Jefferson Lima

[Alex Xu @alexxubyte]

Latency Numbers Every Developer Should Know, Explained in 6 Minutes

[Jefferson Lima @Giibinha]

@guilherme_grimm @RealGalego @meunomeebero @samsantosb Conteúdo muito bom e justificamente onde preciso melhorar, as entrevistas estão chegando, porém sem aprovações até o momento.

[Jefferson Lima @Giibinha]

@apokaiser26 Pode ser filha de bixo foi demais pra pqp kkkkkkkkkkkkkk

[Jefferson Lima @Giibinha]

@BolhaDevs Muito bom isso, vou tentar usar.

[Jefferson Lima @Giibinha]

@BolhaDevs Massa demais

[Jefferson Lima @Giibinha]

@e_opore Very good, I loved it.

[Dhanian 🗯️ @e_opore]

MASTERING PLAN FOR SYSTEM DESIGN CASE STUDIES

System Design Case Studies teach you how real-world applications are architected to handle millions of users, massive amounts of data, high availability, and scalability. This mastering plan takes you from analyzing simple architectures to designing production-grade distributed systems used by the world's largest technology companies.

STEP 1: SYSTEM DESIGN FUNDAMENTALS

→ Functional vs Non-Functional Requirements
→ Scalability Basics
→ Availability and Reliability
→ Latency and Throughput
→ CAP Theorem
→ Trade-offs in System Design

Build a strong foundation before analyzing large-scale systems.
STEP 2: URL SHORTENER (TINYURL / https://t.co/eKhgH9TFSA)
→ Requirements gathering
→ API design
→ Database schema
→ URL generation strategies
→ Caching with Redis
→ Scaling the service
Learn the fundamentals of designing highly scalable web services.
STEP 3: SOCIAL MEDIA FEED (FACEBOOK / X)
→ News feed generation
→ Timeline architecture
→ Fan-out on Write vs Fan-out on Read
→ Ranking algorithms
→ Feed caching
→ Infinite scrolling

Understand how large social platforms deliver personalized content.

STEP 4: CHAT APPLICATION (WHATSAPP)

→ Real-time messaging
→ WebSockets
→ Message delivery guarantees
→ Online presence
→ Group messaging
→ Message synchronization

Design highly available real-time communication systems.
STEP 5: VIDEO STREAMING PLATFORM (YOUTUBE)
→ Video upload pipeline
→ Transcoding architecture
→ CDN integration
→ Recommendation systems
→ Video metadata storage
→ Streaming optimization
Learn how massive media platforms serve billions of videos.
STEP 6: RIDE SHARING SYSTEM (UBER)
→ Driver matching
→ Geo-location indexing
→ ETA calculation
→ Surge pricing
→ Trip lifecycle
→ Real-time tracking

Understand location-based distributed systems.

STEP 7: E-COMMERCE PLATFORM (AMAZON)

→ Product catalog
→ Inventory management
→ Shopping cart design
→ Checkout workflow
→ Order processing
→ Recommendation engine

Build scalable commerce architectures.
STEP 8: PAYMENT SYSTEM (STRIPE / PAYPAL)
→ Payment workflow
→ Transaction consistency
→ Idempotency keys
→ Fraud detection basics
→ Webhooks
→ Payment reconciliation
Design secure and reliable financial systems.
STEP 9: SEARCH ENGINE (GOOGLE)
→ Crawling architecture
→ Indexing pipeline
→ Ranking algorithms
→ Query processing
→ Distributed storage
→ Search optimization

Explore the architecture behind large-scale search systems.

STEP 10: FILE STORAGE SYSTEM (GOOGLE DRIVE / DROPBOX)

→ File uploads
→ Metadata management
→ Chunked uploads
→ File synchronization
→ Version control
→ Storage optimization

Learn distributed storage design principles.
STEP 11: NOTIFICATION SYSTEM
→ Push notifications
→ Email delivery
→ SMS integration
→ Notification queues
→ Retry mechanisms
→ User preferences
Reliable notifications improve user engagement.
STEP 12: ONLINE COLLABORATION SYSTEM
→ Real-time document editing
→ Operational Transformation (OT)
→ CRDT basics
→ Conflict resolution
→ Version history
→ Presence indicators

Study collaborative distributed systems.

