Financial Correctness: Payroll and Accounting Financial systems need stronger invariants than ordinary CRUD. Monetary arithmetic, finalization, periods, rounding, and reproducibility must be explicit business rules.
Money rules • Never use binary floating-point for authoritative monetary amounts. PostgreSQL numeric/decimal is exact and selectable in precision; PostgreSQL also warns against floating point for money because of rounding risk.
• Define currency, scale, rounding mode, and when rounding occurs. Do not let UI formatting decide accounting behavior.
• Persist calculation inputs/version references needed to reproduce a finalized payroll or journal result.
Finalize means something • DRAFT calculations may be recomputed. FINALIZED payroll/journal results should normally be immutable; corrections are new adjustment/reversal records, not silent edits.
• Accounting journal entries require balanced debits and credits before posting; closed periods reject ordinary postings.
• PayrollRun should have a deterministic lifecycle: DRAFT -> CALCULATING -> REVIEW -> FINALIZED -> PAID/ARCHIVED as appropriate. model MoneyExample { amount Decimal @db.Decimal(18, 2) currency String @db.VarChar(3) }
# Never store authoritative payroll money in float/double.
Financial correctness is a domain invariant, not a formatting concern. Official reference: PostgreSQL 18 numeric is exact; floating point should not be used for money due to rounding risk - https://www.postgresql.org/docs/18/datatype-money.html
Enterprise systems are difficult because business invariants, approvals, auditability, effective dates, and permissions interact over long-lived data.
Restate the goal of “Financial Correctness Payroll and Accounting” as one observable behavior. Write the success signal before touching code.
Trace the boundary map: business workflow → invariants → permissions → transaction/audit → operational outcome. Mark the one boundary this lesson changes.
Inspect the current repository state and record the exact files, route, table, or runtime involved. Avoid creating an abstraction before you can name the existing owner.
Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.
Connect the slice to TaskFlow’s phase artifact: Enterprise expansion blueprint for an HRIS/procurement/inventory/payroll-style system with modules, workflows, events, audit, and scaling triggers.. Verify behavior from the outside as a user or API client would.
Create one controlled failure that is safe to reproduce. Predict the symptom first, capture evidence, locate the earliest broken boundary, then fix only that cause.
Close the reference and rebuild or explain the core change from memory. Record what you would monitor or test before trusting the same change in production.
Requisition -> Approval -> Purchase Order -> Goods Receipt -> Invoice
Invariant examples:
- received quantity <= ordered quantity
- finalized approvals are auditable
- closed orders reject new receiptsDo not copy this as a finished answer. Predict what each boundary does, type the smallest useful slice, run it, then compare the result with your prediction.
Return to the nearest observable boundary: input, network, process log, database, or rendered UI. Compare actual evidence with the predicted path.
Change one variable at a time and reproduce the original failure after the fix; otherwise you have correlation, not diagnosis.
Without copying the final source, create a second small scenario that applies “Financial Correctness Payroll and Accounting” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.
Write concrete scaling triggers that would justify extracting a service, queue, cache, or separate database later.
Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.Boolean fields replacing explicit workflow states
Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.Why does this responsibility live here, what input does it trust, what output proves success, and what would go wrong if another layer owned it?
If your explanation is vague, repeat the boundary trace before moving on.Close the reference. Reproduce the core idea from memory or explain the exact request/data flow aloud. Then check your diff or notes for accidental shortcuts that bypass validation, authorization, error handling, or verification earned in earlier phases.