[SPARK-10849][SQL] Adds option to the JDBC data source write for user…

… to specify database column type for the create table

## What changes were proposed in this pull request?
Currently JDBC data source creates tables in the target database using the default type mapping, and the JDBC dialect mechanism.  If users want to specify different database data type for only some of columns, there is no option available. In scenarios where default mapping does not work, users are forced to create tables on the target database before writing. This workaround is probably not acceptable from a usability point of view. This PR is to provide a user-defined type mapping for specific columns.

The solution is to allow users to specify database column data type for the create table  as JDBC datasource option(createTableColumnTypes) on write. Data type information can be specified in the same format as table schema DDL format (e.g: `name CHAR(64), comments VARCHAR(1024)`).

All supported target database types can not be specified ,  the data types has to be valid spark sql data types also.  For example user can not specify target database  CLOB data type. This will be supported in the follow-up PR.

Example:
```Scala
df.write
.option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
.jdbc(url, "TEST.DBCOLTYPETEST", properties)
```
## How was this patch tested?
Added new test cases to the JDBCWriteSuite

Author: sureshthalamati <suresh.thalamati@gmail.com>

Closes #16209 from sureshthalamati/jdbc_custom_dbtype_option_json-spark-10849.

docs/sql-programming-guide.md

@@ -1223,6 +1223,13 @@ the following case-insensitive options:
      This is a JDBC writer related option. If specified, this option allows setting of database-specific table and partition options when creating a table (e.g., <code>CREATE TABLE t (name string) ENGINE=InnoDB.</code>). This option applies only to writing.
    </td>
   </tr>
+
+  <tr>
+    <td><code>createTableColumnTypes</code></td>
+    <td>
+     The database column data types to use instead of the defaults, when creating the table. Data type information should be specified in the same format as CREATE TABLE columns syntax (e.g: <code>"name CHAR(64), comments VARCHAR(1024)")</code>. The specified types should be valid spark sql data types. This option applies only to writing.
+    </td>
+  </tr>  
 </table>
 
 <div class="codetabs">

examples/src/main/java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java

@@ -258,6 +258,11 @@ private static void runJdbcDatasetExample(SparkSession spark) {
 
     jdbcDF2.write()
       .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
+
+    // Specifying create table column data types on write
+    jdbcDF.write()
+      .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
+      .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties);
     // $example off:jdbc_dataset$
   }
 }

examples/src/main/python/sql/datasource.py

@@ -169,6 +169,12 @@ def jdbc_dataset_example(spark):
     jdbcDF2.write \
         .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
               properties={"user": "username", "password": "password"})
+
+    # Specifying create table column data types on write
+    jdbcDF.write \
+        .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)") \
+        .jdbc("jdbc:postgresql:dbserver", "schema.tablename",
+              properties={"user": "username", "password": "password"})
     # $example off:jdbc_dataset$
 
 

examples/src/main/scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala

@@ -181,6 +181,11 @@ object SQLDataSourceExample {
 
     jdbcDF2.write
       .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
+
+    // Specifying create table column data types on write
+    jdbcDF.write
+      .option("createTableColumnTypes", "name CHAR(64), comments VARCHAR(1024)")
+      .jdbc("jdbc:postgresql:dbserver", "schema.tablename", connectionProperties)
     // $example off:jdbc_dataset$
   }
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JDBCOptions.scala

@@ -119,6 +119,7 @@ class JDBCOptions(
   // E.g., "CREATE TABLE t (name string) ENGINE=InnoDB DEFAULT CHARSET=utf8"
   // TODO: to reuse the existing partition parameters for those partition specific options
   val createTableOptions = parameters.getOrElse(JDBC_CREATE_TABLE_OPTIONS, "")
+  val createTableColumnTypes = parameters.get(JDBC_CREATE_TABLE_COLUMN_TYPES)
   val batchSize = {
     val size = parameters.getOrElse(JDBC_BATCH_INSERT_SIZE, "1000").toInt
     require(size >= 1,
@@ -154,6 +155,7 @@ object JDBCOptions {
   val JDBC_BATCH_FETCH_SIZE = newOption("fetchsize")
   val JDBC_TRUNCATE = newOption("truncate")
   val JDBC_CREATE_TABLE_OPTIONS = newOption("createTableOptions")
+  val JDBC_CREATE_TABLE_COLUMN_TYPES = newOption("createTableColumnTypes")
   val JDBC_BATCH_INSERT_SIZE = newOption("batchsize")
   val JDBC_TXN_ISOLATION_LEVEL = newOption("isolationLevel")
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcRelationProvider.scala

