seasonedcc Database Design
ALWAYS load the "kysely" skill before anything else. Follow these principles when designing database tables and writing migrations for this project.
The doctrine: 100% append-only, event-sourced, zero exceptions
The application schema is insert-only. INSERT is the only write the application ever performs. No UPDATE, no DELETE, no TRUNCATE, no ON CONFLICT ... DO UPDATE. This applies to every application table with zero exceptions — including users, sessions, and anything else that feels "infrastructural". If a design seems to require mutating a row, the design is wrong: model the change as a new event row instead.
What this buys us:
- A complete audit trail with no extra machinery — the schema is the audit trail
- Time travel: any past state can be reconstructed by filtering events by
createdAt - No lost-update bugs, no race conditions between readers and writers of the same row
- No sync risk between stored state and the events that produced it
The only tables outside the rule are schemas owned by third parties: graphile-worker's graphile_worker schema and Kysely's migration bookkeeping table mutate themselves internally. We never design tables there and never write to them directly.
Two kinds of tables
Every application table is one of two kinds:
Identity tables hold what is immutable by design: id, createdAt, and ownership foreign keys that can never change (e.g., companyId — a product never moves to another company). Nothing else. When in doubt whether an attribute is truly immutable for the row's entire life, it is not — put it in an event table.
Event tables hold everything that happens to an entity after (and including) its birth. Each event row is a fact that occurred at createdAt and is never modified.
products -- identity: who exists
id UUID PK
company_id UUID NOT NULL (FK)
created_at TIMESTAMPTZ NOT NULL
product_revisions -- event: what the product's details are
id UUID PK
product_id UUID NOT NULL (FK)
name TEXT NOT NULL
sku TEXT NOT NULL
unit_id UUID NOT NULL (FK)
created_at TIMESTAMPTZ NOT NULL -- latest revision wins
product_archivals -- event: the product was archived
id UUID PK
product_id UUID NOT NULL (FK)
created_at TIMESTAMPTZ NOT NULL
Event tables: one per cohesive concern
Slice mutable state into event tables per cohesive concern — fields that change together through one user action share one table:
- A "revision" table snapshots all fields of its concern per edit. The product "edit details" form (name, sku, unit) writes one
product_revisionsrow carrying all those fields — full snapshot, not a diff — so reading current state needs only the latest row. - Separately-actioned state changes each get their own narrow event table:
product_archivals,recipe_approvals,purchase_order_submissions. - Not per-field (table explosion, N-way joins to assemble current state), and not forced whole-entity (couples unrelated concerns into one table).
Creation is a transaction: inserting an entity writes the identity row plus the first row of each relevant event table in one transaction. "Latest event wins" then needs no special case for freshly created entities, and no column ever needs to be nullable while "waiting" for data.
Naming: identity tables are plural nouns (products); event tables are <entity>_<past-action-plural> (product_revisions, purchase_order_approvals, user_session_revocations).
One-way events and paired toggles
Model each state transition by its real shape:
- One-way transitions (revocation, completion, cancellation, termination): a single event table; the existence of a row is the state. A
user_session_revocationsrow means the session is revoked, forever. - Reversible toggles (archive/restore, activate/deactivate, grant/revoke): a pair of event tables; the newer of the two latest events wins. Each direction can carry direction-specific data (e.g., a restoration's reason).
const archivalEvents = db()
.selectFrom('productArchivals')
.select(['productId', 'createdAt', sql<boolean>`true`.as('archived')])
.unionAll(
db()
.selectFrom('productRestorations')
.select(['productId', 'createdAt', sql<boolean>`false`.as('archived')])
)
Take the latest row per productId to know whether the product is archived; no rows means never archived.
Deriving current state
Current state is always computed at query time from events. The canonical patterns:
Latest event wins — DISTINCT ON ordered by recency:
db()
.selectFrom('productRevisions')
.distinctOn('productId')
.orderBy('productId')
.orderBy('createdAt', 'desc')
.orderBy('id', 'desc')
.selectAll()
The id desc tie-break makes ordering deterministic if two events ever share a timestamp — but avoid creating that situation: one user action appends one event per parent per transaction.
