Alex Xu

How do companies ship code to production?

The diagram below illustrates the typical workflow.

Step 1: The process starts with a product owner creating user stories based on requirements.

Step 2: The dev team picks up the user stories from the backlog and puts them into a sprint for a two-week dev cycle.

Step 3: The developers commit source code into the code repository Git.

Step 4: A build is triggered in Jenkins. The source code must pass unit tests, code coverage threshold, and gates in SonarQube.

Step 5: Once the build is successful, the build is stored in artifactory. Then the build is deployed into the dev environment.

Step 6: There might be multiple dev teams working on different features. The features need to be tested independently, so they are deployed to QA1 and QA2.

Step 7: The QA team picks up the new QA environments and performs QA testing, regression testing, and performance testing.

Steps 8: Once the QA builds pass the QA team’s verification, they are deployed to the UAT environment.

Step 9: If the UAT testing is successful, the builds become release candidates and will be deployed to the production environment on schedule.

Step 10: SRE (Site Reliability Engineering) team is responsible for prod monitoring.

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Why is the credit card called “𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐩𝐫𝐨𝐟𝐢𝐭𝐚𝐛𝐥𝐞 product in banks”? How does VISA/Mastercard make money?

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The diagram below shows the economics of the credit card payment flow.

1. The cardholder pays a merchant $100 to buy a product.

2. The merchant benefits from the use of the credit card with higher sales volume, and needs to compensate the issuer and the card network for providing the payment service. The acquiring bank sets a fee with the merchant, called the “𝐦𝐞𝐫𝐜𝐡𝐚𝐧𝐭 𝐝𝐢𝐬𝐜𝐨𝐮𝐧𝐭 𝐟𝐞𝐞.”

3 - 4. The acquiring bank keeps $0.25 as the 𝐚𝐜𝐪𝐮𝐢𝐫𝐢𝐧𝐠 𝐦𝐚𝐫𝐤𝐮𝐩, and $1.75 is paid to the issuing bank as the 𝐢𝐧𝐭𝐞𝐫𝐜𝐡𝐚𝐧𝐠𝐞 𝐟𝐞𝐞. The merchant discount fee should cover the interchange fee.

The interchange fee is set by the card network because it is less efficient for each issuing bank to negotiate fees with each merchant.

5. The card network sets up the 𝐧𝐞𝐭𝐰𝐨𝐫𝐤 𝐚𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐟𝐞𝐞𝐬 with each bank, which pays the card network for its services every month. For example, VISA charges a 0.11% assessment, plus a $0.0195 usage fee, for every swipe.

6. The cardholder pays the issuing bank for its services.

Why should the issuing bank be compensated?
🔹The issuer pays the merchant even if the cardholder fails to pay the issuer.
🔹The issuer pays the merchant before the cardholder pays the issuer.
🔹The issuer has other operating costs, including managing customer accounts, providing statements, fraud detection, risk management, clearing & settlement, etc.

Over to you: Does the card network charge the same interchange fee for big merchants as for small merchants?

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How Do Companies Ship Code to Production?\n\nHere are 11 steps from planning to production:\n\n1 - The Product Owner starts the entire process by creating user stories in a tool like Jira.\n\n2 - The Developer Team performs Sprint Planning activity and adds the user stories to the sprint.\n\n3 - Developers work on the assigned stories. Once a story is finished, they commit the code to Git and push it to GitHub.\n\n4 - Jenkins builds and runs the code through testing and quality check tools such as JUnit, Jacoco, and SonarQube.\n\n5 - If the build is successful, it is stored in the artifactory such as JFrog. Jenkins also deploys the build to the Dev Environment via Docker.\n\n6 - Next up, the feature gets deployed to the QA environment. Since multiple teams may be working on the same code base, multiple QA environments will be created\n\n7 - The QA team uses a particular QA environment and runs multiple test types such as QA, regression, and performance.\n\n8 - Once the QA verification is complete, features are deployed to the UAT (User Acceptance Testing) environment.\n\n9 - UAT testing verifies whether the feature satisfies the user’s requirements.\n\n10 - Once the UAT testing is successful, the builds become release candidates. They are deployed to the production environment based on a specific schedule. \n\n11 - The SRE team uses tools like ELK and Prometheus to monitor the production environment and handle alerts in case of issues. \n\nOver to you: Will you add any other steps to the process?\n\n--\nSubscribe to our weekly newsletter to get a Free System Design PDF (158 pages): https://bit.ly/bbg-social\n \n#systemdesign #coding #interviewtips \n.

My recommended materials for cracking your next technical interview

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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How does Disney Hotstar capture 5 Billion Emojis during a tournament?

