Module 16: System Design Case Studies

Apply everything you’ve learned to real-world database design challenges. Each case study covers requirements, schema design, queries, scaling, and trade-offs.

Estimated Time: 10-12 hours
Format: 6 comprehensive case studies
Interview Use: Practice for 45-60 min design rounds
Skill Level: Senior/Staff

Case Study 1: E-Commerce Platform

Requirements

Product catalog with categories and attributes
User accounts with addresses
Shopping cart and checkout
Order management with status tracking
Inventory management with stock levels
Review and rating system

Schema Design

-- Core entities
CREATE TABLE users (
    id BIGSERIAL PRIMARY KEY,
    email VARCHAR(255) UNIQUE NOT NULL,
    password_hash VARCHAR(255) NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE addresses (
    id BIGSERIAL PRIMARY KEY,
    user_id BIGINT REFERENCES users(id),
    type VARCHAR(20), -- 'shipping', 'billing'
    street TEXT NOT NULL,
    city VARCHAR(100),
    country VARCHAR(2),
    postal_code VARCHAR(20),
    is_default BOOLEAN DEFAULT false
);

-- Product catalog
CREATE TABLE categories (
    id SERIAL PRIMARY KEY,
    name VARCHAR(100) NOT NULL,
    parent_id INT REFERENCES categories(id),
    path ltree  -- requires ltree extension
);

CREATE TABLE products (
    id BIGSERIAL PRIMARY KEY,
    sku VARCHAR(50) UNIQUE NOT NULL,
    name VARCHAR(255) NOT NULL,
    description TEXT,
    category_id INT REFERENCES categories(id),
    base_price DECIMAL(10,2) NOT NULL,
    attributes JSONB DEFAULT '{}',  -- flexible attributes
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE product_variants (
    id BIGSERIAL PRIMARY KEY,
    product_id BIGINT REFERENCES products(id),
    sku VARCHAR(50) UNIQUE NOT NULL,
    name VARCHAR(100),  -- "Red, Large"
    price_modifier DECIMAL(10,2) DEFAULT 0,
    attributes JSONB NOT NULL  -- {"color": "red", "size": "L"}
);

-- Inventory
CREATE TABLE inventory (
    variant_id BIGINT PRIMARY KEY REFERENCES product_variants(id),
    quantity INT NOT NULL DEFAULT 0,
    reserved INT NOT NULL DEFAULT 0,
    CHECK (quantity >= 0),
    CHECK (reserved >= 0),
    CHECK (reserved <= quantity)
);

-- Orders
CREATE TABLE orders (
    id BIGSERIAL PRIMARY KEY,
    user_id BIGINT REFERENCES users(id),
    status VARCHAR(20) DEFAULT 'pending',
    total DECIMAL(12,2) NOT NULL,
    shipping_address_id BIGINT REFERENCES addresses(id),
    created_at TIMESTAMPTZ DEFAULT NOW(),
    updated_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE order_items (
    id BIGSERIAL PRIMARY KEY,
    order_id BIGINT REFERENCES orders(id),
    variant_id BIGINT REFERENCES product_variants(id),
    quantity INT NOT NULL,
    unit_price DECIMAL(10,2) NOT NULL,
    CHECK (quantity > 0)
);

-- Reviews
CREATE TABLE reviews (
    id BIGSERIAL PRIMARY KEY,
    product_id BIGINT REFERENCES products(id),
    user_id BIGINT REFERENCES users(id),
    rating INT NOT NULL CHECK (rating BETWEEN 1 AND 5),
    title VARCHAR(200),
    body TEXT,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    UNIQUE(product_id, user_id)
);

Key Queries

-- Product listing with category tree
WITH RECURSIVE cat_tree AS (
    SELECT id, name, parent_id, ARRAY[name] AS path
    FROM categories WHERE id = $category_id
    UNION ALL
    SELECT c.id, c.name, c.parent_id, ct.path || c.name
    FROM categories c
    JOIN cat_tree ct ON c.parent_id = ct.id
)
SELECT p.*, array_agg(pv.id) AS variant_ids
FROM products p
JOIN cat_tree ct ON p.category_id = ct.id
LEFT JOIN product_variants pv ON pv.product_id = p.id
GROUP BY p.id;

