:35)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:37)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:39)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:41)
> at $iwC$$iwC$$iwC$$iwC$$iwC$$iwC.(:43)
> at $iwC$$iwC$$iwC$$iwC$$iwC.(:45)
> at $iwC$$iwC$$iwC$$iwC.(:47)
> at $iwC$$iwC$$iwC.(:49)
> at $iwC$$iwC.(:51)
> at $iwC.(:53)
> at (:55)
> at .(:59)
> at .()
> at .(:7)
> at .()
> at $print()
> at