SparkException：类上的任务不可序列化：org.apache.avro.generic.GenericDatumReader

Question

我有两个字段的 json 格式输入（大小：BigInteger 和数据：字符串）。这里数据包含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

1. Spark: java.io.NotSerializableException: org.apache.avro.Schema$RecordSchema

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

1. Use schema to convert AVRO messages with Spark to DataFrame

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。

Answer 1

你试过了吗

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
    }