[SPARK-4912][SQL] Persistent tables for the Spark SQL data sources api

With changes in this PR, users can persist metadata of tables created based on the data source API in metastore through DDLs.

Author: Yin Huai <[email protected]>
Author: Michael Armbrust <[email protected]>

Closes #3960 from yhuai/persistantTablesWithSchema2 and squashes the following commits:

069c235 [Yin Huai] Make exception messages user friendly.
c07cbc6 [Yin Huai] Get the location of test file in a correct way.
4456e98 [Yin Huai] Test data.
5315dfc [Yin Huai] rxin's comments.
7fc4b56 [Yin Huai] Add DDLStrategy and HiveDDLStrategy to plan DDLs based on the data source API.
aeaf4b3 [Yin Huai] Add comments.
06f9b0c [Yin Huai] Revert unnecessary changes.
feb88aa [Yin Huai] Merge remote-tracking branch 'apache/master' into persistantTablesWithSchema2
172db80 [Yin Huai] Fix unit test.
49bf1ac [Yin Huai] Unit tests.
8f8f1a1 [Yin Huai] [SPARK-4574][SQL] Adding support for defining schema in foreign DDL commands. #3431
f47fda1 [Yin Huai] Unit tests.
2b59723 [Michael Armbrust] Set external when creating tables
c00bb1b [Michael Armbrust] Don't use reflection to read options
1ea6e7b [Michael Armbrust] Don't fail when trying to uncache a table that doesn't exist
6edc710 [Michael Armbrust] Add tests.
d7da491 [Michael Armbrust] First draft of persistent tables.

Author: yhuai

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

@@ -330,6 +330,7 @@ class SQLContext(@transient val sparkContext: SparkContext)
     def strategies: Seq[Strategy] =
       extraStrategies ++ (
       DataSourceStrategy ::
+      DDLStrategy ::
       TakeOrdered ::
       HashAggregation ::
       LeftSemiJoin ::

sql/core/src/main/scala/org/apache/spark/sql/execution/SparkStrategies.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.sql.execution
 
+import org.apache.spark.sql.sources.{CreateTempTableUsing, CreateTableUsing}
 import org.apache.spark.sql.{SQLContext, Strategy, execution}
 import org.apache.spark.sql.catalyst.expressions._
 import org.apache.spark.sql.catalyst.planning._
@@ -310,4 +311,17 @@ private[sql] abstract class SparkStrategies extends QueryPlanner[SparkPlan] {
       case _ => Nil
     }
   }
+
+  object DDLStrategy extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case CreateTableUsing(tableName, userSpecifiedSchema, provider, true, options) =>
+        ExecutedCommand(
+          CreateTempTableUsing(tableName, userSpecifiedSchema, provider, options)) :: Nil
+
+      case CreateTableUsing(tableName, userSpecifiedSchema, provider, false, options) =>
+        sys.error("Tables created with SQLContext must be TEMPORARY. Use a HiveContext instead.")
+
+      case _ => Nil
+    }
+  }
 }

sql/core/src/main/scala/org/apache/spark/sql/execution/commands.scala

@@ -177,7 +177,6 @@ case class DescribeCommand(
     override val output: Seq[Attribute]) extends RunnableCommand {
 
   override def run(sqlContext: SQLContext) = {
-    Row("# Registered as a temporary table", null, null) +:
-      child.output.map(field => Row(field.name, field.dataType.toString, null))
+    child.output.map(field => Row(field.name, field.dataType.toString, null))
   }
 }

sql/core/src/main/scala/org/apache/spark/sql/sources/ddl.scala

@@ -92,21 +92,21 @@ private[sql] class DDLParser extends StandardTokenParsers with PackratParsers wi
   protected lazy val ddl: Parser[LogicalPlan] = createTable
 
