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Alex Xu is a software engineer and author. His book System Design Interview - An Insider’s Guide is one of Amazon's best-selling books in the “Distributed Systems & Computing” category.  As of December 2020, his book is being translated into 7 different languages (Traditional Chinese, Russian, Japanese, Spanish, Polish, Simplified Chinese, and Korean).

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🎁 Giving Away 100 System Design + ML System Design Books 🎁 
  
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🔹 10 signed physical books  (vol1 + vol2 + ML system design, winners get all 3) 
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𝐇𝐨𝐰 𝐭𝐨 𝐩𝐚𝐫𝐭𝐢𝐜𝐢𝐩𝐚𝐭𝐞:  
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Alex Xu

Follow me for system design & book-writing tips | Author of System Design Interview Series

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Visualizing a SQL query. 
 
SQL statements are executed by the database system in several steps, including: 
- Parsing the SQL statement and checking its validity 
- Transforming the SQL into an internal representation, such as relational algebra 
- Optimizing the internal representation and creating an execution plan that utilizes index information 
- Executing the plan and returning the results 
 
The execution of SQL is highly complex and involves many considerations, such as: 
- The use of indexes and caches 
- The order of table joins 
- Concurrency control 
- Transaction management 
 
Over to you: what is your favorite SQL statement?
 
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Backend burger :)
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Great diagram by Brij Kishore Pandey
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What distinguishes MVC, MVP, MVVM, MVVM-C, and VIPER architecture patterns from each other? 
 
These architecture patterns are among the most commonly used in app development, whether on iOS or Android platforms. Developers have introduced them to overcome the limitations of earlier patterns. So, how do they differ? 
 
🔹 MVC, the oldest pattern, dates back almost 50 years 
🔹 Every pattern has a "view" (V) responsible for displaying content and receiving user input 
🔹 Most patterns include a "model" (M) to manage business data 
🔹 "Controller," "presenter," and "view-model" are translators that mediate between the view and the model ("entity" in the VIPER pattern) 
🔹 These translators can be quite complex to write, so various patterns have been proposed to make them more maintainable 
 
Over to you: keep in mind that this is not an exhaustive list of architecture patterns. Other notable patterns include Flux and Redux. How do they compare to the ones mentioned here? 
 
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Almost every software engineer has used Git before, but only a handful  know how it works :) 
 
To begin with, it's essential to identify where our code is stored. The common assumption is that there are only two locations - one on a remote server like Github and the other on our local machine. However, this isn't entirely accurate. Git maintains three local storages on our machine, which means that our code can be found in four places: 
 
🔹Working directory: where we edit files 
🔹Staging area: a temporary location where files are kept for the next commit 
🔹Local repository: contains the code that has been committed 
🔹Remote repository: the remote server that stores the code 
 
Most Git commands primarily move files between these four locations. 
 
Over to you: Do you know which storage location the "git tag" command operates on? This command can add annotations to a commit. 
 
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Debugging is extremely important for software engineers, but it's never taught in school. 
 
Here are some top debugging techniques you can follow.  

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Google Sheet + ChatGPT: LinkedIn messages are about to 100x :(
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What are the differences between a load balancer and an API gateway? 
 
First, let's clarify some concepts before discussing the differences. 
 
1️⃣ NLB (Network Load Balancer) is usually deployed before the API gateway, handling traffic routing based on IP. It does not parse the HTTP requests. 
 
2️⃣ ALB (Application Load Balancer) routes requests based on HTTP header or URL and thus can provide richer routing rules. We can choose the load balancer based on routing requirements. For simple services with a smaller scale, one load balancer is enough. 
 
3️⃣ The API gateway performs tasks more on the application level. So it has different responsibilities from the load balancer.  
 
The diagram below shows the detail. Often, they are used in combination to provide a scalable and secure architecture for modern web apps. 
 
Option a: ALB is used to distribute requests among different services. Due to the fact that the services implement their own rating limitation, authentication, etc., this approach is more flexible but requires more work at the service level. 
 
Option b: An API gateway takes care of authentication, rate limiting, caching, etc., so there is less work at the service level. However, this option is less flexible compared with the ALB approach. 
 
👉 Over to you: Which one should we use, a load balancer or an API gateway? 
 
If you like this, I’m doing a bigger breakdown on this in our newsletter: https://bit.ly/3FEGliw 
 
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I read something unbelievable today: Levels. fyi scaled to millions of users using 𝐆𝐨𝐨𝐠𝐥𝐞 𝐒𝐡𝐞𝐞𝐭𝐬 𝐚𝐬 𝐚 𝐛𝐚𝐜𝐤𝐞𝐧𝐝! 
 
They started off on Google Forms and Sheets, which helped them reach millions of monthly active users before switching to a proper backend.  
 
To be fair, they do use serverless computing, but using Google Sheets as the database is an interesting choice.  
 
Why do they use Google Sheets as a backend? Using their own words: "It seems like a pretty counterintuitive idea for a site with our traffic volume to not have a backend or any fancy infrastructure, but our philosophy to building products has always been, start simple and iterate. This allows us to move fast and focus on what’s important". 
 
