# articat
[](https://github.com/related-sciences/articat/actions/workflows/build.yml)
[](https://pypi.org/project/articat/)
Minimal metadata catalog to store and retrieve metadata about data artifacts.
## Getting started
At a high level, *articat* is simply a key-value store. Value being the Artifact metadata.
Key a.k.a. "Artifact Spec" being:
* globally unique `id`
* optional timestamp: `partition`
* optional arbitrary string: `version`
To publish a file system Artifact (`FSArtifact`):
```python
from articat import FSArtifact
from pathlib import Path
from datetime import date
# Apart from being a metadata containers, Artifact classes have optional
# convenience methods to help in data publishing flow:
with FSArtifact.partitioned("foo", partition=date(1643, 1, 4)) as fsa:
# To create a new Artifact, always use `with` statement, and
# either `partitioned` or `versioned` methods. Use:
# * `partitioned(...)`, for Artifacts with explicit `datetime` partition
# * `versioned(...)`, for Artifacts with explicit `str` version
# Next we produce some local data, this could be a Spark job,
# ML model etc.
data_path = Path("/tmp/data")
data_path.write_text("42")
# Now let's stage that data, temporary and final data directories/buckets
# are configurable (see below)
fsa.stage(data_path)
# Additionally let's provide some description, here we could also
# save some extra arbitrary metadata like model metrics, hyperparameters etc.
fsa.metadata.description = "Answer to the Ultimate Question of Life, the Universe, and Everything"
```
To retrieve the metadata about the Artifact above:
```python
from articat.fs_artifact import FSArtifact
from datetime import date
from pathlib import Path
# To retrieve the metadata, use Artifact object, and `fetch` method:
fsa = FSArtifact.partitioned("foo", partition=date(1643, 1, 4)).fetch()
fsa.id # "foo"
fsa.created # <CREATION-TIMESTAMP>
fsa.partition # <CREATION-TIMESTAMP>
fsa.metadata.description # "Answer to the Ultimate Question of Life, the Universe, and Everything"
fsa.main_dir # Data directory, this is where the data was stored after staging
Path(fsa.joinpath("data")).read_text() # 42
```
## Features
* store and retrieve metadata about your data artifacts
* no long running services (low maintenance)
* data publishing utils builtin
* IO/data format agnostic
* immutable metadata
* development mode
## Artifact flavours
Currently available Artifact flavours:
* `FSArtifact`: metadata/utils for files or objects (supports: local FS, GCS, S3 and more)
* `BQArtifact`: metadata/utils for BigQuery tables
* `NotebookArtifact`: metadata/utils for Jupyter Notebooks
## Development mode
To ease development of Artifacts, *articat* supports development/dev mode.
Development Artifact can be indicated by `dev` parameter (preferred), or
`_dev` prefix in the Artifact `id`. Dev mode supports:
* overwriting Artifact metadata
* configure separate locations (e.g. `dev_prefix` for `FSArtifact`), with
potentially different retention periods etc
## Backend
* `local`: mostly for testing/demo, metadata is stored locally (configurable, default: `~/.config/articat/local`)
* `gcp_datastore`: metadata is stored in the Google Cloud Datastore
## Configuration
*articat* configuration can be provided in the API, or configuration files. By default configuration
is loaded from `~/.config/articat/articat.cfg` and `articat.cfg` in current working directory. You
can also point at the configuration file via environment variable `ARTICAT_CONFIG`.
