[英]SparkException: Task not serializable on class: org.apache.avro.generic.GenericDatumReader
我输入了 json 格式的两个字段(大小:BigInteger 和数据:字符串)。 这里的数据包含ZStd压缩的Avro记录。 任务是解码这些记录。 我为此使用Spark-avro 。 但是,任务不可序列化异常。
样本数据
{
"data": "7z776qOPevPJF5/0Dv9Rzx/1/i8gJJiQD5MTDGdbeNKKT"
"size" : 231
}
代码
import java.util.Base64
import com.github.luben.zstd.Zstd
import org.apache.avro.Schema
import com.twitter.bijection.Injection
import org.apache.avro.generic.GenericRecord
import com.twitter.bijection.avro.GenericAvroCodecs
import com.databricks.spark.avro.SchemaConverters
import org.apache.spark.sql.types.StructType
import com.databricks.spark.avro.SchemaConverters._
def decode2(input:String,size:Int,avroBijection:Injection[GenericRecord, Array[Byte]], sqlType:StructType): GenericRecord = {
val compressedGenericRecordBytes = Base64.getDecoder.decode(input)
val genericRecordBytes = Zstd.decompress(compressedGenericRecordBytes,size)
avroBijection.invert(genericRecordBytes).get
}
val myRdd = spark.read.format("json").load("/path").rdd
val rows = myRdd.mapPartitions{
lazy val schema = new Schema.Parser().parse(schemaStr)
lazy val avroBijection: Injection[GenericRecord, Array[Byte]] = GenericAvroCodecs.toBinary(schema)
lazy val sqlType = SchemaConverters.toSqlType(schema).dataType.asInstanceOf[StructType]
(iterator) => {
val myList = iterator.toList
myList.map{ x => {
val size = x(1).asInstanceOf[Long].intValue
val data = x(0).asInstanceOf [String]
decode2(data, size, avroBijection,sqlType)
}
}.iterator
}
}
例外
files: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row] = MapPartitionsRDD[987] at rdd at <console>:346
org.apache.spark.SparkException: Task not serializable
at org.apache.spark.util.ClosureCleaner$.ensureSerializable(ClosureCleaner.scala:298)
at org.apache.spark.util.ClosureCleaner$.org$apache$spark$util$ClosureCleaner$$clean(ClosureCleaner.scala:288)
at org.apache.spark.util.ClosureCleaner$.clean(ClosureCleaner.scala:108)
at org.apache.spark.SparkContext.clean(SparkContext.scala:2287)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:794)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitions$1.apply(RDD.scala:793)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
at org.apache.spark.rdd.RDD.withScope(RDD.scala:362)
at org.apache.spark.rdd.RDD.mapPartitions(RDD.scala:793)
... 112 elided
Caused by: java.io.NotSerializableException: org.apache.avro.generic.GenericDatumReader
Serialization stack:
- object not serializable (class: org.apache.avro.generic.GenericDatumReader, value: org.apache.avro.generic.GenericDatumReader@4937cd88)
- field (class: com.twitter.bijection.avro.BinaryAvroCodec, name: reader, type: interface org.apache.avro.io.DatumReader)
- object (class com.twitter.bijection.avro.BinaryAvroCodec, com.twitter.bijection.avro.BinaryAvroCodec@6945439c)
- field (class: $$$$79b2515edf74bd80cfc9d8ac1ba563c6$$$$iw, name: avroBijection, type: interface com.twitter.bijection.Injection)
已经尝试过 SO 帖子
在这篇文章之后,我更新了decode2
方法以将schemaStr
作为输入并在方法内转换为模式和 SqlType。 异常没有变化
使用帖子中提供的代码创建object Injection
然后使用它。 这个也没有帮助。
你有没有尝试过
val rows = myRdd.mapPartitions{
(iterator) => {
val myList = iterator.toList
myList.map{ x => {
lazy val schema = new Schema.Parser().parse(schemaStr)
lazy val avroBijection: Injection[GenericRecord, Array[Byte]] = GenericAvroCodecs.toBinary(schema)
lazy val sqlType = SchemaConverters.toSqlType(schema).dataType.asInstanceOf[StructType]
val size = x(1).asInstanceOf[Long].intValue
val data = x(0).asInstanceOf [String]
decode2(data, size, avroBijection,sqlType)
}
}.iterator
}
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