cloud-fan commented on a change in pull request #30902:
URL: https://github.com/apache/spark/pull/30902#discussion_r569297608
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File path:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
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@@ -408,6 +421,23 @@ object JdbcUtils extends Logging {
(rs: ResultSet, row: InternalRow, pos: Int) =>
row.setFloat(pos, rs.getFloat(pos + 1))
+
+ // SPARK-33888 - sql TIME type represents as physical int in millis
+ // Represents a time of day, with no reference to a particular calendar,
+ // time zone or date, with a precision of one millisecond.
Review comment:
It looks better if the avro schema converter can convert timestamp to
time. After reading time column from JDBC, it becomes `IntegerType` and there
is no context to indicate that this int comes from JDBC time and means
milliseconds. What if the avro logic type is time-micros? With timestamp type,
at least we know the precision is microsecond.
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