José Abraham

Software Engineer. Translate your idea into a real and functional tool. Java and Web Technologies fan.

[Neo Kim @systemdesignone]

If you want to get dangerously good at system design, learn these concepts:

1 Scalability
2 Availability
3 Reliability
4 Latency
5 Throughput
6 Database
7 SQL vs NoSQL
8 Load Balancing
9 Caching
10 Cache Invalidation
11 API Design
12 REST
13 GraphQL
14 gRPC
15 Authentication
16 Fault Tolerance
17 High Availability
18 CAP Theorem
19 Consistency Models
20 Replication
21 Erasure Coding
22 Consensus
23 Leader Election
24 Secrets Management
25 RBAC
26 Sharding
27 Indexing
28 Denormalization
29 ACID
30 BASE
31 Event-Driven
32 Message Queue
33 Pub/Sub
34 Sync vs Async
35 Idempotency
36 Bulkhead
37 Retry Logic
38 Timeout
39 Service Discovery
40 API Gateway
41 Blue-Green Deployment
42 Canary Release
43 Feature Flags
44 Observability
45 Logging
46 Correlation ID
47 Monitoring
48 Alerting
49 Full-Text Search
50 Time Series

(...and many more!)

What else should make this list?

===

👋 PS - Want a simple breakdown of each concept?

Read right now in my newsletter:

→ Part 1: https://t.co/u7BsUK307i
→ Part 2: https://t.co/CJAwmrUXdI
→ Part 3: https://t.co/DOQpnNOnjc

===

💾 Save & RT to help others get good at system design.
👤 Follow @systemdesignone + turn on notifications.

[Dipanshu Kushwaha @Dipanshu_AI]

Anthropic pays engineers $750,000+ a year to understand how LLMs work.

Stanford just put a 2 hour lecture that covers 80% of it for FREE.

Bookmark this. Give it 2 hours today.

[Andrés Manuel @lopezobrador_]

Por el bien de todos, que regrese el otro Trump.

https://t.co/BHQVEInscT

[Shraddha Bharuka @BharukaShraddha]

SYSTEM DESIGN — MASTER TREE 🌲

System Design
│
├── 01. Core Foundations
│ ├── Client-Server Architecture
│ ├── Monolith vs Microservices
│ ├── Stateless vs Stateful
│ ├── Horizontal vs Vertical Scaling
│ └── CAP Theorem
│
├── 02. Networking Layer
│ ├── DNS
│ ├── HTTP / HTTPS
│ ├── TCP vs UDP
│ ├── WebSockets
│ ├── Load Balancers
│ └── Reverse Proxies
│
├── 03. API Layer
│ ├── REST APIs
│ ├── GraphQL
│ ├── gRPC
│ ├── API Gateway
│ ├── Rate Limiting
│ └── Authentication
│
├── 04. Database Layer
│ ├── SQL
│ ├── NoSQL
│ ├── Indexing
│ ├── Replication
│ ├── Sharding
│ └── ACID Transactions
│
├── 05. Caching Layer
│ ├── Redis
│ ├── Memcached
│ ├── CDN
│ ├── Cache Invalidation
│ ├── Write-through Cache
│ └── Cache Eviction Policies
│
├── 06. Messaging Systems
│ ├── Kafka
│ ├── RabbitMQ
│ ├── Pub/Sub
│ ├── Event Streaming
│ ├── Async Processing
│ └── Queue Workers
│
├── 07. Reliability Layer
│ ├── High Availability
│ ├── Fault Tolerance
│ ├── Circuit Breakers
│ ├── Retries
│ ├── Disaster Recovery
│ └── Backpressure Handling
│
├── 08. Observability
│ ├── Logs
│ ├── Metrics
│ ├── Tracing
│ ├── Monitoring
│ ├── Alerting
│ └── SLA / SLO / SLI
│
├── 09. DevOps & Deployment
│ ├── Docker
│ ├── Kubernetes
│ ├── CI/CD
│ ├── Infrastructure as Code
│ ├── Blue-Green Deployment
│ └── Canary Releases
│
└── 10. Future Architectures
├── Serverless
├── Edge Computing
├── AI-Native Systems
├── Autonomous Infrastructure
└── Self-Healing Systems

Most developers learn frameworks.

