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database-patterns
Use when designing PostgreSQL + Redis data models, indexes, caching strategies, JSONB usage, tiered storage, or cache consistency contracts.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Use when designing PostgreSQL + Redis data models, indexes, caching strategies, JSONB usage, tiered storage, or cache consistency contracts.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Automatically diagnose excessive Codex local SQLite diagnostic log writes and give a concrete fix plan. Use when a user asks whether Codex is writing too much to disk, mentions logs_2.sqlite, logs_2.sqlite-wal, block_log_inserts, SSD/TBW wear from Codex logs, or wants Codex log write issues checked, explained, stopped, cleaned up, verified, or restored.
Use when you want Codex to review its own recent history (last N days or specific period) and improve its behavior. Produces minimal, high-signal updates to AGENTS.md and tiny reusable skills. The goal is long-term fluency — Codex gradually becomes better at your specific style, constraints, and workflows.
Use when coordinating complex tasks across multiple AI agents with a centralized handoff document for planning, execution tracking, and feedback fusion.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Guard long, ambiguous, or stateful AI-agent work from drift. Use when the user asks to run or continue a multi-step task, autonomous loop, bug fix, repo change, PR readiness check, compaction handoff, resume from previous context, cost-control checkpoint, or any task likely to span many tool calls, files, sessions, agents, or verification gates.
Audit and standardize a repository's agent-readable context, including AGENTS.md, CLAUDE.md, WARP.md, CONTRIBUTING.md, .agents/skills, and specs/ PRODUCT.md and TECH.md contracts. Use when asked to review, design, create, scaffold, or improve repo instructions, agent onboarding, spec workflows, or cross-repo agent-context conventions.
| name | database-patterns |
| description | Use when designing PostgreSQL + Redis data models, indexes, caching strategies, JSONB usage, tiered storage, or cache consistency contracts. |
| Use Case | Type | Avoid |
|---|---|---|
| Primary Key | UUID / BIGSERIAL | INT (range limits) |
| Timestamps | TIMESTAMPTZ | TIMESTAMP (no timezone) |
| Money | NUMERIC(19,4) | FLOAT (precision loss) |
| Status | TEXT + CHECK | INT (unreadable) |
| Semi-structured | JSONB | JSON (no indexing) |
| Full-text | TSVECTOR | LIKE '%..%' |
-- Use UUID for distributed-friendly IDs
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";
CREATE TABLE users (
id UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
email TEXT UNIQUE NOT NULL,
name TEXT NOT NULL,
status TEXT NOT NULL DEFAULT 'active'
CHECK (status IN ('active', 'inactive', 'suspended')),
metadata JSONB DEFAULT '{}',
created_at TIMESTAMPTZ DEFAULT NOW(),
updated_at TIMESTAMPTZ DEFAULT NOW()
);
-- Updated timestamp trigger
CREATE OR REPLACE FUNCTION update_updated_at()
RETURNS TRIGGER AS $$
BEGIN
NEW.updated_at = NOW();
RETURN NEW;
END;
$$ LANGUAGE plpgsql;
CREATE TRIGGER users_updated_at
BEFORE UPDATE ON users
FOR EACH ROW
EXECUTE FUNCTION update_updated_at();
-- B-Tree: Equality, range, sorting (default)
CREATE INDEX idx_users_email ON users(email);
-- Composite: Leftmost prefix rule
-- Supports: (user_id), (user_id, created_at)
-- Does NOT support: (created_at) alone
CREATE INDEX idx_orders_user_date ON orders(user_id, created_at DESC);
-- Partial: Reduce index size
CREATE INDEX idx_active_users ON users(email)
WHERE status = 'active';