Dedeepya Bonthu wrote an excellent engineering blog that captures this nicely. Here is my understanding of how the system works.

1. Clients send emojis through standard HTTP requests. You can think of Golang Service as a typical Web Server. Golang is chosen because it supports concurrency well. Threads in GoLang are lightweight.

2. Since the write volume is very high, Kafka (message queue) is used as a buffer.

3. Emoji data are aggregated by a streaming processing service called Spark. It aggregates data every 2 seconds, which is configurable. There is a trade-off to be made based on the interval. A shorter interval means emojis are delivered to other clients faster but it also means more computing resources are needed.

4. Aggregated data is written to another Kafka.

5. The PubSub consumers pull aggregated emoji data from Kafka.

6. Emojis are delivered to other clients in real-time through the PubSub infrastructure.

The PubSub infrastructure is interesting. Hotstar considered the following protocols: Socketio, NATS, MQTT, and gRPC, and settled with MQTT. For those who are interested in the tradeoff discussion, see [2].

A similar design is adopted by LinkedIn which streams a million likes/sec [3].

Over to you: What are some of the off-the-shelf Pub-Sub services available? Is there anything you would do differently in this design?

Sources: 
[1] Capturing A Billion Emo(j)i-ons: https://lnkd.in/e24qZK2s
[2] Building Pubsub for 50M concurrent socket connections: https://lnkd.in/eKHqFeef
[3] Streaming a Million Likes/Second: Real-Time Interactions on Live Video: https://lnkd.in/eUthHjv4

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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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What is GraphQL? Is it a replacement for the REST API?

The diagram below shows the quick comparison between REST and GraphQL.

🔹GraphQL is a query language for APIs developed by Meta. It provides a complete description of the data in the API and gives clients the power to ask for exactly what they need.

🔹GraphQL servers sit in between the client and the backend services.

🔹GraphQL can aggregate multiple REST requests into one query. GraphQL server organizes the resources in a graph.

🔹GraphQL supports queries, mutations (applying data modifications to resources), and subscriptions (receiving notifications on schema modifications).

We talked about the REST API in last week’s video and will compare REST vs. GraphQL vs. gRPC in a separate post/video.

Over to you:
1). Is GraphQL a database technology?
2). Do you recommend GraphQL? Why/why not?

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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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How to improve API performance?
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The diagram below shows 5 common tricks to improve API performance.

1. Pagination
This is a common optimization when the size of the result is large. The results are streaming back to the client to improve the service responsiveness.

2. Asynchronous Logging
Synchronous logging deals with the disk for every call and can slow down the system. Asynchronous logging sends logs to a lock-free buffer first and immediately returns. The logs will be flushed to the disk periodically. This significantly reduces the I/O overhead.

3. Caching
We can cache frequently accessed data into a cache. The client can query the cache first instead of visiting the database directly. If there is a cache miss, the client can query from the database. Caches like Redis store data in memory, so the data access is much faster than the database.

4. Payload Compression
The requests and responses can be compressed using gzip etc so that the transmitted data size is much smaller. This speeds up the upload and download.

5. Connection Pool
When accessing resources, we often need to load data from the database. Opening the closing db connections add significant overhead. So we should connect to the db via a pool of open connections. The connection pool is responsible for managing the connection lifecycle.

Over to you: What other tricks do you use to improve API performance?

Reference:
https://twitter[dot]com/Rapid_API/status/1646136138183364608

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15 Open-Source Projects That Changed the World

To come up with the list, we tried to look at the overall impact these projects have created on the industry and related technologies. Also, we’ve focused on projects that have led to a big change in the day-to-day lives of many software developers across the world.

Web Development
- Node.js: The cross-platform server-side Javascript runtime that brought JS to server-side development
- React: The library that became the foundation of many web development frameworks.
- Apache HTTP Server: The highly versatile web server loved by enterprises and startups alike. Served as inspiration for many other web servers over the years.

Data Management
- PostgreSQL: An open-source relational database management system that provided a high-quality alternative to costly systems
- Redis: The super versatile data store that can be used a cache, message broker and even general-purpose storage
- Elasticsearch: A scale solution to search, analyze and visualize large volumes of data

Developer Tools
- Git: Free and open-source version control tool that allows developer collaboration across the globe.
- VSCode: One of the most popular source code editors in the world
- Jupyter Notebook: The web application that lets developers share live code, equations, visualizations and narrative text.

Machine Learning & Big Data
- Tensorflow: The leading choice to leverage machine learning techniques
- Apache Spark: Standard tool for big data processing and analytics platforms
- Kafka: Standard platform for building real-time data pipelines and applications.