STEP 13: MICROSERVICES CASE STUDIES

→ Service decomposition
→ API Gateway
→ Service discovery
→ Event-driven communication
→ Distributed tracing
→ Saga Pattern

Modern applications increasingly rely on microservices.
STEP 14: OBSERVABILITY AND PRODUCTION SYSTEMS
→ Monitoring dashboards
→ Centralized logging
→ Metrics collection
→ Distributed tracing
→ Alerting systems
→ Incident response
Production systems require continuous monitoring and visibility.
STEP 15: INTERVIEW PREPARATION AND ARCHITECTURE REVIEWS
→ FAANG-style system design interviews
→ Requirement clarification
→ Capacity estimation
→ Bottleneck analysis
→ Architecture trade-offs
→ End-to-end design discussions

Real-world case studies build confidence for interviews and production engineering.

SYSTEM DESIGN CASE STUDIES HANDBOOK

Get the complete System Design Case Studies Handbook with detailed architecture breakdowns, scalability strategies, distributed system concepts, real-world case studies, interview preparation, and production-ready design patterns:

https://t.co/ec73poyTBr

[Jefferson Lima @Giibinha]

@AlexsieR4 A pergunta que me fica é como a BMW faz uma vela que quebra ao meio?

[Jefferson Lima @Giibinha]

@SahilExec AI Engineer

[Dhanian 🗯️ @e_opore]

10 Best Practices for System Design.

Grab System Design Ebook: https://t.co/1pR2xx1I3p

[Dhanian 🗯️ @e_opore]

20 concepts to master Building AI Agents from Scratch:

𝟏. 𝐋𝐋𝐌 𝐅𝐮𝐧𝐝𝐚𝐦𝐞𝐧𝐭𝐚𝐥𝐬: Tokens, Context Windows
𝟐. 𝐏𝐫𝐨𝐦𝐩𝐭 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠: Instructions, Few-Shot Learning
𝟑. 𝐀𝐠𝐞𝐧𝐭 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: Planning, Reasoning, Execution
𝟒. 𝐓𝐨𝐨𝐥 𝐂𝐚𝐥𝐥𝐢𝐧𝐠: APIs, External Functions
𝟓. 𝐌𝐞𝐦𝐨𝐫𝐲: Short-Term, Long-Term Context
𝟔. 𝐑𝐀𝐆: Retrieval-Augmented Generation
𝟕. 𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬: Embeddings, Semantic Search
𝟖. 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠𝐬: Text Representation
𝟗. 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: Multi-Step Automation
𝟏𝟎. 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Agent Collaboration
𝟏𝟏. 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐂𝐚𝐥𝐥𝐢𝐧𝐠: Structured Outputs
𝟏𝟐. 𝐀𝐈 𝐆𝐮𝐚𝐫𝐝𝐫𝐚𝐢𝐥𝐬: Safety, Validation
𝟏𝟑. 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭: Token Optimization
𝟏𝟒. 𝐀𝐠𝐞𝐧𝐭 𝐌𝐞𝐦𝐨𝐫𝐲: User Preferences, History
𝟏𝟓. 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Logs, Traces, Metrics
𝟏𝟔. 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧: Accuracy, Reliability
𝟏𝟕. 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐏𝐥𝐚𝐧𝐧𝐢𝐧𝐠: Task Decomposition
𝟏𝟖. 𝐌𝐨𝐝𝐞𝐥 𝐑𝐨𝐮𝐭𝐢𝐧𝐠: Choosing the Right LLM
𝟏𝟗. 𝐀𝐠𝐞𝐧𝐭 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: APIs, Cloud, Containers
𝟐𝟎. 𝐀𝐠𝐞𝐧𝐭 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲: Authentication, Permissions

📘 Grab the Building AI Agents from Scratch Ebook: https://t.co/SWCKKtbPNm

[Dhanian 🗯️ @e_opore]

MASTERING PLAN FOR API DESIGN

STEP 1: UNDERSTAND API FUNDAMENTALS

→ What an API is and how it works
→ Types of APIs (REST, GraphQL, gRPC, SOAP)
→ Request-response lifecycle
→ HTTP fundamentals
→ API architecture basics
→ API-first development

Build a strong foundation before designing APIs.