Existence is state — EXISTS / NOT EXISTS:
.where(({ not, exists, selectFrom }) =>
not(
exists(
selectFrom('userSessionRevocations')
.select('id')
.whereRef('userSessionRevocations.userSessionId', '=', 'userSessions.id')
)
)
)
Aggregates over events — quantities are sums of movements, never stored balances:
db()
.selectFrom('stockMovements')
.select(({ fn }) => fn.sum<string>('quantityDelta').as('onHand'))
.where('stockItemId', '=', stockItemId)
Status from event existence — CASE WHEN ... EXISTS chains: success event exists → succeeded, failure event exists → failed, neither → pending. Only build the derivation query when the UI or business logic actually needs it.
Indexes: every event table gets an index on (parentId, createdAt desc) at creation time — it serves every latest-wins and existence query.
Ordering absolute-set events
Most derivations are order-insensitive (sums) or single-writer latest-wins (revisions). But when an event family mixes delta events (movements) with absolute-set events (count adjustments) over the same derived value, the derivation is latest-absolute-wins plus later deltas — and "later" must mean commit order, not transaction-begin order. now() is frozen at BEGIN, so a slow transaction can stamp an event before an absolute-set that never saw it, and the derivation silently drops the delta.
The rule, for any event family with at least one absolute-set consumer:
- Every writer of every event in the family takes the same advisory lock (
pg_advisory_xact_lock, org-scoped) before inserting, so critical sections are disjoint and inserts happen in commit order. - The ordering column (
createdAt) defaults toclock_timestamp(), notnow(), so the stamp is taken inside the locked section. - Absolute-set comparisons use strict
>; equal stamps cannot occur across transactions under the lock, and one transaction never writes both an absolute-set and a delta for the same derived cell.
The stock placement family (stock_item_inbound_movements, stock_item_outbound_movements, stock_item_transfers, stock_item_count_adjustments) follows this rule; lockOrganization in companies.server.ts is the shared lock. Any new placement writer must take it. If clock monotonicity on the database host ever becomes a real concern, the escalation path is a shared monotonic sequence assigned under the same lock — a new keyed structure, never a rewrite of existing rows.
Lock discipline across modules
Disjoint lock keys guard nothing. A guard that checks "has the concurrent thing already happened" (has-receipts, has-shipments, is-discarded) only works if every writer it excludes takes the SAME advisory lock key before the guard's read. A lock on a different key, or a lock somewhere else in the transaction, leaves the race wide open — the writer commits between the guard's read and the guarded write. When adding a cross-module invariant, trace the lock key of every writer that could race it.
Never call a function that opens its own db().transaction() from inside an already-locked transaction. The inner transaction takes a second pooled connection, which then blocks forever on the advisory lock the outer transaction holds — a deadlock, not an error. If the logic is needed inside a locked transaction, accept a Transaction<DB> parameter or inline the write using the function's lower-level helpers.
Not every check-then-act race needs fixing. When a review flags an existence check racing a concurrent write, do the harm analysis first: if the raced-in event is invisible to every current-state derivation (filtered out by a latest-wins or is-not-deleted predicate), no derived value can go wrong and the check-then-append idiom is acceptable. Escalate to a shared lock only when the race can corrupt a derived value — and then treat it as a lock-family decision (which writers share the key), not a one-module patch.
No mutable columns — period
A column either lives on an identity table because it is immutable by design, or it lives in an event table. There is no third kind.
Deletion is an event
Never DELETE. Removal is a fact that happened, so it is recorded like any other fact:
- User "deletes" a draft → a
purchase_order_discardsevent; queries exclude discarded orders - User detaches an association → the association is itself event-shaped:
recipe_ingredient_additions/recipe_ingredient_removals - Data was entered in error → append a correction event; the erroneous event stays in history
Because rows are never deleted, ON DELETE CASCADE has nothing to attach to — foreign keys are plain references(...) with no delete behavior.
Always use timestamptz
All timestamp columns must use timestamptz (timestamp with time zone), never timestamp. The same applies to timetz over time if time columns are ever needed.
timestamp stores a "wall clock" value with no timezone context — the same value can mean different instants depending on the session's timezone setting. timestamptz stores an unambiguous instant in time (internally UTC), and PostgreSQL automatically converts to/from the session timezone on input/output. This prevents bugs when servers, clients, or sessions use different timezone settings.
