[SPARK-21153] Use project instead of expand in tumbling windows

## What changes were proposed in this pull request?

Time windowing in Spark currently performs an Expand + Filter, because there is no way to guarantee the amount of windows a timestamp will fall in, in the general case. However, for tumbling windows, a record is guaranteed to fall into a single bucket. In this case, doubling the number of records with Expand is wasteful, and can be improved by using a simple Projection instead.

Benchmarks show that we get an order of magnitude performance improvement after this patch.

## How was this patch tested?

Existing unit tests. Benchmarked using the following code:

```scala
import org.apache.spark.sql.functions._

spark.time {
  spark.range(numRecords)
    .select(from_unixtime((current_timestamp().cast("long") * 1000 + 'id / 1000) / 1000) as 'time)
    .select(window('time, "10 seconds"))
    .count()
}
```

Setup:
 - 1 c3.2xlarge worker (8 cores)

![image](https://user-images.githubusercontent.com/5243515/27348748-ed991b84-55a9-11e7-8f8b-6e7abc524417.png)

1 B rows ran in 287 seconds after this optimization. I didn't wait for it to finish without the optimization. Shows about 5x improvement for large number of records.

Author: Burak Yavuz <brkyvz@gmail.com>

Closes #18364 from brkyvz/opt-tumble.