cf-datahive 0.1.0

Canonical result and measurement data storage APIs for Cogniflow

cf_datahive

cf_datahive is the Data Hive package boundary for Python-facing APIs/tooling around the canonical data hive root (workspace/<data_hive>).

Boundary (Current Phase)

• Python package role (sandcastle/cf_datahive): read-oriented API/tooling/validation for pipeline-facing workflows.
• Native role (sandcastle/cf_datahive/cpp): write gatekeeper and only allowed writer under workspace/data_hive.
• Step packages must stay thin wrappers and call the native gatekeeper instead of implementing filesystem/parquet helpers.

Development workflow

• Current development mode is source-first via scripts/fresh_install.ps1.
• The package can now be built and published independently without changing the read/write ownership boundary above.

Canonical layout

workspace/
  data_hive/
    <pipeline_id>/
      runs/
        <run_id>/
          manifest.json
          tables/
            <table_name>/
              part-0000.parquet
              part-0001.parquet
          artifacts/
            <artifact_name>
      latest.txt

• latest.txt stores the committed run_id and is updated atomically.
• manifest.json is the SOT for run metadata, table metadata, file hashes, and artifact hashes.

Usage

from pathlib import Path

from cf_datahive import DataHiveClient

workspace_root = Path("workspace")
client = DataHiveClient(str(workspace_root))

runs = client.list_runs("opcua_fifo_avg")
if runs:
    latest = runs[0].run_id
    manifest = client.load_manifest("opcua_fifo_avg", latest)
    table = client.read_table("opcua_fifo_avg", latest, "measurements")
    print(manifest.status, table.num_rows)

Manifest details

Each run stores a RunManifest (schema_version="1.0") with:

• run lifecycle fields (status: staged|committed|aborted)
• table entries (parquet, schema fingerprint, row/file counts, optional file hashes)
• artifact entries (sha256, media type, size)
• optional semantic_refs placeholder map for future ontology links

Schema fingerprint is sha256 of Arrow schema serialization bytes.

Guardrails

Run the repository guardrail check:

python tools/check_datahive_guardrails.py

The script performs C++/header scans and step-package checks that:

• use canonical workspace/data_hive literals outside the native gatekeeper location (hard fail)
• violate the thin-steps rule in sandcastle/cf_basic_steps/*/src/*/cpp (hard fail)

Testing

Install test dependencies and run:

pip install -e "sandcastle/cf_datahive[test]"
pytest -q sandcastle/cf_datahive/tests

Published distribution name:

pip install cf-datahive

Publishing

cf_datahive is published with the dedicated Windows workflow:

• Workflow: .github/workflows/cf_datahive_windows_publish.yml
• Package directory: sandcastle/cf_datahive
• PyPI tag: cf-datahive-v<version>
• TestPyPI tag: cf-datahive-v<version>-test

Local preflight:

powershell -ExecutionPolicy Bypass -File scripts/mimic_windows_python_publish_workflow.ps1 `
  -WorkflowFile .github/workflows/cf_datahive_windows_publish.yml `
  -PackageDir sandcastle/cf_datahive `
  -PythonExe py `
  -PythonVersion 3.13

Queue a dry-run dispatch:

powershell -ExecutionPolicy Bypass -File scripts/queue_windows_python_publish_workflow.ps1 `
  -WorkflowFile .github/workflows/cf_datahive_windows_publish.yml `
  -PackageDir sandcastle/cf_datahive `
  -PublishTarget testpypi `
  -Ref main `
  -RequireLocalPass `
  -DryRun

Do / Don't

• Do: use DataHiveClient read APIs (list_runs, load_manifest, read_table, open_artifact) for inspection and validation.
• Do: route pipeline write ownership through cf_datahive_cpp in the sink path.
• Don't: write parquet files or artifacts directly into the canonical data hive root from pipeline steps.
• Don't: bypass manifest updates.