[SUPPORT] Can't redefine: array

**Describe the problem you faced**

I have a source with protobuf, after parse them in pyspark df with fixed structure start writing in hudi table. First part of data write done, second write causes an error: org.apache.avro.SchemaParseException: Can't redefine: array

df structure:
```
root
|-- _hoodie_commit_time: string (nullable = true)
|-- _hoodie_commit_seqno: string (nullable = true)
|-- _hoodie_record_key: string (nullable = true)
|-- _hoodie_partition_path: string (nullable = true)
|-- _hoodie_file_name: string (nullable = true)
|-- id: string (nullable = true)
|-- col_1: string (nullable = true)
|-- col_2: string (nullable = true)
|-- col_3: string (nullable = true)
|-- col_4: string (nullable = true)
|-- col_5: string (nullable = true)
|-- col_6: string (nullable = true)
|-- col_7: boolean (nullable = true)
|-- col_8: string (nullable = true)
|-- col_9: string (nullable = true)
|-- col_10: string (nullable = true)
|-- col_11: boolean (nullable = true)
|-- col_12: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- col_2: string (nullable = true)
| | |-- col_8: string (nullable = true)
|-- event_timestamp: timestamp (nullable = true)
|-- event_idempotency: string (nullable = true)
|-- col_13: string (nullable = true)
|-- col_14: string (nullable = true)
|-- body: struct (nullable = true)
| |-- col_15: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- col_16: string (nullable = true)
| | | |-- col_12: array (nullable = true)
| | | | |-- element: struct (containsNull = true)
| | | | | |-- col_2: string (nullable = true)
| | | | | |-- col_8: string (nullable = true)
| |-- col_17: array (nullable = true)
| | |-- element: string (containsNull = true)
| |-- col_18: boolean (nullable = true)
|-- col_19: string (nullable = true)
|-- col_20: string (nullable = true)
|-- load_timestamp: timestamp (nullable = false)
|-- date: string (nullable = true)
```

write options:
```
'hoodie.table.name': 'table_name',
'hoodie.datasource.write.recordkey.field': 'id',
'hoodie.datasource.write.partitionpath.field': 'date',
'hoodie.datasource.write.table.name': 'table_name',
'hoodie.datasource.write.operation': 'upsert',
'hoodie.datasource.write.precombine.field': 'etl__ods_load_timestamp',
'hoodie.upsert.shuffle.parallelism': 32,
'hoodie.insert.shuffle.parallelism': 32,
'hoodie.clustering.async.enabled': 'true'
```

**To Reproduce**

I couldn't reproduce the problem, I tried it locally, on a similar server with the same data, everything is recording successfully. On the same server where the error occurred with different data and a different schema, but with the same code, everything is still successful

