Github user gengliangwang commented on a diff in the pull request:
https://github.com/apache/spark/pull/22037#discussion_r208591866
--- Diff:
external/avro/src/main/scala/org/apache/spark/sql/avro/SchemaConverters.scala
---
@@ -139,7 +142,16 @@ object SchemaConverters {
case FloatType => builder.floatType()
case DoubleType => builder.doubleType()
- case _: DecimalType | StringType => builder.stringType()
+ case StringType => builder.stringType()
+ case d: DecimalType =>
+ val avroType = LogicalTypes.decimal(d.precision, d.scale)
+ if (nullable) {
+ val schema =
avroType.addToSchema(SchemaBuilder.builder().bytesType())
+ builder.`type`(schema)
--- End diff --
For decimal, it requires to set three keys in props: `logicalType`,
`precision`, `scale`.
So here I choose to use such way, instead of setting properties like
logical Timestamp/Date type.
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