AI has become deeply integrated into our daily livesfrom writing code and summarizing documents to analyzing data and automating workflows. With this rise, one fear keeps growing:
Are we becoming less intelligent because of AI? And will AI eventually replace humans entirely?

The truth is simple:
AI can weaken our abilities but only when we outsource our thinking to it.
Used correctly, it doesn’t replace human intelligence; it amplifies it.

1. AI as a cheat sheet really does make us weaker

When we let AI think for us, we slowly lose the ability to:

• analyze problems independently
• make decisions under pressure
• retain complex ideas
• solve unfamiliar tasks creatively

It’s the same phenomenon as overusing a calculator:
If you use it for 2+2, you lose basic arithmetic.
If you use it for advanced calculations, your abilities grow.

AI works the same way.
If we stop thinking, we decline.
If we learn with it, we grow.

2. AI will not replace fields where human judgment is essential

People often claim that AI will replace pilots, surgeons, engineers, lawyers.
This is unrealistic and misunderstands how real-world systems work.

✈️ A plane still needs a human pilot

Autopilot works perfectly until something unexpected happens.
In critical moments, decision-making moves from algorithms to humans.
Responsibility and intuition matter and AI doesn’t have either.

🏥 A surgeon cannot be replaced

AI tools can guide, analyze, predict, and assist.
But the act of performing surgery still requires human hands, skill, empathy, and real-time judgment.
Medicine is not just formulas it is human life.

⚖️ Human decisions involve morality, emotion, and context

AI can process data, but cannot bear responsibility or navigate ethical trade-offs.
That’s why people remain essential in every high-stakes domain.

3. When used correctly, AI becomes an amplifier not a replacement

AI should function as:

• a coach, not a crutch
• a thinking partner, not a brain substitute
• an accelerator, not an escape from effort
• a second mind, not the only mind

When AI handles the repetitive work, we gain more time and mental energy for:

• strategy
• creativity
• problem-solving
• innovation

That’s where human value increases.

4. AI won’t eliminate jobs it will eliminate outdated versions of jobs

Pilots will still exist — but they will operate AI-powered systems.
Surgeons will still exist — but with robotic assistants.
Developers will still exist — but they will design systems that collaborate with AI agents.

The winners of the future are not those who resist AI,
but those who learn to work with it.

We only “get dumber” when we stop learning

AI is not the threat.
The real threat is replacing thinking with copy-paste.

If we use AI to:

• deepen understanding rather than skip explanations
• explore concepts, not avoid them
• enhance our ideas rather than replace them
• analyze, not simply retrieve

then we grow stronger, not weaker.

AI becomes a tool for mastery, not a shortcut.

Conclusion

AI is neither a danger nor a savior.
It is a tool — powerful, transformative, and neutral.

• Use it as a cheat sheet → you decline.
• Use it as a partner → you evolve.

Humans remain essential — in the cockpit, in the operating room, in engineering, in leadership, in decision-making.
Because the human factor is not a flaw.
It is our greatest competitive advantage.

💡 How It Started

As a Laravel backend developer, I’ve always admired how MailerLite builds simple yet powerful tools for developers and marketers.
When I explored their PHP SDK, I noticed that it was missing a few modern features particularly PSR-18/PSR-17 support and e-commerce endpoints for Products, Orders, Customers, and Carts.

That’s where my contribution began.

🧩 What I Built

The goal was to make the SDK more flexible, testable, and aligned with PHP standards.
Here’s what the PR introduced:

• ✅ PSR-18/PSR-17 compliant bridge (HttpLayerPsr, HttpLayerPsrBridge)
• 🛒 Full E-commerce module: Products, Orders, Customers, and Carts endpoints
• 🧱 Refactored internal HTTP layer using SOLID principles and factory pattern
• 🧪 Comprehensive PHPUnit test coverage
• 📘 Updated README with examples and developer setup
• 💅 Followed PSR-12 coding style and CI/CD checks

You can view the merged pull request here:
👉 https://github.com/mailerLite/mailerlite-php/pull/33

🔍 Lessons Learned from the Review Process

The PR went through a detailed technical review by Gaurang S Engineering Team Lead at MailerLite.

