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Project Description


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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.


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( x: x**2).collect())


5 [1, 4, 9, 16, 25]

Methods Implemented


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()


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()


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()
Release History

Release History


This version

History Node

TODO: Figure out how to actually get changelog content.

Changelog content for this version goes here.

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Download Files

Download Files

TODO: Brief introduction on what you do with files - including link to relevant help section.

File Name & Checksum SHA256 Checksum Help Version File Type Upload Date
dummy_spark-0.0.1.tar.gz (10.0 kB) Copy SHA256 Checksum SHA256 Source Jul 21, 2016

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