[SPARK-16848][SQL] Check schema validation for user-specified schema in jdbc and table APIs
## What changes were proposed in this pull request? This PR proposes to throw an exception for both jdbc APIs when user specified schemas are not allowed or useless. **DataFrameReader.jdbc(...)** ``` scala spark.read.schema(StructType(Nil)).jdbc(...) ``` **DataFrameReader.table(...)** ```scala spark.read.schema(StructType(Nil)).table("usrdb.test") ``` ## How was this patch tested? Unit test in `JDBCSuite` and `DataFrameReaderWriterSuite`. Author: hyukjinkwon <gurwls223@gmail.com> Closes #14451 from HyukjinKwon/SPARK-16848.
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@ -165,6 +165,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
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* @since 1.4.0
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*/
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def jdbc(url: String, table: String, properties: Properties): DataFrame = {
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assertNoSpecifiedSchema("jdbc")
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// properties should override settings in extraOptions.
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this.extraOptions ++= properties.asScala
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// explicit url and dbtable should override all
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@ -235,6 +236,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
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table: String,
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predicates: Array[String],
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connectionProperties: Properties): DataFrame = {
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assertNoSpecifiedSchema("jdbc")
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// connectionProperties should override settings in extraOptions.
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val params = extraOptions.toMap ++ connectionProperties.asScala.toMap
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val options = new JDBCOptions(url, table, params)
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@ -475,6 +477,7 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
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* @since 1.4.0
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*/
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def table(tableName: String): DataFrame = {
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assertNoSpecifiedSchema("table")
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sparkSession.table(tableName)
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}
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@ -540,12 +543,19 @@ class DataFrameReader private[sql](sparkSession: SparkSession) extends Logging {
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*/
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@scala.annotation.varargs
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def textFile(paths: String*): Dataset[String] = {
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if (userSpecifiedSchema.nonEmpty) {
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throw new AnalysisException("User specified schema not supported with `textFile`")
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}
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assertNoSpecifiedSchema("textFile")
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text(paths : _*).select("value").as[String](sparkSession.implicits.newStringEncoder)
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}
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/**
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* A convenient function for schema validation in APIs.
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*/
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private def assertNoSpecifiedSchema(operation: String): Unit = {
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if (userSpecifiedSchema.nonEmpty) {
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throw new AnalysisException(s"User specified schema not supported with `$operation`")
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}
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}
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///////////////////////////////////////////////////////////////////////////////////////
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// Builder pattern config options
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///////////////////////////////////////////////////////////////////////////////////////
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@ -25,7 +25,7 @@ import org.h2.jdbc.JdbcSQLException
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import org.scalatest.{BeforeAndAfter, PrivateMethodTester}
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import org.apache.spark.SparkFunSuite
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import org.apache.spark.sql.{DataFrame, Row}
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import org.apache.spark.sql.{AnalysisException, DataFrame, Row}
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import org.apache.spark.sql.catalyst.util.CaseInsensitiveMap
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import org.apache.spark.sql.execution.DataSourceScanExec
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import org.apache.spark.sql.execution.command.ExplainCommand
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@ -900,4 +900,19 @@ class JDBCSuite extends SparkFunSuite
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assert(new JDBCOptions(parameters).asConnectionProperties.isEmpty)
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assert(new JDBCOptions(new CaseInsensitiveMap(parameters)).asConnectionProperties.isEmpty)
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}
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test("SPARK-16848: jdbc API throws an exception for user specified schema") {
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val schema = StructType(Seq(
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StructField("name", StringType, false), StructField("theid", IntegerType, false)))
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val parts = Array[String]("THEID < 2", "THEID >= 2")
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val e1 = intercept[AnalysisException] {
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spark.read.schema(schema).jdbc(urlWithUserAndPass, "TEST.PEOPLE", parts, new Properties())
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}.getMessage
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assert(e1.contains("User specified schema not supported with `jdbc`"))
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val e2 = intercept[AnalysisException] {
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spark.read.schema(schema).jdbc(urlWithUserAndPass, "TEST.PEOPLE", new Properties())
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}.getMessage
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assert(e2.contains("User specified schema not supported with `jdbc`"))
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}
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}
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@ -635,4 +635,14 @@ class DataFrameReaderWriterSuite extends QueryTest with SharedSQLContext with Be
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checkAnswer(spark.table("t"), Row(1, "a") :: Row(2, "b") :: Nil)
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}
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}
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test("SPARK-16848: table API throws an exception for user specified schema") {
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withTable("t") {
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val schema = StructType(StructField("a", StringType) :: Nil)
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val e = intercept[AnalysisException] {
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spark.read.schema(schema).table("t")
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}.getMessage
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assert(e.contains("User specified schema not supported with `table`"))
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}
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}
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}
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