💡 HLD Architecture Handbook: The Intuitive System Designer

Project Goal

This repository, the HLD Architecture Handbook, is designed to be a comprehensive, self-paced learning guide for mastering High-Level Design (HLD) and large-scale system architecture. We focus on providing intuitive definitions and in-depth explanations of core concepts, followed by structured design challenges. The ultimate goal is to help you understand the 'Why' behind every architectural choice—the trade-offs, constraints, and future-proofing considerations necessary for building systems at scale.

Audience: Engineers with basic programming knowledge looking to transition from small-scale development to designing highly scalable, reliable, and performant distributed systems.

📁 Repository Structure

The content is organized into three progressive categories:

Folder	Category Name	Focus
01-principles	Core Principles	Core theoretical concepts: Scale, Availability, CAP Theorem, and foundational architecture styles.
02-components	Components Deep Dive	In-depth analysis of specialized databases, caching, sharding, messaging, and concurrency control.
03-challenges	Design Challenges	Real-world design problems (e.g., URL Shortener, Twitter, E-commerce Flash Sale) applying the concepts learned in the first two categories.
system-design-reference.md	Quick Reference Guide	Latency numbers, comparison tables, formulas, and decision matrices.
resources-and-further-reading.md	Learning Resources	Books, papers, courses, blogs, and tools for continued learning.
README.md	(This File)	The main project index and roadmap.

📊 New: Comprehensive Design Challenges Structure

Each completed design challenge now includes 6 comprehensive files for complete understanding:

03-challenges/
└── 3.x.y-problem-name/
    ├── README.md                        # FULL comprehensive guide (primary document, replaces main design file)
    ├── quick-overview.md                # Quick revision guide with core concepts, architecture flows, key takeaways
    ├── hld-diagram.md                   # System architecture diagrams (10-15 Mermaid diagrams)
    ├── sequence-diagrams.md             # Detailed interaction flows (10-15 Mermaid diagrams)
    ├── this-over-that.md                # In-depth design decisions & trade-offs analysis
    └── pseudocode.md                    # Detailed algorithm implementations (10-20 functions)

Benefits:

• 📊 Visual Learning: 20-30 interactive Mermaid diagrams per challenge for system architecture and sequence flows
• 📁 Better Organization: Separate theory, visuals, design decisions, and implementations
• 🔗 Easy Navigation: README links directly to all supplementary files
• 🎨 Maintainable: Text-based diagrams and pseudocode that are version-controlled
• 🚀 GitHub Native: Renders beautifully in GitHub without external tools
• 🧠 Deep Understanding: Detailed "This Over That" analysis explains WHY each architectural choice was made
• 💻 Implementation Ready: Comprehensive pseudocode with time complexity analysis
• 📖 Quick Revision: quick-overview.md provides concise summaries for fast review

🗺️ Learning Roadmap: Core Concepts

We will cover the following topics in sequence before moving to the Design Challenges.

Category 1: Core Principles (Folder: 01-principles)

Topic ID	Concept
1.1.1	CAP Theorem
1.1.2	Latency, Throughput, and Scaling
1.1.3	Availability and Reliability
1.1.4	Data Consistency Models
1.1.5	Back-of-the-Envelope Calculations
1.1.6	Failure Modes and Fault Tolerance
1.1.7	Idempotency
1.1.8	Data Partitioning and Sharding
1.1.9	Replication Strategies
1.1.10	Message Delivery Guarantees
1.2.1	System Architecture Styles
1.2.2	Networking Components
1.2.3	API Gateway and Service Mesh
1.2.4	Domain-Driven Design (DDD) Basics
1.2.5	Service Discovery

Category 2: Components Deep Dive (Folder: 02-components)

📁 Organized into 7 logical categories for easier navigation:

