When building any software system, jumping straight into coding is tempting. But without a blueprint, teams virtually guarantee confusion, costly rework, and significant delays. That’s where High-Level Design (HLD) comes in.

Think of HLD as the architect’s drawing for your software. It doesn’t show every screw and nail, but it clearly defines the structure, flow, and critical choices that make the system stable, scalable, and secure.

In this comprehensive guide, we’ll break down the core components of an HLD, explore deep technical concepts, and illustrate them with creative real-world examples that bring theory to life.

Core Components of an HLD

1. Introduction & Objectives

Every HLD starts with a clear purpose, what problem the system solves and why it matters. This sets the context for all design choices.

The Problem Statement Framework

A solid objective follows the SMART principle (Specific, Measurable, Achievable, Relevant, Time-bound) and should outline:

• Current State: What exists today and why it’s insufficient

• Desired State: What success looks like

• Success Metrics: Quantifiable KPIs (e.g., response time, cost, uptime)

• Constraints: Budget, timeline, skills, or compliance limits

Example: Concert Ticket Marketplace

Problem: Concert-goers struggle with ticket scalping, fake tickets, and unfair pricing. Existing platforms charge 15–20% fees and have frequent outages during high-demand sales.
Objective: Build a blockchain-verified ticketing platform that:

• Reduces fees to under 5% through serverless architecture

• Handles 100,000 concurrent users during ticket drops

• Prevents ticket fraud through NFT-based verification

• Provides sub-200ms response times globally

• Achieves 99.99% uptime during peak events

AWS Approach: If the objective is a cost-efficient, scalable backend, AWS Lambda (serverless compute) can be chosen as the core execution engine, with DynamoDB for persistent storage and S3 for ticket metadata/images.

2. Architecture Overview

At the heart of an HLD is the big-picture diagram. It should illustrate:

• Core services (frontend, backend, database)

• APIs and external integrations

• Deployment environments

This view gives stakeholders a quick understanding of how everything fits together.

Architectural Patterns

Different systems call for different approaches:

• Monolithic: One deployable unit — simple, fast for MVPs.

• Microservices: Independent services with separate databases — ideal for scaling and clear domain boundaries.

• Serverless: Event-driven, auto-scaling, and pay-per-use, perfect for modern, lean systems.

Layered Architecture

• Presentation Layer: UI and user interactions

• Application Layer: Business logic and orchestration

• Domain Layer: Core entities and rules

• Data Layer: Databases, caches, queues

Example: Concert Ticket Marketplace

Flow Overview:

┌─────────────────────────────────────────────────────────┐
│              CloudFront CDN (Global Edge)               │
│         Static Assets + Dynamic Content Caching         │
└─────────────────────────────────────────────────────────┘
                          │
        ┌─────────────────┴─────────────────┐
        │                                   │
┌───────▼────────┐                 ┌────────▼────────┐
│  S3 + React    │                 │  API Gateway    │
│  SPA Frontend  │                 │  (REST + WS)    │
└────────────────┘                 └────────┬────────┘
                                            │
                    ┌───────────────────────┼───────────────────────┐
                    │                       │                       │
            ┌───────▼────────┐     ┌────────▼────────┐    ┌────────▼────────┐
            │ Auth Lambda    │     │ Ticket Lambda   │    │ Payment Lambda  │
            │ (Cognito JWT)  │     │ (Search/Book)   │    │ (Stripe/Crypto) │
            └───────┬────────┘     └────────┬────────┘    └────────┬────────┘
                    │                       │                       │
        ┌───────────┴───────┐      ┌────────▼────────┐    ┌────────▼────────┐
        │  DynamoDB Users   │      │ DynamoDB Tickets│    │ DynamoDB Orders │
        │  + ElastiCache    │      │ + OpenSearch    │    │                 │
        └───────────────────┘      └────────┬────────┘    └─────────────────┘
                                            │
                                   ┌────────▼────────┐
                                   │  EventBridge    │
                                   │  (Event Bus)    │
                                   └────────┬────────┘
                                            │
                        ┌───────────────────┼───────────────────┐
                        │                   │                   │
                ┌───────▼────────┐  ┌───────▼────────┐  ┌──────▼──────┐
                │ Email Lambda   │  │ Fraud Lambda   │  │ NFT Lambda  │
                │ (SES/SNS)      │  │ (SageMaker ML) │  │ (Blockchain)│
                └────────────────┘  └────────────────┘  └─────────────┘

Key Design Choices:

• Event-Driven: EventBridge decouples services for resilience

• CQRS: DynamoDB for writes, OpenSearch for fast reads

• Real-Time Updates: WebSockets for live seat availability

• Global Scale: Route53 + CloudFront enable multi-region, low-latency performance

AWS Stack: A typical architecture might be API Gateway → Lambda → DynamoDB, with static assets in S3 + CloudFront, and monitoring through CloudWatch.

