[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.

3 files changed, 4 insertions, 4 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
index 8159ffbe2d..173633ce05 100644
--- a/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
+++ b/examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java
@@ -99,7 +99,7 @@ public class JavaSparkSQL {
     // Read in the parquet file created above.
     // Parquet files are self-describing so the schema is preserved.
     // The result of loading a parquet file is also a DataFrame.
-    DataFrame parquetFile = sqlContext.parquetFile("people.parquet");
+    DataFrame parquetFile = sqlContext.read().parquet("people.parquet");
 
     //Parquet files can also be registered as tables and then used in SQL statements.
     parquetFile.registerTempTable("parquetFile");
@@ -120,7 +120,7 @@ public class JavaSparkSQL {
     // The path can be either a single text file or a directory storing text files.
     String path = "examples/src/main/resources/people.json";
     // Create a DataFrame from the file(s) pointed by path
-    DataFrame peopleFromJsonFile = sqlContext.jsonFile(path);
+    DataFrame peopleFromJsonFile = sqlContext.read().json(path);
 
     // Because the schema of a JSON dataset is automatically inferred, to write queries,
     // it is better to take a look at what is the schema.
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)