当试图将spark应用程序分解为类并使用Try时，我遇到了无法序列化的Task的麻烦。
该代码从S3中提取用于架构，从Kafka（主题为具有架构reg的avro格式）中进行流式读取。
我尝试使用类而不使用类...但是在两种情况下，我都遇到与闭包有关的serz错误。我猜想在尝试进行serz时会拉入某些东西。这个错误总是困扰着我。如果有人可以阐明我如何避免这个问题，那将是很棒的。这些Java类似乎比有时值得解决的问题更多。
import java.util.Properties
import com.databricks.spark.avro._
import io.confluent.kafka.schemaregistry.client.rest.RestService
import io.confluent.kafka.serializers.{AbstractKafkaAvroSerDeConfig, KafkaAvroDecoder, KafkaAvroDeserializerConfig}
import org.apache.avro.Schema
import org.apache.avro.generic.GenericData
import org.apache.spark.sql.functions.{col, from_json}
import org.apache.spark.sql.streaming.StreamingQuery
import org.apache.spark.sql.types.StructType
import org.apache.spark.sql.{DataFrame, SparkSession}
import scala.util.{Failure, Success, Try}
case class DeserializedFromKafkaRecord(value: String)
class sparkS3() extends Serializable {
def readpeopleSchemaDF(spark: SparkSession, topicSchemaLocation: String): scala.util.Try[StructType] = {
val read: scala.util.Try[StructType] = Try(
spark
.read
.option("header", "true")
.format("com.databricks.spark.avro")
.load(topicSchemaLocation)
.schema
)
read
}
def writeTopicDF(peopleDFstream: DataFrame,
peopleDFstreamCheckpoint: String,
peopleDFstreamLocation: String): scala.util.Try[StreamingQuery] = {
val write: scala.util.Try[StreamingQuery] = Try(
peopleDFstream
.writeStream
.option("checkpointLocation", peopleDFstreamCheckpoint)
.format("com.databricks.spark.avro")
.option("path", peopleDFstreamLocation)
.start()
)
write
}
}
class sparkKafka() extends Serializable {
def readpeopleTopicDF(spark: SparkSession, topicSchema: StructType): scala.util.Try[DataFrame] = {
val brokers = "URL:9092"
val schemaRegistryURL = "URL:8081"
val kafkaParams = Map[String, String](
"kafka.bootstrap.servers" -> brokers,
"key.deserializer" -> "KafkaAvroDeserializer",
"value.deserializer" -> "KafkaAvroDeserializer",
"group.id" -> "structured-kafka",
//"auto.offset.reset" -> "latest",
"failOnDataLoss" -> "false",
"schema.registry.url" -> schemaRegistryURL
)
var kafkaTopic = "people"
object avroDeserializerWrapper {
val props = new Properties()
props.put(AbstractKafkaAvroSerDeConfig.SCHEMA_REGISTRY_URL_CONFIG, schemaRegistryURL)
props.put(KafkaAvroDeserializerConfig.SPECIFIC_AVRO_READER_CONFIG, "true")
val vProps = new kafka.utils.VerifiableProperties(props)
val deser = new KafkaAvroDecoder(vProps)
val avro_schema = new RestService(schemaRegistryURL).getLatestVersion(kafkaTopic + "-value")
val messageSchema = new Schema.Parser().parse(avro_schema.getSchema)
}
import spark.implicits._
val read: scala.util.Try[DataFrame] = Try(
{
val peopleStringDF = {
spark
.readStream
.format("kafka")
.option("subscribe", kafkaTopic)
.option("kafka.bootstrap.servers", brokers)
.options(kafkaParams)
.load()
.map(x => {
DeserializedFromKafkaRecord(avroDeserializerWrapper.deser.fromBytes(
x
.getAs[Array[Byte]]("value"), avroDeserializerWrapper.messageSchema)
.asInstanceOf[GenericData.Record].toString)
})
}
val peopleJsonDF = {
peopleStringDF
.select(
from_json(col("value")
.cast("string"), topicSchema)
.alias("people"))
}
peopleJsonDF.select("people.*")
})
read
}
}
object peopleDataLakePreprocStage1 {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder
.appName("peoplePreProcConsumerStage1")
.getOrCreate()
val topicSchemaLocation = "URL"
val topicDFstreamCheckpoint = "URL"
val topicDFstreamLocation = "URL"
val sparkKafka = new sparkKafka()
val sparkS3 = new sparkS3()
sparkS3.readpepleSchemaDF(spark, topicSchemaLocation) match {
case Success(topicSchema) => {
sparkKafka.readpeopletTopicDF(spark, topicSchema) match {
case Success(df) => {
sparkS3.writeTopicDF(df, topicDFstreamCheckpoint, topicDFstreamLocation) match {
case Success(query) => {
query.awaitTermination()
}
case Failure(f) => println(f)
}
}
case Failure(f) => println(f)
}
}
case Failure(f) => println(f)
}
}
}
这是错误
