Github user gatorsmile commented on a diff in the pull request:
https://github.com/apache/spark/pull/16209#discussion_r107084604
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala
---
@@ -680,19 +681,63 @@ object JdbcUtils extends Logging {
/**
* Compute the schema string for this RDD.
*/
- def schemaString(schema: StructType, url: String): String = {
+ def schemaString(
+ schema: StructType,
+ url: String,
+ createTableColumnTypes: Option[String] = None): String = {
val sb = new StringBuilder()
val dialect = JdbcDialects.get(url)
+ val userSpecifiedColTypesMap = createTableColumnTypes
+ .map(parseUserSpecifiedCreateTableColumnTypes(schema, _))
+ .getOrElse(Map.empty[String, String])
schema.fields foreach { field =>
val name = dialect.quoteIdentifier(field.name)
- val typ: String = getJdbcType(field.dataType,
dialect).databaseTypeDefinition
+ val typ: String = userSpecifiedColTypesMap.get(field.name)
+ .getOrElse(getJdbcType(field.dataType,
dialect).databaseTypeDefinition)
val nullable = if (field.nullable) "" else "NOT NULL"
sb.append(s", $name $typ $nullable")
}
if (sb.length < 2) "" else sb.substring(2)
}
/**
+ * Parses the user specified createTableColumnTypes option value string
specified in the same
+ * format as create table ddl column types, and returns Map of field
name and the data type to
+ * use in-place of the default data type.
+ */
+ private def parseUserSpecifiedCreateTableColumnTypes(schema: StructType,
+ createTableColumnTypes: String): Map[String, String] = {
+ val userSchema =
CatalystSqlParser.parseTableSchema(createTableColumnTypes)
+ val userColNames = userSchema.fieldNames
+ // check duplicate columns in the user specified column types.
+ if (userColNames.distinct.length != userColNames.length) {
+ val duplicates = userColNames.groupBy(identity).collect {
+ case (x, ys) if ys.length > 1 => x
+ }.mkString(", ")
+ throw new AnalysisException(
+ s"Found duplicate column(s) in createTableColumnTypes option
value: $duplicates")
+ }
+ // check user specified column names exists in the data frame schema.
+ val commonNames = userColNames.intersect(schema.fieldNames)
+ if (commonNames.length != userColNames.length) {
+ val invalidColumns = userColNames.diff(commonNames).mkString(", ")
+ throw new AnalysisException(
+ s"Found invalid column(s) in createTableColumnTypes option value:
$invalidColumns")
+ }
+
+ // char/varchar gets translated to string type. Real data type
specified by the user
+ // is available in the field metadata as HIVE_TYPE_STRING
+ userSchema.fields.map(f =>
+ f.name -> {
+ (if (f.metadata.contains(HIVE_TYPE_STRING)) {
+ f.metadata.getString(HIVE_TYPE_STRING)
+ } else {
+ f.dataType.catalogString
+ }).toUpperCase
--- End diff --
We can create a partial function here.
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