The top 1% learn systems.

[Edgex @SahilExec]

How Redis is single-threaded -
and still handles a million requests per second.

This is one of the most asked interview questions in backend.

Most people know Redis is fast.
Very few can explain why.

[Neo Kim @systemdesignone]

If you're just getting started with SYSTEM DESIGN, learn these 16 concepts (not joking):

1 The Computer Science Stack, Simply Explained
→ https://t.co/qfZnlyCSN5

2 Modular Monolith Architecture
→ https://t.co/VVV6v3KGHJ

3 How RPC Works
→ https://t.co/yeIgcmAxQx

4 How JWT Works
→ https://t.co/SZXXrlBsWH

5 Capacity Planning
→ https://t.co/umTNhM2dVY

6 How Bloom Filters Work
→ https://t.co/ntZXq7LxVn

7 How Consistent Hashing Works
→ https://t.co/7d6EipPcKF

8 How Service Discovery Works
→ https://t.co/BcL3tgxx1u

9 API Versioning - A Deep Dive
→ https://t.co/OHAtKSUgVN

10 Concurrency Is Not Parallelism
→ https://t.co/BwRHeuJ5AF

11 How Idempotent API Works
→ https://t.co/afe7ACuSYE

12 Saga Design Pattern
→ https://t.co/2CffTodOHL

13 How Databases Keep Passwords Securely
→ https://t.co/KSfIhpAT2j

14 API Design Best Practices
→ https://t.co/I2ejJ0kbYq

15 How Apache Kafka Works
→ https://t.co/8rOy9KgCMo

16 Distributed Systems 101
→ https://t.co/yi0K5K5RIE

What else should make this list?

===

👋 PS - Want my System Design Playbook (for free)?

Join my newsletter with 200K+ software engineers now:
→ https://t.co/ByOFTtOihX

===

💾 Save now & RT to help others start with system design.
👤 Follow @systemdesignone + turn on notifications.

[Nikki Siapno @NikkiSiapno]

I wrote 35 articles for 35 system design concepts:

1) Microservices: https://t.co/1CpY04nNxb

2) Redis: https://t.co/YmMKgSvyVk

3) Event-driven architecture: https://t.co/QNUuf1JOy7

4) Database indexing: https://t.co/uNBq3oqmoS

5) Circuit breakers: https://t.co/1U81SrBokz

6) ACID vs BASE: https://t.co/a7nOyylUxk

7) Rate limiting: https://t.co/wr0UAh4sJm

PS - if you want a structured path, get my free 142-page System Design Handbook when you join my free weekly newsletter → https://t.co/rKYuAkfCSL