@@ -69,7 +69,7 @@ class JdbcRelationProvider extends CreatableRelationProvider
             } else {
               // Otherwise, do not truncate the table, instead drop and recreate it
               dropTable(conn, options.table)
-              createTable(conn, df.schema, options)
+              createTable(conn, df, options)
               saveTable(df, Some(df.schema), isCaseSensitive, options)
             }
 
@@ -87,7 +87,7 @@ class JdbcRelationProvider extends CreatableRelationProvider
             // Therefore, it is okay to do nothing here and then just return the relation below.
         }
       } else {
-        createTable(conn, df.schema, options)
+        createTable(conn, df, options)
         saveTable(df, Some(df.schema), isCaseSensitive, options)
       }
     } finally {

sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtils.scala

@@ -30,7 +30,8 @@ import org.apache.spark.sql.{AnalysisException, DataFrame, Row}
 import org.apache.spark.sql.catalyst.InternalRow
 import org.apache.spark.sql.catalyst.encoders.RowEncoder
 import org.apache.spark.sql.catalyst.expressions.SpecificInternalRow
-import org.apache.spark.sql.catalyst.util.{DateTimeUtils, GenericArrayData}
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
+import org.apache.spark.sql.catalyst.util.{CaseInsensitiveMap, DateTimeUtils, GenericArrayData}
 import org.apache.spark.sql.jdbc.{JdbcDialect, JdbcDialects, JdbcType}
 import org.apache.spark.sql.types._
 import org.apache.spark.unsafe.types.UTF8String
@@ -680,18 +681,70 @@ object JdbcUtils extends Logging {
   /**
    * Compute the schema string for this RDD.
    */
-  def schemaString(schema: StructType, url: String): String = {
+  def schemaString(
+      df: DataFrame,
+      url: String,
+      createTableColumnTypes: Option[String] = None): String = {
     val sb = new StringBuilder()
     val dialect = JdbcDialects.get(url)
-    schema.fields foreach { field =>
+    val userSpecifiedColTypesMap = createTableColumnTypes
+      .map(parseUserSpecifiedCreateTableColumnTypes(df, _))
+      .getOrElse(Map.empty[String, String])
+    df.schema.fields.foreach { field =>
       val name = dialect.quoteIdentifier(field.name)
-      val typ: String = getJdbcType(field.dataType, dialect).databaseTypeDefinition
+      val typ = userSpecifiedColTypesMap
+        .getOrElse(field.name, 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(
+      df: DataFrame,
+      createTableColumnTypes: String): Map[String, String] = {
+    def typeName(f: StructField): String = {
+      // char/varchar gets translated to string type. Real data type specified by the user
+      // is available in the field metadata as HIVE_TYPE_STRING
+      if (f.metadata.contains(HIVE_TYPE_STRING)) {
+        f.metadata.getString(HIVE_TYPE_STRING)
+      } else {
+        f.dataType.catalogString
+      }
+    }
+
+    val userSchema = CatalystSqlParser.parseTableSchema(createTableColumnTypes)
+    val nameEquality = df.sparkSession.sessionState.conf.resolver
+
+    // checks duplicate columns in the user specified column types.
+    userSchema.fieldNames.foreach { col =>
+      val duplicatesCols = userSchema.fieldNames.filter(nameEquality(_, col))
+      if (duplicatesCols.size >= 2) {
+        throw new AnalysisException(
+          "Found duplicate column(s) in createTableColumnTypes option value: " +
+            duplicatesCols.mkString(", "))
+      }
+    }
+
+    // checks if user specified column names exist in the DataFrame schema
+    userSchema.fieldNames.foreach { col =>
+      df.schema.find(f => nameEquality(f.name, col)).getOrElse {
+        throw new AnalysisException(
+          s"createTableColumnTypes option column $col not found in schema " +
+            df.schema.catalogString)
+      }
+    }
+
+    val userSchemaMap = userSchema.fields.map(f => f.name -> typeName(f)).toMap
+    val isCaseSensitive = df.sparkSession.sessionState.conf.caseSensitiveAnalysis
+    if (isCaseSensitive) userSchemaMap else CaseInsensitiveMap(userSchemaMap)
+  }
+
   /**
    * Saves the RDD to the database in a single transaction.
    */
@@ -726,9 +779,10 @@ object JdbcUtils extends Logging {
    */
   def createTable(
       conn: Connection,
-      schema: StructType,
+      df: DataFrame,
       options: JDBCOptions): Unit = {
-    val strSchema = schemaString(schema, options.url)
+    val strSchema = schemaString(
+      df, options.url, options.createTableColumnTypes)
     val table = options.table
     val createTableOptions = options.createTableOptions
     // Create the table if the table does not exist.