• 🌐 Communication (Protocols, APIs, Real-time, Load Balancers, API Gateway, Service Mesh, DNS)
• 🗄️ Databases (20 database deep dives including Object Storage, Time Series, Vector DBs, Distributed SQL & CQRS!)
• ⚡ Caching (Redis, Memcached, Consistent Hashing, CDN)
• 📨 Messaging & Streaming (Kafka, Spark, Flink, Message Queues, Event Sourcing)
• 🔒 Security & Observability (Auth, OAuth/JWT, Monitoring, Prometheus/Grafana, Logging, ELK Stack, Distributed Tracing)
• 🧮 Algorithms (Rate Limiting, Consensus, Locking, Bloom Filters)
• 🏗️ Infrastructure (Kubernetes, Docker, Configuration Management, Infrastructure as Code)

2.0 Communication (Folder: 2.0-communication)

Topic ID	Concept	Focus
2.0.1	Foundational Communication Protocols	TCP vs. UDP, HTTP/S, WebSockets, WebRTC, DASH.
2.0.2	API Communication Styles	REST, gRPC, SOAP, GraphQL (Pros, Cons, and Use Cases).
2.0.3	Real-Time Communication	Comparison of techniques for maintaining persistent or near-persistent connections for real-time updates.
2.0.4	Load Balancers Deep Dive	Layer 4 vs Layer 7, algorithms, health checks, SSL termination, sticky sessions.
2.0.5	API Gateway Deep Dive	Request routing, authentication, rate limiting, protocol translation, BFF pattern, service aggregation.
2.0.6	Service Mesh Deep Dive	Sidecar pattern, mTLS, retries, circuit breakers, traffic management, distributed tracing.
2.0.7	DNS Deep Dive	DNS resolution, record types, caching, load balancing, geographic routing, failover, DNSSEC.

2.1 Databases (Folder: 2.1-databases) — 20 Deep Dives

Core Database Concepts

Topic ID	Concept	Focus
2.1.1	RDBMS Deep Dive: SQL & ACID	Transactions, Isolation Levels, ACID vs. BASE.
2.1.2	NoSQL Deep Dive: The BASE Principle	Document Stores, Key-Value Stores, Column-Family.
2.1.3	Specialized Databases	Time-Series, Graph, Geospatial DBs (e.g., Redis Streams, Neo4j).
2.1.4	Database Scaling	Replication (Master-Slave), Federation, Sharding Strategies.
2.1.5	Indexing and Query Optimization	B-Trees, LSM-Trees, Denormalization Trade-offs.
2.1.6	Data Modeling for Scale (CQRS)	Denormalization, Data Decomposition, Command-Query Responsibility Segregation (CQRS).

SQL Databases

Topic ID	Concept	Focus
2.1.7	PostgreSQL Deep Dive	MVCC, JSONB, Full-Text Search, PostGIS, Advanced Indexing (GIN, BRIN), Replication, Extensions.
2.1.8	MySQL Deep Dive	InnoDB Storage Engine, MVCC, Replication (Async, Semi-Sync, Group), Indexing (B+Tree), ProxySQL.

NoSQL Databases

Topic ID	Concept	Focus
2.1.9	Cassandra Deep Dive	Masterless Architecture, Wide-Column Store, Tunable Consistency, Write Path, Compaction, Multi-DC.
2.1.10	MongoDB Deep Dive	Document Model (BSON), Embedded vs. Referenced, Aggregation Framework, Sharding, Change Streams.
2.1.11	Redis Deep Dive	In-Memory Data Structures (Strings, Lists, Sets, Sorted Sets), Persistence (RDB/AOF), Cluster.
2.1.12	DynamoDB Deep Dive	Serverless NoSQL, Partition/Sort Keys, GSI/LSI, On-Demand vs. Provisioned, Global Tables, Streams.

