[SPARK-14447][SQL] Speed up TungstenAggregate w/ keys using VectorizedHashMap

## What changes were proposed in this pull request?

This patch speeds up group-by aggregates by around 3-5x by leveraging an in-memory `AggregateHashMap` (please see https://github.com/apache/spark/pull/12161), an append-only aggregate hash map that can act as a 'cache' for extremely fast key-value lookups while evaluating aggregates (and fall back to the `BytesToBytesMap` if a given key isn't found).

Architecturally, it is backed by a power-of-2-sized array for index lookups and a columnar batch that stores the key-value pairs. The index lookups in the array rely on linear probing (with a small number of maximum tries) and use an inexpensive hash function which makes it really efficient for a majority of lookups. However, using linear probing and an inexpensive hash function also makes it less robust as compared to the `BytesToBytesMap` (especially for a large number of keys or even for certain distribution of keys) and requires us to fall back on the latter for correctness.

## How was this patch tested?

    Java HotSpot(TM) 64-Bit Server VM 1.8.0_73-b02 on Mac OS X 10.11.4
    Intel(R) Core(TM) i7-4960HQ CPU  2.60GHz
    Aggregate w keys:                   Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
    -------------------------------------------------------------------------------------------
    codegen = F                              2124 / 2204          9.9         101.3       1.0X
    codegen = T hashmap = F                  1198 / 1364         17.5          57.1       1.8X
    codegen = T hashmap = T                   369 /  600         56.8          17.6       5.8X

Author: Sameer Agarwal <sameer@databricks.com>

Closes #12345 from sameeragarwal/tungsten-aggregate-integration.