pynock 1.2.1

Description:

pynock 1.2.1

pynock







The following describes a proposed standard NOCK for a Parquet
format that supports efficient distributed serialization of multiple
kinds of graph technologies.
This library pynock provides Examples for working with low-level
Parquet read/write efficiently in Python.
Our intent is to serialize graphs in a way which aligns the data
representations required for popular graph technologies and related
data sources:

semantic graphs (e.g., W3C formats RDF, TTL, JSON-LD, etc.)
labeled property graphs (e.g., openCypher)
probabilistic graphs (e.g., PSL)
spreadsheet import/export (e.g., CSV)
dataframes (e.g., Pandas, Dask, Spark, etc.)
edge lists (e.g., NetworkX, cuGraph, etc.)

This approach also efficient distributed partitions based on Parquet,
which can scale on a cluster to very large (+1 T node) graphs.
For details about the proposed format in Parquet files, see the
FORMAT.md
file.
If you have questions, suggestions, or bug reports, please open
an issue
on our public GitHub repo.
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, the library versioning info is listed in 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 cli.py load-parq --file dat/recipes.parq --debug

python3 cli.py load-rdf --file dat/tiny.ttl --save-csv foo.csv

For further information:
python3 cli.py --help

Usage programmatically in Python
To construct a partition file programmatically, see the
examples
for Jupyter notebooks with sample code and debugging.
Background
For more details about using Arrow and Parquet see:
"Apache Arrow homepage"
"Finer-grained Reading and Writing"
"Apache Arrow: Read DataFrame With Zero Memory"
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.
If you must have an acronym, the proposed standard NOCK stands for
Network Objects for Consistent Knowledge.
Also, the library name had minimal namespace collisions on GitHub and
PyPi :)
Developer updates
To set up the build environment locally, also run:
python3 -m pip install -U pip setuptools wheel
python3 -m pip install -r requirements-dev.txt

Note that we require the use of pre-commit hooks
and to configure that locally:
pre-commit install
git config --local core.hooksPath .git/hooks/

Package releases
First, verify that setup.py will run correctly for the package
release process:
python3 -m pip install -e .
python3 -m pytest -rx tests/
python3 -m pip uninstall pynock

Next, update the semantic version number in setup.py and create a
release on GitHub, and make sure to update the local repo:
git stash
git checkout main
git pull

Make sure that you have set up your 2FA authentication for generating
an API token on PyPi: https://pypi.org/manage/account/token/
Then run our PyPi push script:
./bin/push_pypi.sh

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