DevOps & Containerization
- Docker: The open source solution that allows developers to package and deploy applications in a consistent and portable way.
- Kubernetes: The heart of Cloud-Native architecture and a platform to manage multiple containers
- Linux: The OS that democratized the world of software development.

Over to you: Do you agree with the list? What did we miss?

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Popular interview question: how to diagnose a mysterious process that’s taking too much CPU, memory, IO, etc?

The diagram below illustrates helpful tools in a Linux system.

🔹‘vmstat’ - reports information about processes, memory, paging, block IO, traps, and CPU activity.

🔹‘iostat’ - reports CPU and input/output statistics of the system.

🔹‘netstat’ - displays statistical data related to IP, TCP, UDP, and ICMP protocols.

🔹‘lsof’ - lists open files of the current system.

🔹‘pidstat’ - monitors the utilization of system resources by all or specified processes, including CPU, memory, device IO, task switching, threads, etc.

Credit: Diagram by Brendan Gregg
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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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What does API gateway do?

An API gateway typically provides several important functions. Some common ones are:
- authentication and security policy enforcement
- load balancing and circuit breaking
- protocol translation and service discovery
- monitoring, logging, analytics, and billing
- caching

Watch the whole video here: https://bit.ly/3fm3LAw

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What is an AI Agent?

An AI agent is a software program that can interact with its environment, gather data, and use that data to achieve predetermined goals. AI agents can choose the best actions to perform to meet those goals.

Key characteristics of AI agents are as follows:

An agent can perform autonomous actions without constant human intervention. Also, they can have a human in the loop to maintain control.
- Agents have a memory to store individual preferences and allow for personalization. It can also store knowledge. An LLM can undertake information processing and decision-making functions.
- Agents must be able to perceive and process the information available from their environment.
- Agents can also use tools such as accessing the internet, using code interpreters and making API calls.
- Agents can also collaborate with other agents or humans.

Multiple types of AI agents are available such as learning agents, simple reflex agents, model-based reflex agents, goal-based agents, and utility-based agents.

A system with AI agents can be built with different architectural approaches.

1 - Single Agent: Agents can serve as personal assistants.
2 - Multi-Agent: Agents can interact with each other in collaborative or competitive ways.
3 - Human Machine: Agents can interact with humans to execute tasks more efficiently.

Over to you: Have you used AI Agents?

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Netflix's Tech Stack.
 
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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What does API gateway do?
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The diagram below shows the detail.

Step 1 - The client sends an HTTP request to the API gateway.

Step 2 - The API gateway parses and validates the attributes in the HTTP request.

Step 3 - The API gateway performs allow-list/deny-list checks.

Step 4 - The API gateway talks to an identity provider for authentication and authorization.

Step 5 - The rate limiting rules are applied to the request. If it is over the limit, the request is rejected.

Steps 6 and 7 - Now that the request has passed basic checks, the API gateway finds the relevant service to route to by path matching.

Step 8 - The API gateway transforms the request into the appropriate protocol and sends it to backend microservices.

Steps 9-12: The API gateway can handle errors properly, and deals with faults if the error takes a longer time to recover (circuit break). It can also leverage ELK (Elastic-Logstash-Kibana) stack for logging and monitoring. We sometimes cache data in the API gateway.

Over to you: 1) What’s the difference between a load balancer and an API gateway?
2) Do we need to use different API gateways for PC, mobile and browser separately?

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Best ways to test system functionality. Next week's topic will be listed at the end.

Testing system functionality is a crucial step in software development and engineering processes.

It ensures that a system or software application performs as expected, meets user requirements, and operates reliably.

Here we delve into the best ways:

1. Unit Testing: Ensures individual code components work correctly in isolation.

2. Integration Testing: Verifies that different system parts function seamlessly together.

3. System Testing: Assesses the entire system's compliance with user requirements and performance.

4. Load Testing: Tests a system's ability to handle high workloads and identifies performance issues.

5. Error Testing: Evaluates how the software handles invalid inputs and error conditions.

6. Test Automation: Automates test case execution for efficiency, repeatability, and error reduction.

Over to you: How do you approach testing system functionality in your software development or engineering projects?

Over to you: what's your company's release process look like?

Next week, we will talk about:
- Python vs. C++ vs. Java (YouTube)
- Cloud provider cheatsheet (AWS, GCP, Azure, Oracle, Alibaba Cloud)
- Object stores
- Tech interview materials
- Mobile release

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GET, POST, PUT... Common HTTP “verbs” in one figure.
 
1. HTTP GET 
This retrieves a resource from the server. It is idempotent. Multiple identical requests return the same result. 
 
2. HTTP PUT 
This updates or Creates a resource. It is idempotent. Multiple identical requests will update the same resource. 
 
