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8c941fdf4b
customers, contracts, invoices, payments, train-schedules, locomotives, trains and wagons. 319 fields across the nine datasets, all reusing the existing engine — no change to export.types.ts was needed, which is the result the bookings-first phase was meant to test. Per-dataset notes worth keeping: - trains resolves route, stations and current yard, which the list endpoint never loads — the UI shows raw FK uuids there today. - wagons reads tare/payload/length off wagon_types (they are not on the wagon), and reproduces the service's attachStatusDates() as correlated subqueries. wagon_status_logs stores from_status/to_status, not status. - payments applies no soft-delete guard: freight.payments has neither deleted_at nor updated_at, so the usual predicate is a 42703. Failure columns are failer_code/failer_message. payment_refunds stores MINOR units, so refundedTotal divides by 100. - train-schedules derives freightType from the bookings aboard rather than a column, matching the list service. - customers stays one row per company; profiles, bookings and invoice totals aggregate in subqueries. Verified no row multiplication: trains, customers and contracts each return exactly their counted row count while selecting one-to-many aggregate fields. EXPLAIN-validated against the database: every dataset's widest query, its count query, and all 319 fields individually. That run caught five columns typed varchar rather than timestamp (companies.date_registered, renewal_date, renewed_from, renewed_to and invoices.eims_ack_date), which were being pushed through to_char and would have 500'd the moment anyone ticked them; they now export verbatim. All nine count endpoints verified equal to SELECT count(*) on their table.