**Expected behavior**

I was expecting to see a successful entry like it was before

**Environment Description**

* Hudi version : 0.15.0

* Spark version : 3.0.3

* Storage (HDFS/S3/GCS..) : s3

* Running on Docker? (yes/no) : no

* jars: parquet-common-1.10.1, parquet-hadoop-1.10.1, spark-avro_2.12-3.0.3

**Stacktrace**
```
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.failJobAndIndependentStages(DAGScheduler.scala:2059)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2(DAGScheduler.scala:2008)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$abortStage$2$adapted(DAGScheduler.scala:2007)
at scala.collection.mutable.ResizableArray.foreach(ResizableArray.scala:62)
at scala.collection.mutable.ResizableArray.foreach$(ResizableArray.scala:55)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:49)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:2007)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGScheduler.$anonfun$handleTaskSetFailed$1$adapted(DAGScheduler.scala:973)
at scala.Option.foreach(Option.scala:407)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:973)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2239)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2188)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2177)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:775)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2114)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2135)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2154)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2179)
at org.apache.spark.rdd.RDD.count(RDD.scala:1227)
at org.apache.hudi.HoodieSparkSqlWriterInternal.commitAndPerformPostOperations(HoodieSparkSqlWriter.scala:1073)
at org.apache.hudi.HoodieSparkSqlWriterInternal.writeInternal(HoodieSparkSqlWriter.scala:508)
at org.apache.hudi.HoodieSparkSqlWriterInternal.write(HoodieSparkSqlWriter.scala:187)
at org.apache.hudi.HoodieSparkSqlWriter$.write(HoodieSparkSqlWriter.scala:125)
at org.apache.hudi.DefaultSource.createRelation(DefaultSource.scala:168)
at org.apache.spark.sql.execution.datasources.SaveIntoDataSourceCommand.run(SaveIntoDataSourceCommand.scala:46)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult$lzycompute(commands.scala:70)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.sideEffectResult(commands.scala:68)
at org.apache.spark.sql.execution.command.ExecutedCommandExec.doExecute(commands.scala:90)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$execute$1(SparkPlan.scala:180)
at org.apache.spark.sql.execution.SparkPlan.$anonfun$executeQuery$1(SparkPlan.scala:218)
at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)
at org.apache.spark.sql.execution.SparkPlan.executeQuery(SparkPlan.scala:215)
at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:176)
at org.apache.spark.sql.execution.QueryExecution.toRdd$lzycompute(QueryExecution.scala:127)
at org.apache.spark.sql.execution.QueryExecution.toRdd(QueryExecution.scala:126)
at org.apache.spark.sql.DataFrameWriter.$anonfun$runCommand$1(DataFrameWriter.scala:962)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$5(SQLExecution.scala:100)
at org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:160)
at org.apache.spark.sql.execution.SQLExecution$.$anonfun$withNewExecutionId$1(SQLExecution.scala:87)
at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:767)
at org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:64)
at org.apache.spark.sql.DataFrameWriter.runCommand(DataFrameWriter.scala:962)
at org.apache.spark.sql.DataFrameWriter.saveToV1Source(DataFrameWriter.scala:414)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:398)
at org.apache.spark.sql.DataFrameWriter.save(DataFrameWriter.scala:287)
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:750)
Caused by: org.apache.hudi.exception.HoodieUpsertException: Error upserting bucketType UPDATE for partition :469
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpsertPartition(BaseSparkCommitActionExecutor.java:344)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.lambda$mapPartitionsAsRDD$a3ab3c4$1(BaseSparkCommitActionExecutor.java:254)
at org.apache.spark.api.java.JavaRDDLike.$anonfun$mapPartitionsWithIndex$1(JavaRDDLike.scala:102)
at org.apache.spark.api.java.JavaRDDLike.$anonfun$mapPartitionsWithIndex$1$adapted(JavaRDDLike.scala:102)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsWithIndex$2(RDD.scala:889)
at org.apache.spark.rdd.RDD.$anonfun$mapPartitionsWithIndex$2$adapted(RDD.scala:889)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.$anonfun$getOrCompute$1(RDD.scala:362)
at org.apache.spark.storage.BlockManager.$anonfun$doPutIterator$1(BlockManager.scala:1388)
at org.apache.spark.storage.BlockManager.org$apache$spark$storage$BlockManager$$doPut(BlockManager.scala:1298)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1362)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:1186)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:360)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:311)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:52)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:349)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:313)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:90)
at org.apache.spark.scheduler.Task.run(Task.scala:127)
at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:463)
at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1377)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:466)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
... 1 more
Caused by: org.apache.hudi.exception.HoodieException: org.apache.avro.SchemaParseException: Can't redefine: array
at org.apache.hudi.table.action.commit.HoodieMergeHelper.runMerge(HoodieMergeHelper.java:151)
at org.apache.hudi.table.HoodieSparkTable.runMerge(HoodieSparkTable.java:148)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpdateInternal(BaseSparkCommitActionExecutor.java:376)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpdate(BaseSparkCommitActionExecutor.java:371)
at org.apache.hudi.table.action.commit.BaseSparkCommitActionExecutor.handleUpsertPartition(BaseSparkCommitActionExecutor.java:337)
... 28 more
Caused by: org.apache.avro.SchemaParseException: Can't redefine: array
at org.apache.avro.Schema$Names.put(Schema.java:1128)
at org.apache.avro.Schema$NamedSchema.writeNameRef(Schema.java:562)
at org.apache.avro.Schema$RecordSchema.toJson(Schema.java:690)
at org.apache.avro.Schema$ArraySchema.toJson(Schema.java:805)
at org.apache.avro.Schema$UnionSchema.toJson(Schema.java:882)
at org.apache.avro.Schema$RecordSchema.fieldsToJson(Schema.java:716)
at org.apache.avro.Schema$RecordSchema.toJson(Schema.java:701)
at org.apache.avro.Schema$UnionSchema.toJson(Schema.java:882)
at org.apache.avro.Schema$RecordSchema.fieldsToJson(Schema.java:716)
at org.apache.avro.Schema$RecordSchema.toJson(Schema.java:701)
at org.apache.avro.Schema.toString(Schema.java:324)
at org.apache.avro.Schema.toString(Schema.java:314)
at org.apache.parquet.avro.AvroReadSupport.setAvroReadSchema(AvroReadSupport.java:69)
at org.apache.hudi.io.hadoop.HoodieAvroParquetReader.getIndexedRecordIteratorInternal(HoodieAvroParquetReader.java:170)
at org.apache.hudi.io.hadoop.HoodieAvroParquetReader.getIndexedRecordIterator(HoodieAvroParquetReader.java:101)
at org.apache.hudi.io.hadoop.HoodieAvroParquetReader.getRecordIterator(HoodieAvroParquetReader.java:80)
at org.apache.hudi.table.action.commit.HoodieMergeHelper.runMerge(HoodieMergeHelper.java:128)
... 32 more
```

Start with HoodieMergeHelper.runMerge and HoodieAvroParquetReader.getIndexedRecordIteratorInternal, then trace the Avro Schema.toString path shown in the stack trace. Try to isolate a reproducible upsert using the nested array schema and the stated Hudi 0.15.0, Spark 3.0.3, and S3 environment. Done means the cause is isolated and the failing schema no longer produces the exception.

Written by the indexing model from the issue text.