3. Functional Components

Break the system into major modules, for example:

• Authentication service

• Payment gateway integration

• Notification and messaging service

Each should describe what it does, not how it’s coded.

Aligning with Domain-Driven Design (DDD)

Functional components should follow business domains, not technical layers:

• Ubiquitous Language: Shared vocabulary between devs and business

• Aggregate Roots: Entities that enforce consistency

• Domain Events: Signals for cross-context actions

Example: Concert Marketplace Domains

1. Identity & Access: MFA, OAuth2, RBAC, JWT session management

2. Inventory Management: Seat mapping, real-time availability, presale/lottery strategies

3. Transaction Processing: Payment orchestration, PCI-compliant tokenization, fraud detection

4. Fulfillment & Verification: NFT minting, QR codes, resale marketplace, gate scanning

5. Fan Engagement: Personalized recommendations, waitlists, social sharing, loyalty rewards

AWS Implementation

• Identity: Amazon Cognito user pools with Lambda triggers for custom logic

• Inventory: DynamoDB with conditional writes for atomicity, ElastiCache for read-heavy queries

• Transaction: Step Functions for saga pattern orchestration, SQS for asynchronous processing

• Fulfillment: Lambda + Web3.js for blockchain interaction, S3 for ticket PDFs

• Engagement: Pinpoint for marketing campaigns, Personalize for ML recommendations

4. Data Flow & Interactions

Show how information moves through the system. Sequence diagrams or Data Flow Diagrams (DFDs) work best here, especially for key use cases like login, checkout, or API requests.

Key Insights from Sequence Diagrams

• Synchronous vs. Asynchronous communication patterns

• Failure points and retry strategies

• Latency contributors in the critical path

• Idempotency requirements for safe retries

Example: High-Demand Ticket Drop

Flow:

User → CloudFront → API Gateway → Lambda → SQS → Worker Lambda → DynamoDB → EventBridge → Notifications/Analytics

Steps:

1. User clicks “Buy Tickets” (50K concurrent requests)

2. CloudFront serves cached availability

3. API Gateway rate-limits requests

4. Auth Lambda validates JWT

5. Queue Lambda writes to SQS FIFO (prevents double-booking)

6. Worker Lambda conditionally writes to DynamoDB, holds seat, publishes to EventBridge

7. EventBridge triggers: Email, WebSocket, Analytics

8. Payment Lambda processes payment, updates DynamoDB, queues NFT minting

9. Timeout Lambda releases expired holds

Failure Handling:

• Lambda timeout: SQS visibility timeout > Lambda timeout (65 seconds vs 60 seconds)

• DynamoDB throttling: Exponential backoff with jitter, on-demand capacity mode

• Payment provider downtime: Circuit breaker pattern (fail fast after 3 failures)

• Duplicate requests: SQS FIFO deduplication + DynamoDB conditional writes

AWS Example: A checkout flow may look like API Gateway receives request → Lambda validates → DynamoDB writes order → SNS triggers fulfillment → S3 stores invoice.

5. Technology Stack

Document your chosen stack, frameworks, databases, cloud services and explain the rationale for each selection. This ensures consensus and prevents costly technology drift later on.

• Performance: Throughput, latency, resource efficiency

• Scalability: Horizontal/vertical scaling, stateless vs. stateful

• Cost: Compute, data transfer, managed service premiums

• Developer Experience: Team expertise, local dev, testing/debugging

• Operational Overhead: Maintenance, monitoring, disaster recovery

Example: Concert Marketplace Stack

Alternatives Considered:

Containerized Microservices

• ECS Fargate + ALB + RDS Aurora + ElastiCache + Kafka

• Pros: More control, easier local dev, complex transactions

• Cons: Higher baseline costs (~$500/month), slower scaling, patching burden

Edge-First Architecture

• Cloudflare Workers + Durable Objects + R2 + D1 SQLite

• Pros: Lowest latency (0–50ms), simplified stack, cheaper egress

• Cons: Platform immaturity, smaller ecosystem, vendor lock-in

AWS Example: Tech stack could be Node.js on AWS Lambda, DynamoDB for database, API Gateway for API layer, and S3 for file storage.