-- GIN for JSONB: Containment queries
CREATE INDEX idx_metadata ON users USING GIN (metadata jsonb_path_ops);
-- Expression: Specific JSONB field
CREATE INDEX idx_user_role ON users ((metadata->>'role'));
-- Full-text search
CREATE INDEX idx_search ON products USING GIN (to_tsvector('english', name || ' ' || description));
-- Good: Dynamic attributes, rarely queried fields
CREATE TABLE products (
id UUID PRIMARY KEY,
name TEXT NOT NULL,
price NUMERIC(19,4) NOT NULL,
category TEXT NOT NULL, -- Extracted: frequently queried
attributes JSONB DEFAULT '{}' -- Dynamic: color, size, specs
);
-- Query with containment
SELECT * FROM products
WHERE category = 'electronics' -- B-Tree index
AND attributes @> '{"brand": "Apple"}'; -- GIN index
-- Query specific field
SELECT * FROM products
WHERE attributes->>'color' = 'black'; -- Expression index
-- Update JSONB field
UPDATE products
SET attributes = attributes || '{"featured": true}'
WHERE id = '...';
-- Always use EXPLAIN ANALYZE
EXPLAIN ANALYZE
SELECT u.*, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON o.user_id = u.id
WHERE u.status = 'active'
GROUP BY u.id
ORDER BY u.created_at DESC
LIMIT 20;
-- Watch for:
-- ❌ Seq Scan on large tables → Add index
-- ❌ Sort → Use index for ordering
-- ❌ Nested Loop with many rows → Consider JOIN order
-- ❌ Hash Join on huge tables → Add indexes
// PgBouncer or built-in pool
import { Pool } from 'pg';
const pool = new Pool({
max: 20, // Max connections
idleTimeoutMillis: 30000, // Close idle connections
connectionTimeoutMillis: 2000, // Fail fast
});
// Connection count formula:
// connections = (cores * 2) + effective_spindle_count
// Usually 10-30 is enough
| Use Case | Structure | Example |
|---|---|---|
| Cache objects | String | user:123 → JSON |
| Counters | String + INCR | views:article:456 |
| Sessions | Hash | session:abc → {userId, ...} |
| Leaderboards | Sorted Set | scores → {userId: score} |
| Queues | List/Stream | tasks → LPUSH/RPOP |
| Unique sets | Set | online_users |
| Real-time | Pub/Sub/Stream | Notifications |
# Format: <entity>:<id>:<attribute>
user:123:profile
user:123:settings
order:456:items
session:abc123
# Use colons for hierarchy
# Enables pattern matching with SCAN
SCAN 0 MATCH "user:*:profile" COUNT 100
const TTL = {
SESSION: 24 * 60 * 60, // 24 hours
CACHE: 15 * 60, // 15 minutes
RATE_LIMIT: 60, // 1 minute
LOCK: 30, // 30 seconds
};
// Set with TTL
await redis.set(`cache:user:${id}`, JSON.stringify(user), 'EX', TTL.CACHE);
// Check TTL
const remaining = await redis.ttl(`cache:user:${id}`);
async function getUser(id: string): Promise<User> {
const cacheKey = `user:${id}`;
// 1. Check cache
const cached = await redis.get(cacheKey);
if (cached) {
return JSON.parse(cached);
}
// 2. Cache miss → Query database
const user = await db.user.findUnique({ where: { id } });
if (!user) {
throw new NotFoundError('User not found');
}
// 3. Populate cache
await redis.set(cacheKey, JSON.stringify(user), 'EX', 900);
return user;
}
async function updateUser(id: string, data: UpdateInput): Promise<User> {
// 1. Update database
const user = await db.user.update({
where: { id },
data,
});
// 2. Update cache immediately
await redis.set(`user:${id}`, JSON.stringify(user), 'EX', 900);
return user;
}
async function deleteUser(id: string): Promise<void> {
// 1. Delete from database
await db.user.delete({ where: { id } });
// 2. Invalidate cache
await redis.del(`user:${id}`);
// 3. Invalidate related caches
const keys = await redis.keys(`user:${id}:*`);
if (keys.length > 0) {
await redis.del(...keys);
}
}
async function getUserWithLock(id: string): Promise<User> {
const cacheKey = `user:${id}`;
