System Design

Learn System Design

How to Learn System Design?

Functional vs. Non-functional Requirements

What are Back-of-the-Envelope Estimations?

Things to Avoid During System Design Interview

System Design Basics

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

Why Quorum?

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

Importance of Discussing Trade-offs

Strong vs Eventual Consistency

Latency vs Throughput

ACID vs BASE Properties in Databases

Read-Through vs Write-Through Cache

Batch Processing vs Stream Processing

Load Balancer vs. API Gateway

API Gateway vs Direct Service Exposure

Proxy vs. Reverse Proxy

API Gateway vs. Reverse Proxy

SQL vs. NoSQL

Primary-Replica vs Peer-to-Peer Replication

Data Compression vs Data Deduplication

Server-Side Caching vs Client-Side Caching

REST vs RPC

Polling vs. Long-Polling vs. WebSockets vs. Webhooks

CDN Usage vs Direct Server Serving

Serverless Architecture vs Traditional Server-based

Stateful vs Stateless Architecture

Hybrid Cloud Storage vs All-Cloud Storage

Token Bucket vs Leaky Bucket

Read Heavy vs Write Heavy System

Quiz

System Design Interviews - A step by step guide

System Design Master Template

Designing a URL Shortening Service like TinyURL

Quiz - Designing URL Shortner

Designing Pastebin

Quiz - Designing Pastebin

Designing Instagram

Quiz - Designing Instagram

Designing Dropbox

Quiz - Designing Dropbox

Designing Facebook Messenger

Quiz - Designing Facebook Messenger

Designing Twitter

Quiz - Designing Twitter

Designing Youtube or Netflix

Quiz - Designing Youtube

Designing Typeahead Suggestion

Quiz - Designing Typeahead Suggestion

Designing an API Rate Limiter

Quiz - Designing an API Rate Limiter

Designing Twitter Search

Quiz - Designing Twitter Search

Designing a Web Crawler

Quiz - Designing a Web Crawler

Designing Facebook’s Newsfeed

Quiz - Designing Facebook’s Newsfeed

Designing Yelp or Nearby Friends

Quiz - Designing Yelp or Nearby Friends

Designing Uber backend

Quiz - Designing Uber backend

Designing Ticketmaster

Quiz - Designing Ticketmaster

Dynamo: Introduction

High-Level Architecture

Data Partitioning

Replication

Vector Clocks and Conflicting Data

The Life of Dynamo’s put() & get() Operations

Anti-entropy Through Merkle Trees

Gossip Protocol

Dynamo Characteristics and Criticism

Summary: Dynamo

Quiz: Dynamo

Mock Interview: Dynamo

YouTube Likes Counter

Quiz

Cassandra: Introduction

High-level Architecture

Replication

Cassandra Consistency Levels

Gossiper

Anatomy of Cassandra's Write Operation

Anatomy of Cassandra's Read Operation

Compaction

Tombstones

Summary: Cassandra

Quiz: Cassandra

Mock Interview: Cassandra

Messaging Systems: Introduction

Kafka: Introduction

High-level Architecture

Kafka: Deep Dive

Consumer Groups

Kafka Workflow

Role of ZooKeeper

Controller Broker

Kafka Delivery Semantics

Kafka Characteristics

Summary: Kafka

Quiz: Kafka

Mock Interview: Kafka

Chubby: Introduction

High-level Architecture

Design Rationale

How Chubby Works

File, Directories, and Handles

Locks, Sequencers, and Lock-delays

Sessions and Events

Master Election and Chubby Events

Caching

Database

Scaling Chubby

Summary: Chubby

Quiz: Chubby

Mock Interview: Chubby

Hadoop Distributed File System: Introduction

High-level Architecture

Deep Dive

Anatomy of a Read Operation

Anatomy of a Write Operation

Data Integrity & Caching

Fault Tolerance

HDFS High Availability (HA)

HDFS Characteristics

Summary: HDFS

Quiz: HDFS

Mock Interview: HDFS

Google File System: Introduction

High-level Architecture

Single Master and Large Chunk Size

Metadata

Master Operations

Anatomy of a Read Operation

Anatomy of a Write Operation

Anatomy of an Append Operation

GFS Consistency Model and Snapshotting

Fault Tolerance, High Availability, and Data Integrity

Garbage Collection

Criticism on GFS

Summary: GFS

Quiz: GFS

Mock Interview: GFS

BigTable: Introduction

BigTable Data Model

System APIs

Partitioning and High-level Architecture

SSTable

GFS and Chubby

Bigtable Components

Working with Tablets

The Life of BigTable's Read & Write Operations

Fault Tolerance and Compaction

BigTable Refinements

BigTable Characteristics

Summary: BigTable

Quiz: BigTable

Mock Interview: BigTable

Design Reddit

Quiz

Designing a Notification System

Quiz

Design Google calendar (Medium)