   /**
-   * `CREATE TEMPORARY TABLE avroTable
+   * `CREATE [TEMPORARY] TABLE avroTable
    * USING org.apache.spark.sql.avro
    * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
    * or
-   * `CREATE TEMPORARY TABLE avroTable(intField int, stringField string...)
+   * `CREATE [TEMPORARY] TABLE avroTable(intField int, stringField string...)
    * USING org.apache.spark.sql.avro
    * OPTIONS (path "../hive/src/test/resources/data/files/episodes.avro")`
    */
   protected lazy val createTable: Parser[LogicalPlan] =
   (
-    CREATE ~ TEMPORARY ~ TABLE ~> ident
+    (CREATE ~> TEMPORARY.? <~ TABLE) ~ ident
       ~ (tableCols).? ~ (USING ~> className) ~ (OPTIONS ~> options) ^^ {
-      case tableName ~ columns ~ provider ~ opts =>
+      case temp ~ tableName ~ columns ~ provider ~ opts =>
         val userSpecifiedSchema = columns.flatMap(fields => Some(StructType(fields)))
-        CreateTableUsing(tableName, userSpecifiedSchema, provider, opts)
+        CreateTableUsing(tableName, userSpecifiedSchema, provider, temp.isDefined, opts)
     }
   )
 
@@ -175,13 +175,12 @@ private[sql] class DDLParser extends StandardTokenParsers with PackratParsers wi
     primitiveType
 }
 
-private[sql] case class CreateTableUsing(
-    tableName: String,
-    userSpecifiedSchema: Option[StructType],
-    provider: String,
-    options: Map[String, String]) extends RunnableCommand {
-
-  def run(sqlContext: SQLContext) = {
+object ResolvedDataSource {
+  def apply(
+      sqlContext: SQLContext,
+      userSpecifiedSchema: Option[StructType],
+      provider: String,
+      options: Map[String, String]): ResolvedDataSource = {
     val loader = Utils.getContextOrSparkClassLoader
     val clazz: Class[_] = try loader.loadClass(provider) catch {
       case cnf: java.lang.ClassNotFoundException =>
@@ -199,22 +198,44 @@ private[sql] case class CreateTableUsing(
               .asInstanceOf[org.apache.spark.sql.sources.SchemaRelationProvider]
               .createRelation(sqlContext, new CaseInsensitiveMap(options), schema)
           case _ =>
-            sys.error(s"${clazz.getCanonicalName} should extend SchemaRelationProvider.")
+            sys.error(s"${clazz.getCanonicalName} does not allow user-specified schemas.")
         }
       }
       case None => {
         clazz.newInstance match {
-          case dataSource: org.apache.spark.sql.sources.RelationProvider  =>
+          case dataSource: org.apache.spark.sql.sources.RelationProvider =>
             dataSource
               .asInstanceOf[org.apache.spark.sql.sources.RelationProvider]
               .createRelation(sqlContext, new CaseInsensitiveMap(options))
           case _ =>
-            sys.error(s"${clazz.getCanonicalName} should extend RelationProvider.")
+            sys.error(s"A schema needs to be specified when using ${clazz.getCanonicalName}.")
         }
       }
     }
 
-    sqlContext.baseRelationToSchemaRDD(relation).registerTempTable(tableName)
+    new ResolvedDataSource(clazz, relation)
+  }
+}
+
+private[sql] case class ResolvedDataSource(provider: Class[_], relation: BaseRelation)
+
+private[sql] case class CreateTableUsing(
+    tableName: String,
+    userSpecifiedSchema: Option[StructType],
+    provider: String,
+    temporary: Boolean,
+    options: Map[String, String]) extends Command
+
+private [sql] case class CreateTempTableUsing(
+    tableName: String,
+    userSpecifiedSchema: Option[StructType],
+    provider: String,
+    options: Map[String, String])  extends RunnableCommand {
+
+  def run(sqlContext: SQLContext) = {
+    val resolved = ResolvedDataSource(sqlContext, userSpecifiedSchema, provider, options)
+
+    sqlContext.baseRelationToSchemaRDD(resolved.relation).registerTempTable(tableName)
     Seq.empty
   }
 }

