Database Layer

Resources

scripts/
  database-checklist.sh
references/
  orm-comparison.md

Database Layer Implementation

This skill guides you through implementing database layers in applications, from initial schema design to query optimization. It leverages GoodVibes precision tools and project analysis tools for type-safe, production-ready database implementations.

When to Use This Skill

Use this skill when you need to:

• Set up a new database connection and ORM
• Design and implement database schemas
• Create and run migrations
• Generate type-safe database clients
• Write queries and handle relationships
• Optimize database performance
• Integrate with existing database infrastructure

Workflow

Follow this sequence for database layer implementation:

1. Discover Existing Database Infrastructure

Before implementing any database changes, understand the current state using the detect_stack analysis tool:

detect_stack:
  project_root: "."
  categories: ["database", "orm"]

This identifies:

• Existing database technology (PostgreSQL, MySQL, MongoDB, SQLite)
• ORM/query builder in use (Prisma, Drizzle, Kysely, Mongoose)
• Schema definition files
• Migration tooling
• Connection management patterns

Check project memory for database decisions:

precision_read:
  files:
    - path: ".goodvibes/memory/decisions.json"
    - path: ".goodvibes/memory/patterns.json"
  verbosity: minimal

Look for:

• Previous database technology choices ("Use Prisma for type safety")
• Migration strategies ("Always use reversible migrations")
• Performance patterns ("Add indexes for foreign keys")
• Known issues ("Avoid N+1 queries in user endpoints")

If database already exists, map the current schema:

get_database_schema:
  project_root: "."
  include_relations: true
  include_indexes: true

This returns:

• Table/collection definitions
• Column types and constraints
• Relationships (foreign keys, references)
• Indexes and unique constraints
• Enums and custom types

2. Choose Database Technology

If starting fresh, consult the ORM comparison reference to select the appropriate technology stack.

See: references/orm-comparison.md for decision trees.

Key decision factors:

Record your decision in memory:

After choosing, document the decision in .goodvibes/memory/decisions.json for future reference.

3. Design Schema

Identify entities and relationships first:

Entities: User, Post, Comment, Category

Relationships:
- User 1:N Post (author)
- Post N:M Category (through PostCategory)
- Post 1:N Comment
- User 1:N Comment (author)

Create schema files using precision_write:

For Prisma:

precision_write:
  files:
    - path: "prisma/schema.prisma"
      content: |
        generator client {
          provider = "prisma-client-js"
        }

        datasource db {
          provider = "postgresql"
          url      = env("DATABASE_URL")
        }

        model User {
          id        String   @id @default(cuid())
          email     String   @unique
          name      String?
          posts     Post[]
          comments  Comment[]
          createdAt DateTime @default(now())
          updatedAt DateTime @updatedAt
        }

        model Post {
          id         String     @id @default(cuid())
          title      String
          content    String
          published  Boolean    @default(false)
          author     User       @relation(fields: [authorId], references: [id])
          authorId   String
          categories Category[]
          comments   Comment[]
          createdAt  DateTime   @default(now())
          updatedAt  DateTime   @updatedAt

          @@index([authorId])
          @@index([published, createdAt])
        }

        model Category {
          id    String @id @default(cuid())
          name  String @unique
          posts Post[]
        }

        model Comment {
          id        String   @id @default(cuid())
          content   String
          post      Post     @relation(fields: [postId], references: [id])
          postId    String
          author    User     @relation(fields: [authorId], references: [id])
          authorId  String
          createdAt DateTime @default(now())

