[SPARK-18538][SQL] Fix Concurrent Table Fetching Using DataFrameReade…

…r JDBC APIs

### What changes were proposed in this pull request?
The following two `DataFrameReader` JDBC APIs ignore the user-specified parameters of parallelism degree.

```Scala
  def jdbc(
      url: String,
      table: String,
      columnName: String,
      lowerBound: Long,
      upperBound: Long,
      numPartitions: Int,
      connectionProperties: Properties): DataFrame
```

```Scala
  def jdbc(
      url: String,
      table: String,
      predicates: Array[String],
      connectionProperties: Properties): DataFrame
```

This PR is to fix the issues. To verify the behavior correctness, we improve the plan output of `EXPLAIN` command by adding `numPartitions` in the `JDBCRelation` node.

Before the fix,
```
== Physical Plan ==
*Scan JDBCRelation(TEST.PEOPLE) [NAME#1896,THEID#1897] ReadSchema: struct<NAME:string,THEID:int>
```

After the fix,
```
== Physical Plan ==
*Scan JDBCRelation(TEST.PEOPLE) [numPartitions=3] [NAME#1896,THEID#1897] ReadSchema: struct<NAME:string,THEID:int>
```
### How was this patch tested?
Added the verification logics on all the test cases for JDBC concurrent fetching.

Author: gatorsmile <gatorsmile@gmail.com>

Closes #15975 from gatorsmile/jdbc.

sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala

@@ -159,7 +159,11 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
    * @since 1.4.0
    */
   def jdbc(url: String, table: String, properties: Properties): DataFrame = {
-    jdbc(url, table, JDBCRelation.columnPartition(null), properties)
+    // properties should override settings in extraOptions.
+    this.extraOptions = this.extraOptions ++ properties.asScala
+    // explicit url and dbtable should override all
+    this.extraOptions += (JDBCOptions.JDBC_URL -> url, JDBCOptions.JDBC_TABLE_NAME -> table)
+    format("jdbc").load()
   }
 
   /**
@@ -177,7 +181,8 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
    * @param upperBound the maximum value of `columnName` used to decide partition stride.
    * @param numPartitions the number of partitions. This, along with `lowerBound` (inclusive),
    *                      `upperBound` (exclusive), form partition strides for generated WHERE
-   *                      clause expressions used to split the column `columnName` evenly.
+   *                      clause expressions used to split the column `columnName` evenly. When
+   *                      the input is less than 1, the number is set to 1.
    * @param connectionProperties JDBC database connection arguments, a list of arbitrary string
    *                             tag/value. Normally at least a "user" and "password" property
    *                             should be included. "fetchsize" can be used to control the
@@ -192,9 +197,13 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
       upperBound: Long,
       numPartitions: Int,
       connectionProperties: Properties): DataFrame = {
-    val partitioning = JDBCPartitioningInfo(columnName, lowerBound, upperBound, numPartitions)
-    val parts = JDBCRelation.columnPartition(partitioning)
-    jdbc(url, table, parts, connectionProperties)
+    // columnName, lowerBound, upperBound and numPartitions override settings in extraOptions.
+    this.extraOptions ++= Map(
+      JDBCOptions.JDBC_PARTITION_COLUMN -> columnName,
+      JDBCOptions.JDBC_LOWER_BOUND -> lowerBound.toString,
+      JDBCOptions.JDBC_UPPER_BOUND -> upperBound.toString,
+      JDBCOptions.JDBC_NUM_PARTITIONS -> numPartitions.toString)
+    jdbc(url, table, connectionProperties)
   }
 
   /**
@@ -220,22 +229,14 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
       table: String,
       predicates: Array[String],
       connectionProperties: Properties): DataFrame = {
+    // connectionProperties should override settings in extraOptions.
+    val params = extraOptions.toMap ++ connectionProperties.asScala.toMap
+    val options = new JDBCOptions(url, table, params)
     val parts: Array[Partition] = predicates.zipWithIndex.map { case (part, i) =>
       JDBCPartition(part, i) : Partition
     }
-    jdbc(url, table, parts, connectionProperties)
-  }
-
-  private def jdbc(
-      url: String,
-      table: String,
-      parts: Array[Partition],
-      connectionProperties: Properties): DataFrame = {
-    // connectionProperties should override settings in extraOptions.
-    this.extraOptions = this.extraOptions ++ connectionProperties.asScala
-    // explicit url and dbtable should override all
-    this.extraOptions += ("url" -> url, "dbtable" -> table)
-    format("jdbc").load()
+    val relation = JDBCRelation(parts, options)(sparkSession)
+    sparkSession.baseRelationToDataFrame(relation)
   }
 
   /**

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

@@ -76,9 +76,6 @@ class JDBCOptions(
 
   // the number of partitions
   val numPartitions = parameters.get(JDBC_NUM_PARTITIONS).map(_.toInt)
-  require(numPartitions.isEmpty || numPartitions.get > 0,
-    s"Invalid value `${numPartitions.get}` for parameter `$JDBC_NUM_PARTITIONS`. " +
-      "The minimum value is 1.")
 
