Database Design

Comprehensive guidance for designing relational and non-relational database schemas, applying normalization theory, building effective indexing strategies, writing performant queries, and executing safe schema migrations across PostgreSQL, MySQL, and document stores.

When to Use This Skill

Use this skill for:

• Designing new database schemas from business requirements
• Normalizing or intentionally denormalizing existing schemas
• Creating indexing strategies for read-heavy or write-heavy workloads
• Analyzing and optimizing slow queries with EXPLAIN plans
• Planning zero-downtime schema migrations
• Choosing between relational, document, graph, or time-series databases
• Implementing partitioning, sharding, or replication topologies
• Reviewing connection pooling and resource management
• Designing audit trails, soft deletes, or temporal data models

Trigger phrases: "database design", "schema design", "normalization", "indexing strategy", "query optimization", "EXPLAIN plan", "database migration", "schema migration", "denormalization", "partitioning", "connection pooling", "ER diagram"

What This Skill Does

Provides production-grade database patterns including:

• Schema Modeling: Entity-relationship design, normalization forms (1NF through BCNF), denormalization trade-offs
• Indexing: B-tree, hash, GIN, GiST, composite, partial, and covering index strategies
• Query Optimization: EXPLAIN analysis, N+1 prevention, join strategies, query planning
• Migrations: Schema versioning, backward-compatible changes, zero-downtime deployment
• Database Selection: Relational vs document vs graph vs time-series decision framework
• Operational: Connection pooling, partitioning, replication, backup strategies

Instructions

Step 1: Gather Requirements and Model Entities

Begin by identifying entities, their attributes, and the relationships between them. Translate business language into a conceptual model before writing any DDL.

Entity-Relationship Analysis Checklist:

1. List all nouns in the business domain (these become candidate entities)
2. Identify attributes for each entity (columns)
3. Determine relationships and their cardinality (1:1, 1:N, M:N)
4. Mark required vs optional attributes (NOT NULL constraints)
5. Identify natural keys vs surrogate keys

Example: E-Commerce Domain Model:

┌──────────────┐     1:N     ┌──────────────┐     N:1     ┌──────────────┐
│   Customer   │────────────▶│    Order      │◀───────────│   Product    │
├──────────────┤             ├──────────────┤             ├──────────────┤
│ id (PK)      │             │ id (PK)      │             │ id (PK)      │
│ email (UQ)   │             │ customer_id  │             │ sku (UQ)     │
│ name         │             │ status       │             │ name         │
│ created_at   │             │ total        │             │ price        │
└──────────────┘             │ created_at   │             │ category_id  │
                             └──────┬───────┘             └──────────────┘
                                    │ 1:N
                             ┌──────▼───────┐
                             │  Order Item  │
                             ├──────────────┤
                             │ id (PK)      │
                             │ order_id     │
                             │ product_id   │
                             │ quantity     │
                             │ unit_price   │
                             └──────────────┘

PostgreSQL Schema from the Model:

-- Use UUID primary keys for distributed-friendly design
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

CREATE TABLE customers (
    id          UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    email       VARCHAR(255) NOT NULL UNIQUE,
    name        VARCHAR(200) NOT NULL,
    created_at  TIMESTAMPTZ  NOT NULL DEFAULT now(),
    updated_at  TIMESTAMPTZ  NOT NULL DEFAULT now()
);

CREATE TABLE products (
    id          UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    sku         VARCHAR(50)  NOT NULL UNIQUE,
    name        VARCHAR(300) NOT NULL,
    price       NUMERIC(12,2) NOT NULL CHECK (price >= 0),
    category_id UUID REFERENCES categories(id),
    created_at  TIMESTAMPTZ  NOT NULL DEFAULT now()
);

