Pure Python implementation of the Spark RDD interface.
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
Example: 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. Assume 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[s3,hdfs,http]
- Supports multiple URI scheme: s3://, hdfs://, http:// and file://. 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
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.
A usual pysparkling session starts with either parallelizing a list or by reading data from a file using the methods Context.parallelize(my_list) or Context.textFile("path/to/textfile.txt"). These two methods return an RDD which can then be processed with the methods below.
A Context describes the setup. Instantiating a Context with the default arguments using Context() is the most lightweight setup. All data is just in the local thread and is never serialized or deserialized.
If you want to process the data in parallel, you can use the multiprocessing module. Given the limitations of the default pickle serializer, you can specify to serialize all methods with cloudpickle instead. For example, a common instantiation with multiprocessing looks like this:
c = Context( multiprocessing.Pool(4), serializer=cloudpickle.dumps, deserializer=pickle.loads, )
This assumes that your data is serializable with pickle which is generally faster. You can also specify a custom serializer/deserializer for data.
The functionality provided by this module is used in Context.textFile() for reading and in RDD.saveAsTextFile() for writing. You can use this submodule for writing files directly with File(filename).dump(some_data), File(filename).load() and File.exists(path) to read, write and check for existance of a file. All methods transparently handle http://, s3:// and file:// locations and compression/decompression of .gz and .bz2 files.
Use environment variables AWS_SECRET_ACCESS_KEY and AWS_ACCESS_KEY_ID for auth and use file paths of the form s3://bucket_name/filename.txt.
Fork the Github repository and apply your changes in a feature branch. To run pysparkling’s unit tests locally, install the package and all dependencies with pip install -e .[s3,hdfs,http,tests] and run the tests with nosetests. Don’t run python setup.py test as this will not execute the doctests. When all tests pass, create a Pull Request.
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