          @@index([postId])
          @@index([authorId])
        }
  verbosity: minimal

For Drizzle:

precision_write:
  files:
    - path: "src/db/schema.ts"
      content: |
        import { pgTable, text, timestamp, boolean, index } from 'drizzle-orm/pg-core';
        import { relations } from 'drizzle-orm';

        export const users = pgTable('users', {
          id: text('id').primaryKey().$defaultFn(() => crypto.randomUUID()),
          email: text('email').notNull().unique(),
          name: text('name'),
          createdAt: timestamp('created_at').defaultNow().notNull(),
          updatedAt: timestamp('updated_at').defaultNow().notNull(),
        });

        export const posts = pgTable('posts', {
          id: text('id').primaryKey().$defaultFn(() => crypto.randomUUID()),
          title: text('title').notNull(),
          content: text('content').notNull(),
          published: boolean('published').default(false).notNull(),
          authorId: text('author_id').notNull().references(() => users.id),
          createdAt: timestamp('created_at').defaultNow().notNull(),
          updatedAt: timestamp('updated_at').defaultNow().notNull(),
        }, (table) => ({
          authorIdx: index('author_idx').on(table.authorId),
          publishedCreatedIdx: index('published_created_idx').on(table.published, table.createdAt),
        }));

        export const usersRelations = relations(users, ({ many }) => ({
          posts: many(posts),
        }));

        export const postsRelations = relations(posts, ({ one }) => ({
          author: one(users, {
            fields: [posts.authorId],
            references: [users.id],
          }),
        }));
  verbosity: minimal

Schema best practices:

1. Use appropriate ID strategy:

   • CUID/UUID for distributed systems
   • Auto-increment for simple apps
   • Composite keys for join tables

2. Add timestamps:

   • Always include createdAt
   • Include updatedAt for mutable entities
   • Consider deletedAt for soft deletes

3. Index strategically:

   • Foreign keys (for joins)
   • Frequently queried fields
   • Composite indexes for multi-column filters
   • Unique constraints where applicable

4. Plan for scale:

   • Text vs VARCHAR limits
   • JSONB for flexible data (PostgreSQL)
   • Separate tables for large text/blobs

4. Configure Database Connection

Create environment configuration:

precision_write:
  files:
    - path: ".env.example"
      content: |
        # Database
        DATABASE_URL="postgresql://user:password@localhost:5432/dbname"
        # For Prisma with connection pooling
        # DATABASE_URL="postgresql://user:password@localhost:5432/dbname?pgbouncer=true"
        # DIRECT_URL="postgresql://user:password@localhost:5432/dbname"
      mode: overwrite
  verbosity: minimal

Create database client module:

For Prisma:

precision_write:
  files:
    - path: "src/lib/db.ts"
      content: |
        import { PrismaClient } from '@prisma/client';

        const globalForPrisma = globalThis as unknown as {
          prisma: PrismaClient | undefined;
        };

        export const db =
          globalForPrisma.prisma ??
          new PrismaClient({
            log:
              process.env.NODE_ENV === 'development'
                ? ['query', 'error', 'warn']
                : ['error'],
          });

        if (process.env.NODE_ENV !== 'production') {
          globalForPrisma.prisma = db;
        }
  verbosity: minimal

For Drizzle:

precision_write:
  files:
    - path: "src/lib/db.ts"
      content: |
        import { drizzle } from 'drizzle-orm/postgres-js';
        import postgres from 'postgres';
        import * as schema from '@/db/schema';

        const connectionString = process.env.DATABASE_URL!;

        const client = postgres(connectionString, {
          max: process.env.NODE_ENV === 'production' ? 10 : 1,
        });

        export const db = drizzle(client, { schema });
  verbosity: minimal

5. Run Migrations

Use precision_exec to run migration commands with expectations:

For Prisma:

precision_exec:
  commands:
    - cmd: "npx prisma migrate dev --name init"
      timeout_ms: 60000
      expect:
        exit_code: 0
      # Note: Prisma outputs progress to stderr; this is expected behavior
    - cmd: "npx prisma generate"
      expect:
        exit_code: 0
  verbosity: standard

For Drizzle:

precision_exec:
  commands:
    - cmd: "npx drizzle-kit generate"
      expect:
        exit_code: 0
    - cmd: "npx drizzle-kit push"
      timeout_ms: 60000
      expect:
        exit_code: 0
  verbosity: standard

Migration best practices:

1. Always review generated migrations before applying
2. Use reversible migrations (include both up and down)
3. Test migrations on dev database before production
4. Backup production data before running migrations
5. Use transactions for multi-step migrations