Specialized Databases

Topic ID	Concept	Focus
2.1.13	Elasticsearch Deep Dive	Inverted Indexes, Full-Text Search, Aggregations, Integration with RDBMS (CDC), Sharding, ILM.
2.1.14	Neo4j Deep Dive (Graph Databases)	Property Graph Model, Cypher Query Language, Index-Free Adjacency, Graph Algorithms.
2.1.15	ClickHouse Deep Dive (Columnar)	Columnar Storage, MergeTree Engine, Vectorized Query Execution, OLAP Workloads.
2.1.16	Object Storage Deep Dive	S3, GCS, Azure Blob, multipart uploads, lifecycle policies, storage classes, CDN integration.
2.1.17	Time Series Databases Deep Dive	InfluxDB, TimescaleDB, Prometheus, compression, retention policies, downsampling, IoT data.
2.1.18	Vector Databases Deep Dive	Pinecone, Weaviate, Milvus, FAISS, semantic search, embeddings, k-NN algorithms, AI/ML applications.
2.1.19	Distributed SQL Databases Deep Dive	CockroachDB, TiDB, Google Spanner, YugabyteDB, Raft consensus, multi-region, ACID at scale.
2.1.20	CQRS Deep Dive	Command-Query Responsibility Segregation, read/write separation, eventual consistency, multiple read models, synchronization strategies.

2.2 Caching (Folder: 2.2-caching)

Topic ID	Concept	Focus
2.2.1	Caching Deep Dive	Cache-Aside, Write-Through, CDN vs. App-Level Cache.
2.2.2	Consistent Hashing	Algorithm mechanics, Ring implementation, how it minimizes data movement.
2.2.3	Memcached Deep Dive	In-Memory Key-Value Cache, Slab Allocation, LRU Eviction, Multi-Threading.
2.2.4	CDN Deep Dive	Content Delivery Networks, edge caching, cache invalidation, push vs pull, global distribution.

2.3 Messaging & Streaming (Folder: 2.3-messaging-streaming)

Topic ID	Concept	Focus
2.3.1	Asynchronous Communication	Queues vs. Streams, Pub/Sub Models, Backpressure.
2.3.2	Kafka Deep Dive	Broker, Producer, Consumer Group, Partitions, Offset Management, Log Compaction.
2.3.3	Advanced Message Queues (RabbitMQ, SQS, SNS)	Comparison of broker-based vs. managed queues, Dead-Letter Queues (DLQs).
2.3.4	Distributed Transactions & Idempotency	Two-Phase Commit (2PC), Sagas, ensuring atomic operations.
2.3.5	Batch vs Stream Processing	Detailed look at the Lambda and Kappa Architectures, latency vs. completeness trade-offs.
2.3.6	Push vs Pull Data Flow	Architectural choices in messaging systems (e.g., Kafka (Pull) vs. RabbitMQ (Push)).
2.3.7	Apache Spark Deep Dive	Unified Analytics Engine, RDD/DataFrame API, In-Memory Computing, MLlib, Batch & Stream.
2.3.8	Apache Flink Deep Dive	True Stream Processing, Event-by-Event, Stateful Operators, Exactly-Once, CEP, Ultra-Low Latency.
2.3.9	Event Sourcing Deep Dive	Immutable event logs, state reconstruction, snapshots, event store design, time travel, audit trail.

2.4 Security & Observability (Folder: 2.4-security-observability)

Topic ID	Concept	Focus
2.4.1	Security Fundamentals	Authn/Authz (JWT), TLS/Encryption, Cross-Site Scripting (XSS) & CSRF.
2.4.2	Observability	Logging, Metrics (Prometheus), Distributed Tracing (Jaeger/Zipkin), Alerting.
2.4.3	Prometheus & Grafana Deep Dive	Metrics collection, time-series storage, PromQL, dashboards, alerting, service discovery.
2.4.4	OAuth 2.0 & JWT Deep Dive	OAuth 2.0 flows, JWT structure, token management, refresh tokens, OIDC, security best practices.
2.4.5	ELK Stack & Logging Deep Dive	Elasticsearch, Logstash, Kibana, Beats, log parsing, retention, correlation, full-text search.
2.4.6	Distributed Tracing Deep Dive	Jaeger, Zipkin, OpenTelemetry, span propagation, sampling strategies, trace correlation, performance optimization.

