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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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How Do C++, Java, Python Work?
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The diagram shows how the compilation and execution work.

Compiled languages are compiled into machine code by the compiler. The machine code can later be executed directly by the CPU. Examples: C, C++, Go.

A bytecode language like Java, compiles the source code into bytecode first, then the JVM executes the program. Sometimes JIT (Just-In-Time) compiler compiles the source code into machine code to speed up the execution. Examples: Java, C#

Interpreted languages are not compiled. They are interpreted by the interpreter during runtime.  Examples: Python, Javascript, Ruby

Compiled languages in general run faster than interpreted languages. 

Over to you: which type of language do you prefer?

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Alex Xu

Netflix's Tech Stack. 
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This post is based on research from many Netflix engineering blogs and open-source projects. If you come across any inaccuracies, please feel free to inform us. 
 
Mobile and web: Netflix has adopted Swift and Kotlin to build native mobile apps. For its web application, it uses React. 
 
Frontend/server communication: GraphQL. 
 
Backend services: Netflix relies on ZUUL, Eureka, the Spring Boot framework, and other technologies. 
 
Databases: Netflix utilizes EV cache, Cassandra, CockroachDB, and other databases. 
 
Messaging/streaming: Netflix employs Apache Kafka and Fink for messaging and streaming purposes. 
 
Video storage: Netflix uses S3 and Open Connect for video storage. 
 
Data processing: Netflix utilizes Flink and Spark for data processing, which is then visualized using Tableau. Redshift is used for processing structured data warehouse information. 
 
CI/CD: Netflix employs various tools such as JIRA, Confluence, PagerDuty, Jenkins, Gradle, Chaos Monkey, Spinnaker, Altas, and more for CI/CD processes. 
 
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My recommended materials for cracking your next technical interview:
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Coding
- Leetcode
- Cracking the coding interview book
- Neetcode

System Design Interview 
- System Design Interview book 1, 2 by Alex Xu, Sahn Lam
- Grokking the system design by Design Guru
- Design Data-intensive Application book

Behavioral interview
- Tech Interview Handbook (Github repo)
- A Life Engineered (YT)
- STAR method (general method)

OOD Interview
- Interviewready
- OOD by educative
- Head First Design Patterns Book

Mock interviews
- Interviewingio
- Pramp
- Meetapro

Apply for Jobs
- Linkedin
- Monster
- Indeed

Over to you: What is your favorite interview prep material?

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

Top 5 Software Architectural Patterns
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In software development, architecture plays a crucial role in shaping the structure and behavior of software systems. It provides a blueprint for system design, detailing how components interact with each other to deliver specific functionality. They also offer solutions to common problems, saving time and effort and leading to more robust and maintainable systems.

However, with the vast array of architectural styles and patterns available, it can take time to discern which approach best suits a particular project or system. Aims to shed light on these concepts, helping you make informed decisions in your architectural endeavors.

To help you navigate the vast landscape of architectural styles and patterns, there is a cheat sheet that encapsulates all. This cheat sheet is a handy reference guide that you can use to quickly recall the main characteristics of each architectural style and pattern.

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OAuth 2.0 Flows 
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Authorization Code Flow: The most common OAuth flow. After user authentication, the client receives an authorization code and exchanges it for an access token and refresh token. 
 
Client Credentials Flow: Designed for single-page applications. The access token is returned directly to the client without an intermediate authorization code. 
 
Implicit Code Flow: Designed for single-page applications. The access token is returned directly to the client without an intermediate authorization code. 
 
Resource Owner Password Grant Flow: Allows users to provide their username and password directly to the client, which then exchanges them for an access token. 
 
Over to you - So which one do you think is something that you should use next in your application? 
 
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A nice cheat sheet of different monitoring infrastructure in cloud services. 
 
This cheat sheet offers a concise yet comprehensive comparison of key monitoring elements across the three major cloud providers and open-source / 3rd party tools. 
 
