Grokking System Design Fundamentals
Rate this course
Ask Author
Contribute
Back to course home

6% completed

Vote For New Content
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
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
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
High Availability and Fault Tolerance

Redundancy and failover strategies for load balancers

To ensure high availability and fault tolerance, load balancers should be designed and deployed with redundancy in mind. This means having multiple instances of load balancers that can take over if one fails. Redundancy can be achieved through several failover strategies:

  • Active-passive configuration: In this setup, one load balancer (the active instance) handles all incoming traffic while the other (the passive instance) remains on standby. If the active load balancer fails, the passive instance takes over and starts processing requests. This configuration provides a simple and reliable failover mechanism but does not utilize the resources of the passive instance during normal operation.

  • Active-active configuration: In this setup, multiple load balancer instances actively process incoming traffic simultaneously. Traffic is distributed among the instances using methods such as DNS load balancing or an additional load balancer layer. If one instance fails, the others continue to process traffic with minimal disruption. This configuration provides better resource utilization and increased fault tolerance compared to the active-passive setup.

Health checks and monitoring

Effective health checks and monitoring are essential components of high availability and fault tolerance for load balancers. Health checks are periodic tests performed by the load balancer to determine the availability and performance of backend servers. By monitoring the health of backend servers, load balancers can automatically remove unhealthy servers from the server pool and avoid sending traffic to them, ensuring a better user experience and preventing cascading failures.

Monitoring the load balancer itself is also crucial. By keeping track of performance metrics, such as response times, error rates, and resource utilization, we can detect potential issues and take corrective action before they lead to failures or service degradation.

In addition to regular health checks and monitoring, it is essential to have proper alerting and incident response procedures in place. This ensures that the appropriate personnel are notified of any issues and can take action to resolve them quickly.

Synchronization and State Sharing

In active-active and active-passive configurations, it is crucial to ensure that the load balancer instances maintain a consistent view of the system's state, including the status of backend servers, session data, and other configuration settings. This can be achieved through various mechanisms, such as:

  • Centralized configuration management: Using a centralized configuration store (e.g., etcd, Consul, or ZooKeeper) to maintain and distribute configuration data among load balancer instances ensures that all instances are using the same settings and are aware of changes.

  • State sharing and replication: In scenarios where load balancers must maintain session data or other state information, it is crucial to ensure that this data is synchronized and replicated across instances. This can be achieved through database replication, distributed caching systems (e.g., Redis or Memcached), or built-in state-sharing mechanisms provided by the load balancer software or hardware.

By addressing these aspects of high availability and fault tolerance, we can design and deploy load balancers that provide reliable, consistent service even in the face of failures or other issues.

Stateless vs. Stateful Load Balancing
Scalability and Performance
Mark as Completed

On this page

Redundancy and failover strategies for load balancers

Health checks and monitoring

Synchronization and State Sharing