2.5 Distributed Algorithms (Folder: 2.5-algorithms)

Topic ID	Concept	Focus
2.5.1	Rate Limiting Algorithms	Token Bucket, Leaky Bucket, Fixed Window counter mechanisms.
2.5.2	Consensus Algorithms	Paxos / Raft, Distributed Locks (ZooKeeper/etcd), solving the concurrency problem.
2.5.3	Distributed Locking	$\text{Redis}$ locks, $\text{TTL}$, Fencing Tokens, ensuring mutual exclusion.
2.5.4	Bloom Filters	Intuition, Hash Functions, False Positives, use cases (e.g., CDN cache lookups).

2.6 Infrastructure (Folder: 2.6-infrastructure)

Topic ID	Concept	Focus
2.6.1	Kubernetes and Docker Deep Dive	Container orchestration, pods, services, deployments, auto-scaling, service discovery.
2.6.2	Configuration Management Deep Dive	etcd, Consul, Vault, service discovery, leader election, secrets management, watch API.
2.6.3	Infrastructure as Code Deep Dive	Terraform, CloudFormation, Pulumi, state management, modules, multi-environment, CI/CD.

🗺️ Design Challenges Roadmap (Category 3)

📊 Each challenge folder contains 6 comprehensive files:

• [README.md] - Complete comprehensive guide with all content (primary document, replaces main design file)
• [quick-overview.md] - Quick revision guide with core concepts, architecture flows, and key takeaways
• [hld-diagram.md] - 10-15 system architecture diagrams with detailed flow explanations
• [sequence-diagrams.md] - 10-15 interaction flows with step-by-step explanations
• [this-over-that.md] - In-depth analysis of 5-10 major design decisions and trade-offs
• [pseudocode.md] - 10-20 detailed algorithm implementations with complexity analysis

📊 Each challenge includes comprehensive visual diagrams (Mermaid) for system architecture and sequence flows!

Easy Challenges (Focus: Core Components, Caching, Databases)

These problems require solid application of scaling fundamentals, hashing, and database choices.

Problem ID	System Name	Key Concepts Applied
3.1.1	Design a URL Shortener ($\text{TinyURL}$)	Hashing, Base62 Encoding, Read-Heavy Scaling, Sharding Key, $\text{Cache}$ $\text{Aside}$, Multi-region deployment
3.1.2	Design a Distributed Cache ($\text{Redis}$ / $\text{Memcached}$)	Consistent Hashing, Eviction Policies ($\text{LRU}$), Replication, Failover, $\text{TTL}$, Cache Stampede Prevention
3.1.3	Design a Distributed ID Generator ($\text{Snowflake}$)	64-bit ID Structure, Worker ID Assignment, Clock Drift Handling, Sequence Management, $\text{etcd}$ Coordination

Medium Challenges (Focus: Asynchrony, Feeds, Microservices, Geo-Spatial)

These problems involve decoupling services, handling fan-out, and managing complex data models.

Problem ID	System Name	Key Concepts Applied
3.2.1	Design a Twitter/X Timeline	$\text{Fanout}$ $\text{on}$ $\text{Write}$ vs. $\text{Fanout}$ $\text{on}$ $\text{Read}$, $\text{Caching}$ $\text{Hierarchy}$, $\text{Queuing}$ ($\text{Kafka}$).
3.2.2	Design a Notification Service	$\text{Multi}$-$\text{Channel}$ ($\text{Email}$/$\text{SMS}$/$\text{Push}$/$\text{Web}$), $\text{WebSockets}$, $\text{Kafka}$ $\text{Streams}$, $\text{Circuit}$ $\text{Breakers}$, $\text{Rate}$ $\text{Limiting}$, $\text{DLQ}$.
3.2.3	Design a Distributed Web Crawler	$\text{URL}$ $\text{Frontier}$, $\text{Bloom}$ $\text{Filter}$, $\text{Duplicate}$ $\text{Detection}$, $\text{Politeness}$, $\text{Rate}$ $\text{Limiting}$, $\text{robots}$.$\text{txt}$.
3.2.4	Design a Global Rate Limiter	$\text{Token}$ $\text{Bucket}$, $\text{Sliding}$ $\text{Window}$, $\text{Atomic}$ $\text{INCR}$, $\text{Circuit}$ $\text{Breakers}$, $\text{Fail}$-$\text{Open}$, $\text{Hot}$ $\text{Key}$ $\text{Mitigation}$.