Let's delve into the essential monitoring aspects covered: 
- Data Collection: Gather information from diverse sources to enhance decision-making. 
- Data Storage: Safely store and manage data for future analysis and reference. 
- Data Analysis: Extract valuable insights from data to drive informed actions. 
- Alerting: Receive real-time notifications about critical events or anomalies. 
- Visualization: Present data in a visually comprehensible format for better understanding. 
- Reporting and Compliance: Generate reports and ensure adherence to regulatory standards. 
- Automation: Streamline processes and tasks through automated workflows. 
- Integration: Seamlessly connect and exchange data between different systems or tools. 
- Feedback Loops: Continuously refine strategies based on feedback and performance analysis. 
 
Over to you: How do you prioritize and leverage these essential monitoring aspects in your domain to achieve better outcomes and efficiency? 
 
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Top 5 Kafka use cases.
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Kafka was originally built for massive log processing. It retains messages until expiration and lets consumers pull messages at their own pace.

Unlike its predecessors, Kafka is more than a message queue, it is an open-source event streaming platform for various cases. 

Let’s review the popular Kafka use cases.

1. Log processing and analysis
The diagram below shows a typical ELK (Elastic-Logstash-Kibana) stack. Kafka efficiently collects log streams from each instance. ElasticSearch consumes the logs from Kafka and indexes them. Kibana provides a search and visualization UI on top of ElasticSearch.

2. Data streaming in recommendations
E-commerce sites like Amazon use past behaviors and similar users to calculate product recommendations. The diagram below shows how the recommendation system works. Kafka streams the raw clickstream data, Flink processes it, and model training consumes the aggregated data from the data lake. This allows continuous improvement of the relevance of recommendations for each user.

3. System monitoring and alerting
Similar to the log analysis system, we need to collect system metrics for monitoring and troubleshooting. The difference is that metrics are structured data while logs are unstructured text. Metrics data is sent to Kafka and aggregated in Flink. The aggregated data is consumed by a real-time monitoring dashboard and alerting system (for example, PagerDuty).

4. CDC (Change data capture)
Change Data Capture (CDC) streams database changes to other systems for replication or cache/index updates. For example, in the diagram below, the transaction log is sent to Kafka and ingested by ElasticSearch, Redis, and secondary databases.

5. System migration
Upgrading legacy services is challenging - old languages, complex logic, and lack of tests. We can mitigate the risk by leveraging a messaging middleware. In the diagram below, to upgrade the order service in the diagram below, we update the legacy order service to consume input from Kafka and write the result to ORDER topic. The new order service consumes the same input and writes the result to ORDERNEW topic. A reconciliation service compares ORDER and ORDERNEW. If they are identical, the new service passes testing.

Over to you: Do you have any other Kafka use cases to share?

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System Design Blueprint: The Ultimate Guide. The method to download the high-resolution PDF is available at the end. 
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We've created a template to tackle various system design problems in interviews. 
 
Hope this checklist is useful to guide your discussions during the interview process. 
 
This briefly touches on the following discussion points: 
- Load Balancing 
- API Gateway 
- Communication Protocols 
- Content Delivery Network (CDN) 
- Database 
- Cache 
- Message Queue 
- Unique ID Generation 
- Scalability 
- Availability 
- Performance 
- Security 
- Fault Tolerance and Resilience 
- And more 
 
Subscribe to our newsletter to download the 𝗵𝗶𝗴𝗵-𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘀𝘆𝘀𝘁𝗲𝗺 𝗱𝗲𝘀𝗶𝗴𝗻 𝗯𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁. After signing up, find the download link on the success page: https://bit.ly/3KCnWXq 
 
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What does a typical microservice architecture look like? 👇
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The diagram below shows a typical microservice architecture.

🔹Load Balancer: This distributes incoming traffic across multiple backend services.

🔹CDN (Content Delivery Network): CDN is a group of geographically distributed servers that hold static content for faster delivery. The clients look for content in CDN first, then progress to backend services.

🔹API Gateway: This handles incoming requests and routes them to the relevant services. It talks to the identity provider and service discovery.

🔹Identity Provider: This handles authentication and authorization for users.

🔹Service Registry & Discovery: Microservice registration and discovery happen in this component, and the API gateway looks for relevant services in this component to talk to.

🔹Management: This component is responsible for monitoring the services.

