[SPARK-35446] Override getJDBCType in MySQLDialect to map FloatType to FLOAT

### What changes were proposed in this pull request?

Override `getJDBCType` method in `MySQLDialect` so that `FloatType` is mapped to `FLOAT` instead of `REAL`

### Why are the changes needed?

MySQL treats `REAL` as a synonym to `DOUBLE` by default (see https://dev.mysql.com/doc/refman/8.0/en/numeric-types.html). Therefore, when creating a table with a column of `REAL` type, it will be created as `DOUBLE`. However, currently, `MySQLDialect` does not provide an implementation for `getJDBCType`, and will thus ultimately fall back to `JdbcUtils.getCommonJDBCType`, which maps `FloatType` to `REAL`. This change is needed so that we can properly map the `FloatType` to `FLOAT` for MySQL.

### Does this PR introduce _any_ user-facing change?
Prior to this PR, when writing a dataframe with a `FloatType` column to a MySQL table, it will create a `DOUBLE` column. After the PR, it will create a `FLOAT` column.

### How was this patch tested?
Added a test case in `JDBCSuite` that verifies the mapping.

Closes #32605 from mariosmeim-db/SPARK-35446.

Authored-by: Marios Meimaris <marios.meimaris@databricks.com>
Signed-off-by: Hyukjin Kwon <gurwls223@apache.org>

docs/sql-migration-guide.md

@@ -93,6 +93,8 @@ license: |
 
   - In Spark 3.2, special datetime values such as `epoch`, `today`, `yesterday`, `tomorrow`, and `now` are supported in typed literals only, for instance, `select timestamp'now'`. In Spark 3.1 and 3.0, such special values are supported in any casts of strings to dates/timestamps. To keep these special values as dates/timestamps in Spark 3.1 and 3.0, you should replace them manually, e.g. `if (c in ('now', 'today'), current_date(), cast(c as date))`.
   
+  - In Spark 3.2, `FloatType` is mapped to `FLOAT` in MySQL. Prior to this, it used to be mapped to `REAL`, which is by default a synonym to `DOUBLE PRECISION` in MySQL. 
+
 ## Upgrading from Spark SQL 3.0 to 3.1
 
   - In Spark 3.1, statistical aggregation function includes `std`, `stddev`, `stddev_samp`, `variance`, `var_samp`, `skewness`, `kurtosis`, `covar_samp`, `corr` will return `NULL` instead of `Double.NaN` when `DivideByZero` occurs during expression evaluation, for example, when `stddev_samp` applied on a single element set. In Spark version 3.0 and earlier, it will return `Double.NaN` in such case. To restore the behavior before Spark 3.1, you can set `spark.sql.legacy.statisticalAggregate` to `true`.

sql/core/src/main/scala/org/apache/spark/sql/jdbc/MySQLDialect.scala

@@ -20,7 +20,8 @@ package org.apache.spark.sql.jdbc
 import java.sql.{SQLFeatureNotSupportedException, Types}
 import java.util.Locale
 
-import org.apache.spark.sql.types.{BooleanType, DataType, LongType, MetadataBuilder}
+import org.apache.spark.sql.execution.datasources.jdbc.JdbcUtils
+import org.apache.spark.sql.types.{BooleanType, DataType, FloatType, LongType, MetadataBuilder}
 
 private case object MySQLDialect extends JdbcDialect {
 
@@ -94,4 +95,11 @@ private case object MySQLDialect extends JdbcDialect {
   override def getTableCommentQuery(table: String, comment: String): String = {
     s"ALTER TABLE $table COMMENT = '$comment'"
   }
+
+  override def getJDBCType(dt: DataType): Option[JdbcType] = dt match {
+    // See SPARK-35446: MySQL treats REAL as a synonym to DOUBLE by default
+    // We override getJDBCType so that FloatType is mapped to FLOAT instead
+    case FloatType => Option(JdbcType("FLOAT", java.sql.Types.FLOAT))
+    case _ => JdbcUtils.getCommonJDBCType(dt)
+  }
 }

sql/core/src/test/scala/org/apache/spark/sql/jdbc/JDBCSuite.scala

@@ -899,6 +899,11 @@ class JDBCSuite extends QueryTest
       Option(TimestampType))
   }
 
+  test("SPARK-35446: MySQLDialect type mapping of float") {
+    val mySqlDialect = JdbcDialects.get("jdbc:mysql://127.0.0.1/db")
+    assert(mySqlDialect.getJDBCType(FloatType).map(_.databaseTypeDefinition).get == "FLOAT")
+  }
+
   test("PostgresDialect type mapping") {
     val Postgres = JdbcDialects.get("jdbc:postgresql://127.0.0.1/db")
     val md = new MetadataBuilder().putLong("scale", 0)