3. HTTP POST 
This is used to create new resources. It is not idempotent, making two identical POST will duplicate the resource creation. 
 
4. HTTP DELETE 
This is used to delete a resource. It is idempotent. Multiple identical requests will delete the same resource. 
 
5. HTTP PATCH 
The PATCH method applies partial modifications to a resource. 
 
6. HTTP HEAD 
The HEAD method asks for a response identical to a GET request but without the response body. 
 
7. HTTP CONNECT 
The CONNECT method establishes a tunnel to the server identified by the target resource. 
 
8. HTTP OPTIONS 
This describes the communication options for the target resource. 
 
9. HTTP TRACE 
This performs a message loop-back test along the path to the target resource. 
 
Over to you: What other HTTP verbs have you used?

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A handy cheat sheet for the most popular cloud services.

What’s included?
- AWS, Azure, Google Cloud, Oracle Cloud, Alibaba Cloud
- Cloud servers
- Databases
- Message queues and streaming platforms
- Load balancing, DNS routing software
- Security
- Monitoring

Over to you - which company is the best at naming things?

Subscribe to our newsletter to download the 𝐡𝐢𝐠𝐡-𝐫𝐞𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐜𝐡𝐞𝐚𝐭 𝐬𝐡𝐞𝐞𝐭. After signing up, find the download link on the success page: https://bit.ly/3tiMC1B

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Just noticed that I passed 300,000 Linkedin followers. 
 
I Googled what 300,000 people look like in one place. The stadium below can hold 150k people, so it’s this x2 :)
 
I appreciate every single one of you. Thank you!

Here, I usually talk about:
- how complex systems work
- book writing tips
- tech interviews
- dev tools

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IBM MQ -> RabbitMQ -> Kafka ->Pulsar, How do message queue architectures evolve?

🔹 IBM MQ
IBM MQ was launched in 1993. It was originally called MQSeries and was renamed WebSphere MQ in 2002. It was renamed to IBM MQ in 2014. IBM MQ is a very successful product widely used in the financial sector. Its revenue still reached 1 billion dollars in 2020.

🔹 RabbitMQ
RabbitMQ architecture differs from IBM MQ and is more similar to Kafka concepts. The producer publishes a message to an exchange with a specified exchange type. It can be direct, topic, or fanout. The exchange then routes the message into the queues based on different message attributes and the exchange type. The consumers pick up the message accordingly.

🔹 Kafka
In early 2011, LinkedIn open sourced Kafka, which is a distributed event streaming platform. It was named after Franz Kafka. As the name suggested, Kafka is optimized for writing. It offers a high-throughput, low-latency platform for handling real-time data feeds. It provides a unified event log to enable event streaming and is widely used in internet companies.

Kafka defines producer, broker, topic, partition, and consumer. Its simplicity and fault tolerance allow it to replace previous products like AMQP-based message queues.

🔹 Pulsar
Pulsar, developed originally by Yahoo, is an all-in-one messaging and streaming platform. Compared with Kafka, Pulsar incorporates many useful features from other products and supports a wide range of capabilities. Also, Pulsar architecture is more cloud-native, providing better support for cluster scaling and partition migration, etc.

There are two layers in Pulsar architecture: the serving layer and the persistent layer. Pulsar natively supports tiered storage, where we can leverage cheaper object storage like AWS S3 to persist messages for a longer term.

Over to you: which message queues have you used?

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We are often asked to design for high availability, high scalability, and high throughput. What do they mean exactly?

The diagram below is a system design cheat sheet with common solutions.

1. High Availability
This means we need to ensure a high agreed level of uptime. We often describe the design target as “3 nines” or “4 nines”. “4 nines”, 99.99% uptime, means the service can only be down 8.64 seconds per day.

To achieve high availability, we need to design redundancy in the system. There are several ways to do this:

- Hot-hot: two instances receive the same input and send the output to the downstream service. In case one side is down, the other side can immediately take over. Since both sides send output to the downstream, the downstream system needs to dedupe.

- Hot-warm: two instances receive the same input and only the hot side sends the output to the downstream service. In case the hot side is down, the warm side takes over and starts to send output to the downstream service.

- Single-leader cluster: one leader instance receives data from the upstream system and replicates to other replicas.

- Leaderless cluster: there is no leader in this type of cluster. Any write will get replicated to other instances. As long as the number of write instances plus the number of read instances are larger than the total number of instances, we should get valid data.

2. High Throughput
This means the service needs to handle a high number of requests given a period of time. Commonly used metrics are QPS (query per second) or TPS (transaction per second).

