Database Patterns

Skill — Database Patterns

When this skill activates

Any task involving database schema design, query writing, migrations, or ORM configuration.

Mandatory actions when this skill is active

Schema design standards

Naming conventions (PostgreSQL default):

-- Tables: snake_case, plural
CREATE TABLE user_profiles (...);
CREATE TABLE order_items (...);

-- Columns: snake_case
user_id, created_at, updated_at, deleted_at

-- Primary keys: always id (UUID preferred over auto-increment)
id UUID PRIMARY KEY DEFAULT gen_random_uuid()

-- Foreign keys: {referenced_table_singular}_id
user_id UUID REFERENCES users(id) ON DELETE CASCADE
order_id UUID REFERENCES orders(id) ON DELETE SET NULL

-- Indexes: idx_{table}_{column(s)}
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_orders_user_id_created_at ON orders(user_id, created_at DESC);

Standard columns every table should have:

id          UUID PRIMARY KEY DEFAULT gen_random_uuid(),
created_at  TIMESTAMPTZ NOT NULL DEFAULT NOW(),
updated_at  TIMESTAMPTZ NOT NULL DEFAULT NOW(),
-- For soft delete (preferred over hard delete):
deleted_at  TIMESTAMPTZ  -- NULL = active, timestamp = deleted

UUID strategy:

• Prefer UUIDv7 or ULID for time-ordered inserts (better index locality)
• Use random UUIDv4 for high-entropy IDs when ordering is not important
• Document the choice in ARCHITECTURE.md and keep it consistent across tables

Soft delete implementation: Use soft delete (setting deleted_at) instead of hard delete for:

• User records (GDPR right to erasure exception: anonymise, don't delete)
• Financial records (audit requirements)
• Any record that might be referenced by other records

For hard delete: cascade must be configured. Document the cascade in ARCHITECTURE.md.

Query standards

N+1 query detection and prevention:

// ❌ N+1 pattern: 1 query for users + N queries for their orders
const users = await db.users.findMany()
for (const user of users) {
  user.orders = await db.orders.findMany({ where: { userId: user.id } })
}

// ✅ Single query with JOIN or include
const users = await db.users.findMany({
  include: { orders: true }  // Prisma generates a single JOIN query
})

// ✅ Or batch load with WHERE IN
const userIds = users.map(u => u.id)
const orders = await db.orders.findMany({ where: { userId: { in: userIds } } })
const ordersByUser = groupBy(orders, 'userId')

Framework-agnostic SQL example (N+1):

-- ❌ N+1 pattern (application loops)
-- SELECT * FROM users LIMIT 50;
-- then for each user:
-- SELECT * FROM orders WHERE user_id = ?;

-- ✅ Single query with JOIN
SELECT u.*, o.*
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.id IN (:user_ids);

Pagination patterns:

// ❌ OFFSET pagination (slow on large datasets — scans all previous rows)
SELECT * FROM posts ORDER BY created_at DESC LIMIT 20 OFFSET 10000;

// ✅ Compound cursor — handles duplicate timestamps correctly
// Application layer: encode (created_at, id) as the cursor
SELECT * FROM posts
WHERE (created_at, id) < (:cursor_time::timestamptz, :cursor_id::uuid)
ORDER BY created_at DESC, id DESC
LIMIT 20;

// Cursor encoding (application layer):
// encode: btoa(JSON.stringify({ t: row.created_at, id: row.id }))
// decode: JSON.parse(atob(cursor))
// Return:
{
  "data": [...],
  "nextCursor": "[base64 of {t, id} pair]",
  "hasMore": true
}

Why compound cursors matter

Single-field cursors (created_at only) produce incorrect pagination when multiple records share the same timestamp — common in batch imports and high-write systems. Always use at least (timestamp, id) as a compound cursor.

For simple cases where records are created sequentially and timestamps are guaranteed unique (e.g., a single-writer queue): a single-field cursor is acceptable. Document this assumption in the code.

Transaction usage:

// Use transactions whenever multiple writes must succeed or fail together
await db.$transaction(async (tx) => {
  const order = await tx.orders.create({ data: orderData })
  await tx.orderItems.createMany({ data: items.map(i => ({...i, orderId: order.id})) })
  await tx.inventory.updateMany({ /* deduct stock */ })
  // All three succeed or all three roll back
})

For financial operations or ledger updates, use SERIALIZABLE isolation or explicit row-level locks to prevent lost updates and double-spend conditions.

Index strategy

Always index:

• All foreign key columns (ORM does not always do this automatically)
• Columns used in WHERE clauses on large tables
• Columns used in ORDER BY on large tables
• Columns used in JOIN conditions

Composite indexes:

• Order columns from most selective (highest cardinality) to least
• A composite index on (a, b) is used for queries filtering on a alone, or on both a and b. Not for b alone.
• Example: index on (user_id, created_at DESC) for "get user's recent items"

Never index:

• Boolean columns on large tables (index selectivity too low to help)
• Columns that change very frequently (index maintenance overhead)
• Tables with fewer than 10,000 rows (full scan is faster)

Migration standards

# Every schema change must have a migration file
# Naming: [timestamp]_[descriptive-name].sql or per ORM convention

# Migration must be:
# 1. Idempotent: safe to run multiple times
# 2. Reversible: has both UP and DOWN migration
# 3. Non-blocking: avoid full table locks in production migrations

# Non-blocking pattern for adding a column (PostgreSQL):
# Step 1: Add column with default as NULL (instant)
ALTER TABLE users ADD COLUMN phone_verified BOOLEAN;

# Step 2: Backfill in batches (separate deployment)
UPDATE users SET phone_verified = false
WHERE id IN (SELECT id FROM users WHERE phone_verified IS NULL LIMIT 1000);

# Step 3: Add NOT NULL constraint + default (after backfill completes)
ALTER TABLE users ALTER COLUMN phone_verified SET NOT NULL;
ALTER TABLE users ALTER COLUMN phone_verified SET DEFAULT false;

Query performance checklist

Before committing any query-writing task:

• Ran EXPLAIN ANALYZE on all non-trivial queries
• All WHERE/JOIN/ORDER BY columns have indexes
• No N+1 queries in list-fetching code
• Large queries paginated (cursor-based for > 1K rows)
• Transactions used for multi-write operations
• Connection pooling configured (not creating connections per request)