6. Non-Functional Requirements (NFRs)

Beyond features (what the system does), NFRs define the system’s quality attributes (how well it performs and runs). Include:

• Performance: API latency under 200 ms

• Scalability: auto-scaling policies

• Availability: 99.9% uptime

• Security: encryption, RBAC, compliance (GDPR, HIPAA, PCI)

• Maintainability & Extensibility

Key Metrics (SLIs, SLOs, SLAs)

• SLIs: Request latency, error rate, throughput, uptime

• SLOs: e.g., 99% of API requests <200ms, 99.9% uptime, <0.1% error rate

• SLAs: Contractual guarantees, e.g., 99.5% uptime or service credits, regulatory data retention

Example: Concert Marketplace

Performance:

Scalability & Availability:

Vertical Scaling: Not applicable (serverless)

Horizontal Scaling:

• Lambda: 1,000 concurrent executions per region (can request increase to 10,000+)

• DynamoDB: On-demand capacity mode (auto-scales without limits)

• API Gateway: 10,000 requests/second per region (soft limit, can increase)

• CloudFront: Unlimited (petabyte-scale proven)

Auto-Scaling Policies:

# Example: DynamoDB Table Auto-Scaling (Provisioned Mode)
ReadCapacityScaling:
  MinCapacity: 5
  MaxCapacity: 500
  TargetValue: 70% # Scale when utilization exceeds 70%
  ScaleInCooldown: 60s
  ScaleOutCooldown: 0s # Immediate scale-out

Availability & Resilience

• Multi-AZ & multi-region active-active deployments

• RTO <15 min, RPO <1 min, automated backups to S3 Glacier

• Chaos testing monthly

Security:

• OAuth2/OIDC + MFA, JWT tokens, RBAC, rate-limited API keys

• Encryption at rest (AES-256) & in transit (TLS 1.3), KMS key rotation, Secrets Manager

• Compliance: PCI-DSS, GDPR, CCPA, SOC 2 Type II

• Network security: WAF, Shield, VPC private subnets

• Vulnerability management: Snyk, penetration tests, bug bounty

Maintainability & Extensibility:

• 80% test coverage, ESLint + Prettier, JSDoc/OpenAPI

• Blue-green & canary deployments, feature flags, rollback <5 min

• Extensible via EventBridge, API versioning, webhooks

AWS Example: Lambda provides horizontal scaling, DynamoDB has auto-scaling throughput, CloudWatch monitors latency, and KMS ensures encryption.

7. Infrastructure & Deployment

A snapshot of how the system runs in production:

• Cloud/on-prem deployment strategy

• CI/CD pipeline overview

• Backup and disaster recovery plans

Infrastructure as Code (IaC)

Version-controlled, peer-reviewed infrastructure using tools like:

IaC Tools Comparison:

CI/CD Pipeline (Concert Marketplace Example)

1. Feature branch → local dev with LocalStack → unit & integration tests

2. Build & Test: ESLint/Prettier, Jest coverage, Lambda/Docker artifacts

3. Security Scan: Snyk, Checkov, secrets detection, SAST

4. Deploy to Dev: CDK synth/diff/deploy, smoke tests

5. Integration Tests: API contracts, load testing, security/performance checks

6. Manual Approval → Deploy to Production: Blue-green & canary releases, DB migrations, automated rollback

7. Post-Deployment: Monitoring, chaos engineering, performance baselines, notifications

Deployment Strategies

• Blue-Green:

// Lambda Alias Configuration
const blueAlias = new lambda.Alias(this, 'BlueAlias', {
  aliasName: 'blue',
  version: lambdaFunction.currentVersion,
});

const greenAlias = new lambda.Alias(this, 'GreenAlias', {
  aliasName: 'green',
  version: lambdaFunction.version('$LATEST'),
});
// API Gateway routes 100% to blue initially
// After validation, switch to green
// If issues, instant rollback to blue

