Metadata-Version: 1.2
Name: streamz-latest
Version: 0.3.0
Summary: Mrocklin streamz library. It's the latest version 
Home-page: http://github.com/mrocklin/streamz/
Maintainer: Matthew Rocklin
Maintainer-email: mrocklin@gmail.com
License: BSD
Description: Streamz
        =======
        
        Streamz helps you build pipelines to manage continuous streams of data.  It is
        simple to use in simple cases, but also supports complex pipelines that involve
        branching, joining, flow control, feedback, back pressure, and so on.
        
        Optionally, Streamz can also work with Pandas dataframes to provide sensible
        streaming operations on continuous tabular data.
        
        To learn more about how to use streams, visit :doc:`Core documentation <core>`.
        
        
        Motivation
        ----------
        
        Continuous data streams arise in many applications like the following:
        
        1.  Log processing from web servers
        2.  Scientific instrument data like telemetry or image processing pipelines
        3.  Financial time series
        4.  Machine learning pipelines for real-time and on-line learning
        5.  ...
        
        Sometimes these pipelines are very simple, with a linear sequence of processing
        steps:
        
        .. image:: docs/source/images/simple.svg
           :alt: a simple streamz pipeline
        
        And sometimes these pipelines are more complex, involving branching, look-back
        periods, feedback into earlier stages, and more.
        
        .. image:: docs/source/images/complex.svg
           :alt: a more complex streamz pipeline
        
        Streamz endeavors to be simple in simple cases, while also being powerful
        enough to let you define custom and powerful pipelines for your application.
        
        Why not Python generator expressions?
        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
        
        Python users often manage continuous sequences of data with iterators or
        generator expressions.
        
        .. code-block:: python
        
            def fib():
                a, b = 0, 1
                while True:
                    yield a
                    a, b = b, a + b
        
            sequence = (f(n) for n in fib())
        
        However iterators become challenging when you want to fork them or control the
        flow of data.  Typically people rely on tools like ``itertools.tee``, and
        ``zip``.
        
        .. code-block:: python
        
            x1, x2 = itertools.tee(x, 2)
            y1 = map(f, x1)
            y2 = map(g, x2)
        
        However this quickly become cumbersome, especially when building complex
        pipelines.
        
        
        Related Work
        ------------
        
        Streamz is similar to reactive
        programming systems like `RxPY <https://github.com/ReactiveX/RxPY>`_ or big
        data streaming systems like `Apache Flink <https://flink.apache.org/>`_,
        `Apache Beam <https://beam.apache.org/get-started/quickstart-py/>`_ or
        `Apache Spark Streaming <https://beam.apache.org/get-started/quickstart-py/>`_.
Keywords: streams
Platform: UNKNOWN