STEP 2: MASTER HTTP PROTOCOL

→ HTTP methods (GET, POST, PUT, PATCH, DELETE)
→ Status codes
→ Headers and cookies
→ Content types (JSON, XML, Form Data)
→ URL structure
→ HTTPS and TLS basics

HTTP is the backbone of modern APIs.

STEP 3: DESIGN RESTFUL APIS

→ REST principles
→ Resource-based URLs
→ Naming conventions
→ CRUD operations
→ Versioning strategies
→ Idempotency

REST remains the most widely used API architecture.

STEP 4: REQUEST AND RESPONSE DESIGN

→ JSON request bodies
→ Response structures
→ Pagination
→ Filtering and sorting
→ Searching resources
→ Standardized error responses

Consistent APIs are easier to consume and maintain.

STEP 5: AUTHENTICATION AND AUTHORIZATION

→ API keys
→ JWT authentication
→ OAuth 2.0
→ OpenID Connect basics
→ Role-Based Access Control (RBAC)
→ Session vs token authentication

Security is essential for every API.

STEP 6: API VALIDATION AND ERROR HANDLING

→ Input validation
→ Data sanitization
→ Validation libraries
→ Error handling strategies
→ Error codes and messages
→ Exception handling

Reliable APIs gracefully handle invalid requests.

STEP 7: DATABASE DESIGN FOR APIS

→ SQL vs NoSQL databases
→ Database relationships
→ Efficient querying
→ Transactions
→ Data consistency
→ ORM best practices

Database design directly impacts API performance.

STEP 8: PERFORMANCE AND SCALABILITY

→ Caching strategies
→ Redis integration
→ Rate limiting
→ Compression
→ Asynchronous processing
→ Load balancing basics

Scalable APIs support growing applications.

STEP 9: API DOCUMENTATION

→ OpenAPI (Swagger)
→ API specifications
→ Interactive documentation
→ Example requests and responses
→ SDK generation
→ Developer experience (DX)

Good documentation makes APIs easy to adopt.

STEP 10: TESTING APIS

→ Unit testing
→ Integration testing
→ API testing with Postman
→ Automated testing
→ Mock servers
→ Performance testing

Testing ensures reliability before deployment.

STEP 11: API SECURITY

→ HTTPS everywhere
→ CORS configuration
→ Preventing SQL injection
→ Preventing XSS and CSRF
→ Secrets management
→ API security best practices

Secure APIs protect users and data.

STEP 12: DEPLOYMENT AND MONITORING

→ API gateways
→ Reverse proxies
→ Docker deployment
→ Kubernetes basics
→ Logging and monitoring
→ CI/CD for APIs

Production APIs require strong deployment strategies.

STEP 13: ADVANCED API ARCHITECTURES

→ GraphQL fundamentals
→ gRPC basics
→ WebSockets
→ Event-driven APIs
→ Webhooks
→ Microservices communication

Modern applications often use multiple API styles.

STEP 14: BUILD REAL-WORLD PROJECTS

→ Authentication API
→ E-commerce API
→ Blog API
→ Payment API integration
→ Chat application backend
→ Microservices API system

Projects transform knowledge into practical expertise.

STEP 15: INTERVIEW PREPARATION AND PRODUCTION SKILLS

→ API design interview questions
→ System design fundamentals
→ Performance optimization scenarios
→ Security best practices
→ Debugging production APIs
→ Real-world architecture discussions

Practical experience is the key to mastering API design.

---

API DESIGN HANDBOOK

Get the complete API Design Handbook with deep explanations, REST best practices, authentication strategies, scalable architectures, security techniques, and production-ready API development:

https://t.co/t2KOeauy6O

[Dhanian 🗯️ @e_opore]

Day 51 of Learning Backend Engineering.

Monitoring and Health Check Endpoints.

Grab the Backend Engineering Ebook: https://t.co/qQcR0x4fAo

[Dhanian 🗯️ @e_opore]

𝗗𝗮𝘆 𝟯𝟯/𝟲𝟬 𝗼𝗳 𝗦𝗤𝗟 𝗦𝗲𝗿𝗶𝗲𝘀 — 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗼𝘁𝗮𝗹𝘀 𝗮𝗻𝗱 𝗠𝗼𝘃𝗶𝗻𝗴 𝗔𝘃𝗲𝗿𝗮𝗴𝗲𝘀 – 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀

Businesses rarely care about a single row of data.

They care about trends—how sales are growing over time, how revenue accumulates, and how averages change from one period to the next.