Instead of:
.addColumn('createdAt', 'timestamp', (col) =>
col.defaultTo(sql`now()`).notNull(),
)
Do:
.addColumn('createdAt', 'timestamptz', (col) =>
col.defaultTo(sql`now()`).notNull(),
)
In an event-sourced schema createdAt is the event time — the one timestamp every table has and every derivation orders by.
No nullable columns — zero exceptions
Every column in every table must be non-nullable. If data isn't available at insert time, it belongs in a separate event table that gets created when that data becomes available.
Instead of:
titles
id UUID NOT NULL
markdown TEXT -- nullable, filled after OCR
Do:
titles
id UUID NOT NULL
title_ocr_results
id UUID NOT NULL
title_id UUID NOT NULL (FK)
markdown TEXT NOT NULL
created_at TIMESTAMPTZ NOT NULL
"Optional" attributes are not nullable columns either: model them as their own event table with zero-or-more rows per parent.
No updatedAt columns
Never add updatedAt to any table. When a timestamp for a state change is needed, the event row's createdAt is the timestamp for when the change occurred. In a schema where rows are never updated, an updatedAt column is not just wrong — it is meaningless.
No derivable columns
Never store a column whose value can be inferred from event records. If a status is always set alongside inserting an event record, the event record is the status — the column is redundant and creates sync risk. This is why identity tables carry no status, no archived, no current* columns: all of it derives from events.
Instead of:
plot_summary_intakes
id, plot_summary_id, status, created_at
-- status is 'pending' | 'succeeded' | 'failed'
Do:
plot_summary_intakes
id, plot_summary_id, created_at
-- status derived: success record exists → succeeded
-- failure record exists → failed
-- neither exists → pending
No unique constraints on event table FKs
Event tables must never have unique constraints on the parent foreign key. Allow multiple records per parent so that actions can be rerun and historical results are preserved. The latest record by createdAt represents the current state.
Instead of:
.addColumn('plotSummaryIntakeId', 'uuid', (col) =>
col.notNull().unique().references('plotSummaryIntakes.id'),
)
Do:
.addColumn('plotSummaryIntakeId', 'uuid', (col) =>
col.notNull().references('plotSummaryIntakes.id'),
)
No unnecessary defaults
Only use defaultTo(...) for truly auto-generated values like id and createdAt. When a column has defaultTo(...), Kysely's type generator wraps it in Generated<T>, making it optional on insert. This silently loses type safety — forgetting to pass the value won't produce a compiler error.
Instead of:
.addColumn('seriesName', 'text', (col) =>
col.defaultTo('').notNull(),
)
Do:
.addColumn('seriesName', 'text', (col) =>
col.notNull(),
)
The first generates seriesName: Generated<string> (optional on insert). The second generates seriesName: string (required on insert), ensuring every insert site is forced to provide the value.
Store full resource locators
When persisting references to external resources (S3 objects, Google Drive files, etc.), store all components needed to locate the resource — not just the key/path. For S3, this means storing the bucket name alongside every S3 key. For Google Drive, it means storing both the file ID and the folder ID.
This makes stored references self-contained. If an environment variable like AWS_S3_BUCKET changes, existing records still point to the correct resource.
Instead of:
plot_summary_intake_successes
id UUID NOT NULL
temp_s3_key TEXT NOT NULL
created_at TIMESTAMPTZ NOT NULL
Do:
plot_summary_intake_successes
id UUID NOT NULL
temp_s3_key TEXT NOT NULL
temp_s3_bucket TEXT NOT NULL
created_at TIMESTAMPTZ NOT NULL
The naming convention pairs each *S3Key column with a corresponding *S3Bucket column using the same prefix (e.g., pdfS3Key / pdfS3Bucket, markdownS3Key / markdownS3Bucket).
Self-contained migrations
Never import application code (~/business/, etc.) in migration files. Migrations are frozen snapshots — they must produce the same result regardless of how the application evolves after they were written.
If a migration needs logic that already exists in the application (e.g., a normalization function for a backfill), duplicate that logic directly inside the migration file. This makes the migration immune to future changes in the imported module.