8) Observability: https://t.co/VjfECfyB9d

9) System design quality attributes: https://t.co/v9WJoUPevt

10) CAP theorem: https://t.co/ONwpduTOD1

11) Idempotency: https://t.co/2sItwlz1oe

12) REST APIs: https://t.co/9N3UHDtUyk

13) Sync vs Async: https://t.co/Pim4BeGD9G

14) Network protocols: https://t.co/tx7MlZQIwE

15) Change Data Capture (CDC): https://t.co/tgwwoTitCA

16) CI/CD pipelines: https://t.co/SM2YvhioIX

17). SSO (single sign-on): https://t.co/2kun4Q9K6N

18) JWT: https://t.co/Kuv7DAj6B9

19) gRPC: https://t.co/QwgTXr1N9z

20) Health checks vs heartbeats: https://t.co/r5SalP6CCh

21) API gateway vs load balancer vs reverse proxy: https://t.co/Tg3EhT60tU

22) HTTPS: https://t.co/wc3CQOsmPS

23) Load balancing algorithms: https://t.co/VCLCKOZzni

24) Database caching: https://t.co/23QdZATj2o

25) API protocols: https://t.co/2CEu4Wnhsv

26) CDN: https://t.co/MbaSzBnZPQ

27) Database types: https://t.co/T0tUF1xYPI

28) Message Queues: https://t.co/7Tz5sevNA8

29) Hashing vs encryption vs tokenization: https://t.co/7GfH69yVpL

30) Service Discovery: https://t.co/rcKkXkWWcX

31) Pub/Sub: https://t.co/HF0Zr5R4SK

32) Connection pooling: https://t.co/39SsEo4kk3

33) Forward proxy vs reverse proxy: https://t.co/0P6NM8kh8u

34) Consistent hashing: https://t.co/8d8o74EsaS

35) SQL vs NoSQL:https://t.co/oDTRpsnQUn

——

👋 PS: Get my free 142-page System Design Handbook when you join my free weekly newsletter.

Join 33,000+ engineers → https://t.co/cjSsAXhnsG

——

♻️ Repost to help others learn system design.

➕ Follow me ( Nikki Siapno ) to become good at system design.

[Nitin.nn @NitinthisSide_]

𝟯𝟬 𝗕𝗹𝗼𝗴𝘀 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝟯𝟬 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀:

1. APIs: https://t.co/rotPrzmqsW
2. API Gateways: https://t.co/H8o6JdAvtK
3. JWTs: https://t.co/xDKeDaUwKc
4. Webhooks: https://t.co/DnlnlnQHeA
5. REST vs GraphQL: https://t.co/nxE4WDGLtv
6. Load Balancing: https://t.co/oHhi0BCJg4
7. Proxy vs Reverse Proxy: https://t.co/9yybEKFKpY
8. Scalability: https://t.co/RZAnwKWRfc
9. Availability: https://t.co/6otmmY4qab
10. SPOF: https://t.co/hMwAshtd9W
11. CAP Theorem: https://t.co/1mtupEuHiS
12. SQL vs NoSQL: https://t.co/Yiuz6ohNeG
13. ACID Transactions: https://t.co/oJfATYxxtw
14. Database Indexes: https://t.co/zIUkaDPtxy
15. Database Sharding: https://t.co/f9X4H3nBhf
16. Consistent Hashing: https://t.co/T3kslsUovA
17. CDC: https://t.co/AbAEINF7Ql
18. Caching: https://t.co/wZ9jvagVgg
19. Caching Strategies: https://t.co/4VgCVdCZV4
20. Cache Eviction Policies: https://t.co/2ki1YZPVSv
21. CDN: https://t.co/9BJXTl6osT
22. Rate Limiting Algorithms: https://t.co/IxdITzUgjI
23. Message Queues: https://t.co/qG1LmQdZSS
24. Bloom Filters: https://t.co/HgS8txD8aW
25. Idempotency: https://t.co/jboMnpQjAV
26. Concurrency vs Parallelism: https://t.co/gNDO7kiNUu
27. Long Polling vs WebSockets: https://t.co/yrxQTC4Gip
28. Stateful vs. Stateless Architecture: https://t.co/IoF9d93ECe
29. Batch vs Stream Processing: https://t.co/iLOkjLjKOh
30. Geohashing: https://t.co/T2xROIXPEj

🔁 Repost to help a friend.
📌 Save this post for later.

[Dhanian 🗯️ @e_opore]

15 SYSTEM DESIGN PRINCIPLES

1. SCALABILITY

→ Design systems to handle growth in users and traffic
→ Use horizontal scaling (adding more servers)
→ Avoid single-machine limitations

A good system should grow without breaking.

2. AVAILABILITY

→ Ensure your system is always accessible
→ Use redundancy and failover strategies
→ Minimize downtime

High availability = better user trust.

3. RELIABILITY

→ Systems should perform consistently over time
→ Handle failures gracefully
→ Ensure data correctness

Users should depend on your system without surprises.

4. PERFORMANCE

→ Reduce latency and response time
→ Optimize database queries
→ Use caching strategies

Fast systems improve user experience.

5. FAULT TOLERANCE

→ Design systems to continue working even when parts fail
→ Use backups and replication
→ Implement retry mechanisms

Failures are inevitable — plan for them.

6. LOAD BALANCING

→ Distribute traffic across multiple servers
→ Prevent server overload
→ Improve system responsiveness

No single server should carry all the load.

