- System Analysis and Design - Home
- System Analysis & Design - Overview
- Differences between System Analysis and System Design
- System Analysis and Design - Communication Protocols
- Horizontal and Vertical Scaling in System Design
- Capacity Estimation in Systems Design
- Roles of Web Server and Proxies in Designing Systems
- Clustering and Load Balancing
- System Development Life Cycle
- System Analysis and Design - Requirement Determination
- System Analysis and Design - Systems Implementation
- System Analysis and Design - System Planning
- System Analysis and Design - Structured Analysis
- System Design
- System Analysis and Design - Design Strategies
- System Analysis and Design - Software Deployment
- Software Deployment Example Using Docker
- Functional Vs. Non-functional Requirements
- Data Flow Diagrams(DFD)
- Data Flow Diagram - What It Is?
- Data Flow Diagram - Types and Components
- Data Flow Diagram - Development
- Data Flow Diagram - Balancing
- Data Flow Diagram - Decomposition
- Databases in System Design
- System Design - Databases
- System Design - Database Sharding
- System Design - Database Replication
- System Design - Database Federation
- System Design - Designing Authentication System
- Database Design Vs. Database Architecture
- Database Federation Vs. Database Sharding
- High Level Design(HLD)
- System Design - High Level Design
- System Design - Availability
- System Design - Consistency
- System Design - Reliability
- System Design - CAP Theorem
- System Design - API Gateway
- Low Level Design(LLD)
- System Design - Low Level Design
- System Design - Authentication Vs. Authorization
- System Design - Performance Optimization Techniques
- System Design - Containerization Architecture
- System Design - Modularity and Interfaces
- System Design - CI/CD Pipelines
- System Design - Data Partitioning Techniques
- System Design - Essential Security Measures
- System Implementation
- Input / Output & Forms Design
- Testing and Quality Assurance
- Implementation & Maintenance
- System Security and Audit
- Object-Oriented Approach
- System Analysis & Design Resources
- Quick Guide
- Useful Resources
- Discussion
Clustering and Load Balancing
Introduction to Clustering and Load Balancing
Clustering and load balancing are essential for modern applications to ensure they are scalable, highly available, and perform well under varying loads. Here's why they are significant.
Clustering
High Availability− Clustering ensures that if one server goes down, others can take over, minimizing downtime and ensuring continuous availability.
Scalability− By adding more nodes to a cluster, applications can handle more users and more data without performance degradation.
Fault Tolerance− Clusters are designed to continue operating even when individual nodes fail, which enhances the resilience of the application.
Resource Management− Distributes workloads across multiple nodes, optimizing resource usage and preventing any single node from becoming a bottleneck.
Load Balancing
Efficient Resource Utilization− Load balancing distributes incoming traffic across multiple servers, ensuring that no single server is overwhelmed, which optimizes resource utilization.
Improved Performance− By balancing the load, applications can respond faster to user requests, enhancing the overall user experience.
Redundancy− Load balancing ensures that if one server fails, traffic can be redirected to other operational servers, providing redundancy.
Scalability− Easily scales by adding more servers to the pool, allowing applications to handle increasing traffic seamlessly.
Key Concepts of Clustering
Types of Clustering
High-Availability (HA) Clustering− For fault tolerance and minimal downtime.
Load Balancing Clustering− Distributing workloads to multiple nodes. If a node fails, the request is transferred to the next node.
Storage Clustering− For managing data in distributed systems.
Examples of clustering solutions− Kubernetes, Apache Kafka, Hadoop.
Key Concepts of Load Balancing
Objectives− Avoid overloading any single server, reduce response times, and optimize resource usage.
Types of Load Balancers
Hardware Load Balancers− Specialized devices.
Software Load Balancers− Run on commodity hardware or virtual instances.
DNS Load Balancing− Uses DNS (Domain Name System) to route requests to different servers.
Load Balancing Algorithms and Techniques
Round Robin− Requests are distributed sequentially across servers.
Least Connections− Directs traffic to the server with the fewest active connections.
Weighted Round Robin and Least Connections− Assigns weights to servers based on capacity.
IP Hashing− Routes requests based on the clients IP address.
Random− Routes requests to random servers.
Dynamic Load Balancing− Adapts based on current server performance.
Tools and Technologies for Load Balancing
Nginx− A popular open-source reverse proxy and load balancer.
HAProxy− A fast and reliable load balancer for TCP and HTTP based applications.
AWS Elastic Load Balancing (ELB)− Load balancing for AWS resources, including EC2 and containers.