The only allowed imports in migration files are kysely (and its sql helper) and Node.js built-in modules.
Migrations evolve the schema, and they are bound by the doctrine's spirit: a backfill populates a new structure with INSERTs derived from existing rows; a migration never rewrites or erases recorded events. When an existing event table needs a new NOT NULL column, introduce a new event table for the extended concern instead of reshaping history.
The dev seed builds demo state onto an empty database
The development seed (app/db/dev-seed/) is empty-database-only. seed.ts runs assertDevelopmentDatabase() then assertEmptyDatabase() before any section, aborting unless the database name ends with _development and every application table is empty — only Kysely's migration bookkeeping and the migration-populated measure_units/measure_unit_revisions are exempt. There is no idempotency machinery: no "already seeded" guards, no per-natural-key convergence. Each section assumes a blank database and only inserts. One deliberate exception: the staff-administrator-role.ts helpers stay find-or-create because the E2E test seed (tests/seed/) shares them, and that seed converges over a persistent database by contract (see the testing skill) — on the dev seed's empty-database path they always take the create branch.
There is deliberately no reset script. Because the seed only builds onto an empty database, reseeding is a manual drop, recreate, migrate, seed — every run is a complete build from a known-clean slate, never a patch over existing rows.
seed.ts is a thin ordered orchestrator; each surface owns one file. A section's prerequisite lookup — the product a stock item needs, the recipe a run needs — must throw naming the missing key rather than fall back to a default: a blank-database run has no earlier state to lean on, so a missing prerequisite is a seed-ordering bug to surface loudly, not to paper over.
Three rules keep a section demo-ready and CI-durable. Compute every date relative to now() in SQL (an interval off today, in the relevant company's timezone) — the manifest test runs the seed in CI forever, and a hardcoded calendar date rots. Prerequisite lookups key on natural business keys (a product code, an email), never on insertion order — bulk-seeded rows share timestamps, so orderBy('createdAt') is undefined, and orderBy('id') is a coin flip too, because ids are gen_random_uuid(). And sections drive real business functions with form-shaped input: boolean-ish fields go through checkboxSchema, which expects the string 'on', not true.
The timestamp-tie problem also decides on-screen order. Sibling rows inserted in one transaction share that transaction's now() to the microsecond, so any view that orders them by createdAt falls through to its id tiebreak and reshuffles on every reseed — which flips demo screens and screenshot captures nondeterministically. When a seeded view's row order matters, stagger the siblings' createdAt with small now()-relative offsets (now() - make_interval(secs => index)); never change the product query's ordering to compensate.
A seed lookup that can match more than one row must order deterministically down to a unique tiebreaker — pin a natural business key (a product code, a location code), or order by createdAt plus a final id. An unordered executeTakeFirstOrThrow first-row pick reads whatever heap order the query plan happens to return, so it silently switches to a different row the day a later section inserts earlier in the pipeline, and the swap only surfaces downstream as changed demo state or a drifted docs-screenshot signature — far from the change that caused it.
A feature that ships a new user-facing surface ships its dev-seed section in the same PR. The seed-coverage manifest test (pnpm run test:seed) runs the real seed in CI and asserts every product surface carries its demo-critical state, so a surface added without demo data fails the build.
Performance: derive first, then escalate
Query-time derivation is the default and stays the default until a real query is measurably slow. When that happens, escalate in strict order:
EXPLAIN ANALYZEand indexes. Most latest-wins and existence derivations are index problems. Composite(parentId, createdAt desc)indexes, partial indexes, and covering indexes go a very long way.- Partitioned tables. High-volume event tables (stock movements, processing heartbeats) partition naturally by time range. Declarative partitioning keeps hot partitions small without changing the write path or the doctrine.
- RisingWave materialized views. When a derivation must be served near real time and is beyond what indexes and partitions can do, the plan is a RisingWave service ingesting Postgres events and maintaining incrementally-updated materialized views — a pure read-side layer, rebuildable from Postgres at any time, never written back into the application schema. This layer is intentionally not built yet. Do not add it, plan for it, or design around it until the first two rungs are exhausted on a real, measured bottleneck.
Never write a derived value back into the application schema from application code — a cache that lives in an app table is a mutable column with extra steps.