To achieve high throughput, we often add caches to the architecture so that the request can return without hitting slower I/O devices like databases or disks. We can also increase the number of threads for computation-intensive tasks. However, adding too many threads can deteriorate the performance. We then need to identify the bottlenecks in the system and increase its throughput. Using asynchronous processing can often effectively isolate heavy-lifting components.

3. High Scalability
This means a system can quickly and easily extend to accommodate more volume (horizontal scalability) or more functionalities (vertical scalability). Normally we watch the response time to decide if we need to scale the system.

Over to you: Do you have other things to share in your design toolbox?

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Top 6 most popular API architecture styles.

With billions of API calls made every day, understanding API architecture styles has never been more important. In this video, we take a closer look at these styles. They are the backbone of our interconnected digital world. We will talk about:

- SOAP
- RESTful
- GraphQL
- gRPC
- WebSocket
- Webhook

Watch & subscribe here: https://lnkd.in/e2Jwg9w8

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How do DevOps, NoOps change the software development lifecycle (SDLC)?

The diagram below compares traditional SDLC, DevOps and NoOps.

In a traditional software development, code, build, test, release and monitoring are siloed functions. Each stage works independently and hands over to the next stage.

DevOps, on the other hand, encourages continuous development and collaboration between developers and operations. This shortens the overall life cycle and provides continuous software delivery.

NoOps is a newer concept with the development of serverless computing. Since we can architect the system using FaaS (Function-as-a-Service) and BaaS (Backend-as-a-Service), the cloud service providers can take care of most operations tasks. The developers can focus on feature development and automate operations tasks.

NoOps is a pragmatic and effective methodology for startups or smaller-scale applications, which moves shortens the SDLC even more than DevOps.

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What is so important about HTTPS that, these days, most websites require it? How does HTTPS work?

We will answer these questions in tomorrow’s video. Subscribe to our YouTube channel so you won’t miss it ⇩:

YouTube: https://bit.ly/3xWM7Kk

In the age of AI, what subjects should be taught in schools?

An interesting list of subjects that should be mandatory in schools by startup_rules.

While academics are essential, it's crucial to acknowledge that many elements in this diagram would have been beneficial to learn earlier.

Over to you: What else should be on the list? What are the top 3 skills you wish schools would teach?

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In today's video, we're diving into the world of computer memory and storage.

We will talk about:
- The fundamental duo: RAM and ROM
- DDR4 and DDR5
- Firmware and BIOS
- SRAM and DRAM
- HDD, SSD, USB Drive, SD Card
- and more

Watch the video now: https://lnkd.in/egQVXVG6

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How does Docker work?
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The diagram below shows the architecture of Docker and how it works when we run “docker build”, “docker pull” and “docker run”.

There are 3 components in Docker architecture:

🔹 Docker client
The docker client talks to the Docker daemon.

🔹 Docker host
The Docker daemon listens for Docker API requests and manages Docker objects such as images, containers, networks, and volumes.

🔹 Docker registry
A Docker registry stores Docker images. Docker Hub is a public registry that anyone can use.

Let’s take the “docker run” command as an example.
1. Docker pulls the image from the registry.
2. Docker creates a new container.
3. Docker allocates a read-write filesystem to the container.
4. Docker creates a network interface to connect the container to the default network.
5. Docker starts the container.

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These are really hard times, especially for people on visas. I thought we could leverage the community to help. Let’s create a thread where companies and candidates can find each other.

1. If your company is hiring, please comment with the job openings

2. Everyone else, like and or share for reach

3. Candidates can check the comments for current job openings

Tech industry is resilient. This too shall pass. Let’s support each other during tough times.

#systemdesign #layoffs #career

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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10 Essential Components of a Production Web Application.

1 - It all starts with CI/CD pipelines that deploy code to the server instances. Tools like Jenkins and GitHub help over here.
2 - The user requests originate from the web browser. After DNS resolution, the requests reach the app servers.
3 - Load balancers and reverse proxies (such as Nginx & HAProxy) distribute user requests evenly across the web application servers.
4 - The requests can also be served by a Content Delivery Network (CDN).
5 - The web app communicates with backend services via APIs.
6 - The backend services interact with database servers or distributed caches to provide the data.
7 - Resource-intensive and long-running tasks are sent to job workers using a job queue.
8 - The full-text search service supports the search functionality. Tools like Elasticsearch and Apache Solr can help here.
9 - Monitoring tools (such as Sentry, Grafana, and Prometheus) store logs and help analyze data to ensure everything works fine. 
10 - In case of issues, alerting services notify developers through platforms like Slack for quick resolution.

Over to you: What other components would you add to the architecture of a production web app?

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REST API Cheatsheet. The method to download high-resolution PDF is available at the end.

This guide is designed to help you understand the world of RESTful APIs in a clear and engaging way.