His feedback helped me understand the importance of clarity and maintainability in SDK design.

A few key lessons:

• Readable control flow is more important than clever code.
• Always squash commits for clean contribution history.
• CI/CD consistency (coding standards, PSR-12, and cross-version tests) matters as much as feature logic.

That review process was one of the most valuable learning experiences I’ve had as a developer.

🤝 Why Open Source Matters

Contributing to open source gives you:

• Real-world code review experience with professional teams
• Insight into how large projects maintain consistency and stability
• A public portfolio that speaks louder than a CV
• And, most importantly, the joy of seeing your code shipped in production 🎉
• When my code went live in version v1.0.5

🔗 Final Thoughts

Open source isn’t just about “free software.”
It’s about connecting with people who care about quality, architecture, and collaboration just like MailerLite’s engineering team does.

I’m grateful to the maintainers for their feedback and to the open-source community for inspiring me to keep improving.
If you’re considering your first contribution start small, learn the process, and be patient. Every line of code counts.

✍️ About Me

I’m Ismoil Shifoev, a backend engineer specializing in Laravel, clean architecture, and SOLID principles
I’m passionate about building reliable systems, contributing to open source, and exploring the intersection of software engineering and innovation.

📍 GitHub: @ishifoev

💼 LinkedIn: Ismoil Shifoev

The idea was to improve how the tool displays license compliance results,
so I implemented JSON and Text output formatters with full test coverage and Psalm support.

A few days later, the maintainers merged it into the main branch
and my code became part of the new official release:
👉 v2.1 Changelog

This was a small step in code but a huge step for me as a developer.
Open source teaches you real collaboration: code reviews, clean architecture,
and the importance of writing something the whole community can rely on.

Now my contribution helps teams around the world check open-source license compliance more efficiently.
And that’s the best feeling in software engineering 💡

🧩 Related work

If you’re working with Laravel and databases,
check out my own package — ishifoev/laravel-extended-grammars

🧩 A story about contribution, rejection — and turning an idea into a reusable Laravel package.

A few days ago, I opened a pull request to the Laravel framework

💡 The Idea

I wanted to make something like this possible:

DB::table('users')->insertOrUpdateUsing(
    ['id', 'name', 'email'],
    DB::table('imports')->select('id', 'name', 'email'),
    ['name', 'email']
);

which generates:

✅ MySQL

INSERT INTO users (id, name, email)
SELECT id, name, email FROM imports
ON DUPLICATE KEY UPDATE
    name = VALUES(name),
    email = VALUES(email);

✅ PostgreSQL

INSERT INTO users (id, name, email)
SELECT id, name, email FROM imports
ON CONFLICT (id) DO UPDATE SET
    name = EXCLUDED.name,
    email = EXCLUDED.email;

✅ SQLite

INSERT INTO users (id, name, email)
SELECT id, name, email FROM imports
ON CONFLICT(id) DO UPDATE SET
    name = excluded.name,
    email = excluded.email;

⚙️ Submitting the Pull Request

I implemented the grammar extensions for all three drivers — MySQL, Postgres, and SQLite and sent a PR to the Laravel core.

But a few days later, Taylor Otwell replied

that while the idea was great, they wanted to keep the core minimal to reduce maintenance load.

And that’s completely understandable.

🧱 Turning Rejection into a Package

Instead of letting the idea die, I decided to turn it into a reusable open-source package
so that the Laravel community could still use it.

That’s how ishifoev/laravel-extended-grammars was born.

It extends Laravel’s database grammars and adds the insertOrUpdateUsing() method
to both Eloquent models and Query Builder (DB::table()).

