system-design-primer

name	system-design-primer
description	Large-scale system design reference. Use when designing distributed systems, scaling applications, or preparing for system design interviews. Reference concepts like CAP theorem, caching strategies, load balancing, database sharding, and more.

System Design Primer

Comprehensive reference from https://github.com/donnemartin/system-design-primer (344k stars)

Key Principles

"Everything is a trade-off" — Key principle throughout system design.

Fundamental Trade-offs

Trade-off	Description
Performance vs Scalability	Performance = fast for single user; Scalability = fast under heavy load
Latency vs Throughput	Latency = time to complete action; Throughput = actions per unit time
Availability vs Consistency	CAP Theorem — choose 2 of 3

Consistency Patterns

Pattern	Description	Use Cases
Weak	Reads may not see writes	VoIP, gaming
Eventual	Writes propagate asynchronously	DNS, email
Strong	Synchronous replication	RDBMS, file systems

Availability Metrics

Availability	Downtime/Year	Downtime/Month
99.9% (3 nines)	8h 46m	43m 50s
99.99% (4 nines)	52m 36s	4m 23s
99.999% (5 nines)	5m 15s	26s

Core Topics

Networking & Delivery

• DNS: Hierarchical name resolution, A/CNAME/MX/NS records
• CDN: Push (upload to CDN) vs Pull (lazy load on first request)
• Load Balancer: Layer 4 (transport) vs Layer 7 (application)
• Reverse Proxy: Hide backend servers, SSL termination, caching

Databases

• RDBMS: ACID transactions, complex joins
• NoSQL: Key-value, document, wide-column, graph stores
• Scaling: Replication, Federation, Sharding, Denormalization

Caching Strategies

# Cache-aside pattern (lazy loading)
def get_user(user_id):
    user = cache.get(f"user.{user_id}")
    if user is None:
        user = db.query("SELECT * FROM users WHERE user_id = ?", user_id)
        cache.set(f"user.{user_id}", json.dumps(user))
    return user

Strategy	Description
Cache-aside	Lazy loading, application manages
Write-through	Synchronous write to cache + DB
Write-behind	Async write to cache, then DB
Refresh-ahead	Pre-expire popular entries

4-Step System Design Interview Approach

1. Outline use cases, constraints, assumptions — Scope problem, ask clarifying questions
2. Create high-level design — Sketch main components
3. Design core components — Dive into details
4. Scale the design — Address bottlenecks: load balancing, caching, sharding

Reference Tables

Powers of Two

Power	Value	Approx
10	1,024	1 KB
20	1,048,576	1 MB
30	1,073,741,824	1 GB
40	1,099,511,627,776	1 TB

Latency Reference

Operation	Latency
L1 cache reference	0.5 ns
L2 cache reference	7 ns
Main memory reference	100 ns
SSD random read	150 μs
HDD seek	10 ms
Round trip datacenter	500 μs
Round trip internet	50 ms

Solutions Reference

Located at /opt/system-design-primer/solutions/system_design/:

• mint — Payment system
• pastebin — URL shortener
• query_cache — Key-value store
• twitter — Twitter timeline/search
• social_graph — Social network
• web_crawler — Web crawler
• sales_rank — Amazon sales ranking
• scaling_aws — AWS scalable system
• template — Template for new designs

Additional Resources

• Real World Architectures: MapReduce, Spark, BigTable, Cassandra, DynamoDB, HDFS, Kafka
• Company Engineering Blogs: Google, Facebook, Netflix, Twitter, Uber, Airbnb