What's inside:
- An exploration of the six fundamental principles of REST API design.
- Insights into key components such as HTTP methods, protocols, versioning, and more.
- A special focus on practical aspects like pagination, filtering, and endpoint design.

Whether you're beginning your API journey or looking to refresh your knowledge, this blog and cheat sheet combo is the perfect toolkit for success.

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How does VISA work when we 𝐬𝐰𝐢𝐩𝐞 𝐚 𝐜𝐫𝐞𝐝𝐢𝐭 𝐜𝐚𝐫𝐝 at a merchant’s shop?
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VISA, Mastercard, and American Express act as card networks for the clearing and settling of funds. The card acquiring bank and the card issuing bank can be – and often are – different. If banks were to settle transactions one by one without an intermediary, each bank would have to settle the transactions with all the other banks. This is quite inefficient. 
 
The diagram below shows VISA’s role in the credit card payment process. There are two flows involved. Authorization flow happens when the customer swipes the credit card. Capture and settlement flow happens when the merchant wants to get the money at the end of the day.
 
🔹Authorization Flow
Step 0: The card issuing bank issues credit cards to its customers. 
 
Step 1: The cardholder wants to buy a product and swipes the credit card at the Point of Sale (POS) terminal in the merchant’s shop.
 
Step 2: The POS terminal sends the transaction to the acquiring bank, which has provided the POS terminal.
 
Steps 3 and 4: The acquiring bank sends the transaction to the card network, also called the card scheme. The card network sends the transaction to the issuing bank for approval.
 
Steps 4.1, 4.2 and 4.3: The issuing bank freezes the money if the transaction is approved. The approval or rejection is sent back to the acquirer, as well as the POS terminal. 
 
🔹Capture and Settlement Flow
Steps 1 and 2: The merchant wants to collect the money at the end of the day, so they hit ”capture” on the POS terminal. The transactions are sent to the acquirer in batch. The acquirer sends the batch file with transactions to the card network.
 
Step 3: The card network performs clearing for the transactions collected from different acquirers, and sends the clearing files to different issuing banks.
 
Step 4: The issuing banks confirm the correctness of the clearing files, and transfer money to the relevant acquiring banks.
 
Step 5: The acquiring bank then transfers money to the merchant’s bank. 
 
Step 4: The card network clears the transactions from different acquiring banks. Clearing is a process in which mutual offset transactions are netted, so the number of total transactions is reduced.
 
In the process, the card network takes on the burden of talking to each bank and receives service fees in return.
 
Over to you: Do you think this flow is way too complicated? What will be the future of payments in your opinion?

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Some of you asked to get a PDF version of my LinkedIn topics. Here is the first part. It contains all payment-related topics. This document is a work-in-progress. Subscribe to receive updates or new parts here: blog.bytebytego.com

    🔹 Payment system
    🔹 Payment security
    🔹 Double charge
    🔹 Reconciliation
    🔹 Painful payment reconciliation
    🔹 Clearing & settlement
    🔹 Foreign exchange
    🔹 SWIFT

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Evolution of the Netflix API Architecture.

The Netflix API architecture went through 4 main stages.

- Monolith
- Direct access
- Gateway aggregation layer
- Federated gateway

We explain the evolution in a 4-minute video.

Watch and subscribe here: https://lnkd.in/e9yycpU6

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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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Top 12 Tips for API Security 
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- Use HTTPS 
- Use OAuth2 
- Use WebAuthn 
- Use Leveled API Keys 
- Authorization 
- Rate Limiting 
- API Versioning 
- Whitelisting 
- Check OWASP API Security Risks 
- Use API Gateway 
- Error Handling 
- Input Validation

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10 Key Data Structures We Use Every Day 
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- list: keep your Twitter feeds 
- stack: support undo/redo of the word editor 
- queue: keep printer jobs, or send user actions in-game 
- hash table: cashing systems 
- Array: math operations 
- heap: task scheduling 
- tree: keep the HTML document, or for AI decision 
- suffix tree: for searching string in a document 
- graph: for tracking friendship, or path finding 
- r-tree: for finding the nearest neighbor 
- vertex buffer: for sending data to GPU for rendering

Over to you: Which additional data structures have we overlooked?

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Uber is one of the most innovative companies in the engineering field. Let’s take a look at their CI/CD tech stacks.

Note: This post is based on research on Uber engineering blogs. If you spot any inaccuracies, please let us know.

Project planning: JIRA

Backend services: Spring Boot to develop their backend services. And to make things even faster, they've created a nifty configuration system called Flipr that allows for speedy configuration releases.

Code issues: They developed NullAway to tackle NullPointer problems and NEAL to lint the code. Plus, they built Piranha to clean out-dated feature flags.