-- Checkout with inventory reservation
BEGIN;
SELECT * FROM inventory 
WHERE variant_id = $variant_id 
FOR UPDATE;  -- Lock row

UPDATE inventory 
SET reserved = reserved + $quantity
WHERE variant_id = $variant_id
  AND (quantity - reserved) >= $quantity;

-- Check affected rows, if 0 then out of stock
COMMIT;

-- Product with aggregated reviews
SELECT 
    p.*,
    COUNT(r.id) AS review_count,
    AVG(r.rating) AS avg_rating,
    jsonb_build_object(
        '5', COUNT(*) FILTER (WHERE r.rating = 5),
        '4', COUNT(*) FILTER (WHERE r.rating = 4),
        '3', COUNT(*) FILTER (WHERE r.rating = 3),
        '2', COUNT(*) FILTER (WHERE r.rating = 2),
        '1', COUNT(*) FILTER (WHERE r.rating = 1)
    ) AS rating_distribution
FROM products p
LEFT JOIN reviews r ON r.product_id = p.id
WHERE p.id = $product_id
GROUP BY p.id;

Scaling Strategy

┌─────────────────────────────────────────────────────────────────────────────┐
│                    E-COMMERCE SCALING                                        │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   Read Path (90% of traffic):                                               │
│   ┌─────────────┐     ┌─────────────┐     ┌─────────────┐                  │
│   │   CDN       │────▶│   Redis     │────▶│   Replica   │                  │
│   │ (static)    │     │ (products)  │     │  (queries)  │                  │
│   └─────────────┘     └─────────────┘     └─────────────┘                  │
│                                                                              │
│   Write Path (10% of traffic):                                              │
│   ┌─────────────┐     ┌─────────────┐                                       │
│   │    App      │────▶│   Primary   │                                       │
│   │  (orders)   │     │  (writes)   │                                       │
│   └─────────────┘     └─────────────┘                                       │
│                                                                              │
│   Indexing:                                                                  │
│   • products(category_id, created_at DESC) - listings                       │
│   • products USING GIN(attributes) - attribute search                       │
│   • inventory(variant_id) - stock checks                                    │
│   • orders(user_id, created_at DESC) - order history                        │
│                                                                              │
│   Partitioning:                                                              │
│   • orders by created_at (monthly) - historical data                        │
│   • reviews by product_id hash - even distribution                          │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Case Study 2: Real-Time Analytics

Requirements

Ingest millions of events per day
Sub-second dashboard queries
Time-series aggregations (hourly, daily, weekly)
Retention: raw data 30 days, aggregates forever

Schema Design

-- Time-series events with partitioning
CREATE TABLE events (
    id BIGSERIAL,
    event_time TIMESTAMPTZ NOT NULL,
    event_type VARCHAR(50) NOT NULL,
    user_id BIGINT,
    properties JSONB,
    PRIMARY KEY (event_time, id)
) PARTITION BY RANGE (event_time);

-- Create monthly partitions
CREATE TABLE events_2024_01 PARTITION OF events
    FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');
-- ... more partitions

-- Pre-aggregated summaries
CREATE TABLE event_stats_hourly (
    hour TIMESTAMPTZ NOT NULL,
    event_type VARCHAR(50) NOT NULL,
    count BIGINT DEFAULT 0,
    unique_users BIGINT DEFAULT 0,
    PRIMARY KEY (hour, event_type)
);

CREATE TABLE event_stats_daily (
    date DATE NOT NULL,
    event_type VARCHAR(50) NOT NULL,
    count BIGINT DEFAULT 0,
    unique_users BIGINT DEFAULT 0,
    properties_agg JSONB,  -- aggregated metrics
    PRIMARY KEY (date, event_type)
);

-- HyperLogLog for unique counts (requires extension)
CREATE EXTENSION IF NOT EXISTS hll;

ALTER TABLE event_stats_hourly 
ADD COLUMN user_hll hll DEFAULT hll_empty();