Hard Challenges (Focus: Consistency, Low-Latency, Transactions, Consensus, Real-Time Geo)

These problems require advanced pattern usage, strong consistency guarantees, and managing complex real-time state.

Problem ID	System Name	Key Concepts Applied
3.3.1	Design a Live Chat System ($\text{WhatsApp}$ / $\text{Slack}$)	$\text{WebSockets}$, $\text{Kafka}$ $\text{Ordering}$, $\text{Presence}$ $\text{Service}$, $\text{Sequence}$ $\text{IDs}$, $\text{Read}$ $\text{Receipts}$, $\text{Group}$ $\text{Chat}$.
3.3.2	Design Uber/Lyft Ride Matching	$\text{Redis}$ $\text{Geo}$, $\text{Geohash}$ $\text{Indexing}$, $\text{Kafka}$ $\text{Buffer}$, $\text{ETA}$ $\text{Calculation}$, $\text{Geographic}$ $\text{Sharding}$, $\text{750K}$ $\text{writes/sec}$.
3.3.3	Design an E-commerce Flash Sale	$\text{Redis}$ $\text{Atomic}$ $\text{DECR}$, $\text{Saga}$ $\text{Pattern}$, $\text{Load}$ $\text{Shedding}$, $\text{Idempotency}$ $\text{Keys}$, $\text{Split}$ $\text{Counter}$, $\text{100K}$ $\text{QPS}$ $\text{hot}$ $\text{key}$.
3.3.4	Design a Distributed Database	$\text{Raft}$ $\text{Consensus}$, $\text{2PC}$, $\text{Range}$ $\text{Sharding}$, $\text{LSM}$ $\text{Tree}$, $\text{MVCC}$, $\text{Timestamp}$ $\text{Oracle}$, $\text{Multi}$-$\text{Region}$, $\text{1M}$ $\text{QPS}$.
3.4.1	Design a Stock Exchange Matching Engine	$\text{LMAX}$ $\text{Disruptor}$, $\text{DPDK}$ $\text{Kernel}$ $\text{Bypass}$, $\text{Red}$-$\text{Black}$ $\text{Tree}$, $\text{Fixed}$-$\text{Point}$ $\text{Integers}$, $\text{WAL}$, $\text{<100μs}$ $\text{p99}$, $\text{1M}$ $\text{orders/sec}$.
3.4.2	Design a Global News Feed ($\text{Google}$ $\text{News}$ / $\text{Aggregator}$)	$\text{NLP}$ $\text{Pipelines}$, $\text{LSH}$ $\text{Deduplication}$, $\text{Elasticsearch}$, $\text{Kappa}$ $\text{Architecture}$, $\text{Real}$-$\text{Time}$ $\text{Feature}$ $\text{Store}$, $\text{Multi}$-$\text{Region}$, $\text{100M}$ $\text{articles/day}$.
3.4.3	Design a Distributed Monitoring System ($\text{Prometheus}$ / $\text{Datadog}$)	$\text{M3DB}$ $\text{TSDB}$, $\text{Delta}$-$\text{of}$-$\text{Delta}$ $\text{Encoding}$, $\text{Rollup}$ $\text{Aggregations}$, $\text{Stream}$ $\text{Alerting}$, $\text{Cardinality}$ $\text{Control}$, $\text{100M}$ $\text{writes/sec}$, $\text{10PB}$ $\text{storage}$.
3.4.4	Design a Recommendation System ($\text{Netflix}$ / $\text{Amazon}$)	$\text{Lambda}$ $\text{Architecture}$, $\text{ALS}$ $\text{Collaborative}$ $\text{Filtering}$, $\text{FAISS}$ $\text{ANN}$, $\text{Redis}$ $\text{Feature}$ $\text{Store}$, $\text{TensorFlow}$ $\text{Serving}$, $\text{50ms}$ $\text{p99}$, $\text{100k}$ $\text{QPS}$.