Azure Load Balancer− Manages traffic for applications on Microsoft Azure.
Traefik− A modern load balancer for microservices, with built-in support for Kubernetes.
Clustering Technologies and Architectures
Apache Kafka− A distributed streaming platform that supports clustering.
Kubernetes− Manages containerized applications and scales them automatically.
Apache Cassandra− A distributed NoSQL database designed for clustering and fault tolerance.
Active-Active vs. Active-Passive Clustering− In an active-active setup, all nodes (servers) in the cluster are actively processing requests simultaneously. In an active-passive setup, only one node (or a primary set of nodes) is actively handling requests at any time, while the other node(s) remain on standby.
Configuring Load Balancers for Different Applications
Web Applications− Using HTTP/HTTPS load balancing.
Database Load Balancing− Balancing read and write requests (e.g., with MySQL).
Microservices and APIs− Configuring API gateways with load balancing.
Real-time Applications− Configuring WebSocket load balancing for low latency.
Monitoring and Maintaining Clustering and Load Balancing Systems
Importance of Monitoring− Ensure uptime, performance, and detect issues.
Tools for Monitoring
Prometheus and Grafana− Metric collection and visualization.
Datadog and New Relic− End-to-end monitoring for cloud and on-premise environments.
ELK Stack− Logs analysis for load balancer and cluster events.
Common Maintenance Tasks− Updating configurations, scaling up/down, handling node failures.
Identifying and resolving common load balancing and clustering issues.
Here's a look at common issues that arise in load balancing and clustering, along with strategies to identify and resolve them. These issues often relate to misconfiguration, capacity limitations, and network constraints, and addressing them effectively helps maintain high availability and performance.
Uneven Load Distribution
Symptoms− Some servers experience high CPU or memory usage, while others remain underutilized.
Causes− This can be due to a poorly configured load balancing algorithm (e.g., Round Robin may not work well if servers have unequal processing capabilities) or an incorrect weighting setup in Weighted Round Robin or Least Connections algorithms.
Resolution
Adjust the load balancing algorithm to one that matches the applications requirements. Use a Weighted Load Balancing approach to match server capacities.
For cloud-based solutions, consider auto-scaling policies to add resources automatically under high load conditions.
Session Persistence (Sticky Sessions) Issues
Sticky sessions, also known as session affinity, is a technique used in load balancing to ensure that a user's requests are always directed to the same server throughout a session.
Symptoms− Users are logged out unexpectedly or lose session data when redirected to different servers.
Causes− Load balancers may be configured without sticky sessions, leading to loss of session continuity if a users requests are routed to different servers.
Resolution
Enable session persistence (sticky sessions) on the load balancer to ensure that requests from a given client in the same session are routed to the same server.
For more scalable solutions, implement distributed session management (e.g., session data stored in a database or distributed cache like Redis) to avoid dependency on individual servers.
Configuration Drift
Symptoms− Inconsistent behaviour across nodes, such as different software versions or configurations.
Causes− Manual configuration changes lead to mismatches across cluster nodes.
Resolution
Use configuration management tools like Ansible, Puppet, or Chef to ensure consistent configurations across all nodes.
Implement infrastructure as code (IaC) practices, using tools like Terraform to enforce versioned and consistent configuration states.
DNS Caching Issues in DNS Load Balancing
Symptoms− Clients are directed to unhealthy nodes even after.
Causes− DNS caching at the client side or intermediary resolvers can keep IP mappings of decommissioned or faulty nodes.
Resolution
Reduce the Time-to-Live (TTL) on DNS records to ensure faster propagation of changes in DNS-based load balancers.
Use failover DNS records that redirect traffic to alternative nodes in case primary nodes are unreachable.
Logging and Monitoring Challenges
Symptoms− Lack of insight into traffic patterns, unbalanced loads, or delays in troubleshooting issues.
Causes− Inadequate monitoring or logging on the load balancer and clustering nodes.
Resolution
Integrate monitoring tools such as Prometheus, Grafana, or Datadog for real-time metrics.
Use centralized logging (e.g., ELK Stack or Fluentd) to aggregate logs from different nodes and provide unified access.
Set up alerting systems to notify administrators of unusual patterns, such as sudden traffic spikes, server failures, or high latencies.
Future of Clustering and Load Balancing
Trends in Clustering and Load Balancing
Edge Computing− Deploying clusters closer to data sources for latency reduction.
AI-driven Load Balancing− Using machine learning to optimize request routing.
Serverless Architectures− Impact of serverless on traditional load balancing.
Potential Challenges− Increased complexity in managing distributed systems, security concerns.