CREATE TABLE orders (
    id          UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    customer_id UUID NOT NULL REFERENCES customers(id),
    status      VARCHAR(20) NOT NULL DEFAULT 'pending'
                CHECK (status IN ('pending','confirmed','shipped','delivered','cancelled')),
    total       NUMERIC(14,2) NOT NULL DEFAULT 0 CHECK (total >= 0),
    created_at  TIMESTAMPTZ  NOT NULL DEFAULT now()
);

CREATE TABLE order_items (
    id          UUID PRIMARY KEY DEFAULT uuid_generate_v4(),
    order_id    UUID NOT NULL REFERENCES orders(id) ON DELETE CASCADE,
    product_id  UUID NOT NULL REFERENCES products(id),
    quantity    INT  NOT NULL CHECK (quantity > 0),
    unit_price  NUMERIC(12,2) NOT NULL CHECK (unit_price >= 0),
    UNIQUE (order_id, product_id)
);

Step 2: Apply Normalization Rules

Normalization eliminates redundancy and prevents update anomalies. Apply each form sequentially.

First Normal Form (1NF): Every column holds atomic values; no repeating groups.

-- VIOLATION: comma-separated tags in a single column
CREATE TABLE products_bad (
    id   SERIAL PRIMARY KEY,
    name TEXT,
    tags TEXT  -- 'electronics,sale,featured'
);

-- 1NF FIX: separate junction table
CREATE TABLE products (
    id   SERIAL PRIMARY KEY,
    name TEXT NOT NULL
);

CREATE TABLE tags (
    id   SERIAL PRIMARY KEY,
    name VARCHAR(50) NOT NULL UNIQUE
);

CREATE TABLE product_tags (
    product_id INT REFERENCES products(id),
    tag_id     INT REFERENCES tags(id),
    PRIMARY KEY (product_id, tag_id)
);

Second Normal Form (2NF): Every non-key column depends on the entire primary key (relevant for composite keys).

-- VIOLATION: product_name depends only on product_id, not on (order_id, product_id)
CREATE TABLE order_items_bad (
    order_id     INT,
    product_id   INT,
    product_name TEXT,   -- depends only on product_id
    quantity     INT,
    PRIMARY KEY (order_id, product_id)
);

-- 2NF FIX: move product_name to the products table
CREATE TABLE order_items (
    order_id   INT REFERENCES orders(id),
    product_id INT REFERENCES products(id),
    quantity   INT NOT NULL,
    PRIMARY KEY (order_id, product_id)
);
-- product_name lives in products table only

Third Normal Form (3NF): No transitive dependencies (non-key column depends on another non-key column).

-- VIOLATION: city depends on zip_code, not directly on customer_id
CREATE TABLE customers_bad (
    id       SERIAL PRIMARY KEY,
    name     TEXT,
    zip_code VARCHAR(10),
    city     TEXT           -- transitively dependent via zip_code
);

-- 3NF FIX: extract zip-to-city mapping
CREATE TABLE zip_codes (
    zip_code VARCHAR(10) PRIMARY KEY,
    city     TEXT NOT NULL,
    state    VARCHAR(2) NOT NULL
);

CREATE TABLE customers (
    id       SERIAL PRIMARY KEY,
    name     TEXT NOT NULL,
    zip_code VARCHAR(10) REFERENCES zip_codes(zip_code)
);

Boyce-Codd Normal Form (BCNF): Every determinant is a candidate key. Relevant when a table has overlapping composite candidate keys.

When to Denormalize:

Scenario	Denormalization Technique	Trade-off
Read-heavy dashboards	Materialized views	Stale data between refreshes
Frequently joined columns	Redundant column copy	Must sync on update
Aggregation queries	Pre-computed summary tables	Write amplification
High-traffic lookups	Caching layer (Redis)	Cache invalidation complexity

Step 3: Design an Indexing Strategy

Indexes accelerate reads but slow writes. Choose index types based on query patterns.

