Phase 10 · Lesson 08

TaskFlow Production Data Model

≈ 2 min core reading · 448 source/companion words
Learning contract

What you should be able to do after this

  • Explain TaskFlow Production Data Model without relying on framework buzzwords.
  • Connect the lesson to the phase outcome: Translate TaskFlow business relationships into a relational model that can enforce membership, ordering, labels, comments, ownership, and lifecycle rules.
  • Reproduce the relevant behavior and verify it with evidence.
  • Diagnose at least one representative failure instead of guessing.
Figure 10.8 — TaskFlow boundary map
Business nouns
entities
relationships/cardinality
constraints
Prisma models
migration
seeded graph
This lesson belongs to Phase 10. Keep the full request/data path visible even when today's code changes only one box.
Instructor workflow: predict first → implement the smallest slice → run it → inspect evidence → break one assumption → diagnose → explain it back. Do not copy the whole code block before understanding the first failing boundary.

TaskFlow Production Data Model Assemble the first coherent relational model used by later phases

Phase 10 - Relational Data Modeling Volume 03 - Database Engineering

Architecture milestone

PostgreSQL + Prisma infrastructure

↓

Production-ready relational model

↓

Persistent TaskFlow CRUD

Phase 10 decides what the durable data should mean before Phase 11 writes application behavior against it.

The model we are committing to Figure 1 - TaskFlow production relational model

User

↓

Membership

↓

Workspace

↓

Project

↓

Task

The visual is simplified: Membership connects User <-> Workspace; Workspace owns Projects; Project owns Tasks; Task may reference an assignee User.

Model overview Model Core responsibility

User Application identity/profile reference

Workspace Tenant/collaboration boundary

Membership User participation and workspace-scoped role

Project Container of related work inside workspace

Task Work item inside project

Prisma schema snapshot enum MembershipRole { OWNER ADMIN MEMBER }

enum TaskStatus { TODO IN_PROGRESS DONE CANCELED }

enum TaskPriority { LOW

MEDIUM HIGH URGENT }

model User { id String @id @default(uuid()) email String @unique displayName String? createdAt DateTime @default(now()) updatedAt DateTime @updatedAt

memberships Membership[] assignedTasks Task[] @relation("TaskAssignee") }

model Workspace { id String @id @default(uuid()) name String slug String @unique createdAt DateTime @default(now()) updatedAt DateTime @updatedAt

memberships Membership[] projects Project[] }

model Membership { id String @id @default(uuid()) userId String workspaceId String role MembershipRole @default(MEMBER) joinedAt DateTime @default(now())

user User @relation(fields: [userId], references: [id], onDelete: Cascade) workspace Workspace @relation(fields: [workspaceId], references: [id], onDelete: Cascade)

Code
@@unique([workspaceId, userId])
@@index([userId])
}

model Project { id String @id @default(uuid()) workspaceId String name String description String? createdAt DateTime @default(now()) updatedAt DateTime @updatedAt

workspace Workspace @relation(fields: [workspaceId], references: [id], onDelete: Cascade) tasks Task[]

Code
@@index([workspaceId])
}

model Task { id String @id @default(uuid()) projectId String assigneeId String? title String description String? status TaskStatus @default(TODO) priority TaskPriority @default(MEDIUM) dueAt DateTime? completedAt DateTime? createdAt DateTime @default(now()) updatedAt DateTime @updatedAt

project Project @relation(fields: [projectId], references: [id], onDelete: Cascade) assignee User? @relation("TaskAssignee", fields: [assigneeId], references: [id], onDelete: SetNull)

Code
@@index([projectId, status])
Code
@@index([assigneeId, status])
}

Important service-layer invariant The schema can prove that assigneeId references a real User, but it does not prove that the user belongs to the task project's workspace. Later Todos/Tasks service logic must verify workspace membership before assignment. This is a perfect example of database integrity plus application business rules working together.