sql/core/src/main/scala/org/apache/spark/sql/sources/interfaces.scala

@@ -24,8 +24,8 @@ import org.apache.spark.sql.catalyst.expressions.{Expression, Attribute}
 /**
  * ::DeveloperApi::
  * Implemented by objects that produce relations for a specific kind of data source.  When
- * Spark SQL is given a DDL operation with a USING clause specified, this interface is used to
- * pass in the parameters specified by a user.
+ * Spark SQL is given a DDL operation with a USING clause specified (to specify the implemented
+ * RelationProvider), this interface is used to pass in the parameters specified by a user.
  *
  * Users may specify the fully qualified class name of a given data source.  When that class is
  * not found Spark SQL will append the class name `DefaultSource` to the path, allowing for
@@ -46,17 +46,22 @@ trait RelationProvider {
 
 /**
  * ::DeveloperApi::
- * Implemented by objects that produce relations for a specific kind of data source.  When
- * Spark SQL is given a DDL operation with
- * 1. USING clause: to specify the implemented SchemaRelationProvider
- * 2. User defined schema: users can define schema optionally when create table
+ * Implemented by objects that produce relations for a specific kind of data source
+ * with a given schema.  When Spark SQL is given a DDL operation with a USING clause specified (
+ * to specify the implemented SchemaRelationProvider) and a user defined schema, this interface
+ * is used to pass in the parameters specified by a user.
  *
  * Users may specify the fully qualified class name of a given data source.  When that class is
  * not found Spark SQL will append the class name `DefaultSource` to the path, allowing for
  * less verbose invocation.  For example, 'org.apache.spark.sql.json' would resolve to the
  * data source 'org.apache.spark.sql.json.DefaultSource'
  *
  * A new instance of this class with be instantiated each time a DDL call is made.
+ *
+ * The difference between a [[RelationProvider]] and a [[SchemaRelationProvider]] is that
+ * users need to provide a schema when using a SchemaRelationProvider.
+ * A relation provider can inherits both [[RelationProvider]] and [[SchemaRelationProvider]]
+ * if it can support both schema inference and user-specified schemas.
  */
 @DeveloperApi
 trait SchemaRelationProvider {

sql/core/src/test/scala/org/apache/spark/sql/sources/TableScanSuite.scala

@@ -314,4 +314,34 @@ class TableScanSuite extends DataSourceTest {
       sql("SELECT * FROM oneToTenDef"),
       (1 to 10).map(Row(_)).toSeq)
   }
+
+  test("exceptions") {
+    // Make sure we do throw correct exception when users use a relation provider that
+    // only implements the RelationProvier or the SchemaRelationProvider.
+    val schemaNotAllowed = intercept[Exception] {
+      sql(
+        """
+          |CREATE TEMPORARY TABLE relationProvierWithSchema (i int)
+          |USING org.apache.spark.sql.sources.SimpleScanSource
+          |OPTIONS (
+          |  From '1',
+          |  To '10'
+          |)
+        """.stripMargin)
+    }
+    assert(schemaNotAllowed.getMessage.contains("does not allow user-specified schemas"))
+
+    val schemaNeeded = intercept[Exception] {
+      sql(
+        """
+          |CREATE TEMPORARY TABLE schemaRelationProvierWithoutSchema
+          |USING org.apache.spark.sql.sources.AllDataTypesScanSource
+          |OPTIONS (
+          |  From '1',
+          |  To '10'
+          |)
+        """.stripMargin)
+    }
+    assert(schemaNeeded.getMessage.contains("A schema needs to be specified when using"))
+  }
 }

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveContext.scala

@@ -115,6 +115,16 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     catalog.createTable("default", tableName, ScalaReflection.attributesFor[A], allowExisting)
   }
 
