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From Michael Albert <m_albert...@yahoo.com.INVALID>
Subject Re: avro + parquet + vector<string> + NullPointerException while reading
Date Fri, 07 Nov 2014 00:59:20 GMT
Thanks for the advice!
What seems to work for is is that I define the array type as:   "type": { "type": "array",
"items": "string", "java-class": "java.util.ArrayList" }It seems to be creating an avro.Generic.List,
which spark doesn't know how to serialize, instead of a guava.util.List, which spark likes.
Hive at 0.13.1 still can't read it though...Thanks!-Mike

      From: Michael Armbrust <michael@databricks.com>
 To: Michael Albert <m_albert137@yahoo.com> 
Cc: "user@spark.apache.org" <user@spark.apache.org> 
 Sent: Tuesday, November 4, 2014 2:37 PM
 Subject: Re: avro + parquet + vector<string> + NullPointerException while reading
   
You might consider using the native parquet support built into Spark SQL instead of using
the raw library: 
http://spark.apache.org/docs/latest/sql-programming-guide.html#parquet-files



On Mon, Nov 3, 2014 at 7:33 PM, Michael Albert <m_albert137@yahoo.com.invalid> wrote:

Greetings!
I'm trying to use avro and parquet with the following schema:{    "name": "TestStruct", 
  "namespace": "bughunt",    "type": "record",    "fields": [        {       
    "name": "string_array",            "type": { "type": "array", "items": "string"
}         }    ]}The writing process seems to be OK, but when I try to read it with
Spark, I get:com.esotericsoftware.kryo.KryoException: java.lang.NullPointerExceptionSerialization
trace:string_array (bughunt.TestStruct) at com.esotericsoftware.kryo.serializers.FieldSerializer$ObjectField.read(FieldSerializer.java:626)
at com.esotericsoftware.kryo.serializers.FieldSerializer.read(FieldSerializer.java:221) at
com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:732)When I try to read it with
Hive, I get this:Failed with exception java.io.IOException:org.apache.hadoop.hive.ql.metadata.HiveException:
java.lang.ClassCastException: org.apache.hadoop.io.BytesWritable cannot be cast to org.apache.hadoop.io.ArrayWritableWhich
would lead me to suspect that this might be related to this one: https://github.com/Parquet/parquet-mr/issues/281 ,
but that one seems to be Hive specific, and I am not seeing Spark read the data it claims
to have written itself.
I'm running on an Amazon EMR cluster using the "version 2.4.0" hadoop code and spark 1.1.0.Has
anyone else observed this sort of behavior?
For completeness, here is the code that writes the data:package bughunt
import org.apache.hadoop.mapreduce.Job
import org.apache.spark.SparkConfimport org.apache.spark.SparkContextimport org.apache.spark.SparkContext._

import parquet.avro.AvroWriteSupportimport parquet.avro.AvroParquetOutputFormatimport parquet.hadoop.ParquetOutputFormat
import java.util.ArrayList

object GenData {    val outputPath = "/user/xxxxx/testdata"    val words = List(    
                List("apple", "banana", "cherry"),                    List("car",
"boat", "plane"),                    List("lion", "tiger", "bear"),         
          List("north", "south", "east", "west"),                    List("up",
"down", "left", "right"),                    List("red", "green", "blue"))
    def main(args: Array[String]) {        val conf = new SparkConf(true)       
            .setAppName("IngestLoanApplicattion")                    //.set("spark.kryo.registrator", 
                  //            classOf[CommonRegistrator].getName)     
              .set("spark.serializer",                            "org.apache.spark.serializer.KryoSerializer") 
                  .set("spark.kryoserializer.buffer.mb", 4.toString)         
          .set("spark.kryo.referenceTracking", "false")
        val sc = new SparkContext(conf)
        val rdd = sc.parallelize(words)
        val job = new Job(sc.hadoopConfiguration)
        ParquetOutputFormat.setWriteSupportClass(job, classOf[AvroWriteSupport])   
    AvroParquetOutputFormat.setSchema(job,                    TestStruct.SCHEMA$)
        rdd.map(p => {                     val xs = new java.util.ArrayList[String] 
                  for (z<-p) { xs.add(z) }                    val bldr
= TestStruct.newBuilder()                    bldr.setStringArray(xs)       
            (null, bldr.build()) })           .saveAsNewAPIHadoopFile(outputPath, 
              classOf[Void],                classOf[TestStruct],       
        classOf[ParquetOutputFormat[TestStruct]],                job.getConfiguration) 
  }}
To read the data, I use this sort of code from the spark-shell::paste
import bughunt.TestStruct
import org.apache.hadoop.mapreduce.Jobimport org.apache.spark.SparkContext
import parquet.hadoop.ParquetInputFormatimport parquet.avro.AvroReadSupport
def openRddSpecific(sc: SparkContext) = {    val job = new Job(sc.hadoopConfiguration)
    ParquetInputFormat.setReadSupportClass(job,            classOf[AvroReadSupport[TestStruct]])
    sc.newAPIHadoopFile("/user/malbert/testdata",            classOf[ParquetInputFormat[TestStruct]], 
          classOf[Void],            classOf[TestStruct],            job.getConfiguration)}I
start the Spark shell as follows:spark-shell \    --jars ../my-jar-containing-the-class-definitions.jar
\    --conf mapreduce.user.classpath.first=true \    --conf spark.kryo.referenceTracking=false
\    --conf spark.kryoserializer.buffer.mb=4 \    --conf spark.serializer=org.apache.spark.serializer.KryoSerializer 
I'm stumped.  I can read and write records and maps, but arrays/vectors elude me.Am I missing
something obvious?
Thanks!
Sincerely, Mike Albert



  
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