SPARK-1251 Support for optimizing and executing structured queries

This pull request adds support to Spark for working with structured data using a simple SQL dialect, HiveQL and a Scala Query DSL.

*This is being contributed as a new __alpha component__ to Spark and does not modify Spark core or other components.*

The code is broken into three primary components:
 - Catalyst (sql/catalyst) - An implementation-agnostic framework for manipulating trees of relational operators and expressions.  
 - Execution (sql/core) - A query planner / execution engine for translating Catalyst’s logical query plans into Spark RDDs.  This component also includes a new public interface, SqlContext, that allows users to execute SQL or structured scala queries against existing RDDs and Parquet files.
 - Hive Metastore Support (sql/hive) - An extension of SqlContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes.  There are also wrappers that allows users to run queries that include Hive UDFs, UDAFs, and UDTFs.

A more complete design of this new component can be found in [the associated JIRA](https://spark-project.atlassian.net/browse/SPARK-1251).

[An updated version of the Spark documentation, including API Docs for all three sub-components,](http://people.apache.org/~pwendell/catalyst-docs/sql-programming-guide.html) is also available for review.

With this PR comes support for inferring the schema of existing RDDs that contain case classes.  Using this information, developers can now express structured queries that are automatically compiled into RDD operations.

```scala
// Define the schema using a case class.
case class Person(name: String, age: Int)
val people: RDD[Person] =
  sc.textFile("people.txt").map(_.split(",")).map(p => Person(p(0), p(1).toInt))

// The following is the same as 'SELECT name FROM people WHERE age >= 10 && age <= 19'
val teenagers = people.where('age >= 10).where('age <= 19).select('name).toRdd
```

RDDs can also be registered as Tables, allowing SQL queries to be written over them.
```scala
people.registerAsTable("people")
val teenagers = sql("SELECT name FROM people WHERE age >= 10 && age <= 19")
```

The results of queries are themselves RDDs and support standard RDD operations:
```scala
teenagers.map(t => "Name: " + t(0)).collect().foreach(println)
```

Finally, with the optional Hive support, users can read and write data located in existing Apache Hive deployments using HiveQL.
```scala
sql("CREATE TABLE IF NOT EXISTS src (key INT, value STRING)")
sql("LOAD DATA LOCAL INPATH 'src/main/resources/kv1.txt' INTO TABLE src")

// Queries are expressed in HiveQL
sql("SELECT key, value FROM src").collect().foreach(println)
```

## Relationship to Shark

Unlike Shark, Spark SQL does not act as a drop in replacement for Hive or the HiveServer. Instead this new feature is intended to make it easier for Spark developers to run queries over structured data, using either SQL or the query DSL. After this sub-project graduates from Alpha status it will likely become a new optimizer/backend for the Shark project.