Ingestion Pattern

-- Batch insert with conflict handling
INSERT INTO events (event_time, event_type, user_id, properties)
VALUES 
    ($1, $2, $3, $4),
    ($5, $6, $7, $8),
    -- ... batch of 1000
ON CONFLICT DO NOTHING;

-- Continuous aggregation (run every hour)
INSERT INTO event_stats_hourly (hour, event_type, count, unique_users, user_hll)
SELECT 
    date_trunc('hour', event_time) AS hour,
    event_type,
    COUNT(*) AS count,
    COUNT(DISTINCT user_id) AS unique_users,
    hll_add_agg(hll_hash_bigint(user_id)) AS user_hll
FROM events
WHERE event_time >= $last_processed_hour
  AND event_time < $current_hour
GROUP BY 1, 2
ON CONFLICT (hour, event_type) DO UPDATE SET
    count = event_stats_hourly.count + EXCLUDED.count,
    unique_users = EXCLUDED.unique_users,
    user_hll = hll_union(event_stats_hourly.user_hll, EXCLUDED.user_hll);

Query Patterns

-- Dashboard query (uses pre-aggregates)
SELECT 
    date_trunc('day', hour) AS date,
    event_type,
    SUM(count) AS total_events,
    hll_cardinality(hll_union_agg(user_hll)) AS unique_users
FROM event_stats_hourly
WHERE hour >= NOW() - INTERVAL '7 days'
GROUP BY 1, 2
ORDER BY 1, 2;

-- Drill-down to raw events
SELECT 
    event_time,
    event_type,
    properties
FROM events
WHERE event_time >= $start AND event_time < $end
  AND event_type = $type
ORDER BY event_time DESC
LIMIT 100;

Data Lifecycle

-- Drop old partitions (automated)
DROP TABLE events_2023_01;  -- Instant, no locking

-- Compress old data with TimescaleDB (optional)
-- Or move to columnar storage (Citus columnar)
SELECT alter_table_set_access_method('events_2023_12', 'columnar');

Requirements

Users follow other users
Home feed shows posts from followed users
Support for 100M users, 10M DAU
Feed must be < 100ms

Schema Design

CREATE TABLE users (
    id BIGSERIAL PRIMARY KEY,
    username VARCHAR(50) UNIQUE NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE follows (
    follower_id BIGINT NOT NULL REFERENCES users(id),
    following_id BIGINT NOT NULL REFERENCES users(id),
    created_at TIMESTAMPTZ DEFAULT NOW(),
    PRIMARY KEY (follower_id, following_id)
);

CREATE TABLE posts (
    id BIGSERIAL PRIMARY KEY,
    author_id BIGINT NOT NULL REFERENCES users(id),
    content TEXT NOT NULL,
    media_urls TEXT[],
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- Fan-out timeline (for < 10K followers)
CREATE TABLE timelines (
    user_id BIGINT NOT NULL,
    post_id BIGINT NOT NULL REFERENCES posts(id),
    author_id BIGINT NOT NULL,
    created_at TIMESTAMPTZ NOT NULL,
    PRIMARY KEY (user_id, created_at DESC, post_id)
);

-- Celebrity posts (for > 10K followers)
-- Not fanned out, merged at read time
CREATE INDEX idx_posts_author_time ON posts(author_id, created_at DESC);

Fan-Out Strategy

-- On new post from regular user (< 10K followers)
INSERT INTO timelines (user_id, post_id, author_id, created_at)
SELECT 
    follower_id,
    $post_id,
    $author_id,
    $created_at
FROM follows
WHERE following_id = $author_id;

-- Async worker for large fan-outs
-- Use message queue (RabbitMQ, SQS) for batching

Feed Query (Hybrid)