Repository: They believe in Monorepo. It uses Bazel on a large scale.

Testing: They use SLATE to manage short-lived testing environments and rely on Shadower for load testing by replaying production traffic. They even developed Ballast to ensure a smooth user experience.

Experiment platform: it is based on deep learning and they've generously open-sourced parts of it, like Pyro.

Build: Uber packages their services into containers using uBuild. It's their go-to tool, powered by Buildkite, for all the packaging tasks.

Deploying applications: Netflix Spinnaker. It's their trusted tool for getting things into production smoothly and efficiently.

Monitoring: Uber built their own monitoring systems. They use the uMetric platform, built on Cassandra, to keep things consistent.

Special tooling: Uber relies on Peloton for capacity planning, scheduling, and operations. Crane builds a multi-cloud infrastructure to optimize costs. And with uAct and the OnCall dashboard, they've got event tracing and on-call duty management covered.

Have you ever used any of Uber's tech stack for CI/CD? What are your thoughts on their CI/CD setup?

Some updates based on feedback:
Project planning: JIRA is the main task management portal
Backend Services: UberFx https://lnkd.in/e3AyVSHZ and Glue https://lnkd.in/eKXNe8RK for Golang. 
JFx framework using Springboot for Java. Flipr for configuration. 
Cadence for workflow systems: https://lnkd.in/e9jaZA_7
Experiment Platform: https://lnkd.in/e9VgtBBG
Deploying Applications: Up, https://lnkd.in/eU-5jaej 
Monitoring: M3DB for metrics https://lnkd.in/eUnQ2uNh 
uMetric is for standardized business metrics: https://lnkd.in/ew8WNKsx 
Metrics data: stored in https://lnkd.in/eZv3mTXV

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How to design a 𝐬𝐞𝐜𝐮𝐫𝐞 web API access for your website?
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When we open web API access to users, we need to make sure each API call is authenticated. This means the user must be whom they claim to be.
 
In this post, we explore two common ways:
1. Token based authentication
2. HMAC (Hash-based Message Authentication Code) authentication
 
The diagram below illustrates how they work.
 
𝐓𝐨𝐤𝐞𝐧 𝐛𝐚𝐬𝐞𝐝
Step 1 - the user enters their password into the client, and the client sends the password to the Authentication Server.
 
Step 2 - the Authentication Server authenticates the credentials and generates a token with an expiry time.
 
Steps 3 and 4 - now the client can send requests to access server resources with the token in the HTTP header. This access is valid until the token expires.
 
𝐇𝐌𝐀𝐂 𝐛𝐚𝐬𝐞𝐝
This mechanism generates a Message Authentication Code (signature) by using a hash function (SHA256 or MD5).
 
Steps 1 and 2 - the server generates two keys, one is Public APP ID (public key) and the other one is API Key (private key).
Step 3 - we now generate a HMAC signature on the client side (hmac A). This signature is generated with a set of attributes listed in the diagram.
 
Step 4 - the client sends requests to access server resources with hmac A in the HTTP header.
 
Step 5 - the server receives the request which contains the request data and the authentication header. It extracts the necessary attributes from the request and uses the API key that’s stored on the server side to generate a signature (hmac B.)
 
Steps 6 and 7 - the server compares hmac A (generated on the client side) and hmac B (generated on the server side). If they are matched, the requested resource will be returned to the client.
 
Question - How does HMAC authentication ensure data integrity? Why do we include “request timestamp” in HMAC signature generation?
 
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10 Good Coding Principles to improve code quality.

Software development requires good system designs and coding standards. We list 10 good coding principles in the diagram below.

🔹 01 Follow Code Specifications
When we write code, it is important to follow the industry's well-established norms, like “PEP 8”, “Google Java Style”, adhering to a set of agreed-upon code specifications ensures that the quality of the code is consistent and readable.

🔹 02 Documentation and Comments
Good code should be clearly documented and commented to explain complex logic and decisions, and comments should explain why a certain approach was taken (“Why”) rather than what exactly is being done (“What”). Documentation and comments should be clear, concise, and continuously updated.

🔹 03 Robustness
Good code should be able to handle a variety of unexpected situations and inputs without crashing or producing unpredictable results. Most common approach is to catch and handle exceptions.

🔹 04 Follow the SOLID principle
“Single Responsibility”, “Open/Closed”, “Liskov Substitution”, “Interface Segregation”, and “Dependency Inversion” - these five principles (SOLID for short) are the cornerstones of writing code that scales and is easy to maintain.

🔹 05 Make Testing Easy
Testability of software is particularly important. Good code should be easy to test, both by trying to reduce the complexity of each component, and by supporting automated testing to ensure that it behaves as expected.

