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Multi-tenant data isolation with one Rivet Actor per tenant: the actor key is the tenant id, so each tenant gets its own isolated dataset and migrations.
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SOC 職業分類に基づく
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Live cursors and multiplayer presence with Rivet Actors: per-connection cursor state, realtime updates over events or raw WebSockets, and throttling.
Pragmatic patterns for building multiplayer games: matchmaking, tick loops, realtime state, interest management, and validation.
| name | per-tenant-database |
| description | Multi-tenant data isolation with one Rivet Actor per tenant: the actor key is the tenant id, so each tenant gets its own isolated dataset and migrations. |
IMPORTANT: Before doing anything, you MUST read BASE_SKILL.md in this skill's directory. It contains essential guidance on debugging, error handling, state management, deployment, and project setup. Those rules and patterns apply to all RivetKit work. Everything below assumes you have already read and understood it.
If you need a reference implementation, read the raw working example code in these templates:
Patterns for database-per-tenant architectures with RivetKit. Instead of one shared database with a tenant_id column on every table, each tenant gets its own Rivet Actor, and that actor owns the tenant's entire dataset.
Start with the working example on GitHub and adapt it. The example stores each tenant's dataset in JSON actor state and serves a React dashboard with live event updates.
| Topic | Summary |
|---|---|
| Isolation | One companyDatabase actor per tenant, keyed by company name. Switching tenants swaps the entire dataset. |
| State | JSON actor state holding employees and projects arrays plus timestamps. No SQLite, no queues, no scheduling. |
| Realtime | Every write action mutates state, then broadcasts a typed event (employeeAdded, projectAdded) to all connected clients of that tenant. |
| Auth | None. The sign-in screen is cosmetic. Production guidance is in the security checklist. |
The actor key is the tenant id. The client connects with useActor({ name: "companyDatabase", key: [companyName] }) and the actor reads c.key[0] in createState to seed that tenant's dataset. This gives you:
companyDatabase[tenantId] addresses exactly one actor instance. Two tenants can never share an actor.tenant_id column to filter incorrectly. Cross-tenant leaks require constructing the wrong key, not forgetting a WHERE clause.key: [tenantId] cannot be escaped the way "tenant:" + tenantId string concatenation can. See Keys.The example's test (tests/per-tenant-database.test.ts) proves the isolation: data written to companyDatabase["Alpha Co"] never appears in companyDatabase["Beta Co"].
The example uses plain JSON actor state. The same key-equals-tenant model works with any actor state backend.
| Backend | Use When | Docs | Working Code |
|---|---|---|---|
| JSON actor state | Small datasets, simple reads, whole dataset fits comfortably in memory. What the example uses. | State | GitHub |
Actor SQLite (rivetkit/db) | Tables, indexes, SQL queries, larger-than-memory data, per-tenant relational schema. | SQLite | GitHub |
| SQLite + Drizzle | Typed schema, query builder, and generated migration files on top of actor SQLite. | SQLite + Drizzle | GitHub |
With either SQLite option, every tenant gets its own embedded SQLite database, since the database is scoped to the actor and the actor is scoped to the tenant.
The per-tenant example has no migrations because JSON state has no schema. When you adopt SQLite, migrations run per tenant database:
db({ onMigrate }) runs your migration SQL inside a SQLite savepoint before the actor serves traffic. If onMigrate throws, all migration SQL rolls back atomically and the actor does not start. See SQLite.drizzle-kit generates migration files from your typed schema, and db({ schema, migrations }) applies them when the actor wakes. See SQLite + Drizzle.Because each tenant has its own database, migrations roll out per actor as each tenant's actor wakes, rather than as one large migration against a shared database.
The example's sign-in is cosmetic: the client picks any company string and that string becomes the actor key, so any visitor can read and write any tenant's data. Do not ship this. As a required production extension (not implemented by the example):
c.key in onBeforeConnect (pass/fail) or createConnState (store the verified user on connection state). See Authentication and Connections.companyDatabase[companyName] (single-element array key; c.key[0] is the company name)addEmployeelistEmployeesaddProjectlistProjectsgetStatsemployeeAddedprojectAddedcompany_nameemployeesprojectscreated_atupdated_atEvery write action follows the same mutate-then-broadcast shape: push the record into c.state, bump updated_at, broadcast the typed event, return the record. See Actions and Events.
sequenceDiagram
participant A as Tenant A client
participant DA as companyDatabase A
participant B as Tenant B client
participant DB as companyDatabase B
Note over A: authenticate and derive tenant id
A->>DA: connect with key [tenantA]
Note over DA: createState seeds company_name, employees, projects
A->>DA: listEmployees() + listProjects() + getStats()
A->>DA: addEmployee(name, role)
DA-->>A: employeeAdded event
B->>DB: connect with key [tenantB]
Note over DB: separate actor, separate dataset
B->>DB: listEmployees()
DB-->>B: tenant B data only
In the example, the "authenticate" step is a free-text company picker. The rest of the flow matches the diagram: createState seeds the dataset on first creation, the dashboard loads with listEmployees, listProjects, and getStats, and every connected client of the same tenant receives employeeAdded and projectAdded events.
The example ships with none of these. Apply all of them before production.
onBeforeConnect or createConnState, verify the credential's tenant claim matches c.key and reject mismatches.addEmployee, addProject), not just at connect time. See Access Control.key: [tenantId]). Never interpolate tenant ids into key strings, and never build keys from one tenant's input to address another tenant's actor.employees and projects arrays. The example lets them grow unboundedly in JSON state; move to SQLite when the dataset outgrows memory.