Metadata-Version: 1.1
Name: dummyrdd
Version: 0.0.3
Summary: A pure python mocked version of pyspark's rdd class
Home-page: https://github.com/wdm0006/dummyrdd
Author: Will McGinnis
Author-email: will@pedalwrencher.com
License: MIT
Download-URL: https://github.com/wdm0006/dummyrdd/tarball/0.0.3
Description: DummyRDD
        ========
        
        [![Coverage Status](https://coveralls.io/repos/github/wdm0006/DummyRDD/badge.svg?branch=master)](https://coveralls.io/github/wdm0006/DummyRDD?branch=master)
        [![Build Status](https://travis-ci.org/wdm0006/DummyRDD.svg?branch=master)](https://travis-ci.org/wdm0006/DummyRDD)
        
        Contributors
        ------------
        
         * [Henrique Souza](https://github.com/htssouza)
         * [Will McGinnis](https://gitbhub.com/wdm0006)
         
        Overview
        --------
        
        A test class that walks like and RDD, talks like an RDD but is just a list.
        
        Contains 3 primary classes:
        
         * SparkConf
         * SparkContext
         * RDD
         
        All of which implement the exact same API as the real spark methods, but use a simple
        python list as the actual datastore.  Many functions such as the Hadoop API, partitioning, complex
        operations, and other things are not implemented.  See below for detailed list of implemented functions and
        their caveats. 
        
        Note that for now this is experimental, and may later be useful for testing or development, but anything
        developed using this should always be checked on real spark to make sure that things actually work there. Because
        none of the code is actually distributed in this environment, some things will behave differently.
        
        It is intended that this library can be used as a drop in replacement for a real spark context, without erroring out
        but maybe not actually doing anything (in the case of irrelevant configuration options, for example).
        
        Currently there is no support for the dataframe api, or for that matter most features of anything, very much
        still a work in progress.
        
        Example
        -------
        
        A quick example:
        
            from dummy_spark import SparkContext, SparkConf
            
            sconf = SparkConf()
            sc = SparkContext(master='', conf=sconf)
            rdd = sc.parallelize([1, 2, 3, 4, 5])
            
            print(rdd.count())
            print(rdd.map(lambda x: x**2).collect())
           
        yields:
            
            5
            [1, 4, 9, 16, 25]
        
        
        Methods Implemented
        ===================
        
        SparkConf
        ---------
        
        SparkConf has everything implemented, but nothing is actually ever set.  There are no real configuration settings for 
        the dummy version, so the object simply contains a dictionary of configuration parameters. Implemented functions are therefore:
        
         * \_\_init\_\_()
         * contains()
         * get()
         * getAll()
         * set()
         * setAll()
         * setAppName()
         * setExecutorEnv()
         * setIfMissing()
         * setMaster()
         * setSparkHome()
         * toDebugString()
        
        SparkContext
        ------------
        
        Implemented functions are:
        
         * \_\_init\_\_()
         * \_\_enter\_\_()
         * \_\_exit\_\_()
         * defaultMinPartitions()
         * defaultParallelism()
         * emptyRDD()
         * parallelize()
         * NewAPIHadoopRDD() (only for elasticsearch via elasticsearch-py)
         * range()
         * startTime()
         * stop()
         * textFile() (including from s3 via tinys3)
         * version()
        
        RDD
        ---
        
        Implemented functions are:
        
         * \_\_init\_\_()
         * \_\_add\_\_()
         * \_\_repr\_\_()
         * cache()
         * cartesian()
         * checkpoint()
         * cogroup()
         * collect()
         * context()
         * count()
         * countApprox()
         * countApproxDistinct()
         * distinct()
         * filter()
         * first()
         * flatMap()
         * flatMapValues()
         * foreach()
         * foreachPartition()
         * getNumPartitions()
         * glom()
         * groupBy()
         * groupByKey()
         * id()
         * intersection()
         * isEmpty()
         * lookup()
         * map()
         * mapPartitions()
         * mapValues()
         * max()
         * mean()
         * meanApprox()
         * min()
         * name()
         * persist()
         * reduceByKey()
         * repartitionAndSortWithinPartitions()
         * sample()
         * setName()
         * sortBy()
         * sortByKey()
         * sum()
         * take()
         * takeSample()
         * toLocalIterator()
         * union()
         * zip()
         * zipWithIndex()
        
Keywords: pyspark spark testing
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Programming Language :: Python :: 3