🧩 How It Works

You can use it like this:

use Illuminate\Support\Facades\DB;
use App\Models\User;

DB::table('users')->insertOrUpdateUsing(
    ['id', 'name', 'email'],
    DB::table('imports')->select('id', 'name', 'email'),
    ['name', 'email']
);

// or with Eloquent:
User::insertOrUpdateUsing(
    ['id', 'name', 'email'],
    DB::table('imports')->select('id', 'name', 'email'),
    ['name', 'email']
);

It works seamlessly with Laravel 10, 11, and 12
and supports MySQL, PostgreSQL, and SQLite drivers.

🔧 Installation

composer require ishifoev/laravel-extended-grammars

That’s it — it automatically registers via Laravel’s auto-discovery.

🧠 Implementation Details

The package introduces:

• A custom grammar for each database engine
  (MySqlGrammar, PostgresGrammar, SQLiteGrammar)
• A SupportsInsertOrUpdateUsing trait for models
• A macro on the Query\Builder to extend DB::table()

Under the hood, it uses the compileInsertUsing() method already in Laravel,
then extends it with SQL dialect–specific ON DUPLICATE KEY / ON CONFLICT logic.

🧪 Testing

Each grammar has its own unit test:

• ✅ MySQL: ON DUPLICATE KEY UPDATE
• ✅ PostgreSQL: ON CONFLICT DO UPDATE
• ✅ SQLite: ON CONFLICT(id) DO UPDATE SET

All tests run through Docker and GitHub Actions with Laravel 10–12.

🌍 Open Source Release

The package is published on Packagist and GitHub:

🔗 Packagist: ishifoev/laravel-extended-grammars

🔗 GitHub: github.com/ishifoev/laravel-extended-grammars

It follows PSR-12, SOLID, and includes:

• ✅ PHPUnit tests
• ✅ Psalm static analysis
• ✅ Laravel Pint code style
• ✅ CI-ready Dockerfile

💬 Reflection: Lessons Learned

Submitting code to Laravel taught me a few things:

1. Keep core lean — not every great feature belongs in the framework.
2. Packages are power — Laravel’s ecosystem thrives on open-source extensions.
3. Don’t take “no” as failure — a rejected PR can become something even bigger.

Sometimes, a “no” from Taylor Otwell is actually the beginning of your first open-source release. 😄

🧑‍💻 About the Author

Ismoil Shifoev
Laravel Developer @ Alif Bank
Open-source contributor, backend engineer, and builder of developer tools.

🔗 GitHub

🔗 Packagist

🔗 LinkedIn

🔗 Medium

❤️ Conclusion

What started as a pull request to the Laravel core turned into a standalone, community-driven tool.
That’s the beauty of open source.

If you’ve ever had an idea that didn’t make it into a framework — 
don’t stop there. Make it a package. Share it with the world.
You might help thousands of other developers.

#Laravel #PHP #OpenSource #SoftwareEngineering #Eloquent #BackendDevelopment #ORM

1. Architecture Designed for High Throughput

Kafka is fundamentally designed as a distributed streaming platform. Unlike RabbitMQ, which is primarily designed as a message broker using queues and exchanges, Kafka stores data in append-only logs across partitions. This append-only architecture allows Kafka to handle extremely high volumes of messages with minimal latency.

RabbitMQ relies heavily on message acknowledgment and queuing, which introduces overhead, especially at scale. Kafka’s streamlined, commit-log based system allows it to write and read data sequentially from disk, significantly improving I/O performance.

2. Batch Processing and Sequential Writes

Kafka maximizes performance through batch processing. Instead of sending each message individually, Kafka producers can batch multiple messages together before sending them over the network. This reduces network overhead and increases throughput dramatically.

RabbitMQ typically handles messages individually, adding overhead due to repeated network operations, acknowledgments, and higher CPU usage per message.

3. Persistent Storage and Disk Performance

Kafka efficiently leverages disk storage by writing data sequentially. Sequential writes are fast on modern hardware, making Kafka’s throughput approach or even surpass network speed in many scenarios. Kafka achieves durability by leveraging the operating system’s file system cache effectively.