7. CACHING

→ Store frequently accessed data temporarily
→ Reduce database load
→ Improve speed

Examples: Redis, in-memory caching

8. DATABASE DESIGN

→ Choose the right database (SQL vs NoSQL)
→ Normalize or denormalize based on use case
→ Index for faster queries

Data design is the backbone of your system.

9. CONSISTENCY VS AVAILABILITY (CAP THEOREM)

→ You can’t have full consistency, availability, and partition tolerance at once
→ Choose trade-offs based on system needs

Understanding trade-offs is key in distributed systems.

10. MICROSERVICES ARCHITECTURE

→ Break systems into smaller independent services
→ Each service handles a specific responsibility
→ Enables scalability and flexibility

Avoid tightly coupled monoliths when scaling.

11. API DESIGN

→ Use REST or GraphQL
→ Keep APIs consistent and predictable
→ Version your APIs

APIs are the communication bridge between systems.

12. MESSAGE QUEUES

→ Handle asynchronous tasks
→ Improve system resilience
→ Decouple services

Examples: RabbitMQ, Kafka

13. SECURITY

→ Encrypt data (HTTPS, TLS)
→ Implement authentication & authorization
→ Protect against attacks (SQL injection, XSS)

Security must be built from day one.

14. MONITORING & LOGGING

→ Track system performance
→ Log errors and events
→ Use tools like Prometheus, ELK

You can’t fix what you can’t see.

15. DEPLOYMENT & CI/CD

→ Automate builds and deployments
→ Use pipelines for testing and release
→ Ensure smooth updates

Faster delivery with fewer errors.

System design is about making the right trade-offs while building systems that are scalable, reliable, and efficient.

If you want to master system design in depth:

Grab the System Design Handbook:
https://t.co/WIMretRdFc

[Neo Kim @systemdesignone]

If you want to get started with system design, then learn these 12 concepts:

1 How RPC Works:
↳ https://t.co/yeIgcmAxQx

2 Idempotent API:
↳ https://t.co/afe7ACuSYE

3 Saga Design Pattern:
↳ https://t.co/2CffTodOHL

4 How Bloom Filters Work:
↳ https://t.co/ntZXq7LxVn

5. How Consistent Hashing Works:
↳ https://t.co/7d6EipPcKF

6. Service Discovery:
↳ https://t.co/BcL3tgxx1u

7. Microservices Lessons From Netflix:
↳ https://t.co/XgS7VQoBFv

8. Modular Monolith Architecture Explained:
↳ https://t.co/VVV6v3KGHJ

9. How Databases Keep Passwords Securely:
↳ https://t.co/KSfIhpAT2j

10. Best Practices for API Design:
↳ https://t.co/I2ejJ0kbYq

11. API Versioning - A Deep Dive:
↳ https://t.co/OHAtKSUgVN

12 How JWT Works:
↳ https://t.co/SZXXrlBsWH

What else should make this list?

===

👋 PS - Want my System Design Playbook for FREE?

Join my newsletter with 200K+ software engineers now:
→ https://t.co/ByOFTtOihX

===

💾 Save & RT to help others get started with system design.
👤 Follow @systemdesignone + turn on notifications.

[Alex Xu @alexxubyte]

API Concepts Every Software Engineer Should Know

[Dhanian 🗯️ @e_opore]

Dynamic programming cheat sheet

[Puneet Patwari @system_monarch]

Your system design knowledge, explaining:

- gRPC
- CDNs
- WebSockets
- Rate Limiting
- API Gateways
- Microservices
- Redis Caching
- Load Balancers
- Message Queues
- Database Sharding
- Consistent Hashing
- Eventual Consistency
- Distributed Tracing
- Horizontal Scaling
- Circuit Breakers
- Event Sourcing
- Reverse Proxy
- CAP Theorem
- Service Mesh
- Saga Pattern
- CQRS
- Kafka

Interview decision:
Sorry, we need someone who can explain why they'd use a database.

Knowing the name of a pattern doesn't mean you understand when to use it.

I've seen engineers confidently explain microservices architecture, then suggest splitting a 3-table CRUD app into 15 services.

