写parquet文件的时候 出现 类转化异常如何解决？
在使用 ParquetAvroWriters.forGenericRecord(Schema schema) 写parquet文件的时候 出现 类转化异常： 下面是我的代码：
// //transfor 2 dataStream // TupleTypeInfo tupleTypeInfo = new TupleTypeInfo(GenericData.Record.class, BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.STRING_TYPE_INFO); TupleTypeInfo tupleTypeInfo = new TupleTypeInfo(BasicTypeInfo.STRING_TYPE_INFO, BasicTypeInfo.STRING_TYPE_INFO); DataStream testDataStream = flinkTableEnv.toAppendStream(test, tupleTypeInfo); testDataStream.print().setParallelism(1); ArrayList<org.apache.avro.Schema.Field> fields = new ArrayList<org.apache.avro.Schema.Field>(); fields.add(new org.apache.avro.Schema.Field("id", org.apache.avro.Schema.create(org.apache.avro.Schema.Type.STRING), "id", JsonProperties.NULL_VALUE)); fields.add(new org.apache.avro.Schema.Field("time", org.apache.avro.Schema.create(org.apache.avro.Schema.Type.STRING), "time", JsonProperties.NULL_VALUE)); org.apache.avro.Schema parquetSinkSchema = org.apache.avro.Schema.createRecord("pi", "flinkParquetSink", "flink.parquet", true, fields); String fileSinkPath = "./xxx.text/rs6/"; StreamingFileSink parquetSink = StreamingFileSink. forBulkFormat(new Path(fileSinkPath), ParquetAvroWriters.forGenericRecord(parquetSinkSchema)) .withRollingPolicy(OnCheckpointRollingPolicy.build()) .build(); testDataStream.addSink(parquetSink).setParallelism(1); flinkTableEnv.execute("ReadFromKafkaConnectorWriteToLocalFileJava");
下面是异常：
09:29:50,283 INFO org.apache.flink.runtime.taskmanager.Task - Sink: Unnamed (1/1) (79505cb6ab2df38886663fd99461315a) switched from RUNNING to FAILED.09:29:50,283 INFO org.apache.flink.runtime.taskmanager.Task - Sink: Unnamed (1/1) (79505cb6ab2df38886663fd99461315a) switched from RUNNING to FAILED.java.lang.ClassCastException: org.apache.flink.api.java.tuple.Tuple2 cannot be cast to org.apache.avro.generic.IndexedRecord
at org.apache.avro.generic.GenericData.getField(GenericData.java:697)
at org.apache.parquet.avro.AvroWriteSupport.writeRecordFields(AvroWriteSupport.java:188)
at org.apache.parquet.avro.AvroWriteSupport.write(AvroWriteSupport.java:165)
at org.apache.parquet.hadoop.InternalParquetRecordWriter.write(InternalParquetRecordWriter.java:128)
at org.apache.parquet.hadoop.ParquetWriter.write(ParquetWriter.java:299)
at org.apache.flink.formats.parquet.ParquetBulkWriter.addElement(ParquetBulkWriter.java:52) at org.apache.flink.streaming.api.functions.sink.filesystem.BulkPartWriter.write(BulkPartWriter.java:50)
at org.apache.flink.streaming.api.functions.sink.filesystem.Bucket.write(Bucket.java:214)
at org.apache.flink.streaming.api.functions.sink.filesystem.Buckets.onElement(Buckets.java:274)
at org.apache.flink.streaming.api.functions.sink.filesystem.StreamingFileSink.invoke(StreamingFileSink.java:445)
at org.apache.flink.streaming.api.operators.StreamSink.processElement(StreamSink.java:56)
at org.apache.flink.streaming.runtime.tasks.OneInputStreamTask$StreamTaskNetworkOutput.emitRecord(OneInputStreamTask.java:173)
