This changes window operator to use the new partitioned state
abstraction for keeping window contents instead of custom internal
state and the checkpointed interface.

For now, timers are still kept as custom checkpointed state, however.

WindowOperator now expects a StateIdentifier for MergingState, this can
either be for ReducingState or ListState but WindowOperator is agnostic
to the type of State. Also the signature of WindowFunction is changed to
include the type of intermediate input. For example, if a ReducingState
is used the input of the WindowFunction is T (where T is the input
type). If using a ListState the input of the WindowFunction would be of
type Iterable[T].

Add new state types ValueState, ListState and ReducingState, where
ListState and ReducingState derive from interface MergingState.

ValueState behaves exactly the same as OperatorState. MergingState is a
stateful list to which elements can be added and for which the elements
that it contains can be obtained. If using a ListState the list of
elements is actually kept, for a ReducingState a reduce function is used
to combine all added elements into one. To create a ValueState the user
passes a ValueStateIdentifier to
StreamingRuntimeContext.getPartitionedState() while they would pass a
ListStateIdentifier or ReducingStateIdentifier for the other state
types.

This change is necessary to give the system more information about the
nature of the operator state. We want this to be able to do incremental
snapshots. This would not be possible, for example, if the user had a
List as a state. Inside OperatorState this list would be opaque and
Flink could not create good incremental snapshots.

This also refactors the StateBackend. Before, the logic for partitioned
state was spread out over StreamingRuntimeContext,
AbstractStreamOperator and StateBackend. Now it is consolidated in
StateBackend.

This also adds support for partitioned state in two-input operators.

This closes #1582, #1576.

Old guides are redirected to the new pages.

The LegacyFetcher used the given KeyedDeserializationSchema across multiple threads even though
it is not thread-safe. This commit fixes the problem by cloning the KeyedDeserializationSchema
before giving it to the SimpleConsumerThread.

Add clone method for Java serializable objects to InstantiationUtil

This closes #1577.

[FLINK-3314] [streaming] Fix case where early cancel messages do not properly cancel a stream operator.

  - Clean up generics
  - Clean and safe disposal of initialized resources
  - Add names to asynchronous materialization threads
  - Fix concurrent modification of  materialization threads set

This deactivates the validation of results, which is not reliably possible under the current model (timeout on feedback).
This test for now only checks that the job executes properly.

Also adds proper logging property files for the examples projects.

This closes #1573

Implements NFA using the SharedBuffer

Implements NFACompiler to compile a Pattern into a NFA

Add CEP operator

Makes NFA and SharedBuffer serializable

Add serializability support to SharedBuffer and NFA

Add keyed cep pattern operator

Adds CEP documentation

Adds online documentation for the CEP library

Copies sequence events before giving them to the UDF

Fix correct scala type suffixes

This closes #1557.

This closes #1514

[hotfix] Remove 'ByteArrayInputView' and replace deserialization in TypeInformationSerializationSchema with more efficient reusable buffers.

The methods snapshotState and notifyCheckpointComplete are always
mutually exclusive. The RMQSource relies on this but the test makes a
false assumption when it calls those two methods at the same time.

This closes #1569.

This reverts commit 6b01a890. The
introduced locks are not necessary. The checkpointing test case only
needs to be adapted to the checkpointing runtime behavior.

The auto-magic for Joda Time was limited to very few classes. It was intransparent what
cases would be handled.

Adds message passing between Hadoop's async resource manager client and
the YarnJobManager actor.

This closes #1561.

This is needed for streaming library features, is identical to the batch API.

Closes #1480

Henceforth, we shall only use the term lower in the context of timestamps
to denote that an event has occurred before another event.

This closes #1534