They knew the pattern.
They didn't know the problem it solves.

Understanding fundamentals means asking better questions, for example:

1. Why would I choose PostgreSQL over MongoDB?
Not "what are the differences" but what specific requirements in my system make one a better fit.

Is it the transaction guarantees?
The query flexibility?
The team's expertise?

2. When should I NOT use Redis caching? Maybe your data changes too frequently.

Maybe cache invalidation becomes more complex than just hitting the database.

Maybe you're solving a performance problem that doesn't exist yet.

3. What's the real cost of event sourcing?

Sure, you get an audit trail and time travel.
But now you're debugging by replaying events, your storage grows unbounded, and your queries need projections.

Is that tradeoff worth it for your use case?

4. Do I actually need Kafka?
Or would a simple queue work fine for my 1000 requests per day?

Are you building for scale you have or scale you imagine?

The best engineers I know can explain why they'd choose a boring SQL database over the latest distributed system.

They understand the problems these patterns solve AND the problems they create.

That's what separates someone who memorized a list from someone who can actually architect systems.

[AlgoMaster.io @algomaster_io]

14 must-know Data Structures for coding interviews:

1. Array
2. Queue
3. Deque
4. Matrix
5. Stack
6. Binary Tree
7. Linked List
8. Doubly Linked List
9. HashMap
10. Binary Search Tree (BST)
11. Heap (Priority Queue)
12. Trie
13. Graph
14. Union Find

♻️ Repost to help others in your network

[Nitin.nn @NitinthisSide_]

🧵 Day 11/30 — #SystemDesign

Event-Driven Architecture: How modern systems react in real time

Many systems fail when one service depends directly on another. If Payment Service is down, Order Service breaks. If Notification Service slows, user actions feel delayed. Tight coupling creates fragile systems.

That’s why large-scale systems use Event-Driven Architecture (EDA).
Instead of services calling each other directly every time, services publish events when something happens. Other services subscribe and react independently.

Example:
User places order → OrderCreated event published.
Then different services can react:
→ Payment Service charges user
→ Inventory reserves stock
→ Email Service sends confirmation
→ Analytics logs purchase
→ Recommendation engine updates behavior
One action triggers many workflows asynchronously.

⸻

Core Idea
Producer emits event.
Consumers listen and process.
Flow:
Service A → Event Bus / Queue → Multiple Consumers
The producer doesn’t need to know who is listening. This creates loose coupling and better scalability.

⸻

Why Companies Love It
Benefits:
→ Independent services
→ Easier scaling
→ Faster feature additions
→ Better fault isolation
→ Async processing
→ Cleaner microservices communication
→ Real-time workflows
Instead of changing one giant system, teams add new consumers.

⸻

Real-World Examples
→ Uber: Trip booked triggers pricing, driver matching, notifications
→ Amazon: Order placed triggers payment, shipment, invoice, emails
→ Netflix: User watches content triggers analytics + recommendations
→ Stripe: Payment events power webhooks and integrations
Modern products run on events everywhere.

⸻

Common Event Types
→ UserSignedUp
→ OrderCreated
→ PaymentSucceeded
→ MessageSent
→ RideStarted
→ InventoryLow
→ SubscriptionExpired
Good event names describe something that already happened.

⸻

Popular Tools
→ Kafka
→ RabbitMQ
→ AWS SNS + SQS
→ Google Pub/Sub
→ NATS
→ Redis Streams
Tool choice depends on throughput, durability, ordering, and complexity needs.

⸻

Challenges Most Ignore
EDA sounds elegant, but introduces real complexity:
→ Duplicate events
→ Event ordering issues
→ Debugging across many services
→ Eventual consistency
→ Retry storms
→ Schema evolution problems
Distributed systems always charge rent.

⸻

Golden Rule
Use events when many services must react independently.
Don’t use events just because it sounds advanced.
For a small CRUD app, direct calls may be simpler.

⸻

Key Insight
Monoliths share functions.
Service architectures share events.
That shift changes how systems scales.