6. Generate Type-Safe Client

Use the generate_types project tool to generate TypeScript types from your schema:

generate_types:
  project_root: "."
  source: "database"
  output_path: "src/types/db.ts"

This creates type definitions for:

• Table/collection types
• Insert types (omitting auto-generated fields)
• Update types (all fields optional)
• Select types (with relations)

Verify type generation:

precision_exec:
  commands:
    - cmd: "npm run typecheck"
      expect:
        exit_code: 0
  verbosity: minimal

7. Implement Queries

Start with basic CRUD operations:

precision_write:
  files:
    - path: "src/db/queries/users.ts"
      content: |
        import { db } from '@/lib/db';

        export async function createUser(data: { email: string; name?: string }) {
          return db.user.create({
            data,
          });
        }

        export async function getUserById(id: string) {
          return db.user.findUnique({
            where: { id },
            include: {
              posts: true,
            },
          });
        }

        export async function updateUser(
          id: string,
          data: { email?: string; name?: string }
        ) {
          return db.user.update({
            where: { id },
            data,
          });
        }

        export async function deleteUser(id: string) {
          return db.user.delete({
            where: { id },
          });
        }
  verbosity: minimal

Check for N+1 query patterns using project tools:

get_prisma_operations:
  project_root: "."
  analyze_performance: true

This identifies:

• N+1 query opportunities (missing include or select)
• Missing indexes on frequently queried fields
• Inefficient relationship loading

Optimize queries:

1. Use select to limit fields:

   db.user.findMany({
     select: { id: true, email: true }, // Don't fetch unused fields
   });
   

2. Eager load relationships:

   db.post.findMany({
     include: { author: true }, // Prevents N+1
   });
   

3. Use pagination:

   db.post.findMany({
     take: 20,
     skip: (page - 1) * 20,
   });
   

4. Add database-level constraints:

   @@index([userId, createdAt(sort: Desc)])
   

8. Implement Transactions

For multi-step operations, use transactions:

Prisma:

export async function createPostWithCategories(
  postData: { title: string; content: string; authorId: string },
  categoryIds: string[]
) {
  return db.$transaction(async (tx) => {
    const post = await tx.post.create({
      data: {
        ...postData,
        categories: {
          connect: categoryIds.map((id) => ({ id })),
        },
      },
    });

    await tx.user.update({
      where: { id: postData.authorId },
      data: { updatedAt: new Date() },
    });

    return post;
  });
}

Drizzle:

export async function createPostWithCategories(
  postData: { title: string; content: string; authorId: string },
  categoryIds: string[]
) {
  return db.transaction(async (tx) => {
    const [post] = await tx.insert(posts).values(postData).returning();

    await tx.insert(postCategories).values(
      categoryIds.map((categoryId) => ({
        postId: post.id,
        categoryId,
      }))
    );

    return post;
  });
}

9. Seed Development Data

Create seed script for local development:

precision_write:
  files:
    - path: "prisma/seed.ts"
      content: |
        import { PrismaClient } from '@prisma/client';

        const prisma = new PrismaClient();

        async function main() {
          // Clear existing data
          await prisma.comment.deleteMany();
          await prisma.post.deleteMany();
          await prisma.user.deleteMany();
          await prisma.category.deleteMany();

          // Create users
          const alice = await prisma.user.create({
            data: {
              email: 'alice@example.com',
              name: 'Alice',
            },
          });

          const bob = await prisma.user.create({
            data: {
              email: 'bob@example.com',
              name: 'Bob',
            },
          });

          // Create categories
          const tech = await prisma.category.create({
            data: { name: 'Technology' },
          });

          const news = await prisma.category.create({
            data: { name: 'News' },
          });

          // Create posts
          await prisma.post.create({
            data: {
              title: 'First Post',
              content: 'This is the first post',
              published: true,
              authorId: alice.id,
              categories: {
                connect: [{ id: tech.id }],
              },
            },
          });

          console.log('Database seeded successfully');
        }

        main()
          .catch((e) => {
            console.error(e);
            process.exit(1);
          })
          .finally(async () => {
            await prisma.$disconnect();
          });
  verbosity: minimal

Update package.json:

precision_edit:
  edits:
    - path: "package.json"
      find: '"scripts": {'
      hints:
        near_line: 2
      replace: |
        "prisma": {
          "seed": "tsx prisma/seed.ts"
        },
        "scripts": {
  verbosity: minimal

10. Validate Implementation

Run the database checklist script:

./plugins/goodvibes/skills/outcome/database-layer/scripts/database-checklist.sh .

