[SPARK-2119][SQL] Improved Parquet performance when reading off S3

JIRA issue: [SPARK-2119](https://issues.apache.org/jira/browse/SPARK-2119)

Essentially this PR fixed three issues to gain much better performance when reading large Parquet file off S3.

1. When reading the schema, fetching Parquet metadata from a part-file rather than the `_metadata` file

   The `_metadata` file contains metadata of all row groups, and can be very large if there are many row groups. Since schema information and row group metadata are coupled within a single Thrift object, we have to read the whole `_metadata` to fetch the schema. On the other hand, schema is replicated among footers of all part-files, which are fairly small.

1. Only add the root directory of the Parquet file rather than all the part-files to input paths

   HDFS API can automatically filter out all hidden files and underscore files (`_SUCCESS` & `_metadata`), there's no need to filter out all part-files and add them individually to input paths. What make it much worse is that, `FileInputFormat.listStatus()` calls `FileSystem.globStatus()` on each individual input path sequentially, each results a blocking remote S3 HTTP request.

1. Worked around [PARQUET-16](https://issues.apache.org/jira/browse/PARQUET-16)

   Essentially PARQUET-16 is similar to the above issue, and results lots of sequential `FileSystem.getFileStatus()` calls, which are further translated into a bunch of remote S3 HTTP requests.

   `FilteringParquetRowInputFormat` should be cleaned up once PARQUET-16 is fixed.

Below is the micro benchmark result. The dataset used is a S3 Parquet file consists of 3,793 partitions, about 110MB per partition in average. The benchmark is done with a 9-node AWS cluster.

- Creating a Parquet `SchemaRDD` (Parquet schema is fetched)

  ```scala
  val tweets = parquetFile(uri)
  ```

  - Before: 17.80s
  - After: 8.61s

- Fetching partition information

  ```scala
  tweets.getPartitions
  ```

  - Before: 700.87s
  - After: 21.47s

- Counting the whole file (both steps above are executed altogether)

  ```scala
  parquetFile(uri).count()
  ```

  - Before: ??? (haven't test yet)
  - After: 53.26s

Yes, we are making this assumption, will add a comment here. (And checking schema consistency can be potentially inefficient for large Parquet file with lots of row groups.)