🔹 06 Abstraction
Abstraction requires us to extract the core logic and hide the complexity, thus making the code more flexible and generic. Good code should have a moderate level of abstraction, neither over-designed nor neglecting long-term expandability and maintainability.

🔹 07 Utilize Design Patterns, but don't over-design
Design patterns can help us solve some common problems. However, every pattern has its applicable scenarios. Overusing or misusing design patterns may make your code more complex and difficult to understand.

🔹 08 Reduce Global Dependencies
We can get bogged down in dependencies and confusing state management if we use global variables and instances. Good code should rely on localized state and parameter passing. Functions should be side-effect free.

🔹 09 Continuous Refactoring
Good code is maintainable and extensible. Continuous refactoring reduces technical debt by identifying and fixing problems as early as possible.

🔹 10 Security is a Top Priority
Good code should avoid common security vulnerabilities.

Over to you: which one do you prefer, and with which one do you disagree?

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How does Docker Work?
 
Docker's architecture comprises three main components: 
 
🔹 Docker Client 
This is the interface through which users interact. It communicates with the Docker daemon. 
 
🔹 Docker Host 
Here, the Docker daemon listens for Docker API requests and manages various Docker objects, including images, containers, networks, and volumes. 
 
🔹 Docker Registry 
This is where Docker images are stored. Docker Hub, for instance, is a widely-used public registry.

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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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I created a visual mindmap with the aim of helping people understand important concepts related to caching systems ↓

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How does Garbage Collection work?
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Garbage collection is an automatic memory management feature used in programming languages to reclaim memory no longer used by the program.

🔹 Java
Java provides several garbage collectors, each suited for different use cases:

1. Serial Garbage Collector: Best for single-threaded environments or small applications.

2. Parallel Garbage Collector: Also known as the “Throughput Collector.“

3. CMS (Concurrent Mark-Sweep) Garbage Collector: Low-latency collector aiming to minimize pause times.

4. G1 (Garbage-First) Garbage Collector: Aims to balance throughput and latency.

5. Z Garbage Collector (ZGC): A low-latency garbage collector designed for applications that require large heap sizes and minimal pause times.

🔹 Python
Python's garbage collection is based on reference counting and a cyclic garbage collector:

1. Reference Counting: Each object has a reference count; when it reaches zero, the memory is freed.

2. Cyclic Garbage Collector: Handles circular references that can't be resolved by reference counting.

🔹 GoLang
Concurrent Mark-and-Sweep Garbage Collector: Go's garbage collector operates concurrently with the application, minimizing stop-the-world pauses.

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Top 6 Load Balancing Algorithms Every Developer Should Know

🔹 Static Algorithms
1. Round robin
The client requests are sent to different service instances in sequential order. The services are usually required to be stateless.

2. Sticky round-robin
This is an improvement of the round-robin algorithm. If Alice’s first request goes to service A, the following requests go to service A as well.

3. Weighted round-robin
The admin can specify the weight for each service. The ones with a higher weight handle more requests than others.

4. Hash
This algorithm applies a hash function on the incoming requests’ IP or URL. The requests are routed to relevant instances based on the hash function result.

🔹 Dynamic Algorithms
5. Least connections
A new request is sent to the service instance with the least concurrent connections.

6. Least response time
A new request is sent to the service instance with the fastest response time.

Watch the full video explanation here: https://lnkd.in/ezNufJUb

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Top 5 Most-Used Deployment Strategies. New YouTube Video:
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In this video, we will talk about: 
- Big Bang Deployment 
- Rolling Deployment 
- Blue-Green Deployment 
- Canary Deployment 
- Feature Toggle 
 
Watch and subscribe here: https://lnkd.in/enFJi9_d

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A Roadmap for Full-Stack Development.
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A full-stack developer needs to be proficient in a wide range of technologies and tools across different areas of software development. Here’s a comprehensive look at the technical stacks required for a full-stack developer.

🔹 1. Frontend Development
Frontend development involves creating the user interface and user experience of a web application.

🔹 2. Backend Development
Backend development involves managing the server-side logic, databases, and integration of various services.

🔹 3. Database Development
Database development involves managing data storage, retrieval, and manipulation.

🔹 4. Mobile Development
Mobile development involves creating applications for mobile devices.

🔹 5. Cloud Computing
Cloud computing involves deploying and managing applications on cloud platforms.

🔹 6. UI/UX Design
UI/UX design involves designing the user interface and experience of applications.

🔹 7. Infrastructure and DevOps
Infrastructure and DevOps involve managing the infrastructure, deployment, and continuous integration/continuous delivery (CI/CD) of applications.

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