From 3ba9bbf8def8149ff44e8555f14ef54e926a7272 Mon Sep 17 00:00:00 2001
From: Cheng Lian <lian@databricks.com>
Date: Wed, 15 Apr 2015 22:48:33 +0800
Subject: [PATCH] Adds DataFrame.saveAsTable() overrides which support
 partitioning

---
 .../org/apache/spark/sql/DataFrame.scala      | 44 +++++++++++++++++++
 1 file changed, 44 insertions(+)

diff --git a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
index 932a5e2bc5547..27496227f99d4 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/DataFrame.scala
@@ -1324,6 +1324,28 @@ class DataFrame private[sql](
     saveAsTable(tableName, source, mode, options.toMap)
   }
 
+  /**
+   * :: Experimental ::
+   * Creates a table at the given path from the the contents of this DataFrame
+   * based on a given data source, [[SaveMode]] specified by mode, a set of options, and a list of
+   * partition columns.
+   *
+   * Note that this currently only works with DataFrames that are created from a HiveContext as
+   * there is no notion of a persisted catalog in a standard SQL context.  Instead you can write
+   * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
+   * be the target of an `insertInto`.
+   * @group output
+   */
+  @Experimental
+  def saveAsTable(
+      tableName: String,
+      source: String,
+      mode: SaveMode,
+      options: java.util.Map[String, String],
+      partitionColumns: java.util.List[String]): Unit = {
+    ???
+  }
+
   /**
    * :: Experimental ::
    * (Scala-specific)
@@ -1357,6 +1379,28 @@ class DataFrame private[sql](
     sqlContext.executePlan(cmd).toRdd
   }
 
+  /**
+   * :: Experimental ::
+   * Creates a table at the given path from the the contents of this DataFrame
+   * based on a given data source, [[SaveMode]] specified by mode, a set of options, and a list of
+   * partition columns.
+   *
+   * Note that this currently only works with DataFrames that are created from a HiveContext as
+   * there is no notion of a persisted catalog in a standard SQL context.  Instead you can write
+   * an RDD out to a parquet file, and then register that file as a table.  This "table" can then
+   * be the target of an `insertInto`.
+   * @group output
+   */
+  @Experimental
+  def saveAsTable(
+      tableName: String,
+      source: String,
+      mode: SaveMode,
+      options: Map[String, String],
+      partitionColumns: Seq[String]): Unit = {
+    ???
+  }
+
   /**
    * :: Experimental ::
    * Saves the contents of this DataFrame to the given path,