[SPARK-7654][SQL] DataFrameReader and DataFrameWriter for input/output API

This patch introduces DataFrameWriter and DataFrameReader.

DataFrameReader interface, accessible through SQLContext.read, contains methods that create DataFrames. These methods used to reside in SQLContext. Example usage:
```scala
sqlContext.read.json("...")
sqlContext.read.parquet("...")
```

DataFrameWriter interface, accessible through DataFrame.write, implements a builder pattern to avoid the proliferation of options in writing DataFrame out. It currently implements:
- mode
- format (e.g. "parquet", "json")
- options (generic options passed down into data sources)
- partitionBy (partitioning columns)
Example usage:
```scala
df.write.mode("append").format("json").partitionBy("date").saveAsTable("myJsonTable")
```

TODO:

- [ ] Documentation update
- [ ] Move JDBC into reader / writer?
- [ ] Deprecate the old interfaces
- [ ] Move the generic load interface into reader.
- [ ] Update example code and documentation

Author: Reynold Xin <rxin@databricks.com>

Closes #6175 from rxin/reader-writer and squashes the following commits:

b146c95 [Reynold Xin] Deprecation of old APIs.
bd8abdf [Reynold Xin] Fixed merge conflict.
26abea2 [Reynold Xin] Added general load methods.
244fbec [Reynold Xin] Added equivalent to example.
4f15d92 [Reynold Xin] Added documentation for partitionBy.
7e91611 [Reynold Xin] [SPARK-7654][SQL] DataFrameReader and DataFrameWriter for input/output API.

2 files changed, 2 insertions, 2 deletions

diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
index e943d6c889..c95cca7d65 100644
--- a/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
@@ -106,7 +106,7 @@ object DatasetExample {
     df.saveAsParquetFile(outputDir)
 
     println(s"Loading Parquet file with UDT from $outputDir.")
-    val newDataset = sqlContext.parquetFile(outputDir)
+    val newDataset = sqlContext.read.parquet(outputDir)
 
     println(s"Schema from Parquet: ${newDataset.schema.prettyJson}")
     val newFeatures = newDataset.select("features").map { case Row(v: Vector) => v }
diff --git a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
index 6331d1c006..acc89199d5 100644
--- a/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
+++ b/examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
@@ -61,7 +61,7 @@ object RDDRelation {
     df.saveAsParquetFile("pair.parquet")
 
     // Read in parquet file.  Parquet files are self-describing so the schmema is preserved.
-    val parquetFile = sqlContext.parquetFile("pair.parquet")
+    val parquetFile = sqlContext.read.parquet("pair.parquet")
 
     // Queries can be run using the DSL on parequet files just like the original RDD.
     parquetFile.where($"key" === 1).select($"value".as("a")).collect().foreach(println)