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

@@ -869,7 +869,7 @@ class JDBCSuite extends SparkFunSuite
 
   test("SPARK-16387: Reserved SQL words are not escaped by JDBC writer") {
     val df = spark.createDataset(Seq("a", "b", "c")).toDF("order")
-    val schema = JdbcUtils.schemaString(df.schema, "jdbc:mysql://localhost:3306/temp")
+    val schema = JdbcUtils.schemaString(df, "jdbc:mysql://localhost:3306/temp")
     assert(schema.contains("`order` TEXT"))
   }
 

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

@@ -17,15 +17,16 @@
 
 package org.apache.spark.sql.jdbc
 
-import java.sql.DriverManager
+import java.sql.{Date, DriverManager, Timestamp}
 import java.util.Properties
 
 import scala.collection.JavaConverters.propertiesAsScalaMapConverter
 
 import org.scalatest.BeforeAndAfter
 
-import org.apache.spark.sql.{AnalysisException, Row, SaveMode}
-import org.apache.spark.sql.execution.datasources.jdbc.JDBCOptions
+import org.apache.spark.sql.{AnalysisException, DataFrame, Row, SaveMode}
+import org.apache.spark.sql.catalyst.parser.ParseException
+import org.apache.spark.sql.execution.datasources.jdbc.{JDBCOptions, JdbcUtils}
 import org.apache.spark.sql.internal.SQLConf
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -362,4 +363,147 @@ class JDBCWriteSuite extends SharedSQLContext with BeforeAndAfter {
       assert(sql("select * from people_view").count() == 2)
     }
   }
+
+  test("SPARK-10849: test schemaString - from createTableColumnTypes option values") {
+    def testCreateTableColDataTypes(types: Seq[String]): Unit = {
+      val colTypes = types.zipWithIndex.map { case (t, i) => (s"col$i", t) }
+      val schema = colTypes
+        .foldLeft(new StructType())((schema, colType) => schema.add(colType._1, colType._2))
+      val createTableColTypes =
+        colTypes.map { case (col, dataType) => s"$col $dataType" }.mkString(", ")
+      val df = spark.createDataFrame(sparkContext.parallelize(Seq(Row.empty)), schema)
+
+      val expectedSchemaStr =
+        colTypes.map { case (col, dataType) => s""""$col" $dataType """ }.mkString(", ")
+
+      assert(JdbcUtils.schemaString(df, url1, Option(createTableColTypes)) == expectedSchemaStr)
+    }
+
+    testCreateTableColDataTypes(Seq("boolean"))
+    testCreateTableColDataTypes(Seq("tinyint", "smallint", "int", "bigint"))
+    testCreateTableColDataTypes(Seq("float", "double"))
+    testCreateTableColDataTypes(Seq("string", "char(10)", "varchar(20)"))
+    testCreateTableColDataTypes(Seq("decimal(10,0)", "decimal(10,5)"))
+    testCreateTableColDataTypes(Seq("date", "timestamp"))
+    testCreateTableColDataTypes(Seq("binary"))
+  }
+
+  test("SPARK-10849: create table using user specified column type and verify on target table") {
+    def testUserSpecifiedColTypes(
+        df: DataFrame,
+        createTableColTypes: String,
+        expectedTypes: Map[String, String]): Unit = {
+      df.write
+        .mode(SaveMode.Overwrite)
+        .option("createTableColumnTypes", createTableColTypes)
+        .jdbc(url1, "TEST.DBCOLTYPETEST", properties)
+
+      // verify the data types of the created table by reading the database catalog of H2
+      val query =
+        """
+          |(SELECT column_name, type_name, character_maximum_length
+          | FROM information_schema.columns WHERE table_name = 'DBCOLTYPETEST')
+        """.stripMargin
+      val rows = spark.read.jdbc(url1, query, properties).collect()
+
+      rows.foreach { row =>
+        val typeName = row.getString(1)
+        // For CHAR and VARCHAR, we also compare the max length
+        if (typeName.contains("CHAR")) {
+          val charMaxLength = row.getInt(2)
+          assert(expectedTypes(row.getString(0)) == s"$typeName($charMaxLength)")
+        } else {
+          assert(expectedTypes(row.getString(0)) == typeName)
+        }
+      }
+    }
+
+    val data = Seq[Row](Row(1, "dave", "Boston"))
+    val schema = StructType(
+      StructField("id", IntegerType) ::