What are your thoughts? The link to the original article is embedded at the bottom of the diagram. 
 
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DevOps vs. SRE vs. Platform Engineering. What is the difference?
 
The concepts of DevOps, SRE, and Platform Engineering have emerged at different times and have been developed by various individuals and organizations. 

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DevOps as a concept was introduced in 2009 by Patrick Debois and Andrew Shafer at the Agile conference. They sought to bridge the gap between software development and operations by promoting a collaborative culture and shared responsibility for the entire software development lifecycle. 
 
SRE, or Site Reliability Engineering, was pioneered by Google in the early 2000s to address operational challenges in managing large-scale, complex systems. Google developed SRE practices and tools, such as the Borg cluster management system and the Monarch monitoring system, to improve the reliability and efficiency of their services. 
 
Platform Engineering is a more recent concept, building on the foundation of SRE engineering. The precise origins of Platform Engineering are less clear, but it is generally understood to be an extension of the DevOps and SRE practices, with a focus on delivering a comprehensive platform for product development that supports the entire business perspective. 
 
It's worth noting that while these concepts emerged at different times. They are all related to the broader trend of improving collaboration, automation, and efficiency in software development and operations. 

Over to you: Which topics would you like us to address in our next discussion?
 
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What are the API architectural styles? 
 
The diagram below shows the common API architectural styles in one picture. 
🔹 1. REST 
Proposed in 2000, REST is the most used style. It is often used between front-end clients and back-end services. It is compliant with 6 architectural constraints. The payload format can be JSON, XML, HTML, or plain text. 
 
🔹 2. GraphQL 
GraphQL was proposed in 2015 by Meta. It provides a schema and type system, suitable for complex systems where the relationships between entities are graph-like. For example, in the diagram below, GraphQL can retrieve user and order information in one call, while in REST this needs multiple calls. 
 
GraphQL is not a replacement for REST. It can be built upon existing REST services. 
 
🔹 3. Web socket 
Web socket is a protocol that provides full-duplex communications over TCP. The clients establish web sockets to receive real-time updates from the back-end services. Unlike REST, which always “pulls” data, web socket enables data to be “pushed”. 
 
🔹 4. Webhook 
Webhooks are usually used by third-party asynchronous API calls. In the diagram below, for example, we use Stripe or Paypal for payment channels and register a webhook for payment results. When a third-party payment service is done, it notifies the payment service if the payment is successful or failed. Webhook calls are usually part of the system’s state machine.  
 
🔹 5. gRPC 
Released in 2016, gRPC is used for communications among microservices. gRPC library handles encoding/decoding and data transmission. 
 
🔹 6. SOAP 
SOAP stands for Simple Object Access Protocol. Its payload is XML only, suitable for communications between internal systems.  
 
👉 Over to you: What API architectural styles have you used? 
 
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How Discord Stores Trillions Of Messages 
 
The diagram below shows the evolution of message storage at Discord: 
 
    MongoDB ➡️ Cassandra ➡️ ScyllaDB   
 
In 2015, the first version of Discord was built on top of a single MongoDB replica. Around Nov 2015, MongoDB stored 100 million messages and the RAM couldn’t hold the data and index any longer. The latency became unpredictable. Message storage needs to be moved to another database. Cassandra was chosen. 
 
In 2017, Discord had 12 Cassandra nodes and stored billions of messages. 
 
At the beginning of 2022, it had 177 nodes with trillions of messages. At this point, latency was unpredictable, and maintenance operations became too expensive to run. 
 
There are several reasons for the issue: 
- Cassandra uses the LSM tree for the internal data structure. The reads are more expensive than the writes. There can be many concurrent reads on a server with hundreds of users, resulting in hotspots. 
- Maintaining clusters, such as compacting SSTables, impacts performance. 
- Garbage collection pauses would cause significant latency spikes 
 
ScyllaDB is Cassandra compatible database written in C++. Discord redesigned its architecture to have a monolithic API, a data service written in Rust, and ScyllaDB-based storage. 
 
The p99 read latency in ScyllaDB is 15ms compared to 40-125ms in Cassandra. The p99 write latency is 5ms compared to 5-70ms in Cassandra. 
 
👉 Over to you: What kind of NoSQL database have you used? How do you like it? 
 
References: 
scylladb[.]com/product/technology/shard-per-core-architecture/  
discord[.]com/blog/how-discord-stores-trillions-of-messages  
 
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Which HTTP status codes are most common? 
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The response codes for HTTP are divided into five categories: 

Informational (100-199) 
Success (200-299) 
Redirection (300-399) 
Client Error (400-499) 
Server Error (500-599) 
 
These codes are defined in RFC 9110. To save you from reading the entire document (which is about 200 pages), here is a summary of the most common ones: 
 
Over to you: HTTP status code 401 is for Unauthorized. Can you explain the difference between authentication and authorization, and which one does code 401 check for? 
 
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Cloud-native vs. Cloud computing 
 
The term "Cloud Native" seemed to first appear around 10 years ago when Netflix discussed their web-scale application architecture at a 2013 AWS re:Invent talk. 
 
