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.NullPointerException
Serialization 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.ArrayWritable
Which 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.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.SparkContext._
import parquet.avro.AvroWriteSupport
import parquet.avro.AvroParquetOutputFormat
import 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.Job
import org.apache.spark.SparkContext
import parquet.hadoop.ParquetInputFormat
import 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