+  def refreshTable(tableName: String): Unit = {
+    // TODO: Database support...
+    catalog.refreshTable("default", tableName)
+  }
+
+  protected[hive] def invalidateTable(tableName: String): Unit = {
+    // TODO: Database support...
+    catalog.invalidateTable("default", tableName)
+  }
+
   /**
    * Analyzes the given table in the current database to generate statistics, which will be
    * used in query optimizations.
@@ -340,6 +350,8 @@ class HiveContext(sc: SparkContext) extends SQLContext(sc) {
     override def strategies: Seq[Strategy] = extraStrategies ++ Seq(
       DataSourceStrategy,
       HiveCommandStrategy(self),
+      HiveDDLStrategy,
+      DDLStrategy,
       TakeOrdered,
       ParquetOperations,
       InMemoryScans,

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveMetastoreCatalog.scala

@@ -20,10 +20,11 @@ package org.apache.spark.sql.hive
 import java.io.IOException
 import java.util.{List => JList}
 
+import com.google.common.cache.{LoadingCache, CacheLoader, CacheBuilder}
+
 import org.apache.hadoop.util.ReflectionUtils
 import org.apache.hadoop.hive.metastore.TableType
-import org.apache.hadoop.hive.metastore.api.FieldSchema
-import org.apache.hadoop.hive.metastore.api.{Table => TTable, Partition => TPartition}
+import org.apache.hadoop.hive.metastore.api.{Table => TTable, Partition => TPartition, FieldSchema}
 import org.apache.hadoop.hive.ql.metadata.{Hive, Partition, Table, HiveException}
 import org.apache.hadoop.hive.ql.metadata.InvalidTableException
 import org.apache.hadoop.hive.ql.plan.CreateTableDesc
@@ -39,6 +40,7 @@ import org.apache.spark.sql.catalyst.plans.logical
 import org.apache.spark.sql.catalyst.plans.logical._
 import org.apache.spark.sql.catalyst.rules._
 import org.apache.spark.sql.catalyst.types._
+import org.apache.spark.sql.sources.{LogicalRelation, ResolvedDataSource}
 import org.apache.spark.util.Utils
 
 /* Implicit conversions */
@@ -50,8 +52,76 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
   /** Connection to hive metastore.  Usages should lock on `this`. */
   protected[hive] val client = Hive.get(hive.hiveconf)
 
+  // TODO: Use this everywhere instead of tuples or databaseName, tableName,.
+  /** A fully qualified identifier for a table (i.e., database.tableName) */
+  case class QualifiedTableName(database: String, name: String) {
+    def toLowerCase = QualifiedTableName(database.toLowerCase, name.toLowerCase)
+  }
+
+  /** A cache of Spark SQL data source tables that have been accessed. */
+  protected[hive] val cachedDataSourceTables: LoadingCache[QualifiedTableName, LogicalPlan] = {
+    val cacheLoader = new CacheLoader[QualifiedTableName, LogicalPlan]() {
+      override def load(in: QualifiedTableName): LogicalPlan = {
+        logDebug(s"Creating new cached data source for $in")
+        val table = client.getTable(in.database, in.name)
+        val schemaString = table.getProperty("spark.sql.sources.schema")
+        val userSpecifiedSchema =
+          if (schemaString == null) {
+            None
+          } else {
+            Some(DataType.fromJson(schemaString).asInstanceOf[StructType])
+          }
+        // It does not appear that the ql client for the metastore has a way to enumerate all the
+        // SerDe properties directly...
+        val options = table.getTTable.getSd.getSerdeInfo.getParameters.toMap
+
+        val resolvedRelation =
+          ResolvedDataSource(
+            hive,
+            userSpecifiedSchema,
+            table.getProperty("spark.sql.sources.provider"),
+            options)
+
+        LogicalRelation(resolvedRelation.relation)
+      }
+    }
+
+    CacheBuilder.newBuilder().maximumSize(1000).build(cacheLoader)
+  }
+
+  def refreshTable(databaseName: String, tableName: String): Unit = {
+    cachedDataSourceTables.refresh(QualifiedTableName(databaseName, tableName).toLowerCase)
+  }
+
+  def invalidateTable(databaseName: String, tableName: String): Unit = {
+    cachedDataSourceTables.invalidate(QualifiedTableName(databaseName, tableName).toLowerCase)
+  }
+
   val caseSensitive: Boolean = false
 