Index Type Selection Guide:

Index Type	Use Case	PostgreSQL Syntax
B-tree (default)	Equality, range, sorting	CREATE INDEX ...
Hash	Equality only (rare)	CREATE INDEX ... USING hash
GIN	Full-text search, JSONB, arrays	CREATE INDEX ... USING gin
GiST	Geometric, range types, nearest-neighbor	CREATE INDEX ... USING gist
BRIN	Very large, naturally ordered tables	CREATE INDEX ... USING brin

Composite Index Design (column order matters):

-- Query: WHERE customer_id = ? AND status = ? ORDER BY created_at DESC
-- Rule: equality columns first, then range/sort columns
CREATE INDEX idx_orders_customer_status_date
    ON orders (customer_id, status, created_at DESC);

Partial Index (index only relevant rows):

-- Only index active orders (80% of queries target non-cancelled orders)
CREATE INDEX idx_orders_active
    ON orders (customer_id, created_at DESC)
    WHERE status != 'cancelled';

Covering Index (includes all columns the query needs):

-- Query: SELECT email, name FROM customers WHERE email = ?
-- Covering index avoids heap lookup
CREATE INDEX idx_customers_email_covering
    ON customers (email) INCLUDE (name);

JSONB Indexing (PostgreSQL):

-- GIN index for JSONB containment queries (@>)
CREATE INDEX idx_products_metadata
    ON products USING gin (metadata);

-- Targeted path index for a specific key
CREATE INDEX idx_products_metadata_color
    ON products ((metadata->>'color'));

Step 4: Optimize Queries with EXPLAIN

Use EXPLAIN ANALYZE to understand query execution and identify bottlenecks.

Reading EXPLAIN Output (PostgreSQL):

EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT o.id, o.total, c.email
FROM orders o
JOIN customers c ON c.id = o.customer_id
WHERE o.status = 'pending'
  AND o.created_at > now() - INTERVAL '7 days'
ORDER BY o.created_at DESC
LIMIT 50;

Example Output and Analysis:

Limit  (cost=0.72..125.40 rows=50 width=72) (actual time=0.05..1.23 rows=50 loops=1)
  -> Nested Loop  (cost=0.72..5420.18 rows=2170 width=72) (actual time=0.05..1.20 rows=50 loops=1)
       -> Index Scan Backward using idx_orders_active on orders o
            (cost=0.43..2890.12 rows=2170 width=48) (actual time=0.03..0.45 rows=50 loops=1)
              Filter: (status = 'pending' AND created_at > ...)
       -> Index Scan using customers_pkey on customers c
            (cost=0.29..1.17 rows=1 width=24) (actual time=0.01..0.01 rows=1 loops=50)
              Index Cond: (id = o.customer_id)
Planning Time: 0.35 ms
Execution Time: 1.45 ms
Buffers: shared hit=198

Key Metrics to Watch:

Metric	Good	Investigate
Seq Scan on large table	< 1K rows	> 10K rows
Nested Loop	Small inner set	Large inner set
Sort	In-memory	On-disk (work_mem too small)
Buffers shared read	Low	High (data not in cache)
Rows removed by filter	Low ratio	High ratio (index not selective)

Preventing N+1 Queries:

-- BAD: N+1 pattern (1 query for orders + N queries for items)
-- Application code: for order in orders: fetch items(order.id)

-- GOOD: single JOIN query
SELECT o.id, o.total, oi.product_id, oi.quantity
FROM orders o
JOIN order_items oi ON oi.order_id = o.id
WHERE o.customer_id = $1
ORDER BY o.created_at DESC;

-- GOOD: lateral join for "top N per group"
SELECT o.id, items.*
FROM orders o
CROSS JOIN LATERAL (
    SELECT oi.product_id, oi.quantity
    FROM order_items oi
    WHERE oi.order_id = o.id
    ORDER BY oi.quantity DESC
    LIMIT 3
) items
WHERE o.customer_id = $1;

Step 5: Plan Schema Migrations

Safe migrations require backward compatibility, reversibility, and zero-downtime awareness.

