System Design
Learn System Design
Introduction to System Design
How to Learn System Design?
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
Flashcards Review
Chapter Assessment
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
What Happens When You Type a URL into the Browser
Flashcards Review
Chapter Assessment
Long-Polling vs. WebSockets vs. Server-Sent Events
Introduction to Real-Time Communication
What is Long-Polling?
What is WebSocket?
What are Server-Sent Events?
Difference Between Long-Polling, WebSockets, and Server-Sent Events
Flashcards Review
Chapter Assessment
Domain Name System (DNS)
Introduction to DNS
DNS Resolution Process
DNS Load Balancing and High Availability
Flashcards Review
Chapter Assessment
Proxies
What is a Proxy Server?
Uses of Proxies
VPN vs. Proxy Server
Flashcards Review
Chapter Assessment
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
Flashcards Review
Chapter Assessment
API Gateway
Introduction to API Gateway
Usage of API gateway
Advantages and disadvantages of using API gateway
Flashcards Review
Chapter Assessment
API Design
What Is an API?
What Are REST APIs?
Resources, Not Actions
HTTP Methods and Their Semantics
URL Design
Request and Response Shapes
Status Codes and Error Design
Pagination from the Consumer's View
Idempotency Keys
Versioning and Backward Compatibility
Concurrency and Conditional Requests
REST vs gRPC vs GraphQL
Flashcards Review
Chapter Assessment
Rate Limiting and Throttling
What Is Rate Limiting
Rate Limiting Algorithms
Distributed Rate Limiting
Rate Limiting in Practice
Flashcards Review
Chapter Assessment
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
Flashcards Review
Chapter Assessment
CDN
What is CDN?
Origin Server vs. Edge Server
CDN Architecture
Push CDN vs. Pull CDN
Flashcards Review
Chapter Assessment
Data Partitioning
Introduction to Data Partitioning
Partitioning Methods
Data Sharding Techniques
Benefits of Data Partitioning
Common Problems Associated with Data Partitioning
Flashcards Review
Chapter Assessment
Redundancy and Replication
What is Redundancy?
What is Replication?
Replication Methods
Data Backup vs. Disaster Recovery
Flashcards Review
Chapter Assessment
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
Flashcards Review
Chapter Assessment
Indexes
What are Indexes?
How a B-Tree Index Works
Types of Indexes
B-Tree vs. LSM Tree
Indexes in Distributed Systems
Flashcards Review
Chapter Assessment
Bloom Filters
Introduction to Bloom Filters
Benefits & Limitations of Bloom Filters
Variants and Extensions of Bloom Filters
Applications of Bloom Filters
Flashcards Review
Chapter Assessment
Quorum
Why Quorum?
What is Quorum?
Flashcards Review
Chapter Assessment
Leader and Follower
What is Leader and Follower Pattern?
Flashcards Review
Chapter Assessment
Heartbeat
What is Heartbeat?
Flashcards Review
Chapter Assessment
Checksum
What is Checksum?
Uses of Checksum
Flashcards Review
Chapter Assessment
Distributed Messaging System
Introduction to Messaging System
Introduction to Kafka
Messaging patterns
Popular Messaging Queue Systems
RabbitMQ vs. Kafka vs. ActiveMQ
Scalability and Performance
Flashcards Review
Chapter Assessment
Distributed File Systems
What is a Distributed File System?
Architecture of a Distributed File System
Key Components of a DFS
Flashcards Review
Chapter Assessment
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?
Flashcards Review
Chapter Assessment
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
Flashcards Review
Chapter Assessment
Quiz - System Design Fundamentals
Quiz
How to Approach a System Design Interview
System Design Interviews - A step by step guide
Functional vs. Non-functional Requirements
What are Back-of-the-Envelope Estimations?
Things to Avoid During System Design Interview
System Design Master Template
System Design Master Template
Quiz
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
ACID vs BASE Properties
acid
consistency
availability
databases
+3
Transfer money and the system fails halfway. The bank took 100 from your account and never added it to the recipient's. Nobody accepts that from a bank.
Databases have two famous answers to this danger, and they pull in opposite directions.
ACID keeps every transaction strictly correct, even if the system must pause or refuse work to do it. BASE keeps the system answering, even if some answers are briefly out of date.
This lesson explains both. It also clears up the one confusion that costs candidates points: the word consistency means a different thing in each.
