Grokking System Design Fundamentals
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System Design Fundamentals
Introduction to System Design
Load Balancing
Introduction to Load Balancing
Load Balancing Algorithms
Uses of Load Balancing
Load Balancer Types
Stateless vs. Stateful Load Balancing
High Availability and Fault Tolerance
Scalability and Performance
Challenges of Load Balancers
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Key Characteristics of Distributed Systems
Scalability
Availability
Latency and Performance
Concurrency and Coordination
Monitoring and Observability
Resilience and Error Handling
Fault Tolerance vs. High Availability
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Introduction to Caching
Why is Caching Important?
Types of Caching
Cache Replacement Policies
Cache Invalidation
Cache Read Strategies
Cache Coherence and Consistency Models
Caching Challenges
Cache Performance Metrics
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
Introduction to CAP Theorem
Components of CAP Theorem
Trade-offs in CAP Theorem
Examples of CAP Theorem in Practice
Beyond CAP Theorem
System Design Trade-offs in Interviews
Introduction to Databases
SQL Databases
NoSQL Databases
SQL vs. NoSQL
ACID vs BASE Properties
Real-World Examples and Case Studies
SQL Normalization and Denormalization
In-Memory Database vs. On-Disk Database
Data Replication vs. Data Mirroring
Database Federation
What are Indexes?
Types of Indexes
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Difference Between Long-Polling, WebSockets, and Server-Sent Events
What is Quorum?
What is Heartbeat?
What is Checksum?
Uses of Checksum
What is Leader and Follower Pattern?
What is Security and Privacy?
What is Authentication?
What is Authorization?
Authentication vs. Authorization
OAuth vs. JWT for Authentication
What is Encryption?
What are DDoS Attacks?
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Batch Processing vs. Stream Processing
XML vs. JSON
Synchronous vs. Asynchronous Communication
Push vs. Pull Notification Systems
Microservices vs. Serverless Architecture
Message Queues vs. Service Bus
Stateful vs. Stateless Architecture
Event-Driven vs. Polling Architecture
Quiz
Monitoring and Observability

Monitoring and observability are essential aspects of managing distributed systems, as they help identify issues, understand system behavior, and ensure optimal performance. Here's an overview of various components of monitoring and observability in distributed systems:

A. Metrics Collection

Metrics are quantitative measurements that provide insights into the performance, health, and behavior of a distributed system. Collecting and analyzing metrics, such as latency, throughput, error rates, and resource utilization, can help identify performance bottlenecks, potential issues, and areas for improvement. Tools like Prometheus, Graphite, or InfluxDB can be used to collect, store, and query metrics in distributed systems.

B. Distributed Tracing

Distributed tracing is a technique for tracking and analyzing requests as they flow through a distributed system, allowing you to understand the end-to-end performance and identify issues in specific components or services. Implementing distributed tracing using tools like Jaeger, Zipkin, or OpenTelemetry can provide valuable insights into the behavior of your system, making it easier to debug and optimize.

C. Logging

Logs are records of events or messages generated by components of a distributed system, providing a detailed view of system activity and helping identify issues or anomalies. Collecting, centralizing, and analyzing logs from all services and nodes in a distributed system can provide valuable insights into system behavior and help with debugging and troubleshooting. Tools like Elasticsearch, Logstash, and Kibana (ELK Stack) or Graylog can be used for log aggregation and analysis.

D. Alerting and Anomaly Detection

Alerting and anomaly detection involve monitoring the distributed system for unusual behavior or performance issues and notifying the appropriate teams when such events occur. By setting up alerts based on predefined thresholds or detecting anomalies using machine learning algorithms, you can proactively identify issues and take corrective actions before they impact users or system performance. Tools like Grafana, PagerDuty, or Sensu can help you set up alerting and anomaly detection for your distributed system.

E. Visualization and Dashboards

Visualizing metrics, traces, and logs in an easy-to-understand format can help you better comprehend the state of your distributed system and make data-driven decisions. Dashboards are an effective way to aggregate and display this information, providing a unified view of your system's performance and health. Tools like Grafana, Kibana, or Datadog can be used to create customizable dashboards for monitoring and observability purposes.

Concurrency and Coordination
Resilience and Error Handling
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On this page

A. Metrics Collection

B. Distributed Tracing

C. Logging

D. Alerting and Anomaly Detection

E. Visualization and Dashboards