-- Get user's celebrity followings
WITH celebrities AS (
    SELECT following_id 
    FROM follows f
    JOIN user_stats us ON us.user_id = f.following_id
    WHERE f.follower_id = $user_id 
      AND us.follower_count > 10000
),
-- Get pre-materialized timeline
materialized AS (
    SELECT post_id, author_id, created_at
    FROM timelines
    WHERE user_id = $user_id
      AND created_at > NOW() - INTERVAL '7 days'
    ORDER BY created_at DESC
    LIMIT 100
),
-- Get celebrity posts
celebrity_posts AS (
    SELECT id AS post_id, author_id, created_at
    FROM posts
    WHERE author_id IN (SELECT following_id FROM celebrities)
      AND created_at > NOW() - INTERVAL '7 days'
    ORDER BY created_at DESC
    LIMIT 100
)
-- Merge and return
SELECT p.*
FROM posts p
JOIN (
    SELECT * FROM materialized
    UNION ALL
    SELECT * FROM celebrity_posts
) feed ON feed.post_id = p.id
ORDER BY feed.created_at DESC
LIMIT 20;

Case Study 4: Multi-Tenant SaaS

Requirements

Thousands of tenants (companies)
Complete data isolation
Some tenants are very large (1M+ rows)
Compliance requirements (GDPR)

Schema Design (Shared with RLS)

-- Enable RLS
CREATE TABLE tenants (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    plan VARCHAR(20) DEFAULT 'free',
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE users (
    id BIGSERIAL PRIMARY KEY,
    tenant_id UUID NOT NULL REFERENCES tenants(id),
    email VARCHAR(255) NOT NULL,
    role VARCHAR(20) DEFAULT 'member',
    UNIQUE(tenant_id, email)
);

CREATE TABLE projects (
    id BIGSERIAL PRIMARY KEY,
    tenant_id UUID NOT NULL REFERENCES tenants(id),
    name VARCHAR(255) NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

CREATE TABLE tasks (
    id BIGSERIAL PRIMARY KEY,
    tenant_id UUID NOT NULL REFERENCES tenants(id),
    project_id BIGINT REFERENCES projects(id),
    title VARCHAR(255) NOT NULL,
    status VARCHAR(20) DEFAULT 'open',
    assignee_id BIGINT REFERENCES users(id)
);

-- Row Level Security
ALTER TABLE projects ENABLE ROW LEVEL SECURITY;
ALTER TABLE tasks ENABLE ROW LEVEL SECURITY;

CREATE POLICY tenant_isolation_projects ON projects
    USING (tenant_id = current_setting('app.tenant_id')::UUID);

CREATE POLICY tenant_isolation_tasks ON tasks
    USING (tenant_id = current_setting('app.tenant_id')::UUID);

-- Set tenant context (in application)
-- SET app.tenant_id = 'uuid-here';

Connection Management

# Application middleware
def set_tenant_context(tenant_id):
    """Set tenant context for RLS"""
    with get_db_connection() as conn:
        conn.execute(f"SET app.tenant_id = '{tenant_id}'")
        yield conn
        conn.execute("RESET app.tenant_id")

# PgBouncer with tenant routing (advanced)
# Route large tenants to dedicated pools

Tenant Isolation Testing

-- Test: Ensure tenant A can't see tenant B's data
SET app.tenant_id = 'tenant-a-uuid';

SELECT * FROM projects;  -- Should only see tenant A

-- Try to access tenant B (should fail silently - return 0 rows)
SELECT * FROM projects WHERE tenant_id = 'tenant-b-uuid';
-- Returns 0 rows due to RLS

-- Bypass for admin operations
SET ROLE admin_role;  -- Must have BYPASSRLS
SELECT * FROM projects;  -- Sees all tenants

Case Study 5: Event Sourcing System

Requirements

All state changes stored as events (audit trail)
Ability to rebuild state at any point in time
Support for projections (read models)
Event replay for debugging

Schema Design

-- Event store (append-only)
CREATE TABLE events (
    id BIGSERIAL PRIMARY KEY,
    stream_id UUID NOT NULL,
    stream_type VARCHAR(100) NOT NULL,  -- 'Order', 'User', etc.
    event_type VARCHAR(100) NOT NULL,   -- 'OrderCreated', 'ItemAdded'
    event_data JSONB NOT NULL,
    metadata JSONB DEFAULT '{}',
    version INT NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW(),
    UNIQUE(stream_id, version)  -- Optimistic concurrency
);