Quiz

Design a Recommendation System for Netflix

Quiz

Design Gmail

Quiz

Design Google News, a Global News Aggregator System (Medium)

Quiz

Design Unique ID Generator (Easy)

Quiz

Design Code Judging System like LeetCode (Medium)

Quiz

Design Payment System

Quiz

Design a Flash Sale for an E-commerce Site (Hard)

Quiz

Design a Reminder Alert System

Quiz

Introduction: System Design Patterns

1. Bloom Filters

2. Consistent Hashing

3. Quorum

4. Leader and Follower

5. Write-ahead Log

6. Segmented Log

7. High-Water Mark

8. Lease

9. Heartbeat

10. Gossip Protocol

11. Phi Accrual Failure Detection

12. Split Brain

13. Fencing

14. Checksum

15. Vector Clocks

16. CAP Theorem

17. PACELC Theorem

18. Hinted Handoff

19. Read Repair

20. Merkle Trees

Quiz

Load Balancer Types

Load Balancer Types

load balancing

network traffic distribution

high availability

hard
·
9 min
·Updated Jan 2025

A load balancing type refers to the method or approach used to distribute incoming network traffic across multiple servers or resources to ensure efficient utilization, improve overall system performance, and maintain high availability and reliability. Different load balancing types are designed to meet various requirements and can be implemented using hardware, software, or cloud-based solutions.

Each load balancing type has its own set of advantages and disadvantages, making it suitable for specific scenarios and use cases. Some common load balancing types include hardware load balancing, software load balancing, cloud-based load balancing, DNS load balancing, and Layer 4 and Layer 7 load balancing. By understanding the different load balancing types and their characteristics, you can select the most appropriate solution for your specific needs and infrastructure.

1. Hardware Load Balancing

Hardware load balancers are physical devices designed specifically for load balancing tasks. They use specialized hardware components, such as Application-Specific Integrated Circuits (ASICs) or Field-Programmable Gate Arrays (FPGAs), to efficiently distribute network traffic.

Pros:

  • High performance and throughput, as they are optimized for load balancing tasks.
  • Often include built-in features for network security, monitoring, and management.
  • Can handle large volumes of traffic and multiple protocols.

Cons:

  • Can be expensive, especially for high-performance models.
  • May require specialized knowledge to configure and maintain.
  • Limited scalability, as adding capacity may require purchasing additional hardware.

Example: A large e-commerce company uses a hardware load balancer to distribute incoming web traffic among multiple web servers, ensuring fast response times and a smooth shopping experience for customers.

2. Software Load Balancing

Software load balancers are applications that run on general-purpose servers or virtual machines. They use software algorithms to distribute incoming traffic among multiple servers or resources.

Pros:

  • Generally more affordable than hardware load balancers.
  • Can be easily scaled by adding more resources or upgrading the underlying hardware.
  • Provides flexibility, as they can be deployed on a variety of platforms and environments, including cloud-based infrastructure.

Cons:

  • May have lower performance compared to hardware load balancers, especially under heavy loads.
  • Can consume resources on the host system, potentially affecting other applications or services.
  • May require ongoing software updates and maintenance.

Example: A startup with a growing user base deploys a software load balancer on a cloud-based virtual machine, distributing incoming requests among multiple application servers to handle increased traffic.

3. Cloud-based Load Balancing

Cloud-based load balancers are provided as a service by cloud providers. They offer load balancing capabilities as part of their infrastructure, allowing users to easily distribute traffic among resources within the cloud environment.

Pros:

  • Highly scalable, as they can easily accommodate changes in traffic and resource demands.
  • Simplified management, as the cloud provider takes care of maintenance, updates, and security.
  • Can be more cost-effective, as users only pay for the resources they use.

Cons:

  • Reliance on the cloud provider for performance, reliability, and security.
  • May have less control over configuration and customization compared to self-managed solutions.
  • Potential vendor lock-in, as switching to another cloud provider or platform may require significant changes.

Example: A mobile app developer uses a cloud-based load balancer provided by their cloud provider to distribute incoming API requests among multiple backend servers, ensuring smooth app performance and quick response times.