+  def createDataSourceTable(
+      tableName: String,
+      userSpecifiedSchema: Option[StructType],
+      provider: String,
+      options: Map[String, String]) = {
+    val (dbName, tblName) = processDatabaseAndTableName("default", tableName)
+    val tbl = new Table(dbName, tblName)
+
+    tbl.setProperty("spark.sql.sources.provider", provider)
+    if (userSpecifiedSchema.isDefined) {
+      tbl.setProperty("spark.sql.sources.schema", userSpecifiedSchema.get.json)
+    }
+    options.foreach { case (key, value) => tbl.setSerdeParam(key, value) }
+
+    tbl.setProperty("EXTERNAL", "TRUE")
+    tbl.setTableType(TableType.EXTERNAL_TABLE)
+
+    // create the table
+    synchronized {
+      client.createTable(tbl, false)
+    }
+  }
+
   def tableExists(tableIdentifier: Seq[String]): Boolean = {
     val tableIdent = processTableIdentifier(tableIdentifier)
     val databaseName = tableIdent.lift(tableIdent.size - 2).getOrElse(
@@ -72,7 +142,10 @@ private[hive] class HiveMetastoreCatalog(hive: HiveContext) extends Catalog with
       hive.sessionState.getCurrentDatabase)
     val tblName = tableIdent.last
     val table = client.getTable(databaseName, tblName)
-    if (table.isView) {
+
+    if (table.getProperty("spark.sql.sources.provider") != null) {
+      cachedDataSourceTables(QualifiedTableName(databaseName, tblName).toLowerCase)
+    } else if (table.isView) {
       // if the unresolved relation is from hive view
       // parse the text into logic node.
       HiveQl.createPlanForView(table, alias)

sql/hive/src/main/scala/org/apache/spark/sql/hive/HiveStrategies.scala

@@ -30,6 +30,7 @@ import org.apache.spark.sql.execution._
 import org.apache.spark.sql.hive
 import org.apache.spark.sql.hive.execution._
 import org.apache.spark.sql.parquet.ParquetRelation
+import org.apache.spark.sql.sources.CreateTableUsing
 import org.apache.spark.sql.{SQLContext, SchemaRDD, Strategy}
 
 import scala.collection.JavaConversions._
@@ -208,6 +209,16 @@ private[hive] trait HiveStrategies {
     }
   }
 
+  object HiveDDLStrategy extends Strategy {
+    def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
+      case CreateTableUsing(tableName, userSpecifiedSchema, provider, false, options) =>
+        ExecutedCommand(
+          CreateMetastoreDataSource(tableName, userSpecifiedSchema, provider, options)) :: Nil
+
+      case _ => Nil
+    }
+  }
+
   case class HiveCommandStrategy(context: HiveContext) extends Strategy {
     def apply(plan: LogicalPlan): Seq[SparkPlan] = plan match {
       case describe: DescribeCommand =>

sql/hive/src/main/scala/org/apache/spark/sql/hive/TestHive.scala

@@ -395,6 +395,7 @@ class TestHiveContext(sc: SparkContext) extends HiveContext(sc) {
 
       clearCache()
       loadedTables.clear()
+      catalog.cachedDataSourceTables.invalidateAll()
       catalog.client.getAllTables("default").foreach { t =>
         logDebug(s"Deleting table $t")
         val table = catalog.client.getTable("default", t)