-- Example events
INSERT INTO events (stream_id, stream_type, event_type, event_data, version)
VALUES 
    ('order-123', 'Order', 'OrderCreated', 
     '{"customer_id": 456, "items": []}', 1),
    ('order-123', 'Order', 'ItemAdded', 
     '{"product_id": 789, "quantity": 2}', 2),
    ('order-123', 'Order', 'OrderSubmitted', 
     '{"submitted_at": "2024-01-15T10:30:00Z"}', 3);

-- Projection: Materialized current state
CREATE TABLE order_projections (
    order_id UUID PRIMARY KEY,
    customer_id BIGINT NOT NULL,
    items JSONB NOT NULL DEFAULT '[]',
    status VARCHAR(20) NOT NULL,
    total DECIMAL(12,2),
    last_event_id BIGINT NOT NULL,  -- For replay
    updated_at TIMESTAMPTZ DEFAULT NOW()
);

-- Subscription checkpoints
CREATE TABLE subscription_checkpoints (
    subscription_id VARCHAR(100) PRIMARY KEY,
    last_processed_id BIGINT NOT NULL,
    updated_at TIMESTAMPTZ DEFAULT NOW()
);

Event Replay

-- Rebuild projection from events
CREATE OR REPLACE FUNCTION rebuild_order_projection(p_order_id UUID)
RETURNS void AS $$
DECLARE
    evt RECORD;
    current_state JSONB := '{}';
BEGIN
    -- Delete existing projection
    DELETE FROM order_projections WHERE order_id = p_order_id;
    
    -- Replay all events
    FOR evt IN 
        SELECT * FROM events 
        WHERE stream_id = p_order_id 
        ORDER BY version
    LOOP
        current_state := apply_event(current_state, evt.event_type, evt.event_data);
    END LOOP;
    
    -- Insert rebuilt projection
    INSERT INTO order_projections (order_id, customer_id, items, status, last_event_id)
    VALUES (
        p_order_id,
        (current_state->>'customer_id')::BIGINT,
        current_state->'items',
        current_state->>'status',
        evt.id
    );
END;
$$ LANGUAGE plpgsql;

Subscription Processing

# Python async event processor
async def process_events():
    last_id = get_checkpoint('order_projection')
    
    while True:
        events = await db.fetch("""
            SELECT * FROM events 
            WHERE id > $1 
            ORDER BY id 
            LIMIT 100
        """, last_id)
        
        for event in events:
            await update_projection(event)
            last_id = event['id']
        
        await save_checkpoint('order_projection', last_id)
        await asyncio.sleep(0.1)

Case Study 6: Global Application

Requirements

Users in US, EU, Asia
Data residency (EU data stays in EU)
Low latency reads globally
Strong consistency for writes

Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                    GLOBAL DATABASE ARCHITECTURE                              │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                              │
│   US-EAST (Primary for US users)                                            │
│   ┌─────────────────────────────────────────────────────────────────────┐   │
│   │  ┌─────────────┐     ┌─────────────┐                               │   │
│   │  │   Primary   │────▶│   Replica   │ (local reads)                 │   │
│   │  │  (writes)   │     │             │                               │   │
│   │  └──────┬──────┘     └─────────────┘                               │   │
│   └─────────┼───────────────────────────────────────────────────────────┘   │
│             │ async replication (reference data only)                       │
│   ┌─────────┼───────────────────────────────────────────────────────────┐   │
│   │         ▼           EU-WEST (Primary for EU users)                 │   │
│   │  ┌─────────────┐     ┌─────────────┐                               │   │
│   │  │   Primary   │────▶│   Replica   │                               │   │
│   │  │  (writes)   │     │             │                               │   │
│   │  └─────────────┘     └─────────────┘                               │   │
│   │                                                                     │   │
│   │  EU user data ONLY stored here (GDPR)                              │   │
│   └─────────────────────────────────────────────────────────────────────┘   │
│                                                                              │
│   Data Routing:                                                             │
│   • User data: Route to home region based on user.region                   │
│   • Reference data: Replicated globally (products, configs)                │
│   • Cross-region queries: Federation layer or application-level           │
│                                                                              │
└─────────────────────────────────────────────────────────────────────────────┘