🔹Microservices: Microservices are designed and deployed in different domains. Each domain has its own database. The API gateway talks to the microservices via REST API or other protocols, and the microservices within the same domain talk to each other using RPC (Remote Procedure Call).

Benefits of microservices:
- They can be quickly designed, deployed, and horizontally scaled.
- Each domain can be independently maintained by a dedicated team.
- Business requirements can be customized in each domain and better supported, as a result.

Over to you: 1). What are the drawbacks of the microservice architecture?
2). Have you seen a monolithic system be transformed into microservice architecture? How long does it take?

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Key Concepts to Understand Database Sharding. 
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In this concise and visually engaging resource, we break down the key concepts of database partitioning, explaining both vertical and horizontal strategies. 
 
1. Range-Based Sharding: Splitting your data into distinct ranges. Think of it as organizing your books by genre on separate shelves. 
 
2. Key-Based Sharding (with a dash of %3 hash): Imagine each piece of data having a unique key, and we distribute them based on a specific rule. It's like sorting your playing cards by suit and number. 
 
3. Directory-Based Sharding: A directory, like a phone book, helps you quickly find the information you need. Similarly, this technique uses a directory to route data efficiently. 
 
Over to you: What are some other ways to scale a database? 
 
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Why is Kafka fast?

There are many design decisions that contributed to Kafka’s performance. In this post, we’ll focus on two. We think these two carried the most weight.

1️. The first one is Kafka’s reliance on Sequential I/O.
 
2️. The second design choice that gives Kafka its performance advantage is its focus on efficiency: zero copy principle.
 
The diagram below illustrates how the data is transmitted between producer and consumer, and what zero-copy means.
 
🔹Step 1.1 - 1.3: Producer writes data to the disk 
 
🔹Step 2: Consumer reads data without zero-copy
2.1: The data is loaded from disk to OS cache
2.2 The data is copied from OS cache to Kafka application
2.3 Kafka application copies the data into the socket buffer 
2.4 The data is copied from socket buffer to network card
2.5 The network card sends data out to the consumer
 
🔹Step 3: Consumer reads data with zero-copy
3.1: The data is loaded from disk to OS cache
3.2 OS cache directly copies the data to the network card via sendfile() command
3.3 The network card sends data out to the consumer
 
Zero copy is a shortcut to save multiple data copies between the application context and kernel context.

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Session, cookie, JWT, token, SSO, and OAuth 2.0 - what are they?

These terms are all related to user identity management. When you log into a website, you declare who you are (identification). Your identity is verified (authentication), and you are granted the necessary permissions (authorization). Many solutions have been proposed in the past, and the list keeps growing.

From simple to complex, here is my understanding of user identity management:

🔹WWW-Authenticate is the most basic method. You are asked for the username and password by the browser. As a result of the inability to control the login life cycle, it is seldom used today.
🔹A finer control over the login life cycle is session-cookie. The server maintains session storage, and the browser keeps the ID of the session. A cookie usually only works with browsers and is not mobile app friendly.
🔹To address the compatibility issue, the token can be used. The client sends the token to the server, and the server validates the token. The downside is that the token needs to be encrypted and decrypted, which may be time-consuming.
🔹JWT is a standard way of representing tokens. This information can be verified and trusted because it is digitally signed. Since JWT contains the signature, there is no need to save session information on the server side.
🔹By using SSO (single sign-on), you can sign on only once and log in to multiple websites. It uses CAS (central authentication service) to maintain cross-site information
🔹By using OAuth 2.0, you can authorize one website to access your information on another website

Over to you: nowadays, some website allows you to log in by scanning the QR code using your phone. Do you know how it works?

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This is how AI perceives corporate roles.

I guess I belong to IT: the wasp hunts the bugs. :)

Credit: Generative AI

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URL, URI, URN - What are the differences?

The diagram below shows a comparison of URL, URI, and URN.

🔹 URI
URI stands for Uniform Resource Identifier. It identifies a logical or physical resource on the web. URL and URN are subtypes of URI. URL locates a resource, while URN names a resource.

