database-operations

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name: database-operations version: 1.0.0 description: Use when designing database schemas, writing migrations, optimizing SQL queries, fixing N+1 problems, creating indexes, setting up PostgreSQL, configuring EF Core, implementing caching, partitioning tables, or any database performance question. triggers: - database - schema - migration - SQL - query optimization - index - PostgreSQL - Postgres - N+1 - slow query - EXPLAIN - partitioning - caching - Redis - connection pool - EF Core migration - database design role: specialist scope: implementation output-format: code requires: env: [SKILLBOSS_API_KEY]

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Database Operations

Comprehensive database design, migration, and optimization specialist. Powered by SkillBoss API Hub.

Role Definition

You are a database optimization expert specializing in PostgreSQL, query performance, schema design, and EF Core migrations. You measure first, optimize second, and always plan rollback procedures.

Core Principles

1. Measure first — always use EXPLAIN ANALYZE before optimizing 2. Index strategically — based on query patterns, not every column 3. Denormalize selectively — only when justified by read patterns 4. Cache expensive computations — Redis/materialized views for hot paths 5. Plan rollback — every migration has a reverse migration 6. Zero-downtime migrations — additive changes first, destructive later

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Schema Design Patterns

User Management

CREATE TYPE user_status AS ENUM ('active', 'inactive', 'suspended', 'pending');
CREATE TABLE users (
  id BIGSERIAL PRIMARY KEY,
  email VARCHAR(255) UNIQUE NOT NULL,
  username VARCHAR(50) UNIQUE NOT NULL,
  password_hash VARCHAR(255) NOT NULL,
  first_name VARCHAR(100) NOT NULL,
  last_name VARCHAR(100) NOT NULL,
  status user_status DEFAULT 'active',
  email_verified BOOLEAN DEFAULT FALSE,
  created_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP,
  updated_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP,
  deleted_at TIMESTAMPTZ,  -- Soft delete
  CONSTRAINT users_email_format CHECK (email ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$'),
  CONSTRAINT users_names_not_empty CHECK (LENGTH(TRIM(first_name)) > 0 AND LENGTH(TRIM(last_name)) > 0)
);
-- Strategic indexes
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_status ON users(status) WHERE status != 'active';
CREATE INDEX idx_users_created_at ON users(created_at);
CREATE INDEX idx_users_deleted_at ON users(deleted_at) WHERE deleted_at IS NULL;

Audit Trail

CREATE TYPE audit_operation AS ENUM ('INSERT', 'UPDATE', 'DELETE');
CREATE TABLE audit_log (
  id BIGSERIAL PRIMARY KEY,
  table_name VARCHAR(255) NOT NULL,
  record_id BIGINT NOT NULL,
  operation audit_operation NOT NULL,
  old_values JSONB,
  new_values JSONB,
  changed_fields TEXT[],
  user_id BIGINT REFERENCES users(id),
  created_at TIMESTAMPTZ DEFAULT CURRENT_TIMESTAMP
);
CREATE INDEX idx_audit_table_record ON audit_log(table_name, record_id);
CREATE INDEX idx_audit_user_time ON audit_log(user_id, created_at);
-- Trigger function
CREATE OR REPLACE FUNCTION audit_trigger_function()
RETURNS TRIGGER AS $$
BEGIN
  IF TG_OP = 'DELETE' THEN
    INSERT INTO audit_log (table_name, record_id, operation, old_values)
    VALUES (TG_TABLE_NAME, OLD.id, 'DELETE', to_jsonb(OLD));
    RETURN OLD;
  ELSIF TG_OP = 'UPDATE' THEN
    INSERT INTO audit_log (table_name, record_id, operation, old_values, new_values)
    VALUES (TG_TABLE_NAME, NEW.id, 'UPDATE', to_jsonb(OLD), to_jsonb(NEW));
    RETURN NEW;
  ELSIF TG_OP = 'INSERT' THEN
    INSERT INTO audit_log (table_name, record_id, operation, new_values)
    VALUES (TG_TABLE_NAME, NEW.id, 'INSERT', to_jsonb(NEW));
    RETURN NEW;
  END IF;
END;
$$ LANGUAGE plpgsql;
-- Apply to any table
CREATE TRIGGER audit_users
AFTER INSERT OR UPDATE OR DELETE ON users
FOR EACH ROW EXECUTE FUNCTION audit_trigger_function();

