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
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 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
Introduction to Messaging System
distributed messaging
publish-subscribe
queuing
distributed systems
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Background
One of the common challenges among distributed systems is handling a continuous influx of data from multiple sources. Imagine a log aggregation service that is receiving hundreds of log entries per second from different sources. The function of this log aggregation service is to store these logs on disk at a shared server and also build an index so that the logs can be searched later. A few challenges of this service are:
- How will the log aggregation service handle a spike of messages? If the service can handle (or buffer) 500 messages per second, what will happen if it starts receiving a higher number of messages per second? If we decide to have multiple instances of the log aggregation service, how do we divide the work among these instances?
- How can we receive messages from different types of sources? The sources producing (or consuming) these logs need to decide upon a common protocol and data format to send log messages to the log aggregation service. This leads us to a strongly coupled architecture between the producer and consumer of the log messages.
- What will happen to the log messages if the log aggregation service is down or unresponsive for some time?
To efficiently manage such scenarios, distributed systems depend upon a messaging system.
What is a messaging system?
A messaging system is responsible for transferring data among services, applications, processes, or servers. Such a system helps decouple different parts of a distributed system by providing an asynchronous way of transferring messaging between the sender and the receiver. Hence, all senders (or producers) and receivers (or consumers) focus on the data/message without worrying about the mechanism used to share the data.
There are two common ways to handle messages: Queuing and Publish-Subscribe.
Queue
In the queuing model, messages are stored sequentially in a queue. Producers push messages to the rear of the queue, and consumers extract the messages from the front of the queue.
A particular message can be consumed by a maximum of one consumer only. Once a consumer grabs a message, it is removed from the queue such that the next consumer will get the next message. This is a great model for distributing message-processing among multiple consumers. But this also limits the system as multiple consumers cannot read the same message from the queue.
Publish-subscribe messaging system
In the pub-sub (short for publish-subscribe) model, messages are divided into topics. A publisher (or a producer) sends a message to a topic that gets stored in the messaging system under that topic. Subscribers (or the consumer) subscribe to a topic to receive every message published to that topic. Unlike the Queuing model, the pub-sub model allows multiple consumers to get the same message; if two consumers subscribe to the same topic, they will receive all messages published to that topic.
The messaging system that stores and maintains the messages is commonly known as the message broker. It provides a loose coupling between publishers and subscribers, or producers and consumers of data.
The message broker stores published messages in a queue, and subscribers read them from the queue. Hence, subscribers and publishers do not have to be synchronized. This loose coupling enables subscribers and publishers to read and write messages at different rates.
The messaging system's ability to store messages provides fault-tolerance, so messages do not get lost between the time they are produced and the time they are consumed.
To summarize, a message system is deployed in an application stack for the following reasons:
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Messaging buffering: To provide a buffering mechanism in front of processing (i.e., to deal with temporary incoming message spikes that are greater than what the processing app can deal with). This enables the system to safely deal with spikes in workloads by temporarily storing data until it is ready for processing.
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Guarantee of message delivery: Allows producers to publish messages with assurance that the message will eventually be delivered if the consuming application is unable to receive the message when it is published.
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Providing abstraction: Distributed messaging systems enable decoupling of sender and receiver components in a system, allowing them to evolve independently. This architectural pattern promotes modularity, making it easier to maintain and update individual components without affecting the entire system.
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Scalability: Distributed messaging systems can handle a large number of messages and can scale horizontally to accommodate increasing workloads. This allows applications to grow and manage higher loads without significant performance degradation.
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Fault Tolerance: By distributing messages across multiple nodes or servers, these systems can continue to operate even if a single node fails. This redundancy provides increased reliability and ensures that messages are not lost during system failures.
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Asynchronous Communication: These systems enable asynchronous communication between components, allowing them to process messages at their own pace without waiting for immediate responses. This can improve overall system performance and responsiveness, particularly in scenarios with high latency or variable processing times.
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Load Balancing: Distributed messaging systems can automatically distribute messages across multiple nodes, ensuring that no single node becomes a bottleneck. This allows for better resource utilization and improved overall performance.
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Message Persistence: Many distributed messaging systems provide message persistence, ensuring that messages are not lost if a receiver is temporarily unavailable or slow to process messages. This feature helps maintain data consistency and reliability across the system.
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Security: Distributed messaging systems often support various security mechanisms, such as encryption and authentication, to protect sensitive data and prevent unauthorized access.
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Interoperability: These systems often support multiple messaging protocols and can integrate with various platforms and technologies, making it easier to connect different components within a complex system.
Discussion
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