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
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
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
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
Data Sharding Techniques
sharding
data partitioning
geographical sharding
Data sharding, a type of horizontal partitioning, is a technique used to distribute large datasets across multiple storage resources, often referred to as shards. By dividing data into smaller, more manageable pieces, sharding can improve performance, scalability, and resource utilization. Below are several data sharding techniques with examples:
1. Range-based Sharding
In range-based sharding, data is divided into shards based on a specific range of values for a given partitioning key. Each shard is responsible for a specific range, ensuring that the data is distributed in a predictable manner.
Example: An e-commerce platform stores order data and decides to shard it based on order dates. Shards can be created for specific date ranges, such as monthly or yearly intervals. When a query targets a specific date range, only the relevant shard needs to be accessed, which improves query performance.
2. Hash-based Sharding
Hash-based sharding involves applying a consistent hash function to the partitioning key, which generates a hash value that determines the destination shard for each data entry. This method ensures an even distribution of data across shards and is particularly useful when the partitioning key has a large number of distinct values or is not easily divided into ranges.
Example: A social media platform stores user data and decides to shard it based on user IDs. The platform applies a hash function to the user ID, generating a hash value that determines the shard for each user’s data. This approach ensures an even distribution of data across shards, optimizing storage efficiency and query performance.
3. Directory-based Sharding
Directory-based sharding uses a lookup table, often referred to as a directory, to map each data entry to a specific shard. This method offers greater flexibility, as shards can be added, removed, or reorganized without the need to rehash or reorganize the entire dataset. However, it introduces an additional layer of complexity, as the directory must be maintained and kept consistent.
Example: An online gaming platform stores player data and decides to use directory-based sharding. The platform maintains a directory that maps each player’s username to a specific shard. When a query targets a specific player’s data, the system first consults the directory to determine the relevant shard, then retrieves the data from that shard.
4. Geographical Sharding
Geographical sharding involves partitioning data based on geographical locations, such as countries or regions. This method can help reduce latency and improve performance for users in specific locations by storing their data closer to them.
Example: A global streaming service stores user data and decides to shard it based on the user’s country. Each shard contains data for users from a specific country, and these shards are stored in data centers located within or near that country. This approach ensures that users can access their data with lower latency, improving the streaming experience.
5. Dynamic Sharding
Dynamic sharding is an adaptive approach that automatically adjusts the number of shards based on the data’s size and access patterns. This method can help optimize resource utilization and performance by creating shards as needed and merging or splitting them as the data grows or shrinks.
Example: An Internet of Things (IoT) platform collects sensor data from a large number of devices. The platform uses dynamic sharding to automatically adjust the number of shards based on the volume and frequency of incoming data. As more devices are added or removed, the platform can create or merge shards accordingly, ensuring optimal resource utilization and performance.
6. Hybrid Sharding: The Best of Many Worlds
Why stick to one method when you can combine a few and get the best results? Hybrid Sharding is a blend of multiple sharding strategies to optimize performance. It might combine Geo-based with Directory-based sharding, or any other mix that suits a system's needs.
Its Strength: By tailoring solutions and leveraging the strengths of different techniques, systems can achieve unprecedented efficiency levels.
Snapshot: Many cloud service providers, given their diverse clientele and global infrastructure, adopt hybrid sharding. It's their secret sauce to ensure consistent, high-speed services across the board.
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