[SPARK-22002][SQL] Read JDBC table use custom schema support specify partial fields.

docs/sql-programming-guide.md

@@ -1333,7 +1333,7 @@ the following case-insensitive options:
   <tr>
     <td><code>customSchema</code></td>
     <td>
-     The custom schema to use for reading data from JDBC connectors. For example, "id DECIMAL(38, 0), name STRING"). The column names should be identical to the corresponding column names of JDBC table. Users can specify the corresponding data types of Spark SQL instead of using the defaults. This option applies only to reading.
+     The custom schema to use for reading data from JDBC connectors. For example, <code>"id DECIMAL(38, 0), name STRING"</code>. You can also specify partial fields, and the others use the default type mapping. For example, <code>"id DECIMAL(38, 0)"</code>. The column names should be identical to the corresponding column names of JDBC table. Users can specify the corresponding data types of Spark SQL instead of using the defaults. This option applies only to reading.
     </td>
   </tr>
 </table>

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

@@ -301,12 +301,11 @@ object JdbcUtils extends Logging {
       } else {
         rsmd.isNullable(i + 1) != ResultSetMetaData.columnNoNulls
       }
-      val metadata = new MetadataBuilder()
-        .putLong("scale", fieldScale)
+      val metadata = new MetadataBuilder().putLong("scale", fieldScale)
       val columnType =
         dialect.getCatalystType(dataType, typeName, fieldSize, metadata).getOrElse(
           getCatalystType(dataType, fieldSize, fieldScale, isSigned))
-      fields(i) = StructField(columnName, columnType, nullable, metadata.build())
+      fields(i) = StructField(columnName, columnType, nullable)
       i = i + 1
     }
     new StructType(fields)
@@ -768,31 +767,30 @@ object JdbcUtils extends Logging {
   }
 
   /**
-   * Parses the user specified customSchema option value to DataFrame schema,
-   * and returns it if it's all columns are equals to default schema's.
+   * Parses the user specified customSchema option value to DataFrame schema, and
+   * returns a schema that is replaced by the custom schema's dataType if column name is matched.
    */
   def getCustomSchema(
       tableSchema: StructType,
       customSchema: String,
       nameEquality: Resolver): StructType = {
-    val userSchema = CatalystSqlParser.parseTableSchema(customSchema)
+    if (null != customSchema && customSchema.nonEmpty) {
+      val userSchema = CatalystSqlParser.parseTableSchema(customSchema)
 
-    SchemaUtils.checkColumnNameDuplication(
-      userSchema.map(_.name), "in the customSchema option value", nameEquality)
-
-    val colNames = tableSchema.fieldNames.mkString(",")
-    val errorMsg = s"Please provide all the columns, all columns are: $colNames"
-    if (userSchema.size != tableSchema.size) {
-      throw new AnalysisException(errorMsg)
-    }
+      SchemaUtils.checkColumnNameDuplication(
+        userSchema.map(_.name), "in the customSchema option value", nameEquality)
 
-    // This is resolved by names, only check the column names.
-    userSchema.fieldNames.foreach { col =>
-      tableSchema.find(f => nameEquality(f.name, col)).getOrElse {
-        throw new AnalysisException(errorMsg)
+      // This is resolved by names, use the custom filed dataType to replace the default dataType.
+      val newSchema = tableSchema.map { col =>
+        userSchema.find(f => nameEquality(f.name, col.name)) match {
+          case Some(c) => col.copy(dataType = c.dataType)
+          case None => col
+        }
       }
+      StructType(newSchema)
+    } else {
+      tableSchema
     }
-    userSchema
   }
 
   /**

sql/core/src/test/scala/org/apache/spark/sql/execution/datasources/jdbc/JdbcUtilsSuite.scala

@@ -30,57 +30,38 @@ class JdbcUtilsSuite extends SparkFunSuite {
   val caseInsensitive = org.apache.spark.sql.catalyst.analysis.caseInsensitiveResolution
 
   test("Parse user specified column types") {
-    assert(
-      JdbcUtils.getCustomSchema(tableSchema, "C1 DATE, C2 STRING", caseInsensitive) ===
-      StructType(Seq(StructField("C1", DateType, true), StructField("C2", StringType, true))))
-    assert(JdbcUtils.getCustomSchema(tableSchema, "C1 DATE, C2 STRING", caseSensitive) ===
-      StructType(Seq(StructField("C1", DateType, true), StructField("C2", StringType, true))))
+    assert(JdbcUtils.getCustomSchema(tableSchema, null, caseInsensitive) === tableSchema)
+    assert(JdbcUtils.getCustomSchema(tableSchema, "", caseInsensitive) === tableSchema)
+
+    assert(JdbcUtils.getCustomSchema(tableSchema, "c1 DATE", caseInsensitive) ===
+      StructType(Seq(StructField("C1", DateType, false), StructField("C2", IntegerType, false))))
+    assert(JdbcUtils.getCustomSchema(tableSchema, "c1 DATE", caseSensitive) ===
+      StructType(Seq(StructField("C1", StringType, false), StructField("C2", IntegerType, false))))
+
     assert(
       JdbcUtils.getCustomSchema(tableSchema, "c1 DATE, C2 STRING", caseInsensitive) ===
-        StructType(Seq(StructField("c1", DateType, true), StructField("C2", StringType, true))))
-    assert(JdbcUtils.getCustomSchema(
-      tableSchema, "c1 DECIMAL(38, 0), C2 STRING", caseInsensitive) ===
-      StructType(Seq(StructField("c1", DecimalType(38, 0), true),
-        StructField("C2", StringType, true))))
+      StructType(Seq(StructField("C1", DateType, false), StructField("C2", StringType, false))))
+    assert(JdbcUtils.getCustomSchema(tableSchema, "c1 DATE, C2 STRING", caseSensitive) ===
+      StructType(Seq(StructField("C1", StringType, false), StructField("C2", StringType, false))))
 