In contrast, RabbitMQ primarily relies on in-memory message handling for speed, and when persistence is enabled, it incurs additional overhead, resulting in slower disk I/O compared to Kafka’s optimized disk operations.

4. Partitioning and Parallelism

Kafka uses partitions within topics to distribute data across multiple brokers, enabling parallel processing by multiple consumers. This inherent parallelism dramatically increases Kafka’s message processing capabilities.

RabbitMQ, on the other hand, does not natively support partitioning and parallelism in the same straightforward manner. Although it provides clustering, its routing mechanisms require additional overhead, reducing efficiency compared to Kafka’s straightforward partitioning model.

5. Zero-Copy Transfer and Efficient Networking

Kafka utilizes zero-copy transfer, a technique that moves data directly from the file system cache to network sockets without involving the application layer. This significantly reduces CPU usage and improves throughput.

RabbitMQ typically moves data through multiple layers, increasing CPU utilization and reducing maximum throughput compared to Kafka’s efficient data transfer mechanisms.

Final Thoughts

While both Kafka and RabbitMQ have their place in software architecture, Kafka excels in applications that demand extremely high throughput and low latency. Its optimized disk usage, efficient batching, partitioned structure, and zero-copy transfers give it a clear performance advantage for streaming large volumes of data rapidly.

Choosing Kafka for scenarios involving big data, real-time analytics, or event streaming provides a robust foundation for performance-intensive applications, ensuring your infrastructure can scale reliably to meet growing demands.

But there’s a myth spreading with this wave:
That AI will replace developers.

In truth? It will amplify skilled engineers — and quietly expose those lacking core understanding.

AI is a Super Intern — Not a CTO
AI can suggest code, generate classes, and even design DB schemas.
But it can’t understand why your logic is flawed, or what the business really needs. It can’t review code for maintainability or scalability.

You still need human judgment.

Laravel: Already High-Level, Now Turbocharged with AI
Laravel gives you routing, Eloquent ORM, queues, middleware, service containers — a full toolkit.

Add AI on top, and yes, it feels like you’re coding at warp speed.

But if you:

• don’t understand how queries work,
• rely blindly on AI-generated code,
• skip tests or misuse architecture…

You’re building fast — but fragile.

The Risk for Junior Developers
Why learn data structures or request lifecycles if AI “does it for you”?
Because someday the abstraction leaks. Something breaks.
And you won’t even know where to look.

Juniors who skip fundamentals now may pay the price later.

The Future Belongs to Thinkers, Not Prompters
AI is a multiplier. If you’re strong, it makes you unstoppable.
If you’re weak, it covers it up — until it’s too late.

Want to thrive as a Laravel dev in the age of AI?
Understand first principles. Learn architecture. Write your own tests.
Then, use AI as your accelerator — not your crutch.

Introduction:

Twitter, the microblogging platform that has revolutionized online communication, operates at a scale that few other platforms can match. With 150 million active users generating a staggering 300,000 queries per second (QPS) and a constant stream of 22 MB/s of data, Twitter’s architecture must be robust, scalable, and lightning-fast. In this article, we delve into the intricate system design that enables Twitter to handle such massive loads while ensuring tweets are delivered in under 5 seconds.

1. Understanding Twitter’s Requirements:

• Examining the scale: 150 million active users, 300,000 QPS, and 22 MB/s firehose.
• Importance of real-time delivery: Twitter’s core value lies in delivering tweets instantaneously.
• Need for scalability: Twitter’s user base and activity levels continue to grow, necessitating a scalable architecture.

2. Core Components of Twitter’s Architecture: a. Microservices Architecture:

• Breaking down functionality into independently deployable services.
• Enables scalability and fault isolation. b. Distributed Data Storage:
• Utilizing distributed databases like Cassandra and Manhattan to handle massive amounts of data.
• Ensuring high availability and fault tolerance. c. Message Queues and Stream Processing:
• Leveraging technologies like Kafka for real-time data ingestion and processing.
• Facilitating efficient handling of the firehose of tweets and user interactions. d. Caching Layer:
• Employing caching solutions like Redis or Memcached to reduce latency for frequently accessed data.
• Mitigating the load on backend services. e. Load Balancers and CDN:
• Distributing incoming traffic across multiple servers for optimal performance.
• Utilizing Content Delivery Networks (CDNs) to cache and deliver static content closer to users.