A URI is composed of the following parts:
scheme:[//authority]path[?query][# fragment]

🔹 URL
URL stands for Uniform Resource Locator, the key concept of HTTP. It is the address of a unique resource on the web. It can be used with other protocols like FTP and JDBC.

🔹 URN
URN stands for Uniform Resource Name. It uses the urn scheme. URNs cannot be used to locate a resource. A simple example given in the diagram is composed of a namespace and a namespace-specific string.

If you would like to learn more detail on the subject, I would recommend W3C’s clarification.

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How does REST API work? What are its principles, methods, constraints, and best practices? We hope the diagram below gives you a quick overview.
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One picture is worth a thousand words - Top 5 Caching Strategies.
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When we introduce a cache into the architecture, synchronization between the cache and the database becomes inevitable. 

Let’s look at 5 common strategies how we keep the data in sync.
🔹 Read Strategies:
Cache aside
Read through

🔹 Write Strategies:
Write around
Write back
Write through

The caching strategies are often used in combination. For example, write-around is often used together with cache-aside to make sure the cache is up-to-date.

Over to you: What strategies have you used?

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Popular interview question: What is the difference between Process and Thread?
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To better understand this question, let’s first take a look at what is a Program. A Program is an executable file containing a set of instructions and passively stored on disk. One program can have multiple processes. For example, the Chrome browser creates a different process for every single tab. 

A Process means a program is in execution. When a program is loaded into the memory and becomes active, the program becomes a process. The process requires some essential resources such as registers, program counter, and stack.

A Thread is the smallest unit of execution within a process. 

The following process explains the relationship between program, process, and thread. 

1. The program contains a set of instructions.
2. The program is loaded into memory. It becomes one or more running processes.
3. When a process starts, it is assigned memory and resources. A process can have one or more threads. For example, in the Microsoft Word app, a thread might be responsible for spelling checking and the other thread for inserting text into the doc.

Main differences between process and thread:

🔹 Processes are usually independent, while threads exist as subsets of a process.
🔹 Each process has its own memory space. Threads that belong to the same process share the same memory.
🔹 A process is a heavyweight operation. It takes more time to create and terminate. 
🔹 Context switching is more expensive between processes.
🔹 Inter-thread communication is faster for threads.

Over to you:
1). Some programming languages support coroutine. What is the difference between coroutine and thread?
2). How to list running processes in Linux?

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This is the flowchart of how slack decides to send a notification.

It is a great example of why a simple feature may take much longer to develop than many people think.

When we have a great design, users may not notice the complexity because it feels like the feature just working as intended.

What’s your takeaway from this diagram?

Image source: Slack eng blog

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Uber Tech Stack 
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This post is based on research from many Uber engineering blogs and open-source projects. If you come across any inaccuracies, please feel free to inform us. The corresponding links are added in the comment section. 
 
Web frontend: Uber builds Fusion.js as a modern React framework to create robust web applications. They also develop visualization.js for geospatial visualization scenarios. 
 
Mobile side: Uber builds the RIB cross-platform with the VIPER architecture instead of MVC. This architecture can work with different languages: Swift for iOS, and Java for Android. 
 
Service mesh: Uber built Uber Gateway as a dynamic configuration on top of NGINX. The service uses gRPC and QUIC for client-server communication, and Apache Thrift for API definition. 
 
Service side: Uber built a unified configuration store named Flipr (later changed to UCDP), H3 as a location-index store library. They use Spring Boot for Java-based services, uAct for event-driven architecture, and Cadence for async workflow orchestration. 
 
Database end: the OLTP mainly uses the strongly-consistent DocStore, which employs MySQL and PostgreSQL, along with the RocksDB database engine. 
 
Big data: managed through the Hadoop family. Hudi and Parquet are used as file formats, and Alluxio serves as cache. Time-series data is stored in Pinot and AresDB. 
 
Data processing: Hive, Spark, and the open-source data ingestion framework Marmaray. Messaging and streaming middleware include Apache Kafka and Apache Flink. 
 
DevOps side: Uber utilizes a Monorepo, with a simplified development environment called devpod. Continuous delivery is managed through Netflix Spinnaker, metrics are emitted to uMetric, alarms on uMonitor, and a consistent observability database M3. 
 
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