Introduction to High-Level System Design
System Design Fundamentals
- Functional vs. Non-Functional Requirements
- Scalability, Availability, and Reliability
- Latency and Throughput Considerations
- Load Balancing Strategies
Architectural Patterns
- Monolithic vs. Microservices Architecture
- Layered Architecture
- Event-Driven Architecture
- Serverless Architecture
- Model-View-Controller (MVC) Pattern
- CQRS (Command Query Responsibility Segregation)
Scaling Strategies
- Vertical Scaling vs. Horizontal Scaling
- Sharding and Partitioning
- Data Replication and Consistency Models
- Load Balancing Strategies
- CDN and Edge Computing
Database Design in HLD
- SQL vs. NoSQL Databases
- CAP Theorem and its Impact on System Design
- Database Indexing and Query Optimization
- Database Sharding and Partitioning
- Replication Strategies
API Design and Communication
Caching Strategies
- Types of Caching
- Cache Invalidation Strategies
- Redis vs. Memcached
- Cache-Aside, Write-Through, and Write-Behind Strategies
Message Queues and Event-Driven Systems
- Kafka vs. RabbitMQ vs. SQS
- Pub-Sub vs. Point-to-Point Messaging
- Handling Asynchronous Workloads
- Eventual Consistency in Distributed Systems
Security in System Design
Observability and Monitoring
- Logging Strategies (ELK Stack, Prometheus, Grafana)
- API Security Best Practices
- Secure Data Storage and Access Control
- DDoS Protection and Rate Limiting
Real-World System Design Case Studies
- Distributed locking (Locking and its Types)
- Memory leaks and Out of memory issues
- HLD of YouTube
- HLD of WhatsApp
System Design Interview Questions
- Adobe System Design Interview Questions
- Top Atlassian System Design Interview Questions
- Top Amazon System Design Interview Questions
- Top Microsoft System Design Interview Questions
- Top Meta (Facebook) System Design Interview Questions
- Top Netflix System Design Interview Questions
- Top Uber System Design Interview Questions
- Top Google System Design Interview Questions
- Top Apple System Design Interview Questions
- Top Airbnb System Design Interview Questions
- Top 10 System Design Interview Questions
- Mobile App System Design Interview Questions
- Top 20 Stripe System Design Interview Questions
- Top Shopify System Design Interview Questions
- Top 20 System Design Interview Questions
- Top Advanced System Design Questions
- Most-Frequented System Design Questions in Big Tech Interviews
- What Interviewers Look for in System Design Questions
- Critical System Design Questions to Crack Any Tech Interview
- Top 20 API Design Questions for System Design Interviews
- Top 10 Steps to Create a System Design Portfolio for Developers
Distributed Tracing in Microservices: A Complete Guide to Tracking Requests
What is Distributed Tracing?
Distributed tracing in microservices acts as a digital map for tracking requests across complex systems. Imagine following a character’s journey through a city—distributed tracing provides visibility into how a request navigates services like authentication, payment processing, and inventory checks. This is critical for optimizing performance in a microservices architecture and resolving issues efficiently.
Why Distributed Tracing Matters for Modern Applications
Faster Problem Resolution
When delays occur (e.g., a pizza order taking too long), distributed tracing identifies bottlenecks. Was the payment service slow? Did inventory checks fail? Pinpointing issues accelerates fixes and improves system reliability.
Simplified Debugging
Without tracing, debugging microservices feels like searching for a needle in a haystack. By attaching a trace ID to each request, teams track its path, analyze latency, and isolate errors.
Mapping Service Dependencies
Modern applications rely on interconnected services. Distributed tracing reveals dependencies, helping teams optimize workflows and strengthen system design—skills emphasized in advanced web development courses.
How Distributed Tracing Works: A Step-by-Step Breakdown
Step 1: Assigning a Trace ID
Every request receives a unique identifier (trace ID) when initiated, similar to tagging a package for delivery tracking.
Step 2: Span Creation Across Services
As the request moves through services like payment processing or inventory checks, each “span” logs details such as timestamps and metadata.
Step 3: Aggregating Data for Analysis
Post-request, tools like Jaeger or Zipkin compile spans into a visual trace, highlighting performance gaps or failures.
Top Tools for Implementing Distributed Tracing
- Jaeger: Ideal for latency analysis and large-scale microservices monitoring.
- Zipkin: Simplifies trace visualization and dependency mapping.
- OpenTelemetry: A vendor-agnostic solution for collecting telemetry data.
Mastering these tools is essential for roles requiring system design expertise, a core component of DSA and web development training.
Real-World Example: Diagnosing a Slow Pizza Delivery
Imagine ordering pizza online:
- Order Service initiates the request.
- Payment Service validates your card.
- Inventory Service checks ingredient availability.
- Delivery Service assigns a driver.
If the pizza arrives late, distributed tracing reveals the payment service caused a 15-minute delay. Fixing this improves user satisfaction—demonstrating how tracing aligns with data science principles for actionable insights.
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