# Evaluation — Backend-cached invoice editing over SignalR

**Idea (as proposed):** hold invoices that users are editing in a **server-side
cache**, keep a **SignalR / WebSocket** connection open, apply each front-end
change **in the backend**, and **push the recomputed state back** to the browser.
The backend becomes the single source of truth for the in-progress invoice.

This note evaluates that against the current design and recommends a path.

---

## 1. How invoice editing works today

- **Stateless.** The browser holds the editor state. It posts the whole `invc`
  JSON to `req/sprep|sedit|save`; the server computes/persists and returns a PDF
  preview (image collection). No per-user editing state lives on the server.
- Totals/§13b/§35a and now **set pricing** are computed from posted data; the
  invoice **total comes from the registration balance**, not summed lines.
- Auth is cookie-based (`OCORECookieAuthenticationEvents`), SQL-first data access,
  controller is stateless and DI-scoped.

**Implication:** the server is horizontally scalable and crash-tolerant for
editing — there is nothing to lose if a node restarts mid-edit.

---

## 2. What the proposal would buy

| Benefit | Real for Fuchs? |
|---|---|
| Server-authoritative calculation (one place for pricing/VAT/set rules) | **Partly already true** — the PDF/totals are server-computed; the editor only previews. The duplicated logic is the *live* line math in JS. |
| Real-time multi-user co-editing | **Low value** — invoices are edited by one back-office user at a time; concurrent editing of the same draft is rare. |
| Live validation / instant recompute without full round-trips | **Some value** — smoother UX than re-posting the whole `invc` for each tweak. |
| Reduced payload (deltas vs whole invoice) | Marginal — invoices are small. |

---

## 3. Costs and risks

- **Server-side edit state.** Per-user/per-draft cache with lifetime management
  (idle expiry, explicit discard, max size), or the server leaks memory. Needs a
  distributed cache if scaled out (Redis), or **sticky sessions** for SignalR.
- **Concurrency/locking.** Two tabs or two users on the same draft → need
  optimistic concurrency / locking semantics that don't exist today.
- **Connection lifecycle.** Reconnect, replay, and "lost update" handling;
  offline/flaky networks; auth on the socket (cookie works, but token refresh and
  disconnect-on-logout must be handled).
- **Scaling.** SignalR with multiple instances needs a backplane (Redis/Azure
  SignalR). Today the app has none.
- **Big rewrite of the editor.** The 1,200-line `fis.inv_shared.js` becomes an
  event-driven client of server state — a substantial, risky rewrite of working
  code, with new failure modes (desync between optimistic UI and server truth).
- **Testing surface** grows (connection states, races) far beyond the current
  request/response model.

For a single-tenant back-office app with one editor at a time, this is a lot of
**accidental complexity** for modest UX gains.

---

## 4. Recommendation

**Do not do a full SignalR rewrite now.** It optimises a problem (real-time
collaboration, server-held edit state) the business doesn't strongly have, while
adding stateful-server, scaling, and reconnection complexity to an app that is
currently simple and robust.

Prefer an **incremental, lower-risk path** that captures most of the value:

1. **Single source of pricing truth (highest value, do first).**
   Add a stateless endpoint `inv/calc` that takes the `invc` JSON and returns the
   computed lines + totals + set-mode resolution using the **same backend code**
   (`InvoiceSetPricing`, VAT, §13b/§35a). The editor calls it on change (debounced)
   to recompute, instead of duplicating the math in JS. This removes the
   front/back duplication — the actual pain — **without** sockets or server state.
   It also makes the interplay fully unit-testable (the endpoint is pure).

2. **Keep preview as-is** (post → PDF image) but allow it to reuse `inv/calc`.

3. **If/when live UX is still wanted**, add SignalR **only as a transport** on top
   of the stateless calc (push recompute results), keeping persistence stateless.
   Defer server-held draft state until genuine multi-user co-editing is required.

4. **If server-held drafts are truly needed**, scope a pilot: one entity (invoice
   draft), `IDistributedCache`-backed, explicit acquire/release lock, idle TTL,
   and a reconnect/replay protocol — behind a feature flag, measured against the
   current flow before rollout.

### Why this fits the codebase
It aligns with the just-completed DI service layer: the calc lives in
`IInvoiceService`/`InvoiceSetPricing` (already tested), reused by both the preview
and a future socket. We get "changes computed by the backend, reflected in the
frontend" — the stated goal — **without** turning a stateless, scalable web app
into a stateful real-time system prematurely.

**Suggested next step:** implement `inv/calc` (item 1) and migrate the editor's
line math to it; revisit SignalR only if real-time/co-editing becomes a real
requirement.