bilig

Airflow WorkPaper DAG

Use this when an Apache Airflow DAG needs formula-backed task outputs, but the calculation should run through a Node package with a proof object instead of Excel UI automation, browser grid clicks, or stale cached XLSX formula values.

Airflow’s TaskFlow API lets DAG authors write tasks as decorated Python functions and passes task outputs through XCom. This example keeps that boundary: Airflow owns the DAG, retries, task state, XCom summary, and run history, while a small Node step owns WorkPaper formula recalculation and JSON restore proof.

Official Airflow references, checked against the current Airflow 3.2 docs while keeping this example compatible with Airflow 2.10+:

Example Artifact

The runnable source lives in:

examples/airflow-workpaper-dag

It contains:

Run the TypeScript proof locally:

cd examples/airflow-workpaper-dag
pnpm install --ignore-workspace --lockfile=false
pnpm run check
pnpm run smoke

The smoke test edits Inputs!B2, recalculates quote formulas, serializes the WorkPaper document, restores it, verifies that restored calculated values match, prints JSON, and writes .tmp/workpaper-proof.json.

Airflow DAG Shape

The checked-in DAG keeps XCom compact:

try:
    from airflow.sdk import dag, task
except ImportError:
    from airflow.decorators import dag, task


@dag(dag_id="bilig_workpaper_quote", schedule=None, catchup=False)
def bilig_workpaper_quote_dag() -> None:
    @task(retries=2)
    def calculate_quote_fields() -> dict:
        # Calls: npx --no-install tsx workpaper-quote.ts --quantity ...
        # Reads: .tmp/workpaper-proof.json
        # Returns a compact patch/proof summary through XCom.
        ...

    @task
    def verify_formula_proof(result: dict) -> dict:
        # Fails the run when readback or restore proof does not match.
        ...

    verify_formula_proof(calculate_quote_fields())

Mount or copy the example directory so Airflow can see dags/, then make Node and npm dependencies part of the worker image, startup command, or deployment artifact. The DAG can run as a scheduled workflow, manual run, or upstream task dependency.

DAG Output

The full proof file contains:

{
  "patch": {
    "subtotal": 2250,
    "discount_amount": 225,
    "taxable_amount": 2025,
    "tax_amount": 162,
    "total": 2187,
    "margin_amount": 1089
  },
  "proof": {
    "editedCell": "Inputs!B2",
    "before": {
      "total": 1458
    },
    "after": {
      "total": 2187
    },
    "afterRestore": {
      "total": 2187
    },
    "verified": true
  }
}

The DAG returns the calculated patch plus a compact proof summary through XCom. Keep the complete WorkPaper proof file in an artifact path, shared volume, object store, or logs when the workflow needs an audit trail.

Workflow Shape

  1. Run bilig_workpaper_quote from a schedule, manual trigger, dataset event, API trigger, or parent DAG.
  2. Pass record values such as quantity, unit_price, discount_rate, tax_rate, and unit_cost.
  3. Feed patch into the next task or application writeback step.
  4. Keep the proof file as the audit artifact when the workflow needs readback evidence.

Airflow owns scheduling, retries, dependency graph state, XCom summary, and run history. Bilig owns the formula workbook, recalculation, JSON serialization, restore, and readback proof.

When This Fits

Use it for quote approvals, pricing rules, discount calculations, payout checks, import validation, and data workflows where spreadsheet formulas are the most reviewable representation of business logic.

Do not use it to pretend Bilig is desktop Excel. Keep Excel, LibreOffice, Microsoft Graph, or a domain oracle in the loop for macros, pivots, charts, external links, and exact spreadsheet UI behavior.

Outreach Note

If this is shared in an Airflow issue, Slack thread, or example discussion, lead with the concrete boundary it solves:

Airflow owns the DAG and task history. Bilig owns the formula workbook and returns both calculated field values and readback proof.

Do not post it as a generic spreadsheet-engine pitch. Link the runnable example and smoke output, then ask whether a formula-backed DAG that calls a Node WorkPaper step would be useful to Airflow users.

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