System Design
Learn System Design
Introduction to 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
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
Network Essentials
HTTP vs. HTTPS
TCP vs. UDP
HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0
URL vs. URI vs. URN
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Caching
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
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
CAP & PACELC Theorems
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
Databases (SQL vs. NoSQL)
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
Indexes
What are Indexes?
Types of Indexes
Bloom Filters
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Long-Polling vs. WebSockets vs. Server-Sent Events
Difference Between Long-Polling, WebSockets, and Server-Sent Events
Quorum
Why Quorum?
What is Quorum?
Heartbeat
What is Heartbeat?
Checksum
What is Checksum?
Uses of Checksum
Leader and Follower
What is Leader and Follower Pattern?
Security
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?
Distributed Messaging System
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
Distributed File Systems
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Misc Concepts
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 - System Design Fundamentals
Quiz
System Design Trade-offs
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 Master Template
System Design Interviews - A step by step guide
System Design Master Template
Designing a URL Shortening Service like TinyURL
Designing a URL Shortening Service like TinyURL
Quiz - Designing URL Shortner
Designing Pastebin
Designing Pastebin
Quiz - Designing Pastebin
Designing Instagram
Designing Instagram
Quiz - Designing Instagram
Designing Dropbox
Designing Dropbox
Quiz - Designing Dropbox
Designing Facebook Messenger
Designing Facebook Messenger
Quiz - Designing Facebook Messenger
Designing Twitter
Designing Twitter
Quiz - Designing Twitter
Designing Youtube or Netflix
Designing Youtube or Netflix
Quiz - Designing Youtube
Designing Typeahead Suggestion
Designing Typeahead Suggestion
Quiz - Designing Typeahead Suggestion
Designing an API Rate Limiter
Designing an API Rate Limiter
Quiz - Designing an API Rate Limiter
Designing Twitter Search
Designing Twitter Search
Quiz - Designing Twitter Search
Designing a Web Crawler
Designing a Web Crawler
Quiz - Designing a Web Crawler
Designing Facebook’s Newsfeed
Designing Facebook’s Newsfeed
Quiz - Designing Facebook’s Newsfeed
Designing Yelp or Nearby Friends
Designing Yelp or Nearby Friends
Quiz - Designing Yelp or Nearby Friends
Designing Uber backend
Designing Uber backend
Quiz - Designing Uber backend
Designing Ticketmaster
Designing Ticketmaster
Quiz - Designing Ticketmaster
Dynamo: How to design a key value store?
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
Designing YouTube Likes Counter (medium)
YouTube Likes Counter
Quiz
Cassandra: How to Design a Wide-column NoSQL Database?
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
Kafka: How to Design a Distributed Messaging System?
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: How to Design a Distributed Locking Service?
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
HDFS: How to Design File Storage System?
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
GFS: How to Design a Distributed File System Storage?
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: How to Design a Wide Column Storage System?
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
Designing Reddit (medium)
Design Reddit
Quiz
Designing Notification Service (medium)
Designing a Notification System
Quiz
Design Google Calendar (medium)
Design Google calendar (Medium)
Quiz
Design a Recommendation System (medium)
Design a Recommendation System for Netflix
Quiz
Designing Gmail (medium)
Design Gmail
Quiz
Designing Google News (medium)
Design Google News, a Global News Aggregator System (Medium)
Quiz
Designing Unique ID Generator (medium)
Design Unique ID Generator (Easy)
Quiz
Designing Code Judging System (medium)
Design Code Judging System like LeetCode (Medium)
Quiz
Designing Payment System (hard)
Design Payment System
Quiz
Designing Flash Sale System (hard)
Design a Flash Sale for an E-commerce Site (Hard)
Quiz
Designing Reminder Alert System (hard)
Design a Reminder Alert System
Quiz
System Design Patterns
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 balancing
network traffic distribution
high availability
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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