• Canary:

const deployment = new apigateway.Deployment(this, 'Deployment', {
  api: restApi,
  description: 'Canary deployment for v2.1.0',
});

const stage = new apigateway.Stage(this, 'ProdStage', {
  deployment,
  canarySettings: {
    percentTraffic: 10, // 10% to canary
    useStageCache: false,
    deploymentId: deployment.deploymentId,
  },
});

Disaster Recovery

• Region Failure: Route53 health check → failover to secondary region, DynamoDB Global Tables

• DB Corruption: Point-in-time recovery (<15 min)

• DDoS Attack: WAF + Shield Advanced, CloudFront rate limiting

AWS Example: Deploy using AWS CDK or Terraform, CI/CD with CodePipeline + CodeBuild, disaster recovery with DynamoDB global tables and S3 cross-region replication.

8. Data Design

Provide a high-level schema or entity-relationship diagram. Keep it simple, showing key tables/collections and relationships.

NoSQL Principles (DynamoDB)

• Single-Table Design: Multiple entity types in one table, composite keys (PK/SK), GSIs for alternate queries

• Access Pattern First: List query patterns before designing schema; optimize for read-heavy workloads; accept eventual consistency for non-critical reads

Example: Concert Marketplace

DynamoDB Main Table: TicketMarketplace

Access Patterns:

Get user profile: PK = USER#123, SK = PROFILE
Get user's orders: PK = USER#123, SK begins_with ORDER#
Get event details: PK = VENUE#ABC, SK = EVENT#789
Get available tickets for event: PK = EVENT#789, SK begins_with TICKET#, filter status = available
Find order by ID (GSI1): GSI1-PK = ORDER#456
Find events by date (GSI1): GSI1-PK = EVENT#789, GSI1-SK = 2025-06-15
Find ticket by seat (GSI1): GSI1-PK = SEAT#A12

OpenSearch Index: ticket-search

{
  "mappings": {
    "properties": {
      "eventId": { "type": "keyword" },
      "artist": { "type": "text", "fields": { "keyword": { "type": "keyword" } } },
      "venue": { "type": "text" },
      "date": { "type": "date" },
      "city": { "type": "keyword" },
      "location": { "type": "geo_point" },
      "genres": { "type": "keyword" },
      "priceRange": { "type": "integer_range" },
      "availableTickets": { "type": "integer" }
    }
  }
}

Consistency Strategy:

• Strong: Authentication, payment, ticket purchase

• Eventual: Profile reads, order history, event metadata

• Cross-service: DynamoDB → OpenSearch via Streams + Lambda (1–5s lag)

Data Retention & Archival:

• Active: Current events/tickets in DynamoDB, recent orders (<90 days)

• Archived: Completed orders (>90 days) & analytics in S3/Glacier, audit logs (7 years)

AWS Example: DynamoDB tables designed with partition key + sort key, GSIs for alternate queries, and S3 buckets for object-based storage.

9. Observability & Monitoring

Outline how the system’s health will be tracked:

• Metrics, logs, and traces

• Alerts and thresholds

• Tools like Prometheus, Grafana, or ELK stack

Three Pillars of Observability

• Metrics: System (CPU, memory), Application (request rate, errors), Business (tickets sold, revenue)

• Logs: Structured JSON logs with correlation IDs, levels (ERROR, WARN, INFO, DEBUG), centralized aggregation

• Traces: Distributed tracing to track requests across services, identify slow components, and propagate errors

Example: Concert Marketplace

Metrics Pipeline:

Lambda Functions → CloudWatch Metrics (1-minute resolution)
                ↓
          CloudWatch Alarms → SNS → PagerDuty/Slack
                ↓
    Custom Metrics via Embedded Metric Format (EMF)
                ↓
          CloudWatch Dashboards + Grafana

Key Metrics Dashboard:

Golden Signals: Latency (p50/p95/p99), Traffic (RPS), Errors (4xx/5xx), Saturation (throttles, capacity)
Custom Business Metrics:

// Embedded Metric Format in Lambda
const { MetricUnit } = require('aws-embedded-metrics');

await metrics.putMetric('TicketsSold', 1, MetricUnit.Count);
await metrics.putMetric('Revenue', ticketPrice, MetricUnit.None);
await metrics.setProperty('EventId', eventId);
await metrics.setProperty('Genre', genre);
await metrics.flush();

