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artigraph/artigraph

artigraph

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Declarative Data Production

Artigraph is a tool to improve the authorship, management, and quality of data. It emphasizes that the core deliverable of a data pipeline or workflow is the data, not the tasks.

Artigraph is hosted by the LF AI and Data Foundation as a Sandbox project. See our deck or presentation (@6m35s) requesting Sandbox incubation.

Community

We're excited to hear from anyone interested in the project - feel free to introduce yourself over in the Intro Discussions! See our support page for help or our contributing page for guidelines.

Installation

Artigraph can be installed from PyPI on python 3.9+ with pip install arti.

Example

This sample from the spend example highlights computing the total amount spent from a series of purchase transactions:

from pathlib import Path
from typing import Annotated

from arti import Annotation, Artifact, Graph, producer
from arti.formats.json import JSON
from arti.storage.local import LocalFile
from arti.types import Collection, Date, Float64, Int64, Struct
from arti.versions import SemVer

DIR = Path(__file__).parent


class Vendor(Annotation):
    name: str


class Transactions(Artifact):
    """Transactions partitioned by day."""

    type = Collection(
        element=Struct(fields={"id": Int64(), "date": Date(), "amount": Float64()}),
        partition_by=("date",),
    )


class TotalSpend(Artifact):
    """Aggregate spend over all time."""

    type = Float64()
    format = JSON()
    storage = LocalFile()


@producer(version=SemVer(major=1, minor=0, patch=0))
def aggregate_transactions(
    transactions: Annotated[list[dict], Transactions]
) -> Annotated[float, TotalSpend]:
    return sum(txn["amount"] for txn in transactions)


with Graph(name="test-graph") as g:
    g.artifacts.vendor.transactions = Transactions(
        annotations=[Vendor(name="Acme")],
        format=JSON(),
        storage=LocalFile(path=str(DIR / "transactions" / "{date.iso}.json")),
    )
    g.artifacts.spend = aggregate_transactions(
        transactions=g.artifacts.vendor.transactions
    )

The full example can be run easily with docker run --rm artigraph/example-spend:

INFO:root:Writing mock Transactions data:
INFO:root:      /usr/src/app/transactions/2021-10-01.json: [{'id': 1, 'amount': 9.95}, {'id': 2, 'amount': 7.5}]
INFO:root:      /usr/src/app/transactions/2021-10-02.json: [{'id': 3, 'amount': 5.0}, {'id': 4, 'amount': 12.0}, {'id': 4, 'amount': 7.55}]
INFO:root:Building aggregate_transactions(transactions=Transactions(format=JSON(), storage=LocalFile(path='/usr/src/app/transactions/{date.iso}.json'), annotations=(Vendor(name='Acme'),)))...
INFO:root:Build finished.
INFO:root:Final Spend data:
INFO:root:      /tmp/test-graph/spend/7564053533177891797/spend.json: 42.0

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Artigraph is a tool to improve the authorship, management, and quality of data. It emphasizes that the core deliverable of a data pipeline or workflow is the data, not the tasks.

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