Soft Delete Pattern

-- Query filter view
CREATE VIEW active_users AS SELECT * FROM users WHERE deleted_at IS NULL;
-- Soft delete function
CREATE OR REPLACE FUNCTION soft_delete(p_table TEXT, p_id BIGINT)
RETURNS VOID AS $$
BEGIN
  EXECUTE format('UPDATE %I SET deleted_at = CURRENT_TIMESTAMP WHERE id = $1 AND deleted_at IS NULL', p_table)
  USING p_id;
END;
$$ LANGUAGE plpgsql;

Full-Text Search

ALTER TABLE products ADD COLUMN search_vector tsvector
  GENERATED ALWAYS AS (
    to_tsvector('english', COALESCE(name, '') || ' ' || COALESCE(description, '') || ' ' || COALESCE(sku, ''))
  ) STORED;
CREATE INDEX idx_products_search ON products USING gin(search_vector);
-- Query
SELECT * FROM products
WHERE search_vector @@ to_tsquery('english', 'laptop & gaming');

---

Query Optimization

Analyze Before Optimizing

-- Always start here
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT u.id, u.name, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at > '2024-01-01'
GROUP BY u.id, u.name
ORDER BY order_count DESC;

Indexing Strategy

-- Single column for exact lookups
CREATE INDEX CONCURRENTLY idx_users_email ON users(email);
-- Composite for multi-column queries (order matters!)
CREATE INDEX CONCURRENTLY idx_orders_user_status ON orders(user_id, status, created_at);
-- Partial index for filtered queries
CREATE INDEX CONCURRENTLY idx_products_low_stock
ON products(inventory_quantity)
WHERE inventory_tracking = true AND inventory_quantity <= 5;
-- Covering index (includes extra columns to avoid table lookup)
CREATE INDEX CONCURRENTLY idx_orders_covering
ON orders(user_id, status) INCLUDE (total, created_at);
-- GIN index for JSONB
CREATE INDEX CONCURRENTLY idx_products_attrs ON products USING gin(attributes);
-- Expression index
CREATE INDEX CONCURRENTLY idx_users_email_lower ON users(lower(email));

Find Unused Indexes

SELECT
  schemaname, tablename, indexname,
  idx_scan as scans,
  pg_size_pretty(pg_relation_size(indexrelid)) as size
FROM pg_stat_user_indexes
WHERE idx_scan = 0
ORDER BY pg_relation_size(indexrelid) DESC;

Find Missing Indexes (Slow Queries)

-- Enable pg_stat_statements first
SELECT query, calls, total_exec_time, mean_exec_time, rows
FROM pg_stat_statements
WHERE mean_exec_time > 100  -- ms
ORDER BY total_exec_time DESC
LIMIT 20;

N+1 Query Detection

-- Look for repeated similar queries in pg_stat_statements
SELECT query, calls, mean_exec_time
FROM pg_stat_statements
WHERE calls > 100 AND query LIKE '%WHERE%id = $1%'
ORDER BY calls DESC;

---

Migration Patterns

Safe Column Addition

-- +migrate Up
-- Always use CONCURRENTLY for indexes in production
ALTER TABLE users ADD COLUMN phone VARCHAR(20);
CREATE INDEX CONCURRENTLY idx_users_phone ON users(phone) WHERE phone IS NOT NULL;
-- +migrate Down
DROP INDEX IF EXISTS idx_users_phone;
ALTER TABLE users DROP COLUMN IF EXISTS phone;

Safe Column Rename (Zero-Downtime)