Logging:

Lambda Logs → CloudWatch Logs → Subscription Filter → Kinesis Firehose
                                                              ↓
                                          S3 (long-term storage)
                                                              ↓
                                          Athena (SQL queries)
                                                              ↓
                                    QuickSight (visualization)

Log Retention Policy:

• ERROR logs: 90 days in CloudWatch, indefinite in S3

• INFO logs: 7 days in CloudWatch, 30 days in S3

• DEBUG logs: 1 day in CloudWatch, not stored

Distributed Tracing: X-Ray visualizes end-to-end flow, highlights slow segments (e.g., OpenSearch 46% of total time), propagates trace IDs downstream

Alerting:

• P1: Critical, immediate pager (API error rate >1%, payment down)

• P2: High, notify within 15 min (API latency p99 >1s, Lambda throttles)

• P3/P4: Medium/Low, less urgent or daily review

Synthetic Monitoring: CloudWatch Synthetics runs periodic checks on endpoints (homepage, search, login)

Dashboards:

• Executive: Tickets sold, revenue, conversion, CSAT

• Operations: Service map, error rate, Lambda concurrency, DynamoDB throttles

• Security: Failed logins, WAF blocks, certificate expiry

AWS Example: Use CloudWatch Logs/Metrics, X-Ray for tracing, and dashboards in CloudWatch or Grafana with Amazon Managed Grafana.

10. Assumptions, Constraints, and Risks

Be upfront about assumptions (e.g., “API supports 1000 RPS”), constraints (legacy dependencies), and critical risks (vendor lock-in, scaling issues). Highlight mitigation strategies — this turns the document into a proactive risk-management tool.

Risk Assessment and Mitigation

Use a risk matrix to prioritize issues:

Key Assumptions

• Traffic: Avg 10k active users, peak 100k concurrent, burst 10× in 5 min

• User Behavior: 70% mobile, avg session 8 min, 60% cart abandonment

• Data Volume: 10k events/year, 1M tickets/year, 500k transactions/year

• Third-Party Services: Stripe uptime 99.9%, Polygon 2s finality, SES 99.9%

• Cost Assumptions: Lambda 200ms/512MB, DynamoDB 1M reads/500k writes/day, S3 10TB

Constraints

Critical Risks & Mitigation

1. Scalability Bottleneck (Probability: MEDIUM, Impact: HIGH)

• Warm Lambdas, virtual waiting room, load testing, circuit breakers, graceful degradation

2. Payment Fraud (Probability: HIGH, Impact: HIGH)

• CAPTCHA, device fingerprinting, ML fraud detection, 3D Secure, manual review queue

3. Vendor Lock-In (Probability: HIGH, Impact: MEDIUM)

• Abstraction layers, portable data formats, standard APIs, quarterly multi-cloud pilots

4. Data Breach (Probability: LOW, Impact: CRITICAL)

• Zero Trust, least privilege IAM, Secrets Manager, encryption, WAF, pretesting, bug bounty

5. Third-Party Service Outage (Probability: MEDIUM, Impact: HIGH)

• Health checks, circuit breakers, fallback storage, multi-provider setup, status updates

6. Regulatory Compliance Failure (Probability: LOW, Impact: CRITICAL)

• Privacy by design, automated DSARs, contracts with processors, audits, compliance dashboards

7. Blockchain Smart Contract Vulnerability (Probability: LOW, Impact: HIGH)

• Audited contracts, external review, formal verification, proxy upgrade pattern, circuit breaker, gradual rollout

AWS Example: Assume DynamoDB can scale to required RPS, constraint is vendor lock-in with AWS services, and risk is Lambda cold start (mitigated with Provisioned Concurrency).

Best Practices for Writing an HLD

1. Keep It Simple & Visual

Principle: A diagram is worth a thousand words. Use clear, standardized notations that stakeholders can understand at a glance.

• Use clear diagrams: C4, UML, Mermaid, Draw.io, Excalidraw

• Avoid dense paragraphs, overly complex diagrams, inconsistent notation, missing legends

2. Align with Business Goals

Principle: Every technical decision should trace back to a business objective. This ensures engineering efforts deliver value, not just complexity.

