Spark2.4.1はHDFSからAvroファイルを読み取ることができません

データフレームをAvro形式で書き込んでから読み取るための単純なコードブロックがあります。AvrolibはすでにSpark2.4.xに組み込まれているため、

Avroファイルの書き込みは成功し、ファイルはHDFSで生成されます。ただし、ファイルを読み取ると、AbstractMethodError例外がスローされます。誰かが私にいくつかの光を共有できますか？

ZeppelinノードブックSparkインタープリターにパッケージorg.apache.spark：spark-avro_2.11：2.4.1を追加して、Spark内部ライブラリーを使用しました。

私の単純なコードブロック：

%pyspark

test_rows = [ Row(file_name = "test-guangzhou1", topic='camera1', timestamp=1, msg="Test1"),  Row(file_name = "test-guangzhou1", topic='camera1', timestamp=2, msg="Test2"), Row(file_name = "test-guangzhou3", topic='camera3', timestamp=3, msg="Test3"), Row(file_name = "test-guangzhou1", topic='camera1', timestamp=4, msg="Test4") ]

test_df = spark.createDataFrame(test_rows)

test_df.write.format("avro")
    .mode('overwrite').save("hdfs:///tmp/bag_parser279181359_3")

loaded_df =  spark.read.format("avro").load('hdfs:///tmp/bag_parser279181359_3')

loaded_df.show()

私が見たエラーメッセージ：

Py4JJavaError: An error occurred while calling o701.collectToPython.
: java.lang.AbstractMethodError
    at org.apache.spark.sql.execution.FileSourceScanExec.inputRDD$lzycompute(DataSourceScanExec.scala:337)
    at org.apache.spark.sql.execution.FileSourceScanExec.inputRDD(DataSourceScanExec.scala:331)
    at org.apache.spark.sql.execution.FileSourceScanExec.inputRDDs(DataSourceScanExec.scala:357)
    at org.apache.spark.sql.execution.WholeStageCodegenExec.doExecute(WholeStageCodegenExec.scala:627)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:137)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$execute$1.apply(SparkPlan.scala:133)
    at org.apache.spark.sql.execution.SparkPlan$$anonfun$executeQuery$1.apply(SparkPlan.scala:161)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
    at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:158)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:133)
    at org.apache.spark.sql.execution.SparkPlan.getByteArrayRdd(SparkPlan.scala:289)
    at org.apache.spark.sql.execution.SparkPlan.executeTake(SparkPlan.scala:381)
    at org.apache.spark.sql.execution.CollectLimitExec.executeCollect(limit.scala:38)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3259)
    at org.apache.spark.sql.Dataset$$anonfun$collectToPython$1.apply(Dataset.scala:3256)
    at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3373)
    at org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:79)
    at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:144)
    at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:74)
    at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3367)
    at org.apache.spark.sql.Dataset.collectToPython(Dataset.scala:3256)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)
    at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)
    at py4j.Gateway.invoke(Gateway.java:282)
    at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
    at py4j.commands.CallCommand.execute(CallCommand.java:79)
    at py4j.GatewayConnection.run(GatewayConnection.java:238)
    at java.lang.Thread.run(Thread.java:748)

(<class 'py4j.protocol.Py4JJavaError'>, Py4JJavaError(u'An error occurred while calling o701.collectToPython.\n', JavaObject id=o702), <traceback object at 0x7fc031b5c878>)