5f5d71d500
Split the INC-only loader into a dataset-agnostic engine (pipeline.py, renamed from import_tickets.py) parameterized by a Dataset config, with thin per-type entrypoints inc/import_inc.py and crq/import_crq.py. CRQ shares INC's identical 32-column source schema and CDC change stream, so the engine is fully shared. - pipeline.py: Dataset config (name/table/prefixes/key_regex/post_apply); INC keeps the capture_history post-apply hook, CRQ has none yet. geocode_locations now unions tickets.crq (geocoding is cross-dataset: one gazetteer/budget). - crq/import_crq.py: drains automations/crq/changes/ from isptickets into tickets.crq (data layer + map; SLA/dashboard/history deferred). - migrations/13_crq_columns.sql: CRQ mirror of 03 — typed STORED generated columns + indexes on tickets.crq (reuses tickets.eat_ts()). - Deployment: Dockerfile/run_ingest.sh run both via `python -m`; pyproject packages inc/crq. Docs (README, implementation, deployment-and-operations, n8n export ref, phase-1) updated for the split + the one-time CRQ seed runbook. tickets.crq already exists (mig 01, LIKE tickets.inc) and is unioned into reporting.fn_tickets_for_map + resolve_ticket_geoms, so CRQ appears on the existing Tickets map once seeded. Verified locally: ruff-clean new files, engine lists/parses both streams against live S3 (crq=52 files, inc unaffected). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
61 lines
3.2 KiB
Python
61 lines
3.2 KiB
Python
"""
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crq/import_crq.py — Fireside Communications · CRQ (new-installation) ingestion.
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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Thin entrypoint over the shared engine (`pipeline.py`) for the CRQ dataset:
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tickets.crq — new-installation requests (FleetOps "Tickets" CRQ tab)
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CRQ mirrors INC at the data layer — IDENTICAL 32-column CSV schema and the same
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incremental CDC change stream automations/crq/changes/<EAT-ts>.csv in the
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`isptickets` bucket. This loader upserts on ticket_id, advances the per-dataset
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watermark (tickets.import_meta dataset='crq'), and archives each consumed file to
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automations/crq/processed/. CRQ flows onto the existing Tickets map via
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reporting.fn_tickets_for_map (which already unions tickets.crq).
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Scope (current): data layer + map only. CRQ has NO post-apply history capture yet
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(installation-lifecycle SLA/backlog semantics differ from incidents — a future
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migration). Geocoding is CROSS-DATASET and run from the INC entrypoint
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(python -m inc.import_inc --geocode-clusters / --geocode-locations) against the
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shared gazetteer, which covers both inc and crq.
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Usage (needs DATABASE_URL + RUSTFS_* env; see .env.example):
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python -m crq.import_crq --from-bucket --apply
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python -m crq.import_crq --from-bucket --reseed --apply # one-time bucket cutover
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python -m crq.import_crq --crq-csv 2026-06-24T12-55-44.csv --apply
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Pre-requisite: migrations applied (run_migrations.py) — tickets.crq + its typed
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columns (13_crq_columns.sql) + geo_clusters/geo_locations + fn_tickets_for_map.
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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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"""
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from __future__ import annotations
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import argparse
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import pipeline
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# CRQ has no post-apply hook yet (history capture is INC-only — see module docstring).
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DATASET = pipeline.make_dataset("crq", post_apply=None)
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def main() -> None:
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ap = argparse.ArgumentParser(
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description="Ingest CRQ (installation) tickets from CSV (raw-first)")
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ap.add_argument("--apply", action="store_true", help="Write to DB (default: dry-run)")
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ap.add_argument("--from-bucket", action="store_true",
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help="Drain the incremental CRQ change stream (automations/crq/changes/) "
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"from the isptickets S3 bucket: every not-yet-processed file "
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"oldest→newest, upsert on ticket_id, advance the watermark, archive")
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ap.add_argument("--reseed", action="store_true",
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help="Ignore the stored watermark and drain every file in changes/ once "
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"(one-time bucket cutover / reseed). Use with --from-bucket --apply")
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ap.add_argument("--crq-csv", dest="local_csv", default=None,
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help="Local CRQ tickets CSV file (dev)")
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args = ap.parse_args()
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if not (args.from_bucket or args.local_csv):
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ap.error("provide --from-bucket or --crq-csv")
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pipeline.ingest(DATASET, args)
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if __name__ == "__main__":
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main()
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