   // ------------------------------------------------------------
   // Optional parameters only for reading

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

@@ -137,7 +137,8 @@ private[sql] case class JDBCRelation(
   }
 
   override def toString: String = {
+    val partitioningInfo = if (parts.nonEmpty) s" [numPartitions=${parts.length}]" else ""
     // credentials should not be included in the plan output, table information is sufficient.
-    s"JDBCRelation(${jdbcOptions.table})"
+    s"JDBCRelation(${jdbcOptions.table})" + partitioningInfo
   }
 }

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

@@ -657,7 +657,7 @@ object JdbcUtils extends Logging {
       df: DataFrame,
       url: String,
       table: String,
-      options: JDBCOptions) {
+      options: JDBCOptions): Unit = {
     val dialect = JdbcDialects.get(url)
     val nullTypes: Array[Int] = df.schema.fields.map { field =>
       getJdbcType(field.dataType, dialect).jdbcNullType
@@ -667,13 +667,13 @@ object JdbcUtils extends Logging {
     val getConnection: () => Connection = createConnectionFactory(options)
     val batchSize = options.batchSize
     val isolationLevel = options.isolationLevel
-    val numPartitions = options.numPartitions
-    val repartitionedDF =
-      if (numPartitions.isDefined && numPartitions.get < df.rdd.getNumPartitions) {
-        df.coalesce(numPartitions.get)
-      } else {
-        df
-      }
+    val repartitionedDF = options.numPartitions match {
+      case Some(n) if n <= 0 => throw new IllegalArgumentException(
+        s"Invalid value `$n` for parameter `${JDBCOptions.JDBC_NUM_PARTITIONS}` in table writing " +
+          "via JDBC. The minimum value is 1.")
+      case Some(n) if n < df.rdd.getNumPartitions => df.coalesce(n)
+      case _ => df
+    }
     repartitionedDF.foreachPartition(iterator => savePartition(
       getConnection, table, iterator, rddSchema, nullTypes, batchSize, dialect, isolationLevel)
     )

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

@@ -24,12 +24,12 @@ import java.util.{Calendar, GregorianCalendar, Properties}
 import org.h2.jdbc.JdbcSQLException
 import org.scalatest.{BeforeAndAfter, PrivateMethodTester}
 
-import org.apache.spark.{SparkException, SparkFunSuite}
+import org.apache.spark.SparkFunSuite
 import org.apache.spark.sql.{DataFrame, Row}
 import org.apache.spark.sql.execution.DataSourceScanExec
 import org.apache.spark.sql.execution.command.ExplainCommand
 import org.apache.spark.sql.execution.datasources.LogicalRelation
-import org.apache.spark.sql.execution.datasources.jdbc.{JDBCOptions, JDBCRDD, JdbcUtils}
+import org.apache.spark.sql.execution.datasources.jdbc.{JDBCOptions, JDBCRDD, JDBCRelation, JdbcUtils}
 import org.apache.spark.sql.sources._
 import org.apache.spark.sql.test.SharedSQLContext
 import org.apache.spark.sql.types._
@@ -209,6 +209,16 @@ class JDBCSuite extends SparkFunSuite
     conn.close()
   }
 
+  // Check whether the tables are fetched in the expected degree of parallelism
+  def checkNumPartitions(df: DataFrame, expectedNumPartitions: Int): Unit = {
+    val jdbcRelations = df.queryExecution.analyzed.collect {
+      case LogicalRelation(r: JDBCRelation, _, _) => r
+    }
+    assert(jdbcRelations.length == 1)
+    assert(jdbcRelations.head.parts.length == expectedNumPartitions,
+      s"Expecting a JDBCRelation with $expectedNumPartitions partitions, but got:`$jdbcRelations`")
+  }
+
   test("SELECT *") {
     assert(sql("SELECT * FROM foobar").collect().size === 3)
   }
@@ -313,13 +323,23 @@ class JDBCSuite extends SparkFunSuite
   }
 
   test("SELECT * partitioned") {
-    assert(sql("SELECT * FROM parts").collect().size == 3)
+    val df = sql("SELECT * FROM parts")
+    checkNumPartitions(df, expectedNumPartitions = 3)
+    assert(df.collect().length == 3)
   }
 