+        StructField("first#name", StringType) ::
+        StructField("city", StringType) :: Nil)
+    val df = spark.createDataFrame(sparkContext.parallelize(data), schema)
+
+    // out-of-order
+    val expected1 = Map("id" -> "BIGINT", "first#name" -> "VARCHAR(123)", "city" -> "CHAR(20)")
+    testUserSpecifiedColTypes(df, "`first#name` VARCHAR(123), id BIGINT, city CHAR(20)", expected1)
+    // partial schema
+    val expected2 = Map("id" -> "INTEGER", "first#name" -> "VARCHAR(123)", "city" -> "CHAR(20)")
+    testUserSpecifiedColTypes(df, "`first#name` VARCHAR(123), city CHAR(20)", expected2)
+
+    withSQLConf(SQLConf.CASE_SENSITIVE.key -> "false") {
+      // should still respect the original column names
+      val expected = Map("id" -> "INTEGER", "first#name" -> "VARCHAR(123)", "city" -> "CLOB")
+      testUserSpecifiedColTypes(df, "`FiRsT#NaMe` VARCHAR(123)", expected)
+    }
+
+    withSQLConf(SQLConf.CASE_SENSITIVE.key -> "true") {
+      val schema = StructType(
+        StructField("id", IntegerType) ::
+          StructField("First#Name", StringType) ::
+          StructField("city", StringType) :: Nil)
+      val df = spark.createDataFrame(sparkContext.parallelize(data), schema)
+      val expected = Map("id" -> "INTEGER", "First#Name" -> "VARCHAR(123)", "city" -> "CLOB")
+      testUserSpecifiedColTypes(df, "`First#Name` VARCHAR(123)", expected)
+    }
+  }
+
+  test("SPARK-10849: jdbc CreateTableColumnTypes option with invalid data type") {
+    val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val msg = intercept[ParseException] {
+      df.write.mode(SaveMode.Overwrite)
+        .option("createTableColumnTypes", "name CLOB(2000)")
+        .jdbc(url1, "TEST.USERDBTYPETEST", properties)
+    }.getMessage()
+    assert(msg.contains("DataType clob(2000) is not supported."))
+  }
+
+  test("SPARK-10849: jdbc CreateTableColumnTypes option with invalid syntax") {
+    val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+    val msg = intercept[ParseException] {
+      df.write.mode(SaveMode.Overwrite)
+        .option("createTableColumnTypes", "`name char(20)") // incorrectly quoted column
+        .jdbc(url1, "TEST.USERDBTYPETEST", properties)
+    }.getMessage()
+    assert(msg.contains("no viable alternative at input"))
+  }
+
+  test("SPARK-10849: jdbc CreateTableColumnTypes duplicate columns") {
+    withSQLConf(SQLConf.CASE_SENSITIVE.key -> "false") {
+      val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+      val msg = intercept[AnalysisException] {
+        df.write.mode(SaveMode.Overwrite)
+          .option("createTableColumnTypes", "name CHAR(20), id int, NaMe VARCHAR(100)")
+          .jdbc(url1, "TEST.USERDBTYPETEST", properties)
+      }.getMessage()
+      assert(msg.contains(
+        "Found duplicate column(s) in createTableColumnTypes option value: name, NaMe"))
+    }
+  }
+
+  test("SPARK-10849: jdbc CreateTableColumnTypes invalid columns") {
+    // schema2 has the column "id" and "name"
+    val df = spark.createDataFrame(sparkContext.parallelize(arr2x2), schema2)
+
+    withSQLConf(SQLConf.CASE_SENSITIVE.key -> "false") {
+      val msg = intercept[AnalysisException] {
+        df.write.mode(SaveMode.Overwrite)
+          .option("createTableColumnTypes", "firstName CHAR(20), id int")
+          .jdbc(url1, "TEST.USERDBTYPETEST", properties)
+      }.getMessage()
+      assert(msg.contains("createTableColumnTypes option column firstName not found in " +
+        "schema struct<name:string,id:int>"))
+    }
+
+    withSQLConf(SQLConf.CASE_SENSITIVE.key -> "true") {
+      val msg = intercept[AnalysisException] {
+        df.write.mode(SaveMode.Overwrite)
+          .option("createTableColumnTypes", "id int, Name VARCHAR(100)")
+          .jdbc(url1, "TEST.USERDBTYPETEST", properties)
+      }.getMessage()
+      assert(msg.contains("createTableColumnTypes option column Name not found in " +
+        "schema struct<name:string,id:int>"))
+    }
+  }
 }