System Design

Learn 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

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

Introduction to API Gateway

Usage of API gateway

Advantages and disadvantages of using API gateway

Scalability

Availability

Latency and Performance

Concurrency and Coordination

Monitoring and Observability

Resilience and Error Handling

Fault Tolerance vs. High Availability

HTTP vs. HTTPS

TCP vs. UDP

HTTP: 1.0 vs. 1.1 vs 2.0 vs. 3.0

URL vs. URI vs. URN

Introduction to DNS

DNS Resolution Process

DNS Load Balancing and High Availability

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

What is CDN?

Origin Server vs. Edge Server

CDN Architecture

Push CDN vs. Pull CDN

Introduction to Data Partitioning

Partitioning Methods

Data Sharding Techniques

Benefits of Data Partitioning

Common Problems Associated with Data Partitioning

What is a Proxy Server?

Uses of Proxies

VPN vs. Proxy Server

What is Redundancy?

What is Replication?

Replication Methods

Data Backup vs. Disaster Recovery

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

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

What are Indexes?

Types of Indexes

Introduction to Bloom Filters

Benefits & Limitations of Bloom Filters

Variants and Extensions of Bloom Filters

Applications of Bloom Filters

Difference Between Long-Polling, WebSockets, and Server-Sent Events

Why Quorum?

What is Quorum?

What is Heartbeat?

What is Checksum?

Uses of Checksum

What is Leader and Follower Pattern?

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?

Introduction to Messaging System

Introduction to Kafka

Messaging patterns

Popular Messaging Queue Systems

RabbitMQ vs. Kafka vs. ActiveMQ

Scalability and Performance

What is a Distributed File System?

Architecture of a Distributed File System

Key Components of a DFS

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 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 Interviews - A step by step guide

System Design Master Template

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

Latency vs Throughput

Latency vs Throughput

latency

throughput

system performance

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5 min
·Updated Jan 2025

Latency and throughput are two critical performance metrics in software systems, but they measure different aspects of the system's performance.

Latency

  • Definition: Latency measures delay—how long it takes for a single request to be processed from start to finish. In other words, it's the delay between the initiation of a request and the receipt of the response. Think of it as the time you wait at a fast-food drive-thru to get your order.
  • Characteristics:
    • Measured in units of time (milliseconds, seconds).
    • Lower latency indicates a more responsive system.
  • Impact: Latency is particularly important in scenarios where real-time or near-real-time interaction or data transfer is crucial, such as in online gaming, video conferencing, or high-frequency trading.
  • Example: If you click a link on a website, the latency would be the time it takes from the moment you click the link to when the page starts loading.

Throughput

  • Definition: Throughput refers to the amount of data transferred over a network or processed by a system in a given amount of time. It's a measure of how much work or data processing is completed over a specific period.
  • Characteristics:
    • Measured in units of data per time (e.g., Mbps - Megabits per second).
    • Higher throughput indicates a higher data processing capacity.
  • Impact: Throughput is a critical measure in systems where the volume of data processing is significant, such as in data backup systems, bulk data processing, or video streaming services.
  • Example: In a video streaming service, throughput would be the rate at which video data is transferred from the server to your device.

Latency vs Throughput - Key Differences

  • Focus: Latency is about the delay or time, focusing on speed. Throughput is about the volume of work or data, focusing on capacity.
  • Influence on User Experience: High latency can lead to a sluggish user experience, while low throughput can result in slow data transfer rates, affecting the efficiency of data-intensive operations.
  • Trade-offs: In some systems, improving throughput may increase latency, and vice versa. For instance, sending data in larger batches may improve throughput but could also result in higher latency.

Improving latency and throughput often involves different strategies, as optimizing for one can sometimes impact the other. However, there are several techniques that can enhance both metrics:

How to Improve Latency

  1. Optimize Network Routes: Use Content Delivery Networks (CDNs) to serve content from locations geographically closer to the user. This reduces the distance data must travel, decreasing latency.
  2. Caching Frequently Accessed Data: Cache frequently accessed data in memory to eliminate the need to fetch data from the original source repeatedly.
  3. Upgrade Hardware: Faster processors, more memory, and quicker storage (like SSDs) can reduce processing time.
  4. Use Faster Communication Protocols: Protocols like HTTP/2 can reduce latency through features like multiplexing and header compression.
  5. Database Optimization: Use indexing, optimized queries, and in-memory databases to reduce data access and processing time.
  6. Load Balancing: Distribute incoming requests efficiently among servers to prevent any single server from becoming a bottleneck.
  7. Code Optimization: Optimize algorithms and remove unnecessary computations to speed up execution.
  8. Minimize External Calls: Reduce the number of API calls or external dependencies in your application.

How to Improve Throughput

  1. Scale Horizontally: Add more servers to handle increased load. This is often more effective than vertical scaling (upgrading the capacity of a single server).
  2. Implement Caching: Cache frequently accessed data in memory to reduce the need for repeated data processing.
  3. Parallel Processing: Use parallel computing techniques where tasks are divided and processed simultaneously.
  4. Batch Processing: For non-real-time data, processing in batches can be more efficient than processing each item individually.
  5. Optimize Database Performance: Ensure efficient data storage and retrieval. This may include techniques like partitioning and sharding.
  6. Asynchronous Processing: Use asynchronous processes for tasks that don’t need to be completed immediately.
  7. Network Bandwidth: Increase the network bandwidth to accommodate higher data transfer rates.

Conclusion

Low latency is crucial for applications requiring fast response times, while high throughput is vital for systems dealing with large volumes of data.

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