dawidwys commented on a change in pull request #13763:
URL: https://github.com/apache/flink/pull/13763#discussion_r512673931
##########
File path:
flink-formats/flink-avro/src/main/java/org/apache/flink/formats/avro/typeutils/AvroSchemaConverter.java
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@@ -297,17 +299,22 @@ private static DataType convertToDataType(Schema schema) {
* @return Avro's {@link Schema} matching this logical type.
*/
public static Schema convertToSchema(LogicalType logicalType) {
- return convertToSchema(logicalType, "record");
+ return convertToSchema(logicalType, "record", true);
}
/**
* Converts Flink SQL {@link LogicalType} (can be nested) into an Avro
schema.
*
* @param logicalType logical type
* @param rowName the record name
+ * @param top whether it is parsing the root record,
+ * if it is, the logical type nullability would be
ignored
* @return Avro's {@link Schema} matching this logical type.
*/
- public static Schema convertToSchema(LogicalType logicalType, String
rowName) {
+ public static Schema convertToSchema(
+ LogicalType logicalType,
+ String rowName,
+ boolean top) {
Review comment:
Sorry, but I don't understand your arguments.
What do you mean by "inferring" the outer row nullability? There is no
inference. In SQL the outer row is always `NOT NULL` or otherwise the row
simply does not exist.
In the current shape you have two arguments that contradict each other. Take
this example:
```
DataType type = DataTypes.ROW(
DataTypes.FIELD("f0", DataTypes.ROW(
DataTypes.FIELD("f1", DataTypes.BIGINT()
).nullable()
).nullable();
Schema schema = convertToSchema(type, "record", true) // <- you're
overriding the property of type
// you can achieve the same with
// Schema schema = convertToSchema(type.notNull(), "record")
```
It's the Planners task to produce a `notNull` type for a Tables type.
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