3. Handling Real-Time Updates:

• Websockets and server-sent events for real-time communication with clients.
• Pushing updates to users’ timelines in milliseconds.

4. Ensuring Low Latency:

• Optimizing network latency through strategic data center placement.
• Utilizing in-memory caching and pre-computed results to minimize processing time.
• Employing techniques like sharding and replication to distribute load evenly.

5. Fault Tolerance and Disaster Recovery:

• Implementing redundancy at every layer to ensure resilience against failures.
• Regularly testing failover mechanisms and disaster recovery procedures.
• Utilizing geographically distributed data centers for disaster recovery and data replication.

6. Monitoring and Analytics:

• Comprehensive monitoring of system health and performance metrics.
• Real-time analytics to identify bottlenecks and optimize system performance.
• Utilizing tools like Prometheus, Grafana, and Elasticsearch for monitoring and analysis.

7. Evolution and Future Considerations:

• Continuous refinement of architecture to adapt to changing user demands and technological advancements.
• Exploration of emerging technologies like serverless computing, edge computing, and machine learning for further optimization.
• Scaling infrastructure horizontally and vertically to accommodate future growth.

Conclusion:

Twitter’s architecture stands as a testament to the power of scalable, distributed systems in handling massive user bases and data volumes. By leveraging microservices, distributed data storage, real-time processing, and fault-tolerant design principles, Twitter ensures that its platform remains fast, reliable, and resilient even under the most demanding conditions. As Twitter continues to evolve and grow, its architecture will undoubtedly undergo further enhancements and innovations to meet the challenges of tomorrow’s digital landscape.

Facebook Timeline is audacious in scope. It wants to compile a complete scrollable version of your life story from photos, locations, videos, status updates, and everything you do. That could be many decades of data (hopefully) that must stored and made quickly available at any point in time. A huge technical challenge, even for Facebook, which we know are experts in handing big data. And they built it all in 6 months.

Facebook’s Ryan Mack shares quite a bit of Timeline’s own implementation story in his excellent article: Building Timeline: Scaling up to hold your life story.

Five big takeaways from the article are:

• Leverage infrastructure rather than build something new. You might expect that they would pioneer a new infrastructure for Timeline, but given the short schedule, they decided to go with proven technologies inside Facebook: MySQL, Multifeed (a custom distributed system which takes the tens of thousands of updates from friends and picks the most relevant), Thrift, Memcached, Operations. The last point about the operations infrastructure is a huge win for any team. All that just works. They can concentrate on solving the problem and skip the whole tooling dance, which is why new products can be generated amazingly fast inside a “big company”, if the infrastructure is done right.
• Denormalize. Format data in the way you need to use it.
• Denormalzation, creating special purpose objects instead of distributed rows that must be joined, minimizes random IO by reducing the number of trips to the database. Caching can often get around the need for denormalization, but given the amount of timeline data and how much of it is cold, that is it will rarely be viewed, caching everything isn’t a good design.
• Timeline decides the order to display data by calculating a rank based on metadata. The denormalization process brought all that metadata together in a format that meant ranking could be done in a few IOs and streamed efficiently from the database using a primary key range query
• Timeline is like a datamart in a data warehouse. Data must be slurped up from dozens of different systems, cleaned, merged, and reformatted into a new canonical format. Facebook of course did this in a Facebook-like way. They created a custom data conversion language, they deployed hundreds of MySQL servers to extract the data out of “legacy” systems as fast as possible, they deployed flash storage to speed up joins, they created a parallelizing query proxy, and they standardized on the Multifeed data format for future flexibility.
• Keep different types of caches.
• Short term cache. A timeline of recent activity is frequently invalidated because it is changing all the time as you perform actions through your life. This cache is an in RAM row cache inside InnoDB that uses the Flashcache kernel driver to expand the OS cache onto a flash device.
• Long term cache. A query cache is kept in memcached. The results of large queries, like the ranking of all your activities in 2010, can be efficiently cached since they will rarely be invalidated.
• Run operations locally. The Timeline Aggregator (geographically clustering nearby check-ins, ranking status updates, etc) runs on each database so it can max out the disks. Only data that needs to be displayed is sent over the network.
• Parallelize development. The short 6 month development time was partly a product of the quality infrastructure, but of also running significant development activities in parallel. The development team was split into design, front-end engineering, infrastructure engineering, and data migrations. In parallel they built: UI prototypes on a test backend, production UI on a simulated backend, the scalable backend, the denormalization framework, the data warehouse, and simulated load testing.