Implementation

-- Users partitioned by region
CREATE TABLE users (
    id BIGSERIAL,
    region VARCHAR(10) NOT NULL,  -- 'us', 'eu', 'asia'
    email VARCHAR(255) NOT NULL,
    data JSONB,
    PRIMARY KEY (region, id)
) PARTITION BY LIST (region);

CREATE TABLE users_us PARTITION OF users FOR VALUES IN ('us');
CREATE TABLE users_eu PARTITION OF users FOR VALUES IN ('eu');
CREATE TABLE users_asia PARTITION OF users FOR VALUES IN ('asia');

-- Reference data (replicated everywhere)
CREATE TABLE products (
    id BIGSERIAL PRIMARY KEY,
    name VARCHAR(255) NOT NULL,
    -- No region-specific data
);

-- Foreign data wrapper for cross-region queries
CREATE EXTENSION postgres_fdw;

CREATE SERVER eu_server
FOREIGN DATA WRAPPER postgres_fdw
OPTIONS (host 'eu-db.example.com', port '5432', dbname 'app');

CREATE USER MAPPING FOR app_user
SERVER eu_server
OPTIONS (user 'app_user', password 'secret');

CREATE FOREIGN TABLE users_eu_remote (
    id BIGINT,
    region VARCHAR(10),
    email VARCHAR(255)
) SERVER eu_server OPTIONS (table_name 'users_eu');

Summary

You’ve now seen how to apply database design principles to real-world systems:

E-Commerce

Inventory, orders, flexible attributes

Analytics

Time-series, pre-aggregation, partitioning

Social Feed

Fan-out, hybrid approach, caching

Multi-Tenant

RLS, isolation, connection management

Event Sourcing

Append-only, projections, replay

Global

Geo-routing, data residency, federation

Course Completion 🎉

Congratulations! You’ve completed the Database Engineering Mastery course.

You’re now prepared for:

Senior/Staff engineering interviews
PostgreSQL open-source contributions
Production database architecture and operations

What’s Next?

Join the Community

Connect with fellow database engineers

Start Contributing

Make your first PostgreSQL contribution

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​Module 16: System Design Case Studies

​Case Study 1: E-Commerce Platform

​Requirements

​Schema Design

​Key Queries

​Scaling Strategy

​Case Study 2: Real-Time Analytics

​Requirements

​Schema Design

​Ingestion Pattern

​Query Patterns

​Data Lifecycle

​Case Study 3: Social Media Feed

​Requirements

​Schema Design

​Fan-Out Strategy

​Feed Query (Hybrid)

​Case Study 4: Multi-Tenant SaaS

​Requirements

​Schema Design (Shared with RLS)

​Connection Management

​Tenant Isolation Testing

​Case Study 5: Event Sourcing System

​Requirements

​Schema Design

​Event Replay

​Subscription Processing

​Case Study 6: Global Application

​Requirements

​Architecture

​Implementation

​Summary

E-Commerce

Analytics

Social Feed

Multi-Tenant

Event Sourcing

Global

​Course Completion 🎉

​What’s Next?

Join the Community

Start Contributing

Module 16: System Design Case Studies

Case Study 1: E-Commerce Platform

Requirements

Schema Design

Key Queries

Scaling Strategy

Case Study 2: Real-Time Analytics

Requirements

Schema Design

Ingestion Pattern

Query Patterns

Data Lifecycle

Case Study 3: Social Media Feed

Requirements

Schema Design

Fan-Out Strategy

Feed Query (Hybrid)

Case Study 4: Multi-Tenant SaaS

Requirements

Schema Design (Shared with RLS)

Connection Management

Tenant Isolation Testing

Case Study 5: Event Sourcing System

Requirements

Schema Design

Event Replay

Subscription Processing

Case Study 6: Global Application

Requirements

Architecture

Implementation

Summary

Course Completion 🎉

What’s Next?