Skip to main content

Python native implementation of the Spark RDD interface.

Project description


A native Python implementation of Spark’s RDD interface. The primary objective is not to have RDDs that are resilient and distributed, but to remove the dependency on the JVM and Hadoop. The focus is on having a lightweight and fast implementation for small datasets. It is a drop-in replacement for PySpark’s SparkContext and RDD.

Use case: you have a pipeline that processes 100k input documents and converts them to normalized features. They are used to train a local scikit-learn classifier. The preprocessing is perfect for a full Spark task. Now, you want to use this trained classifier in an API endpoint. You need the same pre-processing pipeline for a single document per API call. This does not have to be done in parallel, but there should be only a small overhead in initialization and preferably no dependency on the JVM. This is what pysparkling is for.


pip install pysparkling


  • Supports multiple URI schemes like s3n://, http:// and file://. Specify multiple files separated by comma. Resolves * and ? wildcards.
  • Handles .gz and .bz2 compressed files.
  • Parallelization via multiprocessing.Pool, concurrent.futures.ThreadPoolExecutor or any other Pool-like objects that have a map(func, iterable) method.
  • only dependencies: boto for AWS S3 and requests for http

The change log is in HISTORY.rst.


Word Count

from pysparkling import Context

counts = Context().textFile(
    lambda line: ''.join(ch if ch.isalnum() else ' ' for ch in line)
    lambda line: line.split(' ')
    lambda word: (word, 1)
    lambda a, b: a + b

which prints a long list of pairs of words and their counts. This and a few more advanced examples are demoed here.



  • aggregate(zeroValue, seqOp, combOp): aggregate value in partition with seqOp and combine with combOp
  • aggregateByKey(zeroValue, seqFunc, combFunc): aggregate by key
  • cache(): synonym for persist()
  • cartesian(other): cartesian product
  • coalesce(): do nothing
  • collect(): return the underlying list
  • count(): get length of internal list
  • countApprox(): same as count()
  • countByKey: input is list of pairs, returns a dictionary
  • countByValue: input is a list, returns a dictionary
  • context(): return the context
  • distinct(): returns a new RDD containing the distinct elements
  • filter(func): return new RDD filtered with func
  • first(): return first element
  • flatMap(func): return a new RDD of a flattened map
  • flatMapValues(func): return new RDD
  • fold(zeroValue, op): aggregate elements
  • foldByKey(zeroValue, op): aggregate elements by key
  • foreach(func): apply func to every element
  • foreachPartition(func): apply func to every partition
  • getNumPartitions(): number of partitions
  • getPartitions(): returns an iterator over the partitions
  • groupBy(func): group by the output of func
  • groupByKey(): group by key where the RDD is of type [(key, value), …]
  • histogram(buckets): buckets can be a list or an int
  • id(): currently just returns None
  • intersection(other): return a new RDD with the intersection
  • isCheckpointed(): returns False
  • join(other): join
  • keyBy(func): creates tuple in new RDD
  • keys(): returns the keys of tuples in new RDD
  • leftOuterJoin(other): left outer join
  • lookup(key): return list of values for this key
  • map(func): apply func to every element and return a new RDD
  • mapPartitions(func): apply f to entire partitions
  • mapValues(func): apply func to value in (key, value) pairs and return a new RDD
  • max(): get the maximum element
  • mean(): mean
  • min(): get the minimum element
  • name(): RDD’s name
  • persist(): caches outputs of previous operations (previous steps are still executed lazily)
  • pipe(command): pipe the elements through an external command line tool
  • reduce(): reduce
  • reduceByKey(): reduce by key and return the new RDD
  • repartition(numPartitions): repartition
  • rightOuterJoin(other): right outer join
  • sample(withReplacement, fraction, seed=None): sample from the RDD
  • sampleStdev(): sample standard deviation
  • sampleVariance(): sample variance
  • saveAsTextFile(path): save RDD as text file
  • stats(): return a StatCounter
  • stdev(): standard deviation
  • subtract(other): return a new RDD without the elements in other
  • sum(): sum
  • take(n): get the first n elements
  • takeSample(n): get n random samples
  • toLocalIterator(): get a local iterator
  • union(other): form union
  • variance(): variance
  • zip(other): other has to have the same length
  • zipWithUniqueId(): pairs each element with a unique index


  • __init__(pool=None, serializer=None, deserializer=None, data_serializer=None, data_deserializer=None): takes a pool object (an object that has a map() method, e.g. a multiprocessing.Pool) to parallelize methods. To support functions and lambda functions, specify custom serializers and deserializers, e.g. serializer=dill.dumps, deserializer=dill.loads.
  • broadcast(var): returns an instance of Broadcast() and it’s values are accessed with value.
  • newRddId(): incrementing number
  • textFile(filename): load every line of a text file into a RDD. filename can contain a comma separated list of many files, ? and * wildcards, file paths on S3 (s3n://bucket_name/filename.txt) and local file paths (relative/path/my_text.txt, /absolut/path/my_text.txt or file:///absolute/file/path.txt). If the filename points to a folder containing part* files, those are resolved.
  • version: the version of pysparkling


  • value: access the value it stores


The functionality provided by this module is used in Context.textFile() for reading and in RDD.saveAsTextFile() for writing.

Use environment variables AWS_SECRET_ACCESS_KEY and AWS_ACCESS_KEY_ID for auth and use file paths of the form s3n://bucket_name/filename.txt.

Infers .gz and .bz2 compressions from the file name.

  • File(file_name): file_name is either local, http, on S3 or …
    • [static] exists(path): check for existance of path
    • [static] resolve_filenames(expr): given a glob-like expression with * and ?, get a list of all matching filenames (either locally or on S3).
    • load(): return the contents as BytesIO
    • dump(stream): write the stream to the file
    • make_public(recursive=False): only for files on S3

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for pysparkling, version 0.2.19
Filename, size File type Python version Upload date Hashes
Filename, size pysparkling-0.2.19.tar.gz (17.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page