SQL window functions make these analyses simple without writing complex subqueries.

Today, you'll learn how to calculate running totals and moving averages like a data analyst 👇

1️⃣ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗼𝘁𝗮𝗹?
A running total (also called a cumulative sum) continuously adds values as you move through ordered rows.
Example:

SELECT order_date,
amount,
SUM(amount) OVER (
ORDER BY order_date
) AS running_total
FROM orders;

Each row includes the total of all previous rows plus the current one.
2️⃣ 𝗪𝗵𝘆 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗼𝘁𝗮𝗹𝘀 𝗮𝗿𝗲 𝗨𝘀𝗲𝗳𝘂𝗹
Running totals help answer questions like:
• Total sales so far this month
• Bank account balances
• Website visitors over time
• Inventory tracking
• Revenue growth
They provide cumulative insights without multiple queries.
3️⃣ 𝗛𝗼𝘄 𝗢𝗥𝗗𝗘𝗥 𝗕𝗬 𝗔𝗳𝗳𝗲𝗰𝘁𝘀 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗼𝘁𝗮𝗹𝘀
The ORDER BY clause determines the sequence in which values are accumulated.
Changing the sort order changes the running total.
Always order by a meaningful column such as a date, timestamp, or transaction ID.
4️⃣ 𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗧𝗼𝘁𝗮𝗹𝘀 𝘄𝗶𝘁𝗵 𝗣𝗔𝗥𝗧𝗜𝗧𝗜𝗢𝗡 𝗕𝗬
You can calculate separate running totals for each group.

Example:

SELECT department,
employee_name,
salary,
SUM(salary) OVER (
PARTITION BY department
ORDER BY employee_name
) AS department_total
FROM employees;
Each department maintains its own cumulative total.

5️⃣ 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗠𝗼𝘃𝗶𝗻𝗴 𝗔𝘃𝗲𝗿𝗮𝗴𝗲?
A moving average calculates the average over a sliding window of rows.
It smooths short-term fluctuations and highlights longer-term trends.
6️⃣ 𝗠𝗼𝘃𝗶𝗻𝗴 𝗔𝘃𝗲𝗿𝗮𝗴𝗲 𝗘𝘅𝗮𝗺𝗽𝗹𝗲
SELECT order_date,
amount,
AVG(amount) OVER (
ORDER BY order_date
ROWS BETWEEN 2 PRECEDING
AND CURRENT ROW
) AS moving_average
FROM orders;
This calculates a 3-row moving average using the current row and the previous two rows.

7️⃣ 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝗿𝗮𝗺𝗲𝘀
The ROWS BETWEEN clause defines which rows are included in the calculation.

Common window frames include:
• CURRENT ROW
• 1 PRECEDING
• 2 PRECEDING
• UNBOUNDED PRECEDING
• UNBOUNDED FOLLOWING

Choosing the right frame determines how much data each calculation uses.

8️⃣ 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗨𝘀𝗲 𝗖𝗮𝘀𝗲𝘀
Running totals and moving averages are widely used in:

• Financial dashboards
• Sales reporting
• Stock market analysis
• Website traffic monitoring
• Forecasting
• Business intelligence
9️⃣ 𝗪𝗵𝘆 𝗪𝗶𝗻𝗱𝗼𝘄 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗔𝗿𝗲 𝗕𝗲𝘁𝘁𝗲𝗿
Without window functions, running totals often require self-joins or nested subqueries.

Window functions produce cleaner, faster, and more maintainable SQL.

🔟 𝗠𝗮𝘀𝘁𝗲𝗿 𝗧𝗵𝗲𝘀𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀
Running totals and moving averages are core techniques in data analytics.
Whether you're building reports, dashboards, or forecasting models, these advanced window functions will become part of your everyday SQL toolkit.
💡 𝗞𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆:
Running totals calculate cumulative values over ordered data, while moving averages smooth fluctuations by averaging values across a sliding window.
Combined with PARTITION BY, ORDER BY, and window frames, these techniques enable powerful analytical queries with concise, efficient SQL.
Grab SQL Playbook:
https://t.co/0bRVxTZBbK
Have you used running totals or moving averages in a real-world project? What was your use case?

[Nikki Siapno @NikkiSiapno]

CI/CD Pipeline explained.

