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我有一个简单的代码块来编写然后以 Avro 格式读取数据帧。由于 Spark 2.4.x 中已经内置了 Avro 库，

Avro 文件写入成功，文件在 HDFS 中生成。但是，当我读取文件时会引发 AbstractMethodError 异常。谁能分享我一些光？

我通过在我的 Zeppelin nodebook Spark 解释器中添加包 org.apache.spark:spark-avro_2.11:2.4.1 来使用 Spark 内部库。

我的简单代码块：

%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()

我看到的错误信息：

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>)
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2 回答 2

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抽象方法错误：

当应用程序尝试调用抽象方法时抛出。通常，这个错误会被编译器捕获；如果自上次编译当前执行的方法以来某些类的定义发生了不兼容的更改，则此错误只会在运行时发生。

AFAIK 你必须调查你用来编译和运行的版本。

于 2019-06-10T19:37:10.453 回答
0

这里有一个类似但不同的问题，与在 emr-5.28.0 上使用 spark-avro 有关。这与这个问题中讨论的原因不同（因为这个问题早在 emr-5.28.0 可用之前就被问到了），但它足够相似，我想我会链接到我的答案以防万一由于类似的堆栈跟踪和类似的声音问题，偶然发现了这个问题。

于 2019-12-04T18:40:05.033 回答