Package includes computational graph implementation with examples. Library supports Map / Reduce semantics and requires constant memory to process any size of input data.

To get started clone repo and print command

pip install -e compgraph --force-reinstall

Example of using graph (graph methods defined in compgraph/graph.py) for calculating number of words in documents

graph: Graph = Graph.graph_from_iter('docs') \
        .map(operations.FilterPunctuation(text_column)) \
        .map(operations.LowerCase(text_column)) \
        .map(operations.Split(text_column)) \
        .sort([text_column]) \
        .reduce(operations.Count(count_column), [text_column]) \
        .sort([count_column, text_column])

documents: list[TRow] = [
    {'doc_id': 1, 'text': 'text 1'},
    {'doc_id': 2, 'text': 'text 2'}
]

assert sorted(list(graph.run(docs=lambda: iter(documents)))) == [
    {'count': 1, 'text': '1'}
    {'count': 1, 'text': '2'},
    {'count': 2, 'text': 'text'},
]

Note1: It should be stated that generators supposed to be renewable, because graph structure in case of sorting and joining is nonlinear.

Note2: Before applying join and reduce methods, all involved tables have to be sorted by appropriate keys.

For a correct compilation of typings, Python 3.10 and above versions are recommended.

Library requires installed click package to provide correct work of examples. All requirements can be installed using

pip install -r requirements.txt

As mentioned above, some graphs are already implemented in compgraph/algorithms.py. Besides, in examples are located python scripts, which allows to use graphs by typing

python3 run_word_count input_filename output_filename

For run_maps.py there are two input files.

All functions and classes have doc strings and typing to simpler understanding. To check codestyle was applied

mypy compgraph
flake8 compgraph