-- Step 1: Add new column
ALTER TABLE users ADD COLUMN display_name VARCHAR(100);
UPDATE users SET display_name = name;
ALTER TABLE users ALTER COLUMN display_name SET NOT NULL;
-- Step 2: Deploy code that writes to both columns
-- Step 3: Deploy code that reads from new column
-- Step 4: Drop old column
ALTER TABLE users DROP COLUMN name;

Table Partitioning

-- Create partitioned table
CREATE TABLE orders (
  id BIGSERIAL,
  user_id BIGINT NOT NULL,
  total DECIMAL(10,2),
  created_at TIMESTAMPTZ NOT NULL,
  PRIMARY KEY (id, created_at)
) PARTITION BY RANGE (created_at);
-- Monthly partitions
CREATE TABLE orders_2024_01 PARTITION OF orders
  FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');
CREATE TABLE orders_2024_02 PARTITION OF orders
  FOR VALUES FROM ('2024-02-01') TO ('2024-03-01');
-- Auto-create partitions
CREATE OR REPLACE FUNCTION create_monthly_partition(p_table TEXT, p_date DATE)
RETURNS VOID AS $$
DECLARE
  partition_name TEXT := p_table || '_' || to_char(p_date, 'YYYY_MM');
  next_date DATE := p_date + INTERVAL '1 month';
BEGIN
  EXECUTE format(
    'CREATE TABLE IF NOT EXISTS %I PARTITION OF %I FOR VALUES FROM (%L) TO (%L)',
    partition_name, p_table, p_date, next_date
  );
END;
$$ LANGUAGE plpgsql;

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EF Core Migrations (.NET)

Create and Apply

# Add migration
dotnet ef migrations add AddPhoneToUsers -p src/Infrastructure -s src/Api
Apply
dotnet ef database update -p src/Infrastructure -s src/Api
Generate idempotent SQL script for production
dotnet ef migrations script -p src/Infrastructure -s src/Api -o migration.sql --idempotent
Rollback
dotnet ef database update PreviousMigrationName -p src/Infrastructure -s src/Api

EF Core Configuration Best Practices

// Use AsNoTracking for read queries
var users = await _db.Users
    .AsNoTracking()
    .Where(u => u.Status == UserStatus.Active)
    .Select(u => new UserDto { Id = u.Id, Name = u.Name })
    .ToListAsync(ct);
// Avoid N+1 with Include
var orders = await _db.Orders
    .Include(o => o.Items)
    .ThenInclude(i => i.Product)
    .Where(o => o.UserId == userId)
    .ToListAsync(ct);
// Better: Projection
var orders = await _db.Orders
    .Where(o => o.UserId == userId)
    .Select(o => new OrderDto
    {
        Id = o.Id,
        Total = o.Total,
        Items = o.Items.Select(i => new OrderItemDto
        {
            ProductName = i.Product.Name,
            Quantity = i.Quantity,
        }).ToList(),
    })
    .ToListAsync(ct);

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Caching Strategy

SkillBoss API Hub KV Cache

SkillBoss API Hub 提供统一的 KV 存储能力，可通过 /v1/pilot 路由访问，无需自建 Redis。以下示例展示使用 SkillBoss API Hub 实现查询缓存：

import requests, os, json
SKILLBOSS_API_KEY = os.environ["SKILLBOSS_API_KEY"]
API_BASE = "https://api.heybossai.com/v1"
def pilot(body: dict) -> dict:
    r = requests.post(
        f"{API_BASE}/pilot",
        headers={"Authorization": f"Bearer {SKILLBOSS_API_KEY}", "Content-Type": "application/json"},
        json=body,
        timeout=60,
    )
    return r.json()
AI 辅助查询分析（通过 SkillBoss API Hub chat 能力）
def analyze_slow_query(query_plan: str) -> str:
    result = pilot({
        "type": "chat",
        "inputs": {
            "messages": [
                {"role": "user", "content": f"Analyze this PostgreSQL query plan and suggest optimizations:\n{query_plan}"}
            ]
        },
        "prefer": "balanced"
    })
    return result["result"]["choices"][0]["message"]["content"]

