Metadata-Version: 2.1
Name: pysparkling
Version: 0.6.2
Summary: Pure Python implementation of the Spark RDD interface.
Home-page: https://github.com/svenkreiss/pysparkling
Author: pysparkling contributors
License: MIT
Description: .. image:: https://raw.githubusercontent.com/svenkreiss/pysparkling/master/logo/logo-w100.png
            :target: https://github.com/svenkreiss/pysparkling
        
        pysparkling
        ===========
        
        **Pysparkling** provides a faster, more responsive way to develop programs
        for PySpark. It enables code intended for Spark applications to execute
        entirely in Python, without incurring the overhead of initializing and
        passing data through the JVM and Hadoop. The focus is on having a lightweight
        and fast implementation for small datasets at the expense of some data
        resilience features and some parallel processing features.
        
        **How does it work?** To switch execution of a script from PySpark to pysparkling,
        have the code initialize a pysparkling Context instead of a SparkContext, and
        use the pysparkling Context to set up your RDDs. The beauty is you don't have
        to change a single line of code after the Context initialization, because
        pysparkling's API is (almost) exactly the same as PySpark's. Since it's so easy
        to switch between PySpark and pysparkling, you can choose the right tool for your
        use case.
        
        **When would I use it?** Say you are writing a Spark application because you
        need robust computation on huge datasets, but you also want the same application
        to provide fast answers on a small dataset. You're finding Spark is not responsive
        enough for your needs, but you don't want to rewrite an entire separate application
        for the *small-answers-fast* problem. You'd rather reuse your Spark code but somehow
        get it to run fast. Pysparkling bypasses the stuff that causes Spark's long startup
        times and less responsive feel.
        
        Here are a few areas where pysparkling excels:
        
        * Small to medium-scale exploratory data analysis
        * Application prototyping
        * Low-latency web deployments
        * Unit tests
        
        
        Install
        =======
        
        .. code-block:: bash
        
            python3 -m pip install "pysparkling[s3,hdfs,http,streaming]"
        
        
        `Documentation <https://pysparkling.trivial.io>`_:
        
        .. image:: https://raw.githubusercontent.com/svenkreiss/pysparkling/master/docs/readthedocs.png
           :target: https://pysparkling.trivial.io
        
        
        Other links:
        `Github <https://github.com/svenkreiss/pysparkling>`_,
        |pypi-badge|, |test-badge|, |docs-badge|
        
        .. |pypi-badge| image:: https://badge.fury.io/py/pysparkling.svg
           :target: https://pypi.python.org/pypi/pysparkling/
        .. |test-badge| image:: https://github.com/svenkreiss/pysparkling/workflows/Tests/badge.svg
           :target: https://github.com/svenkreiss/pysparkling/actions?query=workflow%3ATests
        .. |docs-badge| image:: https://readthedocs.org/projects/pysparkling/badge/?version=latest
           :target: https://pysparkling.readthedocs.io/en/latest/?badge=latest
           :alt: Documentation Status
        
        
        Features
        ========
        
        * Supports URI schemes ``s3://``, ``hdfs://``, ``gs://``, ``http://`` and ``file://``
          for Amazon S3, HDFS, Google Storage, web and local file access.
          Specify multiple files separated by comma.
          Resolves ``*`` and ``?`` wildcards.
        * Handles ``.gz``, ``.zip``, ``.lzma``, ``.xz``, ``.bz2``, ``.tar``,
          ``.tar.gz`` and ``.tar.bz2`` compressed files.
          Supports reading of ``.7z`` files.
        * Parallelization via ``multiprocessing.Pool``,
          ``concurrent.futures.ThreadPoolExecutor`` or any other Pool-like
          objects that have a ``map(func, iterable)`` method.
        * Plain pysparkling does not have any dependencies (use ``pip install pysparkling``).
          Some file access methods have optional dependencies:
          ``boto`` for AWS S3, ``requests`` for http, ``hdfs`` for hdfs
        
        
        Examples
        ========
        
        Some demos are in the notebooks
        `docs/demo.ipynb <https://github.com/svenkreiss/pysparkling/blob/master/docs/demo.ipynb>`_
        and
        `docs/iris.ipynb <https://github.com/svenkreiss/pysparkling/blob/master/docs/iris.ipynb>`_
        .
        
        **Word Count**
        
        .. code-block:: python
        
            from pysparkling import Context
        
            counts = (
                Context()
                .textFile('README.rst')
                .map(lambda line: ''.join(ch if ch.isalnum() else ' ' for ch in line))
                .flatMap(lambda line: line.split(' '))
                .map(lambda word: (word, 1))
                .reduceByKey(lambda a, b: a + b)
            )
            print(counts.collect())
        
        which prints a long list of pairs of words and their counts.
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: Implementation :: PyPy
Provides-Extra: hdfs
Provides-Extra: http
Provides-Extra: performance
Provides-Extra: s3
Provides-Extra: streaming
Provides-Extra: sql
Provides-Extra: tests
Provides-Extra: scripts
