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rmancinasandClaude Opus 4.8 9de8e4e6c0 fix(migration): de-duplicate EFECTIVO_BACKUP against EFECTIVO
The reconciliation pass ruled EFECTIVO and EFECTIVO_BACKUP "near-disjoint
ledgers" and the transform loaded both in full. That verdict was a bug, not
a finding.

reconcile.py compared the business key (cl, fecha, monto, conepto) as raw
strings, on the stated premise that "every table went through the same
mdb-export path, so identical source values serialize identically". They
don't: mdb-export formats a numeric column from its Access column type, so
the same amount is emitted as `5000` from one table and `27000.0000` from
the other. No two rows could ever match on `monto`, which is why the pass
reported 2 overlapping rows.

Canonicalizing numeric key columns first shows 12386 of EFECTIVO_BACKUP's
12387 rows already exist verbatim in EFECTIVO — same customer, same
timestamp to the second, same amount, same concept text — leaving exactly
one genuinely new row. The ledger was carrying 12386 duplicated payments,
roughly doubling every customer's historical receipt total.

- reconcile.py: add canon(), which parses a key column to a number when
  nearly every populated cell parses and re-emits it at fixed precision.
  Applied in keyset() and in the folio-conflict comparison. Rewrite the
  group-1 verdict and the module docstring's method note.
- transform_transactions.py: share a business-key `seen` set between the
  two efectivo_like() calls. EFECTIVO loads first and wins collisions.
  De-dup on the business key, never on folio — folio is per-table
  sequential and collides on 12204 different payments.
- Regenerate RECONCILIATION.md. Groups 2 and 3 re-checked under the fix;
  their verdicts are unchanged.

Ledger after re-running run_all.py --env dev: 45861 -> 33475 rows.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2026-07-22 23:28:18 -07:00

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Reconciliation Report — Overlapping Legacy Tables

Migration plan step 2. Generated by reconcile.py from staged Parquet (output/stg_utilities). Regenerate: ./.venv/bin/python reconcile.py > RECONCILIATION.md.

Headline: EFECTIVO_BACKUP is a duplicate of EFECTIVO and must not be double-loaded; the billing tables are genuinely disjoint period runs; COBRO3 is a mislabeled charge batch, not a customer master. See each group's decided rule.

1. Cash ledger — EFECTIVO vs EFECTIVO_BACKUP

  • EFECTIVO: 13697 rows. EFECTIVO_BACKUP: 12387 rows.
  • folio is per-table sequential: 13697 distinct in EFECTIVO (= row count), 12369 in BACKUP. 12363 folio numbers appear in both.
  • But of those 12363 shared folio numbers, 12204 carry a different transaction (differ on ['cl', 'fecha', 'monto', 'conepto']). → folio collides; it is NOT a stable cross-table id, and it cannot be the de-dup key.
  • On the real business key ['cl', 'fecha', 'monto', 'conepto'], canonicalized: 12386 rows in both, 1279 only in EFECTIVO, 1 only in BACKUP — i.e. all but 1 of BACKUP's 12387 rows already exist verbatim in EFECTIVO, same customer, same timestamp to the second, same amount, same concept text.
  • Yearly row counts track each other almost exactly from 2006 to 2023 (e.g. 2017: 1036 vs 994), which is what a stale copy looks like — not two ledgers covering different eras.

Decided rule (corrected): EFECTIVO_BACKUP is a stale backup copy of EFECTIVO, not an independent ledger. Load EFECTIVO in full, and load from EFECTIVO_BACKUP only the rows whose canonicalized business key is absent from EFECTIVO (1 row(s)). Loading both in full double-counts 12386 payments and doubles nearly every customer's historical receipt total, which makes any statement or balance view wrong. De-dup on the business key, not on folio (it collides).

This reverses the original verdict in this report, which read {b} as 2. That number came from string-comparing monto, which mdb-export serializes with a different precision per table (5000 vs 27000.0000) — see the module docstring.

EFECTIVO FM3 (627) and CHEQUE FM3 (157) are a separate stream — fee/tax/multa columns instead of monto — and migrate as distinct transactions, not reconciled against EFECTIVO.

Data-quality note for the transform: monedas has case/spelling variants (PESOS/Pesos, DOLARES/Dolares/DOLLARS) — normalize currency on load.

2. Billing/fee logs — datos2 vs FEE ANUAL vs fee15

  • Rows: datos2 16000, FEE ANUAL 1082, fee15 1030.
  • date spans: datos2 ('2025-01-08 00:00:00', '2026-05-15 00:00:00'), FEE ANUAL ('2018-01-03 00:00:00', '2018-01-03 00:00:00'), fee15 ('2017-01-10 00:00:00', '2017-01-10 00:00:00') — three different periods.
  • FEE ANUAL.refer and fee15.refer are each a single constant value (1/1 distinct) — these are one-shot per-period fee runs, not general logs.
  • Real-identity overlap ['numid', 'date', 'chargecredit']: FEE ANUAL∩datos2 = 0, fee15∩datos2 = 0, FEE ANUAL∩fee15 = 0.

Decided rule: datos2, FEE ANUAL, and fee15 are disjoint historical billing runs from different periods (≈202526, 2018, 2017 respectively), not copies of one another. Migrate all three into the billing side of transactions, provenance-tagged; no de-dup is needed (zero real-identity overlap). Preserve datos2.due_date (the other two lack it — leave null for their rows).

3. Customer master — DATGRAL vs COBRO3

  • DATGRAL 1172 rows / COBRO3 181 rows; both unique on num_id.
  • Every COBRO3.num_id is in DATGRAL (181/181); it adds no new customer id.
  • COBRO3.fee is constant (1 distinct value: ['75']) while DATGRAL.fee varies per customer — so COBRO3 is not a snapshot of the master's fee field.
  • Sampling shows COBRO3 rows carry the flat charge with names/addresses denormalized (some blank), i.e. a saved charge worklist, not the authoritative customer record.

Decided rule: COBRO3 is a billing/charge batch ('cobro' = collection), not a customer table. DATGRAL is the sole utilities customer master. Do not merge COBRO3 into customers or let it overwrite any master field. If the flat charge has value as history, model those 181 rows as charge transactions (amount = the constant fee) keyed to the existing DATGRAL customers — otherwise exclude COBRO3 from the migration entirely. Either way it contributes zero new customers.