Metadata-Version: 2.1
Name: pynock
Version: 1.0.0
Summary: Examples for low-level Parquet read/write in Python
Home-page: https://github.com/DerwenAI/pynock
Author: Paco Nathan
Author-email: paco@derwen.ai
License: MIT
Description: # pynock
        
        This library `pynock` provides Examples for working with low-level
        Parquet read/write efficiently in Python.
        
        Our intent is to serialize graphs which align the data representations
        required for multiple areas of popular graph technologies:
        
          * semantic graphs (e.g., W3C)
          * labeled property graphs (e.g., openCypher)
          * probabilistic graphs (e.g., PSL)
          * edge lists (e.g., NetworkX)
        
        This approach also supports distributed partitions based on Parquet
        which can scale to very large (+1 T node) graphs.
        
        For details about the formatting required in Parquet files, see the
        [`FORMAT.md`](https://github.com/DerwenAI/pynock/blob/main/FORMAT.md)
        page.
        
        
        ## Caveats
        
        Note that the `pynock` library does not provide any support for graph
        computation or querying, merely for manipulating and validating
        serialization formats.
        
        Our intent is to provide examples where others from the broader open
        source developer community can help troubleshoot edge cases in
        Parquet.
        
        
        ## Dependencies
        
        This code has been tested and validated using Python 3.8, and we make
        no guarantees regarding correct behaviors on other versions.
        
        The Parquet file formats depend on Arrow 5.0.x or later.
        
        For the Python dependencies, see the `requirements.txt` file.
        
        
        ## Set up
        
        To install via PIP:
        
        ```
        python3 -m pip install -U pynock
        ```
        
        To set up this library locally:
        
        ```
        python3 -m venv venv
        source venv/bin/activate
        
        python3 -m pip install -U pip wheel
        python3 -m pip install -r requirements.txt
        ```
        
        ## Usage via CLI
        
        To run examples from CLI:
        
        ```
        python3 example.py load-parq --file dat/recipes.parq --debug
        ```
        
        ```
        python3 example.py load-rdf --file dat/tiny.ttl --save-cvs foo.cvs
        ```
        
        For further information:
        
        ```
        python3 example.py --help
        ```
        
        ## Usage programmatically in Python
        
        To construct a partition file programmatically, see the sample code
        [`tiny.py`](https://github.com/DerwenAI/pynock/blob/main/tiny.py)
        which builds the minimal recipe example as an RDF graph.
        
        
        ## Background
        
        For more details about using Arrow and Parquet see:
        
        ["Apache Arrow homepage"](https://arrow.apache.org/)
        
        ["Finer-grained Reading and Writing"](https://arrow.apache.org/docs/python/parquet.html#finer-grained-reading-and-writing)
        
        ["Apache Arrow: Read DataFrame With Zero Memory"](https://towardsdatascience.com/apache-arrow-read-dataframe-with-zero-memory-69634092b1a)  
        Dejan Simic  
        _Towards Data Science_ (2020-06-25)
        
        
        ## Why the name?
        
        A `nock` is the English word for the end of an arrow opposite its point.
        
        
        ## Package Release
        
        First, verify that `setup.py` will run correctly for the package
        release process:
        
        ```
        python3 -m pip install -e .
        python3 -m pytest tests/
        python3 -m pip uninstall pynock
        ```
        
Keywords: knowledge graph,parquet,serialization
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Human Machine Interfaces
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.8
Description-Content-Type: text/markdown