3.4.5	Design a Stock Brokerage Platform ($\text{Zerodha}$ / $\text{Robinhood}$)	$\text{FIX}$ $\text{Protocol}$, $\text{WebSockets}$ $\text{Push}$, $\text{Event}$ $\text{Sourcing}$, $\text{Redis}$ $\text{Quotes}$, $\text{Elasticsearch}$ $\text{Search}$, $\text{Margin}$ $\text{Calls}$, $\text{10M}$ $\text{users}$, $\text{<200ms}$ $\text{latency}$.
3.4.6	Design a Collaborative Editor ($\text{Google}$ $\text{Docs}$)	$\text{OT}$/$\text{CRDT}$, $\text{WebSockets}$, $\text{Event}$ $\text{Sourcing}$, $\text{CQRS}$.
3.4.7	Design an Online Code Editor / Judge	$\text{Execution}$ $\text{Isolation}$ ($\text{Sandboxing}$), $\text{Queue}$ $\text{Priority}$, $\text{Resource}$ $\text{Throttling}$.
3.4.8	Design a Video Streaming System ($\text{YouTube}$ / $\text{Netflix}$)	$\text{CDN}$ $\text{Hierarchy}$, $\text{DASH}$/$\text{HLS}$, $\text{Encoding}$ $\text{Pipelines}$, $\text{DRM}$.
3.5.1	Design a Payment Gateway ($\text{Stripe}$)	$\text{Idempotency}$, $\text{PCI}$ $\text{Compliance}$, $\text{Tokenization}$, $\text{Fraud}$ $\text{Detection}$.
3.5.2	Design Ad Click Aggregator ($\text{Google}$ $\text{Ads}$)	$\text{Kappa}$ $\text{Architecture}$, $\text{Low}$-$\text{Latency}$ $\text{Counters}$, $\text{Batch}$ $\text{Reconciliation}$.
3.5.3	Design $\text{YouTube}$ $\text{Top}$ $\text{K}$ ($\text{Trending}$ $\text{Algorithm}$)	$\text{Redis}$ $\text{Sorted}$ $\text{Sets}$, $\text{Decay}$ $\text{Functions}$, $\text{Real}$-$\text{Time}$ $\text{Ranking}$ $\text{Pipeline}$.
3.5.4	Design Instagram/Pinterest Feed	$\text{Media}$ $\text{Pipeline}$, $\text{Fanout}$ $\text{on}$ $\text{Write}$ $\text{vs.}$ $\text{Recommendation}$ $\text{Merge}$.
3.5.5	Design Live Commenting ($\text{Facebook}$ $\text{Live}$ / $\text{Twitch}$)	$\text{Massive}$ $\text{Fanout}$, $\text{WebSockets}$, $\text{Adaptive}$ $\text{Throttling}$, $\text{Asynchronous}$ $\text{Moderation}$.
3.5.6	Design Yelp/Google Maps	$\text{Geospatial}$ $\text{Search}$, $\text{Geohash}$ $\text{Partitioning}$, $\text{Hierarchical}$ $\text{Sharding}$, $\text{Multi}$-$\text{Cell}$ $\text{Query}$.
3.5.7	Design Authenticator App ($\text{Microsoft}$ $\text{Authenticator}$ / $\text{Google}$ $\text{Authenticator}$)	$\text{TOTP}$ $\text{Algorithm}$, $\text{Offline}$ $\text{Operation}$, $\text{Device}$ $\text{HSM}$, $\text{Cloud}$ $\text{Backup}$, $\text{Multi}$-$\text{Device}$ $\text{Sync}$, $\text{Push}$ $\text{Notifications}$.
3.5.8	Design Single Sign-On (SSO) System ($\text{Okta}$ / $\text{Auth0}$ / $\text{Azure}$ $\text{AD}$)	$\text{OAuth}$ $\text{2.0}$ / $\text{OIDC}$, $\text{SAML}$ $\text{2.0}$, $\text{JWT}$ $\text{Tokens}$, $\text{Token}$ $\text{Rotation}$, $\text{Identity}$ $\text{Federation}$, $\text{Multi}$-$\text{Tenancy}$.