ACID
A transaction is a unit of work with several steps that must count as one. Transferring money is the standard example. Step one takes 100 from account A. Step two adds 100 to account B.
ACID names four promises a database makes about transactions: Atomicity, Consistency, Isolation, and Durability.
Atomicity: all or nothing. The transaction happens completely, or not at all. If step two fails, step one is undone. Money is never taken without being delivered.
Consistency: the rules always hold. Every database has rules about its data. A balance may not go below zero. Every order must belong to a customer. Consistency means each transaction moves the data from one state that obeys the rules to another state that obeys them. In the transfer, the total across both accounts is the same before and after.
Isolation: transactions do not disturb each other. Transactions running at the same time behave as if they ran one after another. A reader sees a stock count before an update or after it, never a half-updated value in between.
Durability: committed means saved. Once the database confirms the transaction, the change survives a crash or a power failure. If a messaging app says your message was sent, it is stored, even if the server dies a moment later.
Relational databases such as MySQL and PostgreSQL are built around these promises.
The promises have a cost. Keeping them means locking and waiting, and across several machines it means a lot of coordination. That cost is what BASE removes.
BASE
BASE describes how many NoSQL and distributed stores behave. It stands for Basically Available, Soft state, Eventually consistent.
Basically Available: the system answers. Even during heavy load or a partial failure, it gives some response. The response may be slightly out of date. During a big sale, the store keeps taking orders while the inventory counts run a little behind.
Soft state: the data can change on its own. Updates spread between the copies in the background. So a copy can change without any new input, as older updates arrive at it.
Eventually consistent: the copies converge. Stop the writes, wait, and every copy ends up with the same value. There is no promise about how long that takes. Strong vs Eventual Consistency covers what that waiting means for readers.
One Word, Two Meanings
Here is the trap. The C in ACID and the C in CAP are not the same thing.
The C in ACID is about one database. It says every transaction leaves the data obeying its own rules. No negative balances. No order without a customer. A single machine can make this promise by itself.
The C in CAP is about many machines. It says every read returns the newest write, no matter which copy answers. This promise only means something when the data lives on more than one machine.
Same letter, different subjects. When someone says a BASE store "gives up consistency", they mean the CAP kind. Copies of the data may briefly disagree with each other. They do not mean the store corrupts data or forgets its rules.
CAP Underneath
The CAP theorem explains why the ACID and BASE styles both exist.
The theorem names Consistency, Availability, and Partition tolerance, and it is often quoted as "pick two of three". That wording misleads, because partition tolerance is not yours to pick. A partition is a network failure that cuts machines off from each other. Networks fail whether you plan for it or not.
So the real choice appears during the failure. While the machines cannot reach each other, a store does one of two things.
ACID-style systems refuse. The user sees an error or a wait, and never a wrong value. BASE-style systems answer. The user always gets a response, and it may be old.
That is the whole trade in one sentence, and it is why the two acronyms exist. The full argument is in CAP Theorem.
The Difference That Matters
| ACID | BASE | |
|---|---|---|
| Priority | Correctness | Availability |
| After a write | Everyone sees it immediately | Everyone sees it eventually |
| During a partition | May refuse to answer | Answers with what it has |
| Who handles disagreement | The database | The application developers |
| Typical home | Relational databases | NoSQL and distributed stores |
The fourth row is the one people miss. In a BASE store, copies can disagree, and someone must decide which value wins. That someone is your application. The work of resolving conflicts does not disappear when you choose BASE. It moves from the database into your code.
💡 In the interview: if you say "we will use BASE", expect the follow-up: what happens when two replicas disagree? Have a concrete answer, such as last write wins, a version vector, or a merge rule for that exact data. And keep the two meanings of consistency separate. BASE relaxes the CAP kind, which is agreement between copies. It does not mean the data stops following its rules. Mixing the two C's up is the kind of slip an interviewer hears at once.
Key takeaway: ACID is four promises about a transaction: all or nothing, the rules always hold, no interference, and committed means saved. BASE is a different bargain: the system always answers, updates spread in the background, and the copies converge in time. The C in ACID is one database obeying its rules, and the C in CAP is many machines agreeing on the newest value. Partitions are a fact, not a choice, so during one a store either refuses like ACID or answers with old data like BASE. And in a BASE store, resolving disagreement between copies is the application's job.
Discussion
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