In the realm of system design interviews, the ability to craft robust and scalable APIs is a skill highly coveted by tech companies. Effective API design not only involves technical prowess but also requires a deep understanding of system architecture, scalability concerns, and developer experience. In this article, we’ll explore the key principles of API design through the lens of system design interviews, providing concrete examples and strategies to tackle common challenges.

Understanding System Design Interviews:

System design interviews often present candidates with complex real-world scenarios and ask them to architect solutions that can handle large-scale data, traffic, and concurrency. APIs play a critical role in these designs, serving as the interface through which different components of the system interact. Therefore, candidates must demonstrate proficiency in designing APIs that are efficient, scalable, and maintainable.

Key Principles of API Design:

1. Scalability: In system design interviews, scalability is a paramount concern. APIs should be designed to handle increasing loads and growing datasets without compromising performance. For example, consider a social media platform like Twitter. The API responsible for fetching tweets should be designed to efficiently retrieve and serve large volumes of data, even during peak usage times.
2. Flexibility: Systems evolve over time, and APIs must be flexible enough to accommodate changing requirements and use cases. For instance, in an e-commerce platform, the checkout API should support various payment methods and shipping options, allowing for seamless integration with third-party services.
3. Consistency: Consistency in API design simplifies development and enhances usability. Interview candidates may be asked to design APIs that adhere to industry standards and follow consistent naming conventions. For example, in a microservices architecture, all service APIs should adopt a standardized format for request and response payloads to facilitate interoperability.
4. Security: Security is a critical consideration in API design, particularly in scenarios involving sensitive data or user authentication. Candidates may be expected to design APIs with robust authentication mechanisms, such as OAuth or JWT tokens, to prevent unauthorized access. For example, in a banking application, the API responsible for transferring funds should enforce stringent authentication and authorization checks to protect against fraud.
5. Documentation: Clear and comprehensive documentation is essential for ensuring that developers can effectively use an API. Candidates may be evaluated based on their ability to articulate API endpoints, parameters, and expected behavior. For instance, in a weather forecasting application, the API documentation should include detailed descriptions of supported endpoints, query parameters, and response formats.

Title: Designing Robust APIs: A System Design Interview Approach

Introduction: In the realm of system design interviews, the ability to craft robust and scalable APIs is a skill highly coveted by tech companies. Effective API design not only involves technical prowess but also requires a deep understanding of system architecture, scalability concerns, and developer experience. In this article, we’ll explore the key principles of API design through the lens of system design interviews, providing concrete examples and strategies to tackle common challenges.

Understanding System Design Interviews: System design interviews often present candidates with complex real-world scenarios and ask them to architect solutions that can handle large-scale data, traffic, and concurrency. APIs play a critical role in these designs, serving as the interface through which different components of the system interact. Therefore, candidates must demonstrate proficiency in designing APIs that are efficient, scalable, and maintainable.