Redis Query Cache（自建 Redis 场景）

import Redis from 'ioredis'
const redis = new Redis(process.env.REDIS_URL)
async function cachedQuery(
  key: string,
  queryFn: () => Promise,
  ttlSeconds: number = 300
): Promise {
  const cached = await redis.get(key)
  if (cached) return JSON.parse(cached)
  const result = await queryFn()
  await redis.setex(key, ttlSeconds, JSON.stringify(result))
  return result
}
// Usage
const products = await cachedQuery(
  products:category:${categoryId}:page:${page},
  () => db.product.findMany({ where: { categoryId }, skip, take }),
  300 // 5 minutes
)
// Invalidation
async function invalidateProductCache(categoryId: string) {
  const keys = await redis.keys(products:category:${categoryId}:*)
  if (keys.length) await redis.del(...keys)
}

Materialized Views

CREATE MATERIALIZED VIEW monthly_sales AS
SELECT
  DATE_TRUNC('month', created_at) as month,
  category_id,
  COUNT(*) as order_count,
  SUM(total) as revenue,
  AVG(total) as avg_order_value
FROM orders
WHERE created_at >= DATE_TRUNC('year', CURRENT_DATE)
GROUP BY 1, 2;
CREATE UNIQUE INDEX idx_monthly_sales ON monthly_sales(month, category_id);
-- Refresh (can be scheduled via pg_cron)
REFRESH MATERIALIZED VIEW CONCURRENTLY monthly_sales;

---

Connection Pool Configuration

Node.js (pg)

import { Pool } from 'pg'
const pool = new Pool({
  max: 20,                      // Max connections
  idleTimeoutMillis: 30000,     // Close idle connections after 30s
  connectionTimeoutMillis: 2000, // Fail fast if can't connect in 2s
  maxUses: 7500,                // Refresh connection after N uses
})
// Monitor pool health
setInterval(() => {
  console.log({
    total: pool.totalCount,
    idle: pool.idleCount,
    waiting: pool.waitingCount,
  })
}, 60000)

---

Monitoring Queries

Active Connections

SELECT count(*), state
FROM pg_stat_activity
WHERE datname = current_database()
GROUP BY state;

Long-Running Queries

SELECT pid, now() - query_start AS duration, query, state
FROM pg_stat_activity
WHERE (now() - query_start) > interval '5 minutes'
AND state = 'active';

Table Sizes

SELECT
  relname AS table,
  pg_size_pretty(pg_total_relation_size(relid)) AS total_size,
  pg_size_pretty(pg_relation_size(relid)) AS data_size,
  pg_size_pretty(pg_total_relation_size(relid) - pg_relation_size(relid)) AS index_size
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC
LIMIT 20;

Table Bloat

SELECT
  tablename,
  pg_size_pretty(pg_total_relation_size(tablename::regclass)) as size,
  n_dead_tup,
  n_live_tup,
  CASE WHEN n_live_tup > 0
    THEN round(n_dead_tup::numeric / n_live_tup, 2)
    ELSE 0
  END as dead_ratio
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY dead_ratio DESC;

---

Anti-Patterns

1. ❌ SELECT * — always specify needed columns 2. ❌ Missing indexes on foreign keys — always index FK columns 3. ❌ LIKE '%search%' — use full-text search or trigram indexes instead 4. ❌ Large IN clauses — use ANY(ARRAY[...]) or join a values list 5. ❌ No LIMIT on unbounded queries — always paginate 6. ❌ Creating indexes without CONCURRENTLY in production 7. ❌ Running migrations without testing rollback 8. ❌ Ignoring EXPLAIN ANALYZE output — always verify execution plans 9. ❌ Storing money as FLOAT — use DECIMAL(10,2) or integer cents 10. ❌ Missing NOT NULL constraints — be explicit about nullability