[Jefferson Lima @Giibinha]

@SahilExec I would queue it to process email sending asynchronously.

[Jefferson Lima @Giibinha]

@SahilExec Because this will update all records, not just a specific one.

[Dhanian 🗯️ @e_opore]

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐄𝐯𝐞𝐧𝐭-𝐃𝐫𝐢𝐯𝐞𝐧 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 (𝐄𝐃𝐀)?

DEFINITION

-> Event-Driven Architecture (EDA) is a software architecture pattern where applications communicate by producing and consuming events

-> An event represents a significant change or action that occurs within a system

-> EDA allows systems to react to events asynchronously without tight coupling

CORE IDEA OF EDA

-> Systems communicate through events instead of direct requests

-> Event producers generate events when something happens

-> Event consumers react automatically when events are received

-> Multiple consumers can process the same event independently

WHY EDA IS NEEDED

-> Modern applications require real-time communication

-> Reduces dependencies between different services

-> Supports highly scalable distributed systems

-> Enables faster response to business events

EXAMPLE

-> A customer places an online order

-> The Order Service publishes an "Order Created" event

-> Payment Service processes the payment

-> Inventory Service updates stock

-> Shipping Service prepares delivery

-> Notification Service sends a confirmation email

KEY COMPONENTS OF EDA

EVENT

-> A record describing something that happened in the system

-> Examples include Order Created, Payment Completed, and User Registered

EVENT PRODUCER

-> Generates and publishes events

-> Does not know which services will consume the events

EVENT CONSUMER

-> Listens for events and performs specific actions

-> Multiple consumers can subscribe to the same event

EVENT BROKER

-> Receives, stores, and distributes events

-> Examples include Apache Kafka, RabbitMQ, AWS EventBridge, and Azure Event Grid

EVENT CHANNEL

-> A communication pathway used to transfer events between producers and consumers

HOW EDA WORKS

STEP 1: EVENT OCCURS

-> A business action triggers an event

STEP 2: EVENT IS PUBLISHED

-> The producer sends the event to an event broker

STEP 3: EVENT ROUTING

-> The broker distributes the event to subscribed consumers

STEP 4: EVENT PROCESSING

-> Consumers process the event independently

STEP 5: BUSINESS ACTIONS

-> Consumers perform their required tasks

STEP 6: NEW EVENTS

-> Consumers may publish additional events to continue the workflow

CHARACTERISTICS OF EDA

-> Loose Coupling

-> Asynchronous Communication

-> Scalability

-> High Availability

-> Event-Based Communication

-> Fault Tolerance

-> Real-Time Processing

COMMON EDA TECHNOLOGIES

-> Apache Kafka

-> RabbitMQ

-> Apache Pulsar

-> AWS EventBridge

-> Azure Event Grid

-> Google Pub/Sub

-> Redis Streams

ADVANTAGES OF EDA

-> Real-time processing

-> High scalability

-> Better fault isolation

-> Loose coupling

-> Faster system responsiveness

-> Easier integration

-> Supports distributed systems

CHALLENGES OF EDA

-> Increased architectural complexity

-> Event ordering challenges

-> Duplicate event handling

-> Monitoring distributed systems

-> Event consistency issues

-> Debugging asynchronous workflows

EDA VS REQUEST-RESPONSE ARCHITECTURE

EDA

-> Event-based communication

-> Asynchronous processing

-> Highly scalable

-> Loosely coupled services

REQUEST-RESPONSE

-> Direct communication

-> Synchronous processing

-> Immediate responses

-> Tightly connected interactions

EDA VS MICROSERVICES

EDA

-> Communication through events

-> Services react independently

-> Ideal for real-time workflows

MICROSERVICES

-> Architectural style for building applications

-> Services communicate using APIs or events

-> Focuses on independent deployment

REAL-WORLD APPLICATIONS OF EDA

-> Banking Systems

-> E-Commerce Platforms

-> IoT Applications

-> Stock Trading Platforms

-> Ride-Sharing Applications

-> Healthcare Systems

-> Logistics and Supply Chain Systems

Grab the Software Architectures Ebook:

https://t.co/rQ5uqOjgfE

[Jefferson Lima @Giibinha]

@e_opore Amazing, what is the difference between EDA and asynchronous communication using queues?

[Jefferson Lima @Giibinha]

Redis Clearly Explained https://t.co/Aa35UaRfju