Framework: Use the “5 Whys” technique

• Decision: Use DynamoDB instead of RDS

• Why? Need millisecond latency for ticket availability checks

• Why? Users abandon if page loads >2 seconds

• Why? Every 100ms delay reduces conversions by 7%

• Why? Revenue directly tied to conversion rate

• Business Goal: Maximize ticket sales revenue

Metrics Alignment:

3. Use Standard Notations

Principle: Consistent, industry-standard notations make HLDs accessible to new team members and external partners.

• Architecture: AWS/Azure/GCP icons, C4

• Data Models: Chen/Crow’s Foot, UML Class Diagrams

• Sequence Diagrams: UML, show actors, messages, activation boxes

4. Define Clear Boundaries

Principle: Explicitly show what’s inside vs. outside your system’s control. This clarifies responsibilities and dependencies.

• System Boundary: What you operate vs external systems

• Trust Boundary: Authenticated vs public requests

• Data Boundary: Where data resides and moves (inside AWS vs third-party)

5. Address NFRs Early

Principle: Non-functional requirements shape architecture from day one. Retrofitting scalability or security is 10x more expensive than building it in.

Early Architecture Decisions:

• Performance: Async patterns, cache, indexes

• Scalability: Stateless services, horizontal scaling, partition keys

• Security: Auth from day 1, least privilege, encryption

• Reliability: Multi-AZ, retries, graceful degradation

• Example: Latency <500ms → OpenSearch, 10k concurrent → ElastiCache, Availability 99.9% → Multi-AZ

6. Collaborate & Review

Principle: HLD is a team sport. Cross-functional input catches blind spots and builds shared understanding.

Review Process:

• Stages: Draft → Tech Review → Stakeholder Review → Final Approval

• Review Checklist:

## HLD Review Checklist

### Architecture
- [ ] Clear system boundaries defined
- [ ] Component responsibilities well-defined
- [ ] Data flow diagrams show key interactions
- [ ] Failure scenarios considered
- [ ] Alternative architectures evaluated
### Scalability
- [ ] Load estimates with justification
- [ ] Auto-scaling strategy defined
- [ ] Bottlenecks identified and mitigated
- [ ] Load testing plan outlined
### Security
- [ ] Authentication/authorization design
- [ ] Data encryption at rest and in transit
- [ ] Secrets management strategy
- [ ] Compliance requirements addressed
### Cost
- [ ] Monthly cost estimate provided
- [ ] Cost optimization strategies outlined
- [ ] TCO comparison with alternatives
### Operations
- [ ] Monitoring and alerting design
- [ ] Deployment strategy defined
- [ ] Disaster recovery plan
- [ ] On-call runbook outline
### Team Readiness
- [ ] Required skills assessment
- [ ] Knowledge gaps identified
- [ ] Training plan if needed

7. Version Control Your HLD

Principle: Treat HLD as code. Every change should be tracked, reviewed, and revertible.

• Treat HLD as code: Git + PRs, diagrams versioned

Repository Structure:
docs/
├── architecture/
│   ├── high-level-design.md
│   ├── diagrams/
│   │   ├── system-context.mmd
│   │   ├── component-diagram.mmd
│   │   └── deployment-diagram.png
│   ├── adrs/  (Architecture Decision Records)
│   │   ├── 001-use-dynamodb.md
│   │   ├── 002-serverless-architecture.md
│   │   └── 003-multi-region-deployment.md
│   └── runbooks/
│       ├── deployment.md
│       └── incident-response.md

• Use ADRs for decisions (e.g., DynamoDB adoption)

• Commit message convention: [HLD] Add multi-region deployment section

8. Make It a Living Document

Principle: HLD is not write-once, read-never. It evolves with the system, reflecting reality not fantasy.

• Triggers: Update on major features, architecture changes, scaling thresholds, post-mortems

• Process: Detect → Document → Review → Publish

• Track Health Metrics: diagram accuracy, SLOs, runbooks, engagement

• Automate drift detection: compare HLD vs actual infrastructure, create GitHub issues

AWS Example: Version control with Git + IaC (CDK/Terraform), diagrams with AWS Architecture Icons, early setup of CloudWatch alerts and IAM policies.