Key Principles of API Design:

1. Scalability: In system design interviews, scalability is a paramount concern. APIs should be designed to handle increasing loads and growing datasets without compromising performance. For example, consider a social media platform like Twitter. The API responsible for fetching tweets should be designed to efficiently retrieve and serve large volumes of data, even during peak usage times.
2. Flexibility: Systems evolve over time, and APIs must be flexible enough to accommodate changing requirements and use cases. For instance, in an e-commerce platform, the checkout API should support various payment methods and shipping options, allowing for seamless integration with third-party services.
3. Consistency: Consistency in API design simplifies development and enhances usability. Interview candidates may be asked to design APIs that adhere to industry standards and follow consistent naming conventions. For example, in a microservices architecture, all service APIs should adopt a standardized format for request and response payloads to facilitate interoperability.
4. Security: Security is a critical consideration in API design, particularly in scenarios involving sensitive data or user authentication. Candidates may be expected to design APIs with robust authentication mechanisms, such as OAuth or JWT tokens, to prevent unauthorized access. For example, in a banking application, the API responsible for transferring funds should enforce stringent authentication and authorization checks to protect against fraud.
5. Documentation: Clear and comprehensive documentation is essential for ensuring that developers can effectively use an API. Candidates may be evaluated based on their ability to articulate API endpoints, parameters, and expected behavior. For instance, in a weather forecasting application, the API documentation should include detailed descriptions of supported endpoints, query parameters, and response formats.

Example Scenario: Let’s consider a system design interview scenario where you’re tasked with designing an API for a ride-sharing service similar to Uber. The API must support functionalities such as requesting rides, updating ride status, and retrieving ride history.

To address scalability concerns, you could design the API to be horizontally scalable, with load balancers distributing incoming requests across multiple instances of the ride service. Additionally, you might implement caching mechanisms to reduce database load and improve response times during peak traffic periods.

For flexibility, the API could offer configurable options for ride preferences, such as vehicle type, payment method, and ride-sharing preferences. This flexibility enables the service to cater to diverse user preferences and market demands.

Consistency in API design can be achieved by adopting RESTful principles and adhering to established naming conventions for endpoints and HTTP methods. For example, the API endpoints for requesting a ride could be /rides/request (POST), /rides/{ride_id} (GET), and /rides/{ride_id}/status (PUT).

Security measures would include implementing token-based authentication for riders and drivers, encrypting sensitive data such as payment information, and enforcing rate limiting to prevent abuse or denial-of-service attacks.

Conclusion:

In system design interviews, the ability to design robust APIs is a key differentiator for candidates vying for engineering roles at top tech companies. By understanding and applying principles such as scalability, flexibility, consistency, security, and documentation, candidates can showcase their expertise in crafting APIs that meet the demands of modern, scalable systems. Through strategic problem-solving and thoughtful design considerations, aspiring engineers can excel in system design interviews and contribute to the development of innovative and resilient software solutions.

Step 1— Propose high-level design and get buy-in
Instead of showing the high-level design diagram from the beginning, we will use a slightly
different approach. We will start with something simple: build everything in a single server.
Then, gradually scale it up to support millions of users. By doing this exercise, it will refresh
your memory about some important topics covered in the book.
Let us start with a single server setup as listed below:
• A web server to upload and download files.
• A database to keep track of metadata like user data, login info, files info, etc.
• A storage system to store files. We allocate 1TB of storage space to store files.
We spend a few hours setting up an Apache web server, a MySql database, and a directory
called drive/ as the root directory to store uploaded files. Under drive/ directory, there is a list
of directories, known as namespaces. Each namespace contains all the uploaded files for that
user. The filename on the server is kept the same as the original file name. Each file or folder
can be uniquely identified by joining the namespace and the relative path.
Figure 15–3 shows an example of how the /drive directory looks like on the left side and its
expanded view on the right side.

