[HUDI-1641] Issue for Integrating Hudi with Kafka using Avro Schema

• Type: Bug
• Status: Closed
• Priority: Major
• Resolution: Fixed
• Fix Version/s: 0.7.0
• Component/s: deltastreamer, spark, Utilities
• Assignee: Unassigned
• Reporter: PRASHANT BHOSALE
• Created: 25/Feb/21 14:04
• Updated: 11/Mar/21 11:36
• Resolved: 11/Mar/21 11:36

Description

I am trying to integrate Hudi with Kafka topic.
teps followed :

1. Created Kafka topic in Confluent with schema defined in schema registry.
2. Using kafka-avro-console-producer, I am trying to produce data.
3. Running Hudi Delta Streamer in continuous mode to consume the data.

Infrastructure :

1. AWS EMR
2. Spark 2.4.4
3. Hudi Utility ( Tried with 0.6.0 and 0.7.0 )
4. Avro ( Tried avro-1.8.2, avro-1.9.2 and avro-1.10.0 )

21/02/24 13:02:08 ERROR TaskResultGetter: Exception while getting task result
org.apache.spark.SparkException: Error reading attempting to read avro data -- encountered an unknown fingerprint: 103427103938146401, not sure what schema to use.  This could happen if you registered additional schemas after starting your spark context.
    at org.apache.spark.serializer.GenericAvroSerializer$$anonfun$4.apply(GenericAvroSerializer.scala:141)
    at org.apache.spark.serializer.GenericAvroSerializer$$anonfun$4.apply(GenericAvroSerializer.scala:138)
    at scala.collection.mutable.HashMap.getOrElseUpdate(HashMap.scala:79)
    at org.apache.spark.serializer.GenericAvroSerializer.deserializeDatum(GenericAvroSerializer.scala:137)
    at org.apache.spark.serializer.GenericAvroSerializer.read(GenericAvroSerializer.scala:162)
    at org.apache.spark.serializer.GenericAvroSerializer.read(GenericAvroSerializer.scala:47)
    at com.esotericsoftware.kryo.Kryo.readObject(Kryo.java:731)
    at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read(DefaultArraySerializers.java:391)
    at com.esotericsoftware.kryo.serializers.DefaultArraySerializers$ObjectArraySerializer.read(DefaultArraySerializers.java:302)
    at com.esotericsoftware.kryo.Kryo.readClassAndObject(Kryo.java:813)
    at org.apache.spark.serializer.KryoSerializerInstance.deserialize(KryoSerializer.scala:371)
    at org.apache.spark.scheduler.DirectTaskResult.value(TaskResult.scala:88)
    at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply$mcV$sp(TaskResultGetter.scala:72)
    at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:63)
    at org.apache.spark.scheduler.TaskResultGetter$$anon$3$$anonfun$run$1.apply(TaskResultGetter.scala:63)
    at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1945)
    at org.apache.spark.scheduler.TaskResultGetter$$anon$3.run(TaskResultGetter.scala:62)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)
21/02/24 13:02:08 INFO YarnScheduler: Removed TaskSet 1.0, whose tasks have all completed, from pool
21/02/24 13:02:08 INFO YarnScheduler: Cancelling stage 1
21/02/24 13:02:08 INFO YarnScheduler: Killing all running tasks in stage 1: Stage cancelled
21/02/24 13:02:08 INFO DAGScheduler: ResultStage 1 (isEmpty at DeltaSync.java:380) failed in 1.415 s due to Job aborted due to stage failure: Exception while getting task result: org.apache.spark.SparkException: Error reading attempting to read avro data -- encountered an unknown fingerprint: 103427103938146401, not sure what schema to use.  This could happen if you registered additional schemas after starting your spark context.
21/02/24 13:02:08 INFO DAGScheduler: Job 5 failed: isEmpty at DeltaSync.java:380, took 1.422265 s
21/02/24 13:02:08 ERROR HoodieDeltaStreamer: Shutting down delta-sync due to exception
org.apache.spark.SparkException: Job aborted due to stage failure: Exception while getting task result: org.apache.spark.SparkException: Error reading attempting to read avro data -- encountered an unknown fingerprint: 103427103938146401, not sure what schema to use.  This could happen if you registered additional schemas after starting your spark context.
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:2041)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2029)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:2028)
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2028)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:966)
    at scala.Option.foreach(Option.scala:257)
    at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:966)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2262)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2211)
    at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2200)
    at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
    at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:777)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082)
    at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101)
    at org.apache.spark.rdd.RDD$$anonfun$take$1.apply(RDD.scala:1364)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.take(RDD.scala:1337)
    at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply$mcZ$sp(RDD.scala:1472)
    at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply(RDD.scala:1472)
    at org.apache.spark.rdd.RDD$$anonfun$isEmpty$1.apply(RDD.scala:1472)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)
    at org.apache.spark.rdd.RDD.withScope(RDD.scala:363)
    at org.apache.spark.rdd.RDD.isEmpty(RDD.scala:1471)
    at org.apache.spark.api.java.JavaRDDLike$class.isEmpty(JavaRDDLike.scala:544)
    at org.apache.spark.api.java.AbstractJavaRDDLike.isEmpty(JavaRDDLike.scala:45)
    at org.apache.hudi.utilities.deltastreamer.DeltaSync.readFromSource(DeltaSync.java:380)
    at org.apache.hudi.utilities.deltastreamer.DeltaSync.syncOnce(DeltaSync.java:255)
    at org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer$DeltaSyncService.lambda$startService$0(HoodieDeltaStreamer.java:587)
    at java.util.concurrent.CompletableFuture$AsyncSupply.run(CompletableFuture.java:1604)
    at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
    at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
    at java.lang.Thread.run(Thread.java:748)