Advanced HLD Techniques (Condensed)

1. Domain-Driven Design (DDD) Integration

When systems grow complex, DDD provides a framework for organizing the HLD around business domains rather than technical layers.

Strategic Design:

• Bounded Contexts: Separate models for Ticketing, Payments, Identity

• Context Mapping: Define relationships (Customer-Supplier, Conformist, Anti-Corruption Layer)

• Ubiquitous Language: Consistent business terms in code & docs

Example Context Map:

[Identity Context] ◄──── [Ticketing Context]
    Customer-Supplier
    (Identity provides auth tokens)

[Ticketing Context] ────► [Payment Context]
    Anti-Corruption Layer
    (Ticketing translates payment domain events)
[Payment Context] ◄──── [Fraud Detection Context]
    Conformist
    (Fraud detection uses Payment's model)

2. Capacity Planning

Include quantitative estimates to validate architecture decisions:

Traffic Modeling:

Assumptions:
- 1,000 events/month (33/day)
- Average 5,000 tickets per event
- Peak: 100K users in 5 minutes = 20K RPS burst
- Ticket purchase: 5 API calls (search, select, hold, pay, confirm)

Load Calculations:
- Peak API requests: 20K users × 5 calls = 100K requests over 5 min = 333 RPS
- Database writes: 20K tickets × 2 writes (hold + confirm) = 40K writes
- DynamoDB required: 40K / 300s = 133 WCU sustained, 500 WCU burst
- Lambda concurrency: 333 RPS × 0.2s avg duration = 67 concurrent executions

3. Cost Optimization

Monthly Costs (10K events, 500K transactions)

Compute:
- Lambda: 50M invocations × $0.20/1M = $10
- Lambda duration: 10M GB-seconds × $0.0000166667 = $167
  Subtotal: $177
Database:
- DynamoDB: 100M reads × $0.25/1M = $25
- DynamoDB: 50M writes × $1.25/1M = $62.50
- DynamoDB storage: 100GB × $0.25 = $25
  Subtotal: $112.50
Storage & CDN:
- S3: 10TB × $0.023 = $230
- CloudFront: 100TB data transfer × $0.085 = $8,500
  Subtotal: $8,730
Data Transfer:
- Inter-region: 10TB × $0.02 = $200
Third-Party:
- Stripe: 500K × $0.05 = $25,000 (passed to customer)
- SendGrid: 1M emails × $0.001 = $1,000
Total AWS: $9,419.50/month
Total with Third-Party: $35,419.50/month
Optimization Opportunities:
1. S3 Intelligent-Tiering: Save 30% on storage = $69/month
2. Reserved Capacity DynamoDB: Save 50% = $56/month
3. CloudFront pricing tier negotiation: Save 15% = $1,275/month
**Potential Savings: $1,400/month (15%)

Final Thoughts

A strong High-Level Design isn’t just documentation — it’s a communication tool, a risk management framework, and a north star for engineering execution. It bridges the gap between business objectives and technical implementation, giving everyone a shared understanding of how the system will work.

By covering the core components with depth, following industry best practices, and continuously updating the HLD as the system evolves, your document becomes the backbone of the project. It helps teams build systems that are not just functional, but robust, scalable, secure, and aligned with long-term business goals.

Key Takeaways:

1. Start with Why: Every technical decision should trace to a business goal

2. Visualize Ruthlessly: Diagrams beat paragraphs for explaining architecture

3. Quantify Everything: Use numbers for capacity, costs, performance targets

4. Plan for Failure: Document risks, assumptions, and mitigation strategies

5. Collaborate Early: Cross-functional review catches blind spots

6. Treat as Code: Version control, peer review, automated validation

7. Keep It Current: HLD reflects reality, not wishful thinking

8. Think Long-Term: Design for extensibility and maintainability

When executed on AWS, this translates into a secure, serverless, and scalable architecture leveraging services like API Gateway, Lambda, DynamoDB, S3, CloudFront, EventBridge, and CloudWatch. The result: a system that can scale from zero to millions of users, maintain sub-200ms latency, achieve 99.99% uptime, and do it all cost-effectively.

Your HLD is the architectural blueprint that transforms ambitious product visions into production-ready systems. Invest the time to get it right, and your team will thank you when they’re deploying with confidence instead of debugging surprises at 3 AM.