at org.apache.flink.streaming.runtime.io.StreamTaskNetworkInput.processElement(StreamTaskNetworkInput.java:151) at org.apache.flink.streaming.runtime.io.StreamTaskNetworkInput.emitNext(StreamTaskNetworkInput.java:128) at org.apache.flink.streaming.runtime.io.StreamOneInputProcessor.processInput(StreamOneInputProcessor.java:69)
at org.apache.flink.streaming.runtime.tasks.StreamTask.processInput(StreamTask.java:311)
at org.apache.flink.streaming.runtime.tasks.mailbox.MailboxProcessor.runMailboxLoop(MailboxProcessor.java:187)
at org.apache.flink.streaming.runtime.tasks.StreamTask.runMailboxLoop(StreamTask.java:487)
at org.apache.flink.streaming.runtime.tasks.StreamTask.invoke(StreamTask.java:470)
at org.apache.flink.runtime.taskmanager.Task.doRun(Task.java:707)
at org.apache.flink.runtime.taskmanager.Task.run(Task.java:532)
at java.lang.Thread.run(Thread.java:748)09:29:50,284
INFO org.apache.flink.runtime.taskmanager.Task - Freeing task resources for Sink: Unnamed (1/1) (79505cb6ab2df38886663fd99461315a).09:29:50,285 INFO org.apache.flink.runtime.taskmanager.Task - Ensuring all FileSystem streams are closed for task Sink: Unnamed (1/1) (79505cb6ab2df38886663fd99461315a) [FAILED]09:29:50,289
INFO org.apache.flink.runtime.taskexecutor.TaskExecutor - Un-registering task and sending final execution state FAILED to JobManager for task Sink: Unnamed (1/1) 79505cb6ab2df38886663fd99461315a.09:29:50,293 INFO org.apache.flink.runtime.executiongraph.ExecutionGraph - Sink: Unnamed (1/1) (79505cb6ab2df38886663fd99461315a)
switched from RUNNING to FAILED.java.lang.ClassCastException: org.apache.flink.api.java.tuple.Tuple2 cannot be cast to org.apache.avro.generic.IndexedRecord at org.apache.avro.generic.GenericData.getField(GenericData.java:697) at org.apache.parquet.avro.AvroWriteSupport.writeRecordFields(AvroWriteSupport.java:188) at org.apache.parquet.avro.AvroWriteSupport.write(AvroWriteSupport.java:165)
请问是否是使用方式不对？还是什么问题？
*来自志愿者整理的flink邮件归档
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从提供的错误信息来看，问题在于尝试将Flink的Tuple2对象写入Parquet文件时，使用了期望IndexedRecord类型的Avro writer。具体异常信息是：
java.lang.ClassCastException: org.apache.flink.api.java.tuple.Tuple2 cannot be cast to org.apache.avro.generic.IndexedRecord
这意味着你的Flink DataStream中的数据类型（即Tuple2<String, String>）与你为Parquet Avro writer指定的期望类型（即GenericRecord，它是IndexedRecord的一个实现）不匹配。
要解决这个问题，你需要在Flink程序中将Tuple数据转换为Avro GenericRecord实例。这里是一个简化的步骤来指导你如何进行转换：
- 创建一个方法来将Tuple转换为Avro Record: 首先，定义一个方法，该方法接收Tuple2作为输入，并返回一个根据你的Avro schema构建的GenericRecord。
public static GenericRecord tupleToGenericRecord(Tuple2<String, String> tuple, Schema avroSchema) {
GenericRecord record = new GenericData.Record(avroSchema);
record.put("id", tuple.f0);
record.put("time", tuple.f1);
return record;
}
- 修改DataStream以转换Tuple为GenericRecord: 在写入sink之前，使用
map操作将DataStream中的每个Tuple转换为GenericRecord。
DataStream<GenericRecord> avroDataStream = testDataStream.map(
tuple -> tupleToGenericRecord(tuple, parquetSinkSchema),
TypeInformation.of(GenericRecord.class)
);
- 更新sink配置以使用转换后的DataStream: 现在，用转换后的
avroDataStream替换原来的testDataStream来创建sink。
avroDataStream.addSink(parquetSink).setParallelism(1);
这样，你就确保了在写入Parquet文件之前，数据已经被正确地转换成了Avro GenericRecord格式，从而避免了类转换异常。完成这些更改后，你的代码应该能够成功运行并生成预期的Parquet文件。
