[SPARK-10820][SQL] Support for the continuous execution of structured queries

This is a follow up to 9aadcffa that extends Spark SQL to allow users to _repeatedly_ optimize and execute structured queries.  A `ContinuousQuery` can be expressed using SQL, DataFrames or Datasets.  The purpose of this PR is only to add some initial infrastructure which will be extended in subsequent PRs.

## User-facing API

- `sqlContext.streamFrom` and `df.streamTo` return builder objects that are analogous to the `read/write` interfaces already available to executing queries in a batch-oriented fashion.
- `ContinuousQuery` provides an interface for interacting with a query that is currently executing in the background.

## Internal Interfaces
 - `StreamExecution` - executes streaming queries in micro-batches

The following are currently internal, but public APIs will be provided in a future release.
 - `Source` - an interface for providers of continually arriving data.  A source must have a notion of an `Offset` that monotonically tracks what data has arrived.  For fault tolerance, a source must be able to replay data given a start offset.
 - `Sink` - an interface that accepts the results of a continuously executing query.  Also responsible for tracking the offset that should be resumed from in the case of a failure.

## Testing
 - `MemoryStream` and `MemorySink` - simple implementations of source and sink that keep all data in memory and have methods for simulating durability failures
 - `StreamTest` - a framework for performing actions and checking invariants on a continuous query

Author: Michael Armbrust <michael@databricks.com>
Author: Tathagata Das <tathagata.das1565@gmail.com>
Author: Josh Rosen <rosenville@gmail.com>

Closes #11006 from marmbrus/structured-streaming.