This validates:

• Schema file exists and is valid
• Migration directory present
• Database URL documented in .env.example
• Type generation configured
• No SQL injection vulnerabilities (string concatenation)
• Connection pooling configured
• Indexes on foreign keys

Run type checking and tests:

precision_exec:
  commands:
    - cmd: "npm run typecheck"
      expect:
        exit_code: 0
    - cmd: "npm run test -- db"
      expect:
        exit_code: 0
  verbosity: minimal

Use query_database to verify data integrity:

query_database:
  project_root: "."
  query: "SELECT COUNT(*) FROM users;"

Common Patterns

Soft Deletes

Add deletedAt field and filter in queries:

model Post {
  id        String    @id
  deletedAt DateTime?
}

// Soft delete
await db.post.update({
  where: { id },
  data: { deletedAt: new Date() },
});

// Query only active records
await db.post.findMany({
  where: { deletedAt: null },
});

Optimistic Locking

Use version field to prevent concurrent updates:

model Post {
  id      String @id
  version Int    @default(0)
}

await db.post.update({
  where: {
    id: postId,
    version: currentVersion,
  },
  data: {
    title: newTitle,
    version: { increment: 1 },
  },
});

Connection Pooling

For serverless environments, use connection pooling:

# PgBouncer
DATABASE_URL="postgresql://user:password@localhost:6543/db?pgbouncer=true"
DIRECT_URL="postgresql://user:password@localhost:5432/db"

datasource db {
  provider  = "postgresql"
  url       = env("DATABASE_URL")
  directUrl = env("DIRECT_URL")
}

Full-Text Search

PostgreSQL:

@@index([content(ops: raw("gin_trgm_ops"))], type: Gin)

await db.$queryRaw`
  SELECT * FROM posts
  WHERE to_tsvector('english', content) @@ to_tsquery('search terms')
`;

Security Checklist

• Database credentials in environment variables (not committed)
• Input validation on all user-provided data
• Parameterized queries (no string concatenation)
• Row-level security for multi-tenant apps
• Rate limiting on expensive queries
• Audit logging for sensitive operations
• Least privilege database user permissions
• SSL/TLS for database connections in production

Performance Checklist

• Indexes on foreign keys
• Composite indexes for multi-column filters
• Connection pooling configured
• Query result pagination
• Eager loading to prevent N+1 queries
• Database query logging in development
• Explain/analyze for slow queries
• Caching for frequently accessed data

Troubleshooting

Migration fails with constraint violation

1. Check existing data conflicts with new constraints
2. Add data migration before schema migration
3. Use multi-step migrations (add column nullable, populate, make required)

N+1 query detected

1. Use get_prisma_operations to identify location
2. Add include or select with relations
3. Consider using dataloader for complex cases

Connection pool exhausted

1. Increase pool size in connection string (?pool_timeout=10)
2. Check for missing await (connections not released)
3. Use connection pooler (PgBouncer, Prisma Accelerate)

Type generation fails

1. Verify schema syntax with npx prisma validate
2. Clear generated files and regenerate
3. Check for circular dependencies in relations

Next Steps

After implementing the database layer:

1. Add caching - Use Redis for frequently accessed data
2. Implement search - Add full-text search or Elasticsearch
3. Add monitoring - Track query performance and slow queries
4. Write tests - Unit tests for queries, integration tests for transactions
5. Document schema - Add comments to schema for team reference
6. Plan backups - Set up automated database backups

For additional reference material and decision trees, see:

• references/orm-comparison.md - ORM selection guide
• scripts/database-checklist.sh - Validation script

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