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https://issues.apache.org/jira/browse/SPARK-9340?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Cheng Lian updated SPARK-9340:
------------------------------
Description:
SPARK-6776 and SPARK-6777 followed {{parquet-avro}} to implement
backwards-compatibility rules defined in {{parquet-format}} spec. However, both
Spark SQL and {{parquet-avro}} neglected the following statement in
{{parquet-format}}:
{quote}
This does not affect repeated fields that are not annotated: A repeated field
that is neither contained by a {{LIST}}- or {{MAP}}-annotated group nor
annotated by {{LIST}} or {{MAP}} should be interpreted as a required list of
required elements where the element type is the type of the field.
{quote}
One of the consequences is that, Parquet files generated by
{{parquet-protobuf}} containing unannotated repeated fields are not correctly
converted to Catalyst arrays.
For example, the following Parquet schema
{noformat}
message root {
repeated int32 f1
}
{noformat}
should be converted to
{noformat}
StructType(StructField("f1", ArrayType(IntegerType, containsNull = false),
nullable = false) :: Nil)
{noformat}
But now it triggers an {{AnalysisException}}.
was:
The way ParquetTypesConverter handles primitive repeated types results in an
incompatible schema being used for querying data. For example, given a schema
like so:
message root {
repeated int32 repeated_field;
}
Spark produces a read schema like:
message root {
optional int32 repeated_field;
}
These are incompatible and all attempts to read fail.
In ParquetTypesConverter.toDataType:
if (parquetType.isPrimitive) {
toPrimitiveDataType(parquetType.asPrimitiveType, isBinaryAsString,
isInt96AsTimestamp)
} else {...}
The if condition should also have !parquetType.isRepetition(Repetition.REPEATED)
And then this case will need to be handled in the else
> CatalystSchemaConverter and CatalystRowConverter don't handle unannotated
> repeated fields correctly
> ---------------------------------------------------------------------------------------------------
>
> Key: SPARK-9340
> URL: https://issues.apache.org/jira/browse/SPARK-9340
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 1.2.0, 1.3.0, 1.4.0, 1.5.0
> Reporter: Damian Guy
> Attachments: ParquetTypesConverterTest.scala
>
>
> SPARK-6776 and SPARK-6777 followed {{parquet-avro}} to implement
> backwards-compatibility rules defined in {{parquet-format}} spec. However,
> both Spark SQL and {{parquet-avro}} neglected the following statement in
> {{parquet-format}}:
> {quote}
> This does not affect repeated fields that are not annotated: A repeated field
> that is neither contained by a {{LIST}}- or {{MAP}}-annotated group nor
> annotated by {{LIST}} or {{MAP}} should be interpreted as a required list of
> required elements where the element type is the type of the field.
> {quote}
> One of the consequences is that, Parquet files generated by
> {{parquet-protobuf}} containing unannotated repeated fields are not correctly
> converted to Catalyst arrays.
> For example, the following Parquet schema
> {noformat}
> message root {
> repeated int32 f1
> }
> {noformat}
> should be converted to
> {noformat}
> StructType(StructField("f1", ArrayType(IntegerType, containsNull = false),
> nullable = false) :: Nil)
> {noformat}
> But now it triggers an {{AnalysisException}}.
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