Migration Versioning with a Tool (e.g., Flyway, Alembic, Knex):

migrations/
  V001__create_customers.sql
  V002__create_products.sql
  V003__create_orders.sql
  V004__add_customer_phone.sql
  V005__add_orders_shipped_at.sql

Zero-Downtime Migration Pattern: Adding a Column:

-- Step 1: Add column as nullable (no lock, backward compatible)
ALTER TABLE customers ADD COLUMN phone VARCHAR(20);

-- Step 2: Backfill in batches (avoid long-running transactions)
UPDATE customers SET phone = 'unknown'
WHERE phone IS NULL AND id IN (
    SELECT id FROM customers WHERE phone IS NULL LIMIT 10000
);

-- Step 3: Add NOT NULL constraint (only after all rows populated)
ALTER TABLE customers ALTER COLUMN phone SET NOT NULL;

-- Step 4: Add default for future inserts
ALTER TABLE customers ALTER COLUMN phone SET DEFAULT '';

Zero-Downtime Migration Pattern: Renaming a Column:

-- NEVER do: ALTER TABLE customers RENAME COLUMN name TO full_name;
-- This breaks application code that references "name".

-- SAFE approach (expand-contract pattern):
-- Phase 1: Add new column, copy data, create trigger
ALTER TABLE customers ADD COLUMN full_name VARCHAR(200);
UPDATE customers SET full_name = name;

CREATE OR REPLACE FUNCTION sync_customer_name() RETURNS TRIGGER AS $$
BEGIN
    IF TG_OP = 'INSERT' OR TG_OP = 'UPDATE' THEN
        NEW.full_name = COALESCE(NEW.full_name, NEW.name);
        NEW.name = COALESCE(NEW.name, NEW.full_name);
    END IF;
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER trg_sync_name BEFORE INSERT OR UPDATE ON customers
FOR EACH ROW EXECUTE FUNCTION sync_customer_name();

-- Phase 2: Deploy application reading from full_name
-- Phase 3: Drop old column and trigger after all consumers migrated
ALTER TABLE customers DROP COLUMN name;
DROP TRIGGER trg_sync_name ON customers;
DROP FUNCTION sync_customer_name();

Alembic Migration Example (Python/SQLAlchemy):

"""Add phone column to customers.

Revision ID: a1b2c3d4
Revises: z9y8x7w6
"""
from alembic import op
import sqlalchemy as sa

revision = 'a1b2c3d4'
down_revision = 'z9y8x7w6'

def upgrade():
    op.add_column('customers', sa.Column('phone', sa.String(20), nullable=True))

def downgrade():
    op.drop_column('customers', 'phone')

Step 6: Choose the Right Database Engine

Decision Framework:

Requirement	Best Fit	Examples
ACID transactions, complex JOINs	Relational	PostgreSQL, MySQL
Flexible schema, nested documents	Document	MongoDB, CouchDB
Relationship traversal (social graphs)	Graph	Neo4j, Amazon Neptune
Time-ordered metrics, IoT data	Time-series	TimescaleDB, InfluxDB
Key-value caching, sessions	Key-value	Redis, DynamoDB
Full-text search, analytics	Search engine	Elasticsearch, OpenSearch

PostgreSQL vs MySQL Quick Comparison:

-- PostgreSQL: richer type system, JSONB, CTEs, window functions
-- Supports partial indexes, GIN/GiST, LISTEN/NOTIFY
SELECT id, name,
       ROW_NUMBER() OVER (PARTITION BY category_id ORDER BY price DESC) as rank
FROM products;

-- MySQL: widely deployed, simpler replication setup
-- InnoDB for ACID; supports JSON type since 5.7
SELECT id, name,
       ROW_NUMBER() OVER (PARTITION BY category_id ORDER BY price DESC) as row_rank
FROM products;

Step 7: Configure Connection Pooling and Operational Patterns

PgBouncer Configuration Example:

; pgbouncer.ini
[databases]
myapp = host=127.0.0.1 port=5432 dbname=myapp

[pgbouncer]
listen_port = 6432
listen_addr = 0.0.0.0
auth_type = md5
auth_file = /etc/pgbouncer/userlist.txt

; Pool sizing: connections = (2 * cpu_cores) + disk_spindles
pool_mode = transaction
default_pool_size = 25
max_client_conn = 200
min_pool_size = 5
reserve_pool_size = 5
reserve_pool_timeout = 3

; Timeouts
server_idle_timeout = 600
client_idle_timeout = 0
query_timeout = 30

Application-Level Pooling (Node.js with pg):

const { Pool } = require('pg');

const pool = new Pool({
  host: process.env.DB_HOST,
  port: 5432,
  database: 'myapp',
  user: process.env.DB_USER,
  password: process.env.DB_PASSWORD,
  max: 20,                  // max connections in pool
  idleTimeoutMillis: 30000, // close idle connections after 30s
  connectionTimeoutMillis: 5000,
  statement_timeout: 30000, // kill queries after 30s
});

// Graceful shutdown
process.on('SIGTERM', async () => {
  await pool.end();
  process.exit(0);
});

Table Partitioning (PostgreSQL):

-- Range partitioning by date (ideal for time-series or log data)
CREATE TABLE events (
    id         UUID NOT NULL DEFAULT uuid_generate_v4(),
    event_type VARCHAR(50) NOT NULL,
    payload    JSONB,
    created_at TIMESTAMPTZ NOT NULL
) PARTITION BY RANGE (created_at);

-- Create monthly partitions
CREATE TABLE events_2026_01 PARTITION OF events
    FOR VALUES FROM ('2026-01-01') TO ('2026-02-01');
CREATE TABLE events_2026_02 PARTITION OF events
    FOR VALUES FROM ('2026-02-01') TO ('2026-03-01');
CREATE TABLE events_2026_03 PARTITION OF events
    FOR VALUES FROM ('2026-03-01') TO ('2026-04-01');

-- Index each partition (automatically inherited)
CREATE INDEX idx_events_type_date ON events (event_type, created_at DESC);

-- Automate partition creation with pg_partman
CREATE EXTENSION pg_partman;
SELECT partman.create_parent('public.events', 'created_at', 'native', 'monthly');

Best Practices

• Always use constraints: CHECK, NOT NULL, UNIQUE, and FOREIGN KEY constraints prevent bad data at the source
• Prefer UUID or BIGSERIAL over SERIAL: UUIDs work well in distributed systems; BIGSERIAL avoids 32-bit overflow
• Use TIMESTAMPTZ, not TIMESTAMP: Always store timestamps with timezone to avoid ambiguity
• Index foreign keys: Every FK column should have a supporting index for JOIN performance
• Name objects consistently: Use snake_case for tables and columns; prefix indexes with idx_
• Version your schema: Use migration tools (Flyway, Alembic, Knex) rather than ad-hoc DDL scripts
• Test migrations against production-size data: A migration that takes 2 seconds on 1,000 rows may lock the table for 20 minutes on 10 million rows
• Monitor slow queries: Enable pg_stat_statements or MySQL slow query log in all environments
• Keep transactions short: Long transactions hold locks and block other operations
• Use read replicas for analytics: Route reporting queries to replicas to protect primary write performance

Common Patterns

Pattern 1: Soft Deletes with Filtered Index

-- Add a deleted_at column instead of actually deleting rows
ALTER TABLE customers ADD COLUMN deleted_at TIMESTAMPTZ;

-- Partial index so active-record queries ignore deleted rows
CREATE INDEX idx_customers_active_email
    ON customers (email)
    WHERE deleted_at IS NULL;

-- Application query automatically uses the filtered index
SELECT * FROM customers WHERE email = $1 AND deleted_at IS NULL;