const lockKey = `lock:user:${id}`;
// Check cache
const cached = await redis.get(cacheKey);
if (cached) {
return JSON.parse(cached);
}
// Try to acquire lock
const acquired = await redis.set(lockKey, '1', 'EX', 10, 'NX');
if (!acquired) {
// Another process is loading, wait and retry
await sleep(100);
return getUserWithLock(id);
}
try {
// Double-check cache (another process might have populated it)
const rechecked = await redis.get(cacheKey);
if (rechecked) {
return JSON.parse(rechecked);
}
// Load from database
const user = await db.user.findUnique({ where: { id } });
await redis.set(cacheKey, JSON.stringify(user), 'EX', 900);
return user;
} finally {
await redis.del(lockKey);
}
}
async function getUserSafe(id: string): Promise<User | null> {
const cacheKey = `user:${id}`;
const cached = await redis.get(cacheKey);
// Check for cached null
if (cached === 'NULL') {
return null;
}
if (cached) {
return JSON.parse(cached);
}
const user = await db.user.findUnique({ where: { id } });
if (!user) {
// Cache null with short TTL
await redis.set(cacheKey, 'NULL', 'EX', 60);
return null;
}
await redis.set(cacheKey, JSON.stringify(user), 'EX', 900);
return user;
}
┌─────────────────────────────────────────────────┐
│ Application │
└─────────────────────────────────────────────────┘
│
┌───────────────┼───────────────┐
▼ ▼ ▼
┌─────────┐ ┌─────────┐ ┌─────────┐
│ Redis │ │ Postgres │ │ Archive │
│ (Hot) │ │ (Warm) │ │ (Cold) │
└─────────┘ └─────────┘ └─────────┘
< 1ms ~10ms ~100ms+
Active data Recent data Historical
Memory SSD Object storage
-- Partition by date range
CREATE TABLE orders (
id UUID NOT NULL,
user_id UUID NOT NULL,
total NUMERIC(19,4) NOT NULL,
created_at TIMESTAMPTZ NOT NULL
) PARTITION BY RANGE (created_at);
-- Create partitions
CREATE TABLE orders_2025_q1 PARTITION OF orders
FOR VALUES FROM ('2025-01-01') TO ('2025-04-01');
CREATE TABLE orders_2025_q2 PARTITION OF orders
FOR VALUES FROM ('2025-04-01') TO ('2025-07-01');
-- Archive old data
CREATE TABLE orders_archive (LIKE orders INCLUDING ALL);
-- Move old data to archive
WITH moved AS (
DELETE FROM orders
WHERE created_at < NOW() - INTERVAL '1 year'
RETURNING *
)
INSERT INTO orders_archive SELECT * FROM moved;
// Use transactions for multi-table operations
async function transferFunds(fromId: string, toId: string, amount: number) {
await db.$transaction(async (tx) => {
// Deduct from source
const from = await tx.account.update({
where: { id: fromId },
data: { balance: { decrement: amount } },
});
if (from.balance < 0) {
throw new Error('Insufficient funds');
}
// Add to destination
await tx.account.update({
where: { id: toId },
data: { balance: { increment: amount } },
});
});
}
-- Add version column
ALTER TABLE products ADD COLUMN version INT DEFAULT 1;
-- Update with version check
UPDATE products
SET
stock = stock - 1,
version = version + 1
WHERE id = $1 AND version = $2
RETURNING *;
-- If no rows returned, concurrent modification occurred
## Schema
- [ ] UUID or BIGSERIAL for primary keys
- [ ] TIMESTAMPTZ for all timestamps
- [ ] NUMERIC for money, not FLOAT
- [ ] CHECK constraints for enums
- [ ] Foreign keys with ON DELETE
## Indexing
- [ ] Index for each WHERE clause pattern
- [ ] Composite indexes match query order
- [ ] GIN index for JSONB containment
- [ ] EXPLAIN ANALYZE for slow queries
## Caching
- [ ] Cache-aside as default pattern
- [ ] TTL on all cached data
- [ ] Cache invalidation on writes
- [ ] Stampede/penetration protection
## Operations
- [ ] Connection pooling configured
- [ ] Slow query logging enabled
- [ ] Backup and recovery tested
- [ ] Partition strategy for growth