---

📚 Additional Resources

• System Design Reference Guide: Quick-lookup tables for latency numbers, database comparisons, caching strategies, and more.
• Resources and Further Reading: Curated books, papers, courses, blogs, and tools to deepen your knowledge.

---

🎉 Contributions

We highly encourage community contributions to expand this resource! Before submitting a Pull Request, please read and follow these guidelines:

General Guidelines

1. Clarity and Depth: Content must maintain the project's goal: providing intuitive, easy-to-understand definitions while retaining technical depth.
2. Naming Convention: All new topic files must be placed in the correct category folder (e.g., 01-principles/, 02-components/) and follow the format: [ID]-[short-name].md (e.g., 1.2.1-architecture-styles.md).

Template for Adding a New Concept Topic (Category 1 or 2)

Use this structure for any new concept file. The file should provide a clear progression from basic intuition to technical details.

# [ID] Topic Title: Subtitle/Focus

## Intuitive Explanation
[Start with a simple, high-level analogy or definition that a beginner can grasp.]

## In-Depth Analysis
[Dive into the technical specifics, internal workings, and algorithms.]

### Key Concepts / Tradeoffs
* **Concept 1:** ...
* **Tradeoff:** [Discuss the pros/cons of a choice, e.g., speed vs. consistency.]

## 💡 Real-World Use Cases
* [List 2-3 specific examples of companies or scenarios where this concept is applied.]

---

## ✏️ Design Challenge
[Create a concise, open-ended question that forces the reader to apply the concepts from the file.]

Template for Adding a New Design Problem (Category 3)

When adding a new design challenge to 03-challenges/, create a folder 3.x.y-problem-name/ with 6 required files:

File Structure:

03-challenges/3.x.y-problem-name/
├── README.md                        # Main comprehensive guide (primary document, replaces old main design file)
├── quick-overview.md                # Quick revision guide with core concepts, architecture flows, key takeaways
├── hld-diagram.md                   # 10-15 architecture diagrams (Mermaid)
├── sequence-diagrams.md             # 10-15 sequence diagrams (Mermaid)
├── this-over-that.md                # In-depth design decision analysis
└── pseudocode.md                    # Algorithm implementations

⚠️ IMPORTANT: The main design file (3.x.y-design-problem-name.md) should NOT exist in the final structure. Its content should be moved to README.md, and a quick-overview.md file should be created for quick revision purposes.

Main File Template (README.md):

REQUIRED STRUCTURE (must follow this exact order):

# [ID] Design a [System Name]

> 📚 **Note on Implementation Details:**
> This document focuses on high-level design concepts and architectural decisions. 
> For detailed algorithm implementations, see **[pseudocode.md](./pseudocode.md)**.

