[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.