[SPARK-13353][SQL] fast serialization for collecting DataFrame/Dataset

## What changes were proposed in this pull request?

When we call DataFrame/Dataset.collect(), Java serializer (or Kryo Serializer) will be used to serialize the UnsafeRows in executor, then deserialize them into UnsafeRows in driver. Java serializer (and Kyro serializer) are slow on millions rows, because they try to find out the same rows, but usually there is no same rows.

This PR will serialize the UnsafeRows as byte array by packing them together, then Java serializer (or Kyro serializer) serialize the bytes very fast (there are fewer blocks and byte array are not compared by content).

The UnsafeRow format is highly compressible, the serialized bytes are also compressed (configurable by spark.io.compression.codec).

## How was this patch tested?

Existing unit tests.

Add a benchmark for collect, before this patch:
```
Intel(R) Core(TM) i7-4558U CPU  2.80GHz
collect:                        Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------
collect 1 million                      3991 / 4311          0.3        3805.7       1.0X
collect 2 millions                  10083 / 10637          0.1        9616.0       0.4X
collect 4 millions                  29551 / 30072          0.0       28182.3       0.1X
```

```
Intel(R) Core(TM) i7-4558U CPU  2.80GHz
collect:                        Best/Avg Time(ms)    Rate(M/s)   Per Row(ns)   Relative
-------------------------------------------------------------------------------------------
collect 1 million                        775 / 1170          1.4         738.9       1.0X
collect 2 millions                     1153 / 1758          0.9        1099.3       0.7X
collect 4 millions                     4451 / 5124          0.2        4244.9       0.2X
```

We can see about 5-7X speedup.

Author: Davies Liu <davies@databricks.com>

Closes #11664 from davies/serialize_row.