@@ -24,7 +24,7 @@ import org.apache.spark.sql.types._
2424import scala .util .matching .Regex
2525
2626/**
27- * ClickHouseDialects
27+ * ClickHouse SQL dialect
2828 */
2929object ClickHouseDialect extends JdbcDialect with Logging {
3030
@@ -42,7 +42,7 @@ object ClickHouseDialect extends JdbcDialect with Logging {
4242
4343 /**
4444 * Inferred schema always nullable.
45- * see [[JDBCRDD.resolveTable(JDBCOptions) ]]
45+ * see [[JDBCRDD.resolveTable ]]
4646 */
4747 override def getCatalystType (sqlType : Int ,
4848 typeName : String ,
@@ -61,8 +61,10 @@ object ClickHouseDialect extends JdbcDialect with Logging {
6161 }
6262 }
6363
64- // Spark use a widening conversion both ways.
65- // see https://github.com/apache/spark/pull/26301#discussion_r347725332
64+ /**
65+ * Spark use a widening conversion both ways, see detail at
66+ * [[https://github.com/apache/spark/pull/26301#discussion_r347725332 ]]
67+ */
6668 private [jdbc] def toCatalystType (typeName : String ,
6769 precision : Int ,
6870 scale : Int ): Option [(Boolean , DataType )] = {
@@ -95,8 +97,10 @@ object ClickHouseDialect extends JdbcDialect with Logging {
9597 case _ => (false , maybeNullableTypeName)
9698 }
9799
98- // NOT recommend auto create ClickHouse table by Spark JDBC, the reason is it's hard to handle nullable because
99- // ClickHouse use `T` to represent ANSI SQL `T NOT NULL` and `Nullable(T)` to represent ANSI SQL `T NULL`,
100+ /**
101+ * NOT recommend auto create ClickHouse table by Spark JDBC, the reason is it's hard to handle nullable because
102+ * ClickHouse use `T` to represent ANSI SQL `T NOT NULL` and `Nullable(T)` to represent ANSI SQL `T NULL`,
103+ */
100104 override def getJDBCType (dt : DataType ): Option [JdbcType ] = dt match {
101105 case StringType => Some (JdbcType (" String" , Types .VARCHAR ))
102106 // ClickHouse doesn't have the concept of encodings. Strings can contain an arbitrary set of bytes,
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