karld 0.2.10

Doing some data things in a memory efficient manner

How To Data

1. Split data.

2. Create a generator that will take the data as an iterator, yielding key,value pairs.

3. Sort each list of key/value pairs by the key.

4. Use heap to merge lists of key/value pairs by the key.

5. Group key/value pairs by the key.

6. Reduce each key grouped values to one value yielding a single key/value pair.

In lieu of a key, you may use a key function as long as it produces the same key throughout the map-sort-merge-group phases.

Split data

Use split_file to split up your data files or use split_csv_file to split up csv files which may have multi-line fields to ensure they are not broken up.:

import os

import karld

big_file_names = [
    "bigfile1.csv",
    "bigfile2.csv",
    "bigfile3.csv"
]

data_path = os.path.join('path','to','data', 'root')


def main():
    for filename in big_file_names:
        # Name the directory to write the split files into based
        # on the name of the file.
        out_dir = os.path.join(data_path, 'split_data', filename.replace('.csv', ''))

        # Split the file, with a default max_lines=200000 per shard of the file.
        karld.io.split_csv_file(os.path.join(data_path, filename), out_dir)


if __name__ == "__main__":
    main()

When you’re generating data and want to shard it out to files based on quantity, use one of the split output functions such as split_file_output_csv, split_file_output or split_file_output_json:

import os
import pathlib

import karld


def main():
    """
    Python 2 version
    """

    items = (str(x) + os.linesep for x in range(2000))

    out_dir = pathlib.Path('shgen')
    karld.io.ensure_dir(str(out_dir))

    karld.io.split_file_output('big_data', items, str(out_dir))


if __name__ == "__main__":
    main()

CSV serializable data:

import pathlib

import karld


def main():
    """
    From a source of data, shard it to csv files.
    """
    if karld.is_py3():
        third = chr
    else:
        third = unichr

    # Your data source
    items = ((x, x + 1, third(x + 10)) for x in range(2000))

    out_dir = pathlib.Path('shard_out_csv')

    karld.io.ensure_dir(str(out_dir))

    karld.io.split_file_output_csv('big_data.csv', items, str(out_dir))


if __name__ == "__main__":
    main()

Rows of json serializable data:

import pathlib

import karld


def main():
    """
    From a source of data, shard it to csv files.
    """
    if karld.is_py3():
        third = chr
    else:
        third = unichr

    # Your data source
    items = ((x, x + 1, third(x + 10)) for x in range(2000))

    out_dir = pathlib.Path('shard_out_json')

    karld.io.ensure_dir(str(out_dir))

    karld.io.split_file_output_json('big_data.json', items, str(out_dir))


if __name__ == "__main__":
    main()

Documentation

Read the docs: http://karld.readthedocs.org/en/latest/

Expanded Getting Started at http://karld.readthedocs.org/en/latest/getting-started.html.

More examples are documented at http://karld.readthedocs.org/en/latest/source/example.html. View the source of the example files, for examples…

Contributing:

Submit any issues or questions here: https://github.com/johnwlockwood/karl_data/issues.

Make pull requests to development branch of

https://github.com/johnwlockwood/karl_data.

Documentation is written in reStructuredText and currently uses the

Sphinx style for field lists http://sphinx-doc.org/domains.html#info-field-lists

Check out closed pull requests to see the flow of development, as almost every change to master is done via a pull request on GitHub. Code Reviews are welcome, even on merged Pull Requests. Feel free to ask questions about the code.