#30DaysOfSystemDesign #EventDrivenArchitecture #BackendEngineering

[Ritesh Roushan @devXritesh]

System Design Series - Day 8/30

API Gateway Patterns – The Front Door of Your Microservices

API Gateway is the single entry point for all your clients.

Without it:
- Mobile/web clients call 10+ different services directly
- Authentication is duplicated everywhere
- Rate limiting, CORS, logging → repeated in every service
- Services are fully exposed to the internet

With it:
- One clean URL for clients
- Centralized auth, rate limiting, routing, aggregation
- Backend services stay hidden and secure

Here’s everything you need to know about API Gateway patterns.

What is an API Gateway?

Think of it as the hotel front desk

Without a front desk:
- Guests wander around looking for rooms
- No security check
- Housekeeping and room service have no coordination

With a front desk:
- Single check-in point
- Routes guests to correct room
- Handles security, coordination, and requests

API Gateway does exactly that for your microservices.

The Problem It Solves

Before API Gateway:

Mobile app needs user profile + orders:
→ Calls User Service directly
→ Calls Order Service directly
→ Calls Payment Service directly

Problems:
- Client knows internal service URLs
- Multiple network calls (slow on mobile)
- Auth tokens sent to every service
- No centralized rate limiting or logging
- Services exposed to the internet

After API Gateway:

Mobile app calls one URL:
https://api.example. com/profile

Gateway handles everything internally:
- Authenticates once
- Routes and aggregates calls
- Returns combined response

Benefits:
- 1 network call from client
- Services completely hidden (security win)
- Centralized cross-cutting concerns
- Much better client experience

Core Responsibilities:
1. Routing
Maps external URLs to internal services
GET /api/users → User Service
GET /api/orders → Order Service

2. Authentication & Authorization
Validates JWT/OAuth once at the gateway.
Services trust the gateway.

3. Rate Limiting
Prevents abuse (e.g., 100 requests/min per user).

4. Request Aggregation
Combines multiple backend calls into one response for the client.

5. Protocol Translation
Client uses REST → Service uses gRPC (handled at gateway).

Advanced Patterns

- Circuit Breaker → Prevents cascading failures when a service is down

- Request/Response Transformation → Convert old → new API formats

- Caching → Cache frequent responses at the gateway level

- Logging & Monitoring → Centralized observability

When to Use API Gateway

Use it when:
- You have multiple microservices
- External clients (mobile, web, third-party)
- You need centralized auth, rate limiting, or aggregation

Don’t use it when:
- Simple monolith (overkill)
- Only internal service-to-service communication
- Ultra-low latency is critical (extra hop)

Popular Solutions

- Kong (open-source, powerful plugins)
- AWS API Gateway (managed, serverless)
- NGINX + Lua (DIY, lightweight)
- Traefik, Envoy, KrakenD

Summary

API Gateway is not just a proxy.

It is the security layer, traffic manager, and aggregator for your entire backend.

It simplifies client code, hides internal complexity, and centralizes cross-cutting concerns.

Trade-offs:
- Extra network hop (adds latency)
- Becomes a critical component (make it highly available)

Used correctly, it’s one of the most valuable pieces in any microservices architecture.

Tomorrow (Day 9): Inter-Service Communication Patterns

Questions about API Gateway?

Drop them below 👇

#SystemDesign #APIGateway #Microservices #Backend

[Dhanian 🗯️ @e_opore]

API DESIGN ROADMAP

FOUNDATIONS

What is an API → Interface that allows systems to communicate
Types of APIs → REST, GraphQL, SOAP, gRPC
HTTP Basics → Methods (GET, POST, PUT, DELETE), Status Codes
Client-Server Architecture → Separation of concerns
JSON & Data Formats → JSON, XML, Protocol Buffers

REST API DESIGN

REST Principles → Statelessness, Uniform Interface
Resource Naming → Use nouns, not verbs (e.g., /users, /orders)
HTTP Methods →
→ GET → Retrieve data
→ POST → Create data
→ PUT/PATCH → Update data
→ DELETE → Remove data
Status Codes → 200, 201, 400, 401, 404, 500
Versioning → /v1/users, /v2/users