At that time, the meaning of the term was likely different than it is today. However, one thing remains the same: there were no clear definitions for it then, and there still are not any clear definitions now. It means different things to different people. 
 
In this video, we provide our interpretation of the term "Cloud Native" and discuss when it is important. 
 
Watch and subscribe here: https://lnkd.in/evAqzU9V 
 
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The 𝐋𝐢𝐧𝐮𝐱 𝐒𝐭𝐨𝐫𝐚𝐠𝐞 𝐒𝐭𝐚𝐜𝐤 𝐃𝐢𝐚𝐠𝐫𝐚𝐦 shows the layout of the the Linux storage stack. Diagram by Thomas-Krenn. 
 
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YouTube handles 500+ hours of video content uploads every minute on average. How does it manage this? 
 
The diagram below shows YouTube’s innovative hardware encoding published in 2021. 
 
🔹 Traditional Software Encoding 
YouTube’s mission is to transcode raw video into different compression rates to adapt to different viewing devices - mobile(720p), laptop(1080p), or high-resolution TV(4k).  
 
Creators upload a massive amount of video content on YouTube every minute. Especially during the COVID-19 pandemic, video consumption is greatly increased as people are sheltered at home. Software-based encoding became slow and costly. This means there was a need for a specialized processing brain tailored made for video encoding/decoding. 
 
🔹 YouTube’s Transcoding Brain - VCU 
Like GPU or TPU was used for graphics or machine learning calculations, YouTube developed VCU (Video transCoding Unit) for warehouse-scale video processing. 
 
Each cluster has a number of VCU accelerated servers. Each server has multiple accelerator trays, each containing multiple VCU cards. Each card has encoders, decoders, etc. [1] 
 
VCU cluster generates video content with different resolutions and stores it in cloud storage. 
 
This new design brought 20-33x improvements in computing efficiency compared to the previous optimized system. [2] 
 
👉 Over to you: Why is a specialized chip so much faster than a software-based solution? 
 
Reference:  
[1] dl. acm. org/doi/abs/10.1145/3445814.3446723  
[2] blog. youtube/inside-youtube/new-era-video-infrastructure/  
 
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Everyone loves burgers, whether it's a full stack burger, a frontend burger, or a backend burger.  
 
While the origin of this innovative burger is unknown, a comparable full-stack burger was shared on Reddit four years ago. We want to give a special shout-out to the original creators.  
 
Watch & subscribe full video here: https://lnkd.in/eFKe4gHd 

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A beginner’s guide to CDN (Content Delivery Network).
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A guest post by Love Sharma. The link to the complete article can be found in the comment section.

CDNs are distributed server networks that help improve the performance, reliability, and security of content delivery on the internet.

Here is the Overall CDN Diagram explains:

Edge servers are located closer to the end user than traditional servers, which helps reduce latency and improve website performance.

Edge computing is a type of computing that processes data closer to the end user rather than in a centralized data center. This helps to reduce latency and improve the performance of applications that require real-time processing, such as video streaming or online gaming.

Cloud gaming is online gaming that uses cloud computing to provide users with high-quality, low-latency gaming experiences.

Together, these technologies are transforming how we access and consume digital content. By providing faster, more reliable, and more immersive experiences for users, they are helping to drive the growth of the digital economy and create new opportunities for businesses and consumers alike.

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Get ready for an exciting week ahead: 
 
- Oauth2 
- How to design APIs 
- Backend burger (Video) 
- Structure of URL 
- How to keep data in sync 
 
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C, C++, Java, Javascript, Typescript, Golang, Rust… 
 
How do programming languages evolve for the past 70 years? 
 
The diagram below shows a brief history of programming languages. 
 
🔹 Perforated cards were the first generation of programming languages. Assembly languages, which are machine-oriented, are the second generation of programming language. Third-generation languages, which are human-oriented, have been around since 1957. 
 
🔹 Early languages like Fortran and LISP proposed garbage collection, recursion, exceptions. These features still exist in modern programming languages. 
 
🔹 In 1972, two influential languages were born: Smalltalk and C. Smalltalk greatly influenced scripting languages and client-side languages. C language was developed for unix programming.  
 
🔹 In the 1980s, object-oriented languages became popular because of its advantage in graphic user interfaces. Object-C and C++ are two famous ones. 
 
🔹 In the 1990s, the PCs became cheaper. The programming languages at this stage emphasized on security and simplicity. Python was born in this decade. It was easy to learn and extend and it quickly gained popularity. In 1995, Java, Javascript, PHP and Ruby were born.  
 
🔹 In 2000, C# was released by Microsoft. Although it was bundled with .NET framework, this language carried a lot of advanced features.  
 
🔹 A number of languages were developed in the 2010s to improve C++ or Java. In the C++ family, we have D, Rust, Zig and most recently Carbon. In the Java family, we have Golang and Kotlin. The use of Flutter made Dart popular, and Typescript was developed to be fully compatible with Javascript. Also, Apple finally released Swift to replace Object-C.  
 
👉 Over to you: What’s your favorite language and why? Will AI change the way we use programming languages? 
 
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