System Design

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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

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

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

Introduction to DNS

DNS Resolution Process

DNS Load Balancing and High Availability

Flashcards Review

Chapter Assessment

What is a Proxy Server?

Uses of Proxies

VPN vs. Proxy Server

Flashcards Review

Chapter Assessment

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

Introduction to API Gateway

Usage of API gateway

Advantages and disadvantages of using API gateway

Flashcards Review

Chapter Assessment

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

What Is Rate Limiting

Rate Limiting Algorithms

Distributed Rate Limiting

Rate Limiting in Practice

Flashcards Review

Chapter Assessment

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

What is CDN?

Origin Server vs. Edge Server

CDN Architecture

Push CDN vs. Pull CDN

Flashcards Review

Chapter Assessment

Introduction to Data Partitioning

Partitioning Methods

Data Sharding Techniques

Benefits of Data Partitioning

Common Problems Associated with Data Partitioning

Flashcards Review

Chapter Assessment

What is Redundancy?

What is Replication?

Replication Methods

Data Backup vs. Disaster Recovery

Flashcards Review

Chapter Assessment

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

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

Flashcards Review

Chapter Assessment

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

Introduction to Bloom Filters

Benefits & Limitations of Bloom Filters

Variants and Extensions of Bloom Filters

Applications of Bloom Filters

Flashcards Review

Chapter Assessment

Why Quorum?

What is Quorum?

Flashcards Review

Chapter Assessment

What is Leader and Follower Pattern?

Flashcards Review

Chapter Assessment

What is Heartbeat?

Flashcards Review

Chapter Assessment

What is Checksum?

Uses of Checksum

Flashcards Review

Chapter Assessment

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

What is a Distributed File System?

Architecture of a Distributed File System

Key Components of a DFS

Flashcards Review

Chapter Assessment

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

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

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

Functional vs. Non-functional Requirements

What are Back-of-the-Envelope Estimations?

Things to Avoid During System Design Interview

System Design Master Template

Quiz

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

Concurrency and Coordination

Concurrency and Coordination

consistency

distributed systems

availability

replication

+1

hard
·
6 min
·Updated Jul 2026·Credit: System Design Fundamentals

In a distributed system, many processes work at the same time, often on the same data. That creates three separate problems: controlling access, coordinating timing, and agreeing on what the data currently is.

Concurrency Control

Concurrency control manages simultaneous access to shared resources or data, so multiple processes can work efficiently without producing conflicts or inconsistencies.

Three techniques do it.

Locking. A lock restricts access to a shared resource or piece of data so that only one process can use it at a time. Simple, and it works by making everyone else wait.

Optimistic concurrency control. This assumes conflicts are rare. Processes are allowed to run simultaneously, and conflicts are detected and resolved afterwards, usually by validating the work and rolling back if it clashed. It is a bet: when conflicts really are rare, nobody waits for anybody.

Transactional memory. Multiple operations are grouped into a transaction that executes atomically, which keeps the data consistent and the operations isolated from each other.

Synchronization

Synchronization coordinates the execution of multiple processes or threads so the system behaves correctly.

Barriers hold processes at a point until they have all reached it, then let them all continue.

Semaphores are signalling mechanisms that control access to shared resources and keep processes in step.

Condition variables let a process wait until a specific condition is true before continuing.

The Difference Between Them

These two get treated as the same thing. They are not.

Concurrency control is about access. Its goal is managing who can reach a shared resource when several processes are running at once, and its concern is what happens when two of them want to change the same data.

Synchronization is about timing. Its goal is coordinating the order and timing of concurrent processes, making sure certain operations happen before others and that processes do not interfere with one another.

Image
Concurrency control decides who may touch shared data, while synchronization decides the order in which processes proceed

One answers "may you touch this?" The other answers "is it your turn yet?"

Coordination Services

Coordination services are specialized components that provide ready-made primitives for the hard parts of running a distributed system: configuration management, service discovery, leader election, and distributed locking.

Apache ZooKeeper, etcd and Consul are the usual examples.

The reason they exist is that every one of those primitives is difficult to get right, and getting them subtly wrong produces failures that only appear under load.

Consistency Models

A consistency model defines the rules for how and when a change made by one operation becomes visible to other operations. Each one trades differently between consistency, availability and partition tolerance.

Strong consistency. Once a write completes, any subsequent read immediately sees the new value. Traditional relational databases like MySQL and PostgreSQL typically offer this.

Eventual consistency. All reads of a data item will eventually return the last written value, with no guarantee of how long that takes. Amazon's DynamoDB works this way.

Causal consistency. Operations that are causally related are seen in the same order by everyone, while unrelated concurrent operations may be seen in different orders on different nodes. In a social app, if someone posts a message and then comments on it, any user who sees the comment must also see the original post.

Read-your-writes consistency. Once a write completes, subsequent reads by that same client see it. Updating your profile and immediately seeing the new version is this guarantee.

Session consistency. A stronger form of read-your-writes that extends the guarantee across a whole session of interactions. Items added to a shopping cart stay consistently visible for the rest of that visit.

Sequential consistency. Operations from all nodes are seen in the same order everywhere. There is a global order, but it does not have to match real time. A distributed logging system merging logs from many servers into one consistent log works this way.

Monotonic read consistency. Once a read has seen a value, later reads never see an older one. Checking a flight status, the departure time may move forward but will not jump backwards.

Linearizability. A stronger form of sequential consistency: every operation is atomic and instantly visible to all nodes, not merely globally ordered. In a distributed key-value store, once a key is written, a read on any node reflects it immediately.

Image
The consistency models arranged from eventual at the weak end to linearizability at the strong end, with the guarantee each one adds

Read that list as a ladder. Each step up adds a guarantee and takes something away in performance, availability, or both. The right rung depends on what the application actually needs.

💡 The strongest answer to "which consistency model?" names the operation, not the system. "Reads of the account balance need linearizability, but the activity feed can be eventually consistent" is a real design. Systems are rarely one model throughout.

Key takeaway: Concurrency control manages access to shared data using locks, optimistic control that assumes conflicts are rare and resolves them afterwards, or transactional memory. Synchronization coordinates timing using barriers, semaphores and condition variables. Coordination services like ZooKeeper, etcd and Consul supply configuration management, service discovery, leader election and distributed locking. Consistency models form a ladder from eventual up to linearizability, each rung adding a guarantee and costing performance or availability.

The next lesson, Monitoring and Observability, covers how you find out what a system this complicated is actually doing.

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