java.lang.IllegalStateException: s3a://... when compacting batch 9 (compactInterval: 10)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:173)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:172)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.NumericRange.foreach(NumericRange.scala:73)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.compact(CompactibleFileStreamLog.scala:172)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.add(CompactibleFileStreamLog.scala:156)
at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol.commitJob(ManifestFileCommitProtocol.scala:64)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:213)
at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:123)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:477)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:475)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:474)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
18/08/10 13:04:07 ERROR MicroBatchExecution: Query [id = 2876ded4-f223-40c4-8634-0c8feec94bf6, runId = 9b9a1347-7a80-4295-bb6e-ff2de18eeaf4] terminated with error
org.apache.spark.SparkException: Job aborted.
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:224)
at org.apache.spark.sql.execution.streaming.FileStreamSink.addBatch(FileStreamSink.scala:123)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3$$anonfun$apply$16.apply(MicroBatchExecution.scala:477)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:77)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch$3.apply(MicroBatchExecution.scala:475)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.org$apache$spark$sql$execution$streaming$MicroBatchExecution$$runBatch(MicroBatchExecution.scala:474)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply$mcV$sp(MicroBatchExecution.scala:133)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1$$anonfun$apply$mcZ$sp$1.apply(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProgressReporter$class.reportTimeTaken(ProgressReporter.scala:271)
at org.apache.spark.sql.execution.streaming.StreamExecution.reportTimeTaken(StreamExecution.scala:58)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution$$anonfun$runActivatedStream$1.apply$mcZ$sp(MicroBatchExecution.scala:121)
at org.apache.spark.sql.execution.streaming.ProcessingTimeExecutor.execute(TriggerExecutor.scala:56)
at org.apache.spark.sql.execution.streaming.MicroBatchExecution.runActivatedStream(MicroBatchExecution.scala:117)
at org.apache.spark.sql.execution.streaming.StreamExecution.org$apache$spark$sql$execution$streaming$StreamExecution$$runStream(StreamExecution.scala:279)
at org.apache.spark.sql.execution.streaming.StreamExecution$$anon$1.run(StreamExecution.scala:189)
Caused by: java.lang.IllegalStateException: s3a://..../_spark_metadata/0 doesn't exist when compacting batch 9 (compactInterval: 10)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4$$anonfun$apply$1.apply(CompactibleFileStreamLog.scala:174)
at scala.Option.getOrElse(Option.scala:121)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:173)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog$$anonfun$4.apply(CompactibleFileStreamLog.scala:172)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)
at scala.collection.immutable.NumericRange.foreach(NumericRange.scala:73)
at scala.collection.TraversableLike$class.map(TraversableLike.scala:234)
at scala.collection.AbstractTraversable.map(Traversable.scala:104)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.compact(CompactibleFileStreamLog.scala:172)
at org.apache.spark.sql.execution.streaming.CompactibleFileStreamLog.add(CompactibleFileStreamLog.scala:156)
at org.apache.spark.sql.execution.streaming.ManifestFileCommitProtocol.commitJob(ManifestFileCommitProtocol.scala:64)
at org.apache.spark.sql.execution.datasources.FileFormatWriter$.write(FileFormatWriter.scala:213)
... 17 more
答案 0 :(得分：1)
解析是两个（或两者）之一。.扩展类的序列化，同一名称空间中的单独文件。我已经更新了上面的代码以反映
答案 1 :(得分：0)
只是一个刺。在 sparkS3 类中，您使用'var'定义这些值-您是说'val'吗？