You use `local` mode without configuration file. Available options:
```toml
[main]
# local or gcp_datastore, default: local
# mode =
# local DB directory, default: ~/.config/articat/local
# local_db_dir =
[fs]
# temporary directory/prefix
# tmp_prefix =
# development data directory/prefix
# dev_prefix =
# production data directory/prefix
# prod_prefix =
[gcp]
# GCP project
# project =
[bq]
# development data BigQuery dataset
# dev_dataset =
# production data BigQuery dataset
# prod_dataset =
```
## Our/example setup
Below you can see a diagram of our setup, Articat is just one piece of our system, and solves a specific problem. This should give you an idea where it might fit into your environment:
<p align="center">
<img src="https://docs.google.com/drawings/d/1wll4Q_PlKGHVu-C2IN8jUIxzFTD8jwFWnvwgFrvq2ls/export/png" alt="Our setup diagram"/>
</p>
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"description": "# articat\n[](https://github.com/related-sciences/articat/actions/workflows/build.yml)\n[](https://pypi.org/project/articat/)\n\nMinimal metadata catalog to store and retrieve metadata about data artifacts.\n\n## Getting started\n\nAt a high level, *articat* is simply a key-value store. Value being the Artifact metadata.\nKey a.k.a. \"Artifact Spec\" being:\n * globally unique `id`\n * optional timestamp: `partition`\n * optional arbitrary string: `version`\n\nTo publish a file system Artifact (`FSArtifact`):\n\n```python\nfrom articat import FSArtifact\nfrom pathlib import Path\nfrom datetime import date\n\n# Apart from being a metadata containers, Artifact classes have optional\n# convenience methods to help in data publishing flow:\n\nwith FSArtifact.partitioned(\"foo\", partition=date(1643, 1, 4)) as fsa:\n # To create a new Artifact, always use `with` statement, and\n # either `partitioned` or `versioned` methods. Use:\n # * `partitioned(...)`, for Artifacts with explicit `datetime` partition\n # * `versioned(...)`, for Artifacts with explicit `str` version\n\n # Next we produce some local data, this could be a Spark job,\n # ML model etc.\n data_path = Path(\"/tmp/data\")\n data_path.write_text(\"42\")\n\n # Now let's stage that data, temporary and final data directories/buckets\n # are configurable (see below)\n fsa.stage(data_path)\n\n # Additionally let's provide some description, here we could also\n # save some extra arbitrary metadata like model metrics, hyperparameters etc.\n fsa.metadata.description = \"Answer to the Ultimate Question of Life, the Universe, and Everything\"\n```\n\nTo retrieve the metadata about the Artifact above:\n\n```python\nfrom articat.fs_artifact import FSArtifact\nfrom datetime import date\nfrom pathlib import Path\n\n# To retrieve the metadata, use Artifact object, and `fetch` method:\nfsa = FSArtifact.partitioned(\"foo\", partition=date(1643, 1, 4)).fetch()\n\nfsa.id # \"foo\"\nfsa.created # <CREATION-TIMESTAMP>\nfsa.partition # <CREATION-TIMESTAMP>\nfsa.metadata.description # \"Answer to the Ultimate Question of Life, the Universe, and Everything\"\nfsa.main_dir # Data directory, this is where the data was stored after staging\nPath(fsa.joinpath(\"data\")).read_text() # 42\n```\n\n## Features\n\n * store and retrieve metadata about your data artifacts\n * no long running services (low maintenance)\n * data publishing utils builtin\n * IO/data format agnostic\n * immutable metadata\n * development mode\n\n## Artifact flavours\n\nCurrently available Artifact flavours:\n * `FSArtifact`: metadata/utils for files or objects (supports: local FS, GCS, S3 and more)\n * `BQArtifact`: metadata/utils for BigQuery tables\n * `NotebookArtifact`: metadata/utils for Jupyter Notebooks\n\n## Development mode\n\nTo ease development of Artifacts, *articat* supports development/dev mode.\nDevelopment Artifact can be indicated by `dev` parameter (preferred), or\n`_dev` prefix in the Artifact `id`. Dev mode supports:\n * overwriting Artifact metadata\n * configure separate locations (e.g. `dev_prefix` for `FSArtifact`), with\n potentially different retention periods etc\n\n## Backend\n\n * `local`: mostly for testing/demo, metadata is stored locally (configurable, default: `~/.config/articat/local`)\n * `gcp_datastore`: metadata is stored in the Google Cloud Datastore\n\n## Configuration\n\n*articat* configuration can be provided in the API, or configuration files. By default configuration\nis loaded from `~/.config/articat/articat.cfg` and `articat.cfg` in current working directory. You\ncan also point at the configuration file via environment variable `ARTICAT_CONFIG`.\n\nYou use `local` mode without configuration file. Available options:\n\n ```toml\n[main]\n# local or gcp_datastore, default: local\n# mode =\n\n# local DB directory, default: ~/.config/articat/local\n# local_db_dir =\n\n[fs]\n# temporary directory/prefix\n# tmp_prefix =\n# development data directory/prefix\n# dev_prefix =\n# production data directory/prefix\n# prod_prefix =\n\n[gcp]\n# GCP project\n# project =\n\n[bq]\n# development data BigQuery dataset\n# dev_dataset =\n# production data BigQuery dataset\n# prod_dataset =\n```\n\n## Our/example setup\n\nBelow you can see a diagram of our setup, Articat is just one piece of our system, and solves a specific problem. This should give you an idea where it might fit into your environment:\n\n<p align=\"center\">\n <img src=\"https://docs.google.com/drawings/d/1wll4Q_PlKGHVu-C2IN8jUIxzFTD8jwFWnvwgFrvq2ls/export/png\" alt=\"Our setup diagram\"/>\n</p>\n",
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