API STRUCTURE & BEST PRACTICES

Consistency → Use standard naming conventions
Pagination → Limit large datasets (?page=1&limit=10)
Filtering & Sorting → Query parameters (?sort=asc)
Error Handling → Clear, structured error responses
Idempotency → Same request → same result
Rate Limiting → Prevent abuse (e.g., 100 requests/min)

AUTHENTICATION & AUTHORIZATION

Authentication → Verify user identity
→ API Keys
→ OAuth 2.0
→ JWT (JSON Web Tokens)
Authorization → Control access levels (roles & permissions)
Secure Endpoints → Protect sensitive routes

API SECURITY

HTTPS → Encrypt communication
Input Validation → Prevent malicious data
CORS → Control cross-origin requests
Rate Limiting → Prevent DDoS attacks
Data Sanitization → Avoid injection attacks
Secrets Management → Secure API keys & tokens

DOCUMENTATION

OpenAPI / Swagger → Standard API documentation
Postman → API testing & documentation
Clear Examples → Request & response samples
Endpoint Descriptions → Explain purpose & usage
Version Documentation → Track API changes

TESTING

Unit Testing → Test individual components
Integration Testing → Test full API flow
Automated Testing → CI/CD pipelines
Load Testing → Handle high traffic
Tools → Postman, Jest, Mocha, Supertest

PERFORMANCE & SCALABILITY

Caching → Redis, CDN
Database Optimization → Indexing, query tuning
Load Balancing → Distribute traffic
Async Processing → Queues (RabbitMQ, Kafka)
Horizontal Scaling → Add more servers

ADVANCED CONCEPTS

GraphQL → Flexible querying
gRPC → High-performance communication
WebSockets → Real-time APIs
API Gateway → Centralized API management
Microservices → Break into smaller services

DEVOPS & DEPLOYMENT

CI/CD → Automate testing and deployment
Docker → Containerize APIs
Kubernetes → Orchestrate services
Monitoring → Logs, metrics (Prometheus, Grafana)
Cloud Deployment → AWS, Azure, GCP

REAL-WORLD BEST PRACTICES

Design First → Plan before coding
Backward Compatibility → Avoid breaking changes
Logging → Track API usage and errors
Throttling → Control traffic spikes
User Experience → Simple, predictable APIs

FINAL LEARNING PATH

Learn HTTP & REST → Understand the basics
Build Simple APIs → CRUD operations
Add Authentication → Secure your API
Document Properly → Use Swagger/Postman
Optimize Performance → Scaling & caching
Deploy to Cloud → Production-ready APIs

Grab the API DESIGN EBOOK:
https://t.co/t2KOeauy6O

[Dhanian 🗯️ @e_opore]

If I had to master API design, I’d learn these concepts:

1. What is an API

2. API Architecture Styles

3. REST Principles

4. RESTful Design

5. GraphQL

6. gRPC

7. SOAP

8. Resource Modeling

9. URI Design

10. HTTP Methods (GET, POST, PUT, PATCH, DELETE)

11. Status Codes

12. Request & Response Structure

13. JSON

14. XML

15. Serialization

16. Deserialization

17. Idempotency

18. Statelessness

19. Versioning Strategies

20. URI Versioning

21. Header Versioning

22. Content Negotiation

23. Pagination

24. Filtering

25. Sorting

26. Searching

27. Rate Limiting

28. Throttling

29. API Gateway

30. Load Balancing

31. Caching Strategies

32. Cache-Control Headers

33. ETags

34. Conditional Requests

35. Authentication

36. Authorization

37. API Keys

38. JWT

39. OAuth 2.0

40. OpenID Connect

41. CORS

42. CSRF Protection

43. Input Validation

44. Data Sanitization

45. Error Handling

46. Error Codes & Messages

47. Logging

48. Monitoring

49. Observability

50. Metrics

51. Tracing

52. Documentation

53. OpenAPI Specification

54. Swagger

55. API Contracts

56. Contract Testing

57. Mock Servers

58. API Testing

59. Postman

60. Load Testing

61. Security Best Practices

62. Encryption (HTTPS/TLS)

63. Secrets Management

64. Rate Limit Headers

65. Webhooks

66. Event-Driven APIs

67. Async APIs

68. Streaming APIs

69. WebSockets

70. Server-Sent Events

71. API Lifecycle Management

72. Deprecation Strategies

73. Backward Compatibility

74. Breaking Changes

75. API Governance

76. Naming Conventions

77. Consistency Rules

78. SDK Generation

79. Developer Experience (DX)

80. API Analytics

81. Usage Tracking

82. Monetization

83. API Marketplace

84. Multi-Tenancy

85. Scalability

86. High Availability

87. Fault Tolerance

88. Circuit Breaker

89. Retry Logic

90. Timeout Handling

91. Service Discovery

92. Microservices Integration

93. BFF (Backend for Frontend)

94. Edge APIs

95. CDN Integration

96. Data Transformation

97. Aggregation Layer

98. Orchestration vs Choreography

(...and more concepts)

===

👋 PS - Want a detailed breakdown of API design best practices?

Read right now:

→ API Design Ebook:
https://t.co/t2KOeauy6O

===

💾 Save this for later & RT to help others learn API design.

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[AlgoMaster.io @algomaster_io]

10 must-know Array patterns for coding interviews:

1. Two Pointers
2. Sliding Window
3. Prefix Sum
4. Kadane's Algorithm
5. Binary Search
6. Cyclic Sort
7. Merge Intervals
8. Monotonic Stack
9. Hash Map Lookup
10. Sorting + Greedy

♻️ Repost to help others in your network

[Neo Kim @systemdesignone]

If you're serious about system design (in 2026), learn these 26 case studies:

1 How Stock Exchange Works:
↳ https://t.co/iFNSX9TM9O

2 How YouTube Works:
↳ https://t.co/kHk3g6jz6t

3 How Kafka Works:
↳ https://t.co/8rOy9KgCMo

4 How Google Docs Works:
↳ https://t.co/W57IkAjXpT

5 How URL Shortener Works:
↳ https://t.co/tGndgdhH0V

6 How WhatsApp Works:
↳ https://t.co/VScq8QwHMr

7 How Airbnb Works:
↳ https://t.co/Bi5SAjfv5S

8 How Spotify Works:
↳ https://t.co/BxrH3oHIFS

9 How Slack Works:
↳ https://t.co/eIo29uOQOJ

10 How Reddit Works:
↳ https://t.co/o6Pw2hhj3T

11 How Bluesky Works:
↳ https://t.co/2rLYlRlky0

12 How Tinder Works:
↳ https://t.co/4E1zfgfvlw

13 How Twitter Timeline Works:
↳ https://t.co/pF2RYmPaIG

14 How Uber Finds Nearby Drivers:
↳ https://t.co/kJ2t8dtmch

15 How Pastebin Works:
↳ https://t.co/8NSUNlYM7q

16 How Amazon S3 Works:
↳ https://t.co/iReWAEHwmj

17 How Do Apple AirTags Work:
↳ https://t.co/upWcgsXwKh

18 How LLMs Actually Work:
↳ https://t.co/5lCKxq2g4N

19 How Uber Computes ETA:
↳ https://t.co/hw1hYJqQmj

20 How Real Time Leaderboard Works:
↳ https://t.co/HEChNTOHWb

21 How ChatGPT Apps Work:
↳ https://t.co/BJTYYnAwO1

22 How Nginx Works:
↳ https://t.co/JTeQTJvyrf

23 How ChatGPT Works:
↳ https://t.co/TZYZ3iddYH

24 How Meta Serverless Works:
↳ https://t.co/NSt6jovxu5

25 How YouTube Was Able to Support 2.49 Billion Users With MySQL:
↳ https://t.co/4VDJ5cs6fL

26 How Google Search Works:
↳ https://t.co/jwOaC4bhnv

What else should make this list?

===

👋 PS - Want my System Design Playbook for FREE?

Join my newsletter with 200K+ software engineers now:
→ https://t.co/ByOFTtOihX

===

1 Save & RT to help other software engineers ace system design.
2 Follow @systemdesignone + turn on notifications.