4. DNS Load Balancing

DNS (Domain Name System) load balancing relies on the DNS infrastructure to distribute incoming traffic among multiple servers or resources. It works by resolving a domain name to multiple IP addresses, effectively directing clients to different servers based on various policies.

Pros:

  • Relatively simple to implement, as it doesn't require specialized hardware or software.
  • Provides basic load balancing and failover capabilities.
  • Can distribute traffic across geographically distributed servers, improving performance for users in different regions.

Cons:

  • Limited to DNS resolution time, which can be slow to update when compared to other load balancing techniques.
  • No consideration for server health, response time, or resource utilization.
  • May not be suitable for applications requiring session persistence or fine-grained load distribution.

Example: A content delivery network (CDN) uses DNS load balancing to direct users to the closest edge server based on their geographical location, ensuring faster content delivery and reduced latency.

5. Global Server Load Balancing (GSLB)

Global Server Load Balancing (GSLB) is a technique used to distribute traffic across geographically dispersed data centers. It combines DNS load balancing with health checks and other advanced features to provide a more intelligent and efficient traffic distribution method.

Pros:

  • Provides load balancing and failover capabilities across multiple data centers or geographic locations.
  • Can improve performance and reduce latency for users by directing them to the closest or best-performing data center.
  • Supports advanced features, such as server health checks, session persistence, and custom routing policies.

Cons:

  • Can be more complex to set up and manage than other load balancing techniques.
  • May require specialized hardware or software, increasing costs.
  • Can be subject to the limitations of DNS, such as slow updates and caching issues.

Example: A multinational corporation uses GSLB to distribute incoming requests for its web applications among several data centers around the world, ensuring high availability and optimal performance for users in different regions.

6. Hybrid Load Balancing

Hybrid load balancing combines the features and capabilities of multiple load balancing techniques to achieve the best possible performance, scalability, and reliability. It typically involves a mix of hardware, software, and cloud-based solutions to provide the most effective and flexible load balancing strategy for a given scenario.

Pros:

  • Offers a high degree of flexibility, as it can be tailored to specific requirements and infrastructure.
  • Can provide the best combination of performance, scalability, and reliability by leveraging the strengths of different load balancing techniques.
  • Allows organizations to adapt and evolve their load balancing strategy as their needs change over time.

Cons:

  • Can be more complex to set up, configure, and manage than single-technique solutions.
  • May require a higher level of expertise and understanding of multiple load balancing techniques.
  • Potentially higher costs, as it may involve a combination of hardware, software, and cloud-based services.

Example: A large-scale online streaming platform uses a hybrid load balancing strategy, combining hardware load balancers in their data centers for high-performance traffic distribution, cloud-based load balancers for scalable content delivery, and DNS load balancing for global traffic management. This approach ensures optimal performance, scalability, and reliability for their millions of users worldwide.

7. Layer 4 Load Balancing

Layer 4 load balancing, also known as transport layer load balancing, operates at the transport layer of the OSI model (the fourth layer). It distributes incoming traffic based on information from the TCP or UDP header, such as source and destination IP addresses and port numbers.

Pros:

  • Fast and efficient, as it makes decisions based on limited information from the transport layer.
  • Can handle a wide variety of protocols and traffic types.
  • Relatively simple to implement and manage.

Cons:

  • Lacks awareness of application-level information, which may limit its effectiveness in some scenarios.
  • No consideration for server health, response time, or resource utilization.
  • May not be suitable for applications requiring session persistence or fine-grained load distribution.

Example: An online gaming platform uses Layer 4 load balancing to distribute game server traffic based on IP addresses and port numbers, ensuring that players are evenly distributed among available game servers for smooth gameplay.

8. Layer 7 Load Balancing

Layer 7 load balancing, also known as application layer load balancing, operates at the application layer of the OSI model (the seventh layer). It takes into account application-specific information, such as HTTP headers, cookies, and URL paths, to make more informed decisions about how to distribute incoming traffic.

Pros:

  • Provides more intelligent and fine-grained load balancing, as it considers application-level information.
  • Can support advanced features, such as session persistence, content-based routing, and SSL offloading.
  • Can be tailored to specific application requirements and protocols.

Cons:

  • Can be slower and more resource-intensive compared to Layer 4 load balancing, as it requires deeper inspection of incoming traffic.
  • May require specialized software or hardware to handle application-level traffic inspection and processing.
  • Potentially more complex to set up and manage compared to other load balancing techniques.

Example: A web application with multiple microservices uses Layer 7 load balancing to route incoming API requests based on the URL path, ensuring that each microservice receives only the requests it is responsible for handling.

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