## 📊 Visual Diagrams & Resources

- **[High-Level Design Diagrams](./hld-diagram.md)** - System architecture, component design, data flow
- **[Sequence Diagrams](./sequence-diagrams.md)** - Detailed interaction flows and failure scenarios
- **[Design Decisions (This Over That)](./this-over-that.md)** - In-depth analysis of architectural choices
- **[Pseudocode Implementations](./pseudocode.md)** - Detailed algorithm implementations

---

## 1. Problem Statement
[Clear problem description]

---

## 2. Requirements and Scale Estimation
### Functional Requirements
* [What the system MUST do]

### Non-Functional Requirements
* **Scale:** [e.g., 500M DAU]
* **QPS:** [Read: 100k, Write: 5k]
* **Latency:** [e.g., <100ms]

### Capacity Estimation
[Back-of-envelope calculations for storage, bandwidth, QPS]

## 3. High-Level Architecture
[ASCII diagram with main components]

## 4. Data Model
[Database schemas - use ```sql for SQL only]

## 5. Component Design
[Detailed component descriptions]

## 6. Why This Over That?
[Inline explanations for major choices: DB, cache, sync/async]
* **Why PostgreSQL over MongoDB?** [Rationale with bullets]
* **Why Kafka over RabbitMQ?** [Rationale with bullets]

## 7. Bottlenecks and Scaling
[Identify bottlenecks and future scaling strategies]

## 8. Common Anti-Patterns
❌ **Anti-Pattern:** [Bad approach]
✅ **Best Practice:** [Good approach]

## 9. Alternative Approaches
[Discuss 2-3 alternative architectures not chosen]

## 10. Monitoring and Observability
[Key metrics, alerts, dashboards]

## 11. Trade-offs Summary
[Final comparison table of all major decisions]

## 12. Real-World Examples
[How Twitter, Uber, etc. solve this problem]

this-over-that.md Template:

# Design Decisions: [System Name]

## Decision 1: [e.g., Fanout Strategy]
### The Problem
[What are we trying to solve?]

### Options Considered
| Option | Pros | Cons | Performance | Cost |
|--------|------|------|-------------|------|
| Option A | ... | ... | ... | ... |
| Option B | ... | ... | ... | ... |

### Decision Made
[What we chose and why - 3-5 bullets]

### Rationale
1. [Detailed point 1]
2. [Detailed point 2]

### Trade-offs Accepted
[What we're sacrificing]

### When to Reconsider
[Conditions that would change this decision]

[Repeat for 5-10 major decisions]

## Summary Comparison
[Final table comparing all decisions]

pseudocode.md Template:

# Pseudocode Implementations: [System Name]

## Table of Contents
- [Section 1: Feature Name](#section-1)
- [Section 2: Feature Name](#section-2)

## Section 1: Feature Name

### function_name()
**Purpose:** One-line description

**Parameters:**
- param1: type - description
- param2: type - description

**Returns:** return_type - description

**Algorithm:**
\`\`\`
function function_name(param1, param2):
  // Detailed implementation
  return result
\`\`\`

**Time Complexity:** O(n)
**Space Complexity:** O(1)

**Example Usage:**
\`\`\`
result = function_name(arg1, arg2)
\`\`\`

[Include 10-20 functions organized by feature]

Key Requirements:

• STANDARDIZED FORMAT: All README files MUST follow this exact structure:
  1. Title
  2. "Note on Implementation Details" block (referencing pseudocode.md)
  3. "📊 Visual Diagrams & Resources" section (with links to all supplementary files)
  4. Section numbering starts at "## 1. Problem Statement"
  5. Continue with "## 2. Requirements...", "## 3. High-Level Architecture", etc.
• README.md: NO programming language code, NO detailed pseudocode (describe in words, reference pseudocode.md)
• quick-overview.md: Concise revision guide (300-600 lines) with core concepts, architecture flows, key design decisions, bottlenecks, anti-patterns, trade-offs, real-world examples, and key takeaways
• All diagrams MUST have flow explanations (steps, benefits, trade-offs)
• this-over-that.md: 5-10 major decisions with detailed analysis
• pseudocode.md: 10-20 functions with complexity analysis
• See 03-challenges/3.1.1-url-shortener/ as reference implementation