Can write but read parquet file with nested arrays

I am trying to read a parquet file in scala using the Avro interface (1.10.). The file was also generated using the same interface.

The data that I am writing looks like this:

 
```java

case class Inner(b: Array[Int])
case class Outer(a: Array[Inner])

val data = Outer(
Array(
Inner(Array(1, 2)),
Inner(Array(3, 4))
)
)
```
 

Using parquet-tools to read read the file looks like this:

 
```java

$ parquet-tools cat /tmp/test.parquet
a:
.array:
..b:
...array = 1
...array = 2
.array:
..b:
...array = 3
...array = 4
```
 

But while trying to read the file I get the following exception:

 

 
```java

 
Exception in thread "main" org.apache.parquet.io.InvalidRecordException: Parquet/Avro schema mismatch: Avro field 'array' not found
at org.apache.parquet.avro.AvroRecordConverter.getAvroField(AvroRecordConverter.java:225)
at org.apache.parquet.avro.AvroRecordConverter.(AvroRecordConverter.java:130)
at org.apache.parquet.avro.AvroRecordConverter.newConverter(AvroRecordConverter.java:279)
at org.apache.parquet.avro.AvroRecordConverter.newConverter(AvroRecordConverter.java:232)
at org.apache.parquet.avro.AvroRecordConverter.access$100(AvroRecordConverter.java:78)
at org.apache.parquet.avro.AvroRecordConverter$AvroCollectionConverter$ElementConverter.(AvroRecordConverter.java:536)
at org.apache.parquet.avro.AvroRecordConverter$AvroCollectionConverter.(AvroRecordConverter.java:486)
at org.apache.parquet.avro.AvroRecordConverter.newConverter(AvroRecordConverter.java:289)
at org.apache.parquet.avro.AvroRecordConverter.(AvroRecordConverter.java:141)
at org.apache.parquet.avro.AvroRecordConverter.(AvroRecordConverter.java:95)
at org.apache.parquet.avro.AvroRecordMaterializer.(AvroRecordMaterializer.java:33)
at org.apache.parquet.avro.AvroReadSupport.prepareForRead(AvroReadSupport.java:138)
at org.apache.parquet.hadoop.InternalParquetRecordReader.initialize(InternalParquetRecordReader.java:183)
at org.apache.parquet.hadoop.ParquetReader.initReader(ParquetReader.java:156)
at org.apache.parquet.hadoop.ParquetReader.read(ParquetReader.java:135)
at raw.runtime.writer.parquet.avro.Lixo$.main(Lixo.scala:78)
at raw.runtime.writer.parquet.avro.Lixo.main(Lixo.scala)

 
```
 

This is the code used to generate this file:
```java

val filename = "/tmp/test.parquet"
val path = Paths.get(filename).toFile
val conf = new Configuration()

val schema: Schema = {

val inner = Schema.createRecord("inner", "some doc", "outer", false,
List(new Schema.Field("b", Schema.createArray(Schema.create(Schema.Type.INT)), "", null: Object)).asJava
)

Schema.createRecord("outer", "", "", false,
List(new Schema.Field("a", Schema.createArray(inner), "", null: Object)).asJava
)
}

val os = new FileOutputStream(path)

val outputFile = new RawParquetOutputFile(os)
val parquetWriter: ParquetWriter[GenericRecord] = AvroParquetWriter.builder[GenericRecord](outputFile)
.withConf(conf)
.withSchema(schema)
.build()

val data = Outer(
Array(
Inner(Array(1, 2)),
Inner(Array(3, 4))
)
)

val record = new GenericData.Record(schema)
val fieldA = schema.getField("a").schema()
val recorData = {
val fieldAType = fieldA.getElementType()
data.a.map { x =>
val innerRecord = new GenericData.Record(fieldAType)
innerRecord.put("b", x.b)
innerRecord
}
}

record.put("a", recorData)
parquetWriter.write(record)
parquetWriter.close()
os.close()
```
 Also if I pass the configuration option 
```java

parquet.avro.add-list-element-records = false

```
I get a different exception:

org.apache.avro.SchemaParseException: Can't redefine: list

 

Am I doing something wrong?

 

 

**Reporter**: [cesar matos](https://issues.apache.org/jira/secure/ViewProfile.jspa?name=Torcato)
#### Related issues:
- [SchemaParseException: Can't redefine: list in AvroIndexedRecordConverter](https://github.com/apache/parquet-java/issues/2239) (is duplicated by)

_{**Note**: *This issue was originally created as [PARQUET-1409](https://issues.apache.org/jira/browse/PARQUET-1409). Please see the [migration documentation](https://issues.apache.org/jira/browse/PARQUET-2502) for further details.*}