yaooqinn commented on a change in pull request #31921:
URL: https://github.com/apache/spark/pull/31921#discussion_r598463339
##########
File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/ParquetSchemaConverter.scala
##########
@@ -130,13 +130,11 @@ class ParquetToSparkSchemaConverter(
case INT32 =>
originalType match {
case INT_8 => ByteType
- case INT_16 => ShortType
- case INT_32 | null => IntegerType
+ case INT_16 | UINT_8 => ShortType
+ case INT_32 | UINT_16 | null => IntegerType
case DATE => DateType
case DECIMAL => makeDecimalType(Decimal.MAX_INT_DIGITS)
- case UINT_8 => typeNotSupported()
- case UINT_16 => typeNotSupported()
- case UINT_32 => typeNotSupported()
+ case UINT_32 => LongType
Review comment:
Thanks, @HyukjinKwon,
Yea, I have checked that PR too. There's also a suggestion that we support
them.
Lately, Wenchen created https://issues.apache.org/jira/browse/SPARK-34786
for reading uint64. As other unsigned types are not supported too, and they
are a bit more clear than uint64 which needs a decimal.
IMO, for Spark, it is worthwhile to be able to support more storage layer
features without breaking our own rules. So I raised this PR to collect more
opinions.
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