Pattern 2: Audit Trail with Trigger

CREATE TABLE audit_log (
    id         BIGSERIAL PRIMARY KEY,
    table_name VARCHAR(100) NOT NULL,
    record_id  UUID NOT NULL,
    action     VARCHAR(10) NOT NULL,  -- INSERT, UPDATE, DELETE
    old_data   JSONB,
    new_data   JSONB,
    changed_by VARCHAR(100),
    changed_at TIMESTAMPTZ NOT NULL DEFAULT now()
);

CREATE OR REPLACE FUNCTION audit_trigger_fn() RETURNS TRIGGER AS $$
BEGIN
    INSERT INTO audit_log (table_name, record_id, action, old_data, new_data, changed_by)
    VALUES (
        TG_TABLE_NAME,
        COALESCE(NEW.id, OLD.id),
        TG_OP,
        CASE WHEN TG_OP IN ('UPDATE','DELETE') THEN to_jsonb(OLD) END,
        CASE WHEN TG_OP IN ('INSERT','UPDATE') THEN to_jsonb(NEW) END,
        current_setting('app.current_user', true)
    );
    RETURN COALESCE(NEW, OLD);
END;
$$ LANGUAGE plpgsql;

CREATE TRIGGER audit_customers
    AFTER INSERT OR UPDATE OR DELETE ON customers
    FOR EACH ROW EXECUTE FUNCTION audit_trigger_fn();

Pattern 3: Optimistic Locking

-- Add a version column for conflict detection
ALTER TABLE products ADD COLUMN version INT NOT NULL DEFAULT 1;

-- Update with version check (application retries on 0 rows affected)
UPDATE products
SET price = $1, version = version + 1
WHERE id = $2 AND version = $3;
-- If rowcount == 0, another writer changed the row; re-read and retry

Pattern 4: Materialized View for Dashboards

CREATE MATERIALIZED VIEW mv_daily_sales AS
SELECT
    date_trunc('day', o.created_at)::DATE AS sale_date,
    COUNT(DISTINCT o.id) AS order_count,
    SUM(o.total) AS revenue,
    COUNT(DISTINCT o.customer_id) AS unique_customers
FROM orders o
WHERE o.status != 'cancelled'
GROUP BY 1;

CREATE UNIQUE INDEX idx_mv_daily_sales_date ON mv_daily_sales (sale_date);

-- Refresh concurrently (no read lock) on a schedule
REFRESH MATERIALIZED VIEW CONCURRENTLY mv_daily_sales;

Quality Checklist

• Entity-relationship model documented before writing DDL
• All tables have a primary key (UUID or BIGSERIAL)
• Foreign key constraints defined for every relationship
• CHECK constraints enforce domain rules (positive prices, valid statuses)
• Schema is in 3NF (intentional denormalization documented with rationale)
• Indexes exist for all foreign keys and common query patterns
• EXPLAIN ANALYZE run on critical queries with production-like data
• No N+1 query patterns in application code
• Migration scripts are versioned and tested with rollback
• Zero-downtime migration strategy for production deployments
• Connection pooling configured with appropriate limits
• Timestamps use TIMESTAMPTZ (not TIMESTAMP)
• Naming conventions are consistent (snake_case, idx_ prefixes)

Related Skills

• sql-expert - Advanced SQL query writing and optimization
• cloud-architect - Managed database service selection (RDS, Cloud SQL, Aurora)
• cicd-architect - Running migrations in deployment pipelines
• performance-review - Identifying database-related performance bottlenecks

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Version: 1.0.0 Last Updated: March 2026

Iterative Refinement Strategy

This skill is optimized for an iterative approach:

1. Execute: Perform the core steps defined above.
2. Review: Critically analyze the output (coverage, quality, completeness).
3. Refine: If targets aren't met, repeat the specific implementation steps with improved context.
4. Loop: Continue until the definition of done is satisfied.