Why User exists before Supabase Auth integration Supabase Auth identity will arrive later. TaskFlow still benefits from an application-facing User/Profile model that can participate in relational data. Phase 17 will define how authenticated identity maps to this model without trusting browser-supplied user IDs. The production model is intentionally capable but not maximal. Comments, attachments, activity events, labels, invitations, notifications and audit logs will be added only when later product phases create concrete requirements.

Executable lesson studio · deployment

Now turn the explanation into engineering skill.

Deployment is the controlled movement of a versioned artifact into a configured runtime with evidence of health and a credible rollback path.

By the end, you should be able to

  • Explain TaskFlow Production Data Model in terms of concrete inputs, ownership, outputs, and evidence.
  • Implement or reproduce the smallest TaskFlow slice that demonstrates this deployment concept.
  • Diagnose one representative deployment failure using observable evidence instead of trial-and-error.
Figure 10.8B — Mental model
versioned artifact
production config
runtime
health/smoke test
rollback path
Read left-to-right while implementing. When debugging, walk right-to-left until the first observed fact differs from your prediction.
Code-along sequence

One coherent change at a time

  1. 01

    Restate the goal of “TaskFlow Production Data Model” as one observable behavior. Write the success signal before touching code.

  2. 02

    Trace the boundary map: versioned artifact → production config → runtime → health/smoke test → rollback path. Mark the one boundary this lesson changes.

  3. 03

    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.

  4. 04

    Implement the smallest vertical slice. Add one coherent change, run the narrowest verification command, and read the output before continuing.

  5. 05

    Connect the slice to TaskFlow’s phase artifact: A complete Prisma/ER model for TaskFlow core entities with keys, relation tables, uniqueness, and important indexes.. Verify behavior from the outside as a user or API client would.

  6. 06

    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.

  7. 07

    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.

txtRelease runbook, not a vague deploy button
1. Build an immutable artifact.
2. Apply production configuration/secrets.
3. Run compatible migrations.
4. Start the new version.
5. Check readiness and smoke tests.
6. Observe errors/latency.
7. Promote or execute rollback.

Do 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.

Verification

Prove the happy path

  1. Run the narrowest relevant command or request and require a clean exit/status.
  2. Inspect the boundary-specific evidence: terminal output, Network panel, database row/query plan, test report, container health, or deployment logs.
  3. Repeat the behavior once after the change so a one-off cached or stale result cannot masquerade as success.
Break it on purpose

Build debugging memory

  1. Using nullable foreign keys where the domain requires ownership
  2. Change one assumption in the deployment example—identity, input, configuration, ordering, connectivity, or state—and predict the exact symptom before running it.
SymptomInvestigation path
Deployment is green but app fails

Separate platform deploy status from application readiness; run smoke tests against real routes and dependencies.

New release breaks schema compatibility

Use expand/contract migrations and keep rollback compatibility explicit before release.

Closed-reference challenge

Can you transfer the idea?

Without copying the final source, create a second small scenario that applies “TaskFlow Production Data Model” to a different TaskFlow resource or input. Write the acceptance criteria first, implement it, and provide the evidence that each criterion is satisfied.

Explain-back

Answer these without reopening the solution

  1. What problem does TaskFlow Production Data Model solve in TaskFlow, and what would be observably wrong without it?
  2. Which part of the deployment boundary owns the decision, and which neighboring layer must not own it?
  3. What evidence proves the happy path instead of merely suggesting it worked?
  4. Name one realistic failure or abuse case and the first place you would inspect.
  5. If requirements doubled tomorrow, which part of this design should stay stable and which part is allowed to change?
Lesson studio

Turn the reading into skill

01

Do it

Model workspace membership as an explicit relation carrying role/state rather than an array on User.

Use the smallest change that demonstrates this lesson. Stop after it works and capture evidence.
02

Break it safely

Using nullable foreign keys where the domain requires ownership

Predict the symptom before causing the failure. Then locate the earliest boundary where reality diverges from expectation.
03

Explain it back

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.
Exit ticket

Before marking complete

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.