A resumable upload is achieved by the following 3 steps [2]:
• Send the initial request to retrieve the resumable URL.
• Upload the data and monitor upload state.
• If upload is disturbed, resume the upload.
2. Download a file from Google Drive
Example API: https://api.example.com/files/download
Params:
• path: download file path.
Example params:
{
“path”: “/recipes/soup/best_soup.txt”
}
3. Get file revisions
Example API: https://api.example.com/files/list_revisions
Params:
• path: The path to the file you want to get the revision history.
• limit: The maximum number of revisions to return.
Example params:
{
“path”: “/recipes/soup/best_soup.txt”,
“limit”: 20
}
All the APIs require user authentication and use HTTPS. Secure Sockets Layer (SSL)
protects data transfer between the client and backend servers.

Title: Revolutionizing File Management: The Design of Google Drive

In the realm of cloud storage and file management, few platforms have had as significant an impact as Google Drive. Since its inception, Google Drive has revolutionized the way individuals and organizations store, access, and collaborate on files. But what lies beneath its user-friendly interface? How was Google Drive designed to meet the diverse needs of users across the globe? Let’s delve into the design principles and features that make Google Drive a cornerstone of modern digital productivity.

User-Centric Design

At the core of Google Drive’s design philosophy is a commitment to user-centricity. From its intuitive interface to its seamless integration with other Google services, every aspect of Google Drive is meticulously crafted to enhance user experience. The design team at Google prioritizes simplicity and accessibility, ensuring that users can effortlessly navigate the platform regardless of their technical expertise.

Unified File Storage

Google Drive serves as a centralized hub for storing various file types, including documents, spreadsheets, presentations, and multimedia files. Its unified approach to file storage eliminates the need for multiple storage solutions, streamlining the organization of digital assets. Whether you’re a student managing class notes or a business professional collaborating on a project, Google Drive offers a versatile solution for storing and accessing files from any device with an internet connection.

Seamless Collaboration

One of Google Drive’s most powerful features is its collaboration capabilities. Multiple users can simultaneously edit documents, comment on files, and track changes in real-time, fostering seamless collaboration regardless of geographical location. Whether you’re co-authoring a report with colleagues or brainstorming ideas with classmates, Google Drive’s collaboration tools facilitate efficient teamwork and communication.

Robust Security Measures

In an age where data privacy is paramount, Google Drive prioritizes security to safeguard users’ sensitive information. Advanced encryption techniques protect files during transmission and storage, ensuring that only authorized individuals can access them. Additionally, Google Drive offers granular permission settings, allowing users to control who can view, edit, or share their files. With built-in safeguards against data breaches and cyber threats, users can trust Google Drive to keep their information secure.

Intelligent Search and Organization

With vast amounts of data stored on Google Drive, finding specific files can be a daunting task. However, Google Drive’s intelligent search capabilities make it effortless to locate files based on keywords, file types, and even content within documents. Moreover, Google Drive automatically organizes files into folders and categories, simplifying the browsing experience and saving users valuable time.

Integration with Third-Party Apps

Recognizing that users often rely on a variety of tools and applications to complete tasks, Google Drive seamlessly integrates with third-party apps and services. Whether you’re editing photos with Adobe Photoshop or creating diagrams with Lucidchart, Google Drive allows you to access and save files directly from your favorite productivity tools. This integration enhances workflow efficiency and empowers users to leverage the full capabilities of their preferred applications.

Continuous Innovation

Google Drive is not static; it evolves continually to adapt to changing user needs and technological advancements. The design team at Google regularly introduces new features and updates to enhance functionality, improve performance, and address emerging challenges. Whether it’s introducing offline access capabilities or enhancing mobile responsiveness, Google Drive remains at the forefront of innovation in the realm of file management.

Conclusion

Google Drive’s design represents a harmonious blend of user-centric principles, innovative features, and robust security measures. By prioritizing simplicity, collaboration, and accessibility, Google Drive has transformed the way individuals and organizations manage their digital files. With its seamless integration with other Google services, robust security measures, and commitment to continuous innovation, Google Drive continues to set the standard for cloud storage and file management platforms. As technology evolves and user needs evolve, one thing remains certain: Google Drive will continue to adapt and innovate, empowering users to work smarter and more efficiently in the digital age.