[Streaming]Add streaming key features introduction

README.rst

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+.. image:: streaming/assets/infinite.svg
+   :target: streaming/assets/infinite.svg
+   :alt: mobius
+
+Mobius : Online Machine Learning.
+============
+`Mobius <https://tech.antfin.com/products/ARCMOBIUS>`_ is an AI infra platform including realtime computing and training.
+
+Ray Streaming
+=============
+
+Ray Streaming is a streaming data processing framework built on ray. It will be helpful for you to build machine learning jobs dealing with real-time data.
+
+Key Features
+------------
+
+
+#. 
+   **Cross Language**. Based on Ray's multi-language actor, Ray Streaming can also run in multiple
+   languages(only Python and Java is supported currently) with high efficiency. You can implement your
+   operator in different languages and run them in one job.
+
+#. 
+   **Single Node Failover**. We designed a special failover mechanism that only needs to rollback the
+   failed node it's own, in most cases, to recover the job. This will be a huge benefit if your job is
+   sensitive about failure recovery time. In other frameworks like Flink, instead, the entire job should
+   be restarted once a node has failure.
+
+#. 
+   **AutoScaling**. (Moved from internal in the future). Generate a new graph with different configurations in runtime without stopping job.
+
+#. 
+   **Fusion Training**. (Moved from internal in the future). Combine TensorFlow/Pytorch and streaming, then buiding an e2e online machine
+   learning pipeline.
+
+Examples
+--------
+
+Python
+^^^^^^
+
+.. code-block:: Python
+
+   import ray
+   from ray.streaming import StreamingContext
+
+   ctx = StreamingContext.Builder() \
+       .build()
+   ctx.read_text_file(__file__) \
+       .set_parallelism(1) \
+       .flat_map(lambda x: x.split()) \
+       .map(lambda x: (x, 1)) \
+       .key_by(lambda x: x[0]) \
+       .reduce(lambda old_value, new_value:
+               (old_value[0], old_value[1] + new_value[1])) \
+       .filter(lambda x: "ray" not in x) \
+       .sink(lambda x: print("result", x))
+   ctx.submit("word_count")
+
+Java
+^^^^
+
+.. code-block:: Java
+
+   StreamingContext context = StreamingContext.buildContext();
+   List<String> text = Collections.singletonList("hello world");
+   DataStreamSource.fromCollection(context, text)
+       .flatMap((FlatMapFunction<String, WordAndCount>) (value, collector) -> {
+           String[] records = value.split(" ");
+           for (String record : records) {
+               collector.collect(new WordAndCount(record, 1));
+           }
+       })
+       .filter(pair -> !pair.word.contains("world"))
+       .keyBy(pair -> pair.word)
+       .reduce((oldValue, newValue) ->
+               new WordAndCount(oldValue.word, oldValue.count + newValue.count))
+       .sink(result -> System.out.println("sink result=" + result));
+   context.execute("testWordCount");
+
+Use Java Operators in Python
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: Python
+
+   import ray
+   from ray.streaming import StreamingContext
+
+   ctx = StreamingContext.Builder().build()
+   ctx.from_values("a", "b", "c") \
+       .as_java_stream() \
+       .map("io.ray.streaming.runtime.demo.HybridStreamTest$Mapper1") \
+       .filter("io.ray.streaming.runtime.demo.HybridStreamTest$Filter1") \
+       .as_python_stream() \
+       .sink(lambda x: print("result", x))
+   ctx.submit("HybridStreamTest")
+
+Use Python Operators in Java
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. code-block:: Java
+
+   StreamingContext context = StreamingContext.buildContext();
+   DataStreamSource<String> streamSource =
+       DataStreamSource.fromCollection(context, Arrays.asList("a", "b", "c"));
+   streamSource
+       .map(x -> x + x)
+       .asPythonStream()
+       .map("ray.streaming.tests.test_hybrid_stream", "map_func1")
+       .filter("ray.streaming.tests.test_hybrid_stream", "filter_func1")
+       .asJavaStream()
+       .sink(value -> System.out.println("HybridStream sink=" + value));
+   context.execute("HybridStreamTestJob");
+
+
+
+Training
+-----------
+
+ To be published