     // Throw AnalysisException
     val duplicate = intercept[AnalysisException]{
       JdbcUtils.getCustomSchema(tableSchema, "c1 DATE, c1 STRING", caseInsensitive) ===
-        StructType(Seq(StructField("c1", DateType, true), StructField("c1", StringType, true)))
+        StructType(Seq(StructField("c1", DateType, false), StructField("c1", StringType, false)))
     }
     assert(duplicate.getMessage.contains(
       "Found duplicate column(s) in the customSchema option value"))
 
-    val allColumns = intercept[AnalysisException]{
-      JdbcUtils.getCustomSchema(tableSchema, "C1 STRING", caseSensitive) ===
-        StructType(Seq(StructField("C1", DateType, true)))
-    }
-    assert(allColumns.getMessage.contains("Please provide all the columns,"))
-
-    val caseSensitiveColumnNotFound = intercept[AnalysisException]{
-      JdbcUtils.getCustomSchema(tableSchema, "c1 DATE, C2 STRING", caseSensitive) ===
-        StructType(Seq(StructField("c1", DateType, true), StructField("C2", StringType, true)))
-    }
-    assert(caseSensitiveColumnNotFound.getMessage.contains(
-      "Please provide all the columns, all columns are: C1,C2;"))
-
-    val caseInsensitiveColumnNotFound = intercept[AnalysisException]{
-      JdbcUtils.getCustomSchema(tableSchema, "c3 DATE, C2 STRING", caseInsensitive) ===
-        StructType(Seq(StructField("c3", DateType, true), StructField("C2", StringType, true)))
-    }
-    assert(caseInsensitiveColumnNotFound.getMessage.contains(
-      "Please provide all the columns, all columns are: C1,C2;"))
-
     // Throw ParseException
     val dataTypeNotSupported = intercept[ParseException]{
       JdbcUtils.getCustomSchema(tableSchema, "c3 DATEE, C2 STRING", caseInsensitive) ===
-        StructType(Seq(StructField("c3", DateType, true), StructField("C2", StringType, true)))
+        StructType(Seq(StructField("c3", DateType, false), StructField("C2", StringType, false)))
     }
     assert(dataTypeNotSupported.getMessage.contains("DataType datee is not supported"))
 
     val mismatchedInput = intercept[ParseException]{
       JdbcUtils.getCustomSchema(tableSchema, "c3 DATE. C2 STRING", caseInsensitive) ===
-        StructType(Seq(StructField("c3", DateType, true), StructField("C2", StringType, true)))
+        StructType(Seq(StructField("c3", DateType, false), StructField("C2", StringType, false)))
     }
     assert(mismatchedInput.getMessage.contains("mismatched input '.' expecting"))
   }

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

@@ -26,6 +26,7 @@ import org.scalatest.{BeforeAndAfter, PrivateMethodTester}
 
 import org.apache.spark.{SparkException, SparkFunSuite}
 import org.apache.spark.sql.{AnalysisException, DataFrame, Row}
+import org.apache.spark.sql.catalyst.parser.CatalystSqlParser
 import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
 import org.apache.spark.sql.execution.DataSourceScanExec
 import org.apache.spark.sql.execution.command.ExplainCommand
@@ -970,30 +971,28 @@ class JDBCSuite extends SparkFunSuite
 
   test("jdbc API support custom schema") {
     val parts = Array[String]("THEID < 2", "THEID >= 2")
+    val customSchema = "NAME STRING, THEID INT"
     val props = new Properties()
-    props.put("customSchema", "NAME STRING, THEID BIGINT")
-    val schema = StructType(Seq(
-      StructField("NAME", StringType, true), StructField("THEID", LongType, true)))
+    props.put("customSchema", customSchema)
     val df = spark.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, props)
     assert(df.schema.size === 2)
-    assert(df.schema === schema)
+    assert(df.schema === CatalystSqlParser.parseTableSchema(customSchema))
     assert(df.count() === 3)
   }
 
   test("jdbc API custom schema DDL-like strings.") {
     withTempView("people_view") {
+      val customSchema = "NAME STRING, THEID INT"
       sql(
         s"""
            |CREATE TEMPORARY VIEW people_view
            |USING org.apache.spark.sql.jdbc
            |OPTIONS (uRl '$url', DbTaBlE 'TEST.PEOPLE', User 'testUser', PassWord 'testPass',
-           |customSchema 'NAME STRING, THEID INT')
+           |customSchema '$customSchema')
         """.stripMargin.replaceAll("\n", " "))
-      val schema = StructType(
-        Seq(StructField("NAME", StringType, true), StructField("THEID", IntegerType, true)))
       val df = sql("select * from people_view")
-      assert(df.schema.size === 2)
-      assert(df.schema === schema)
+      assert(df.schema.length === 2)
+      assert(df.schema === CatalystSqlParser.parseTableSchema(customSchema))
       assert(df.count() === 3)
     }
   }