   test("SELECT WHERE (simple predicates) partitioned") {
-    assert(sql("SELECT * FROM parts WHERE THEID < 1").collect().size === 0)
-    assert(sql("SELECT * FROM parts WHERE THEID != 2").collect().size === 2)
-    assert(sql("SELECT THEID FROM parts WHERE THEID = 1").collect().size === 1)
+    val df1 = sql("SELECT * FROM parts WHERE THEID < 1")
+    checkNumPartitions(df1, expectedNumPartitions = 3)
+    assert(df1.collect().length === 0)
+
+    val df2 = sql("SELECT * FROM parts WHERE THEID != 2")
+    checkNumPartitions(df2, expectedNumPartitions = 3)
+    assert(df2.collect().length === 2)
+
+    val df3 = sql("SELECT THEID FROM parts WHERE THEID = 1")
+    checkNumPartitions(df3, expectedNumPartitions = 3)
+    assert(df3.collect().length === 1)
   }
 
   test("SELECT second field partitioned") {
@@ -370,24 +390,27 @@ class JDBCSuite extends SparkFunSuite
   }
 
   test("Partitioning via JDBCPartitioningInfo API") {
-    assert(
-      spark.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties())
-      .collect().length === 3)
+    val df = spark.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", "THEID", 0, 4, 3, new Properties())
+    checkNumPartitions(df, expectedNumPartitions = 3)
+    assert(df.collect().length === 3)
   }
 
   test("Partitioning via list-of-where-clauses API") {
     val parts = Array[String]("THEID < 2", "THEID >= 2")
-    assert(spark.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties())
-      .collect().length === 3)
+    val df = spark.read.jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties())
+    checkNumPartitions(df, expectedNumPartitions = 2)
+    assert(df.collect().length === 3)
   }
 
   test("Partitioning on column that might have null values.") {
-    assert(
-      spark.read.jdbc(urlWithUserAndPass, "TEST.EMP", "theid", 0, 4, 3, new Properties())
-        .collect().length === 4)
-    assert(
-      spark.read.jdbc(urlWithUserAndPass, "TEST.EMP", "THEID", 0, 4, 3, new Properties())
-        .collect().length === 4)
+    val df = spark.read.jdbc(urlWithUserAndPass, "TEST.EMP", "theid", 0, 4, 3, new Properties())
+    checkNumPartitions(df, expectedNumPartitions = 3)
+    assert(df.collect().length === 4)
+
+    val df2 = spark.read.jdbc(urlWithUserAndPass, "TEST.EMP", "THEID", 0, 4, 3, new Properties())
+    checkNumPartitions(df2, expectedNumPartitions = 3)
+    assert(df2.collect().length === 4)
+
     // partitioning on a nullable quoted column
     assert(
       spark.read.jdbc(urlWithUserAndPass, "TEST.EMP", """"Dept"""", 0, 4, 3, new Properties())
@@ -404,6 +427,7 @@ class JDBCSuite extends SparkFunSuite
       numPartitions = 0,
       connectionProperties = new Properties()
     )
+    checkNumPartitions(res, expectedNumPartitions = 1)
     assert(res.count() === 8)
   }
 
@@ -417,6 +441,7 @@ class JDBCSuite extends SparkFunSuite
       numPartitions = 10,
       connectionProperties = new Properties()
     )
+    checkNumPartitions(res, expectedNumPartitions = 4)
     assert(res.count() === 8)
   }
 
@@ -430,6 +455,7 @@ class JDBCSuite extends SparkFunSuite
       numPartitions = 4,
       connectionProperties = new Properties()
     )
+    checkNumPartitions(res, expectedNumPartitions = 1)
     assert(res.count() === 8)
   }
 
@@ -450,7 +476,9 @@ class JDBCSuite extends SparkFunSuite
   }
 
   test("SELECT * on partitioned table with a nullable partition column") {
-    assert(sql("SELECT * FROM nullparts").collect().size == 4)
+    val df = sql("SELECT * FROM nullparts")
+    checkNumPartitions(df, expectedNumPartitions = 3)
+    assert(df.collect().length == 4)
   }
 
   test("H2 integral types") {
@@ -722,7 +750,8 @@ class JDBCSuite extends SparkFunSuite
     }
     // test the JdbcRelation toString output
     df.queryExecution.analyzed.collect {
-      case r: LogicalRelation => assert(r.relation.toString == "JDBCRelation(TEST.PEOPLE)")
+      case r: LogicalRelation =>
+        assert(r.relation.toString == "JDBCRelation(TEST.PEOPLE) [numPartitions=3]")
     }
   }
 

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

@@ -319,9 +319,12 @@ class JDBCWriteSuite extends SharedSQLContext with BeforeAndAfter {
       df.write.format("jdbc")
         .option("dbtable", "TEST.SAVETEST")
         .option("url", url1)
+        .option("user", "testUser")
+        .option("password", "testPass")
         .option(s"${JDBCOptions.JDBC_NUM_PARTITIONS}", "0")
         .save()
     }.getMessage
-    assert(e.contains("Invalid value `0` for parameter `numPartitions`. The minimum value is 1"))
+    assert(e.contains("Invalid value `0` for parameter `numPartitions` in table writing " +
+      "via JDBC. The minimum value is 1."))
   }
 }