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
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
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
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
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
What are Indexes?
database design
data modeling
performance
databases
+2
There are two ways for a database to find a row: read every row in the table and check each one, or look the answer up in a small sorted structure built for that purpose (an index). The first way is simple and slow. The second way is what keeps real systems fast.
An index is a data structure that stores the values of one or more columns in sorted order, along with a pointer to the full row. Think of it as a table of contents for your data. You do not read the whole book to find one chapter. You read the table of contents, and it tells you which page to turn to.
A Library Catalog
A library catalog is a register that lists the books in a library. It works like a database table with four columns: book title, writer, subject, and date of publication.
Most libraries keep two catalogs. One is sorted by title. The other is sorted by writer name. That way you can start from a writer whose work you enjoy, or from a title you already know.
Those two catalogs are indexes for the library's collection of books. Each one is a sorted list that is easy to search by one piece of information. The books themselves never move.
What an Index Actually Stores
When you create an index on a column, the database stores two things for every row: the value in that column, and a pointer to where the full row lives.
Say we have a Books table with a title, a writer, and a subject. An index on the Title column is a sorted list of titles, and next to each title is a pointer back to the complete row.
The index is much smaller than the table, because it holds one or two columns instead of all of them. Small and sorted is exactly what makes it fast to search.
What Changes When a Query Uses an Index
Without an index, the database has to read every row in the table and test each one against your WHERE clause. This is called a full table scan. On a table with ten million rows, the database reads ten million rows to return one.
With an index, the database searches the sorted index instead, finds the matching entry, and follows the pointer straight to the row. Most databases store the index as a B-tree, a tree structure that stays balanced as data is added, so a lookup takes only a few steps even on a very large table.
Two things get better:
- Fewer disk reads. The database touches a few index pages and one data page instead of the entire table. Disk reads are the slowest part of most queries.
- Free sorting. The index is already in order, so a query that asks for results sorted by the indexed column can skip the sorting step entirely.
Selectivity Decides Whether an Index Helps
Selectivity is how good a column is at narrowing the search. A column with many distinct values, such as an email address, is highly selective: one value matches one row out of millions.
A column with few distinct values is not selective. An index on a gender column, or on a status column that holds only active and inactive, points at half the table. Reading half the table through an index is slower than just scanning the table, because each pointer is a separate jump. Databases know this, and their query planners will often ignore such an index.
The rule of thumb: index the columns you filter on when that filter throws most of the table away.
Indexes at Large Scale
The same idea applies far beyond a single relational table. Imagine a dataset of many terabytes where each record is only about one kilobyte. You cannot iterate over that much data in any reasonable time, so an index is not an optimization, it is a requirement.
A dataset that large is also spread across several physical machines. Something has to tell the system which machine holds the record you want. An index is the usual answer.
Indexes Make Writes Slower
An index is a second copy of some of your data, and copies have to be kept in step.
Every time you insert a row, the database writes the row and then updates every index on that table. The same is true for updates and deletes. Five indexes on a table means one insert becomes six writes.
Indexes also take up storage. On a wide table with many indexes, the indexes can take more space than the data.
So there is a real trade-off:
- Reads get faster. Queries that filter or sort on the indexed column avoid scanning the table.
- Writes get slower. Every insert, update, and delete has more work to do.
If a table is written to constantly and read from rarely, such as a raw event log, extra indexes cost more than they return. Add indexes for the queries you actually run, and drop indexes that no longer serve a query.
💡 In an interview, never answer "add an index" and stop there. Say which column, why that column is selective, and what the write cost is. "Users are looked up by email on every login, and email is unique, so I would index it. The table is written once per signup and read on every request, so the write cost is not a concern." That sentence shows you understand both sides of the trade.
Quick Reference
| Question | Answer |
|---|---|
| What is an index? | A sorted structure holding one or more columns plus a pointer to the full row |
| What problem does it solve? | It avoids reading every row to find a few rows |
| What does it cost? | Extra storage, and extra work on every insert, update, and delete |
| When does it help most? | Selective columns you filter, join, or sort on |
| When does it hurt? | Write-heavy tables, and columns with very few distinct values |
Next up: How a B-Tree Index Works, where we open up the structure behind almost every index and see why it stays only three levels deep on a table of a hundred million rows.
Discussion
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