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Which gen AI operating models can help banking leaders compete?
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by "Only McKinsey" <publishing@email.mckinsey.com> - 11:06 - 7 Apr 2024 -
The quarter’s top Themes
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In the first quarter of 2024, our top ten posts from McKinsey Themes look at highlights from the World Economic Forum’s annual meeting, generative AI, the state of talent, and more. At No. 1 is “10 key takeaways from Davos 2024,” which includes a collection of must-read insights for today’s business leaders by McKinsey’s Michael Chui, Homayoun Hatami, Dana Maor, Kate Smaje, Bob Sternfels, Rodney Zemmel, and others. Read on for our full top 10.
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by "McKinsey Top Ten" <publishing@email.mckinsey.com> - 06:09 - 7 Apr 2024 -
The week in charts
The Week in Charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:20 - 6 Apr 2024 -
EP106: How Does JavaScript Work?
EP106: How Does JavaScript Work?
Forwarded this email? Subscribe here for moreThis week’s system design refresher:
Roadmap for Learning SQL (Youtube video)
Can Kafka lose messages?
9 Best Practices for building microsercvices
Roadmap for Learning Cyber Security
How does Javascript Work?
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Roadmap for Learning SQL
Can Kafka lose messages?
Error handling is one of the most important aspects of building reliable systems.
Today, we will discuss an important topic: Can Kafka lose messages?
A common belief among many developers is that Kafka, by its very design, guarantees no message loss. However, understanding the nuances of Kafka's architecture and configuration is essential to truly grasp how and when it might lose messages, and more importantly, how to prevent such scenarios.
The diagram below shows how a message can be lost during its lifecycle in Kafka.
Producer
When we call producer.send() to send a message, it doesn't get sent to the broker directly. There are two threads and a queue involved in the message-sending process:
1. Application thread
2. Record accumulator
3. Sender thread (I/O thread)
We need to configure proper ‘acks’ and ‘retries’ for the producer to make sure messages are sent to the broker.Broker
A broker cluster should not lose messages when it is functioning normally. However, we need to understand which extreme situations might lead to message loss:
1. The messages are usually flushed to the disk asynchronously for higher I/O throughput, so if the instance is down before the flush happens, the messages are lost.
2. The replicas in the Kafka cluster need to be properly configured to hold a valid copy of the data. The determinism in data synchronization is important.
Consumer
Kafka offers different ways to commit messages. Auto-committing might acknowledge the processing of records before they are actually processed. When the consumer is down in the middle of processing, some records may never be processed.
A good practice is to combine both synchronous and asynchronous commits, where we use asynchronous commits in the processing loop for higher throughput and synchronous commits in exception handling to make sure the the last offset is always committed.
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9 Best Practices for building microsercvices
Creating a system using microservices is extremely difficult unless you follow some strong principles.
9 best practices that you must know before building microservices:
Design For Failure
A distributed system with microservices is going to fail.
You must design the system to tolerate failure at multiple levels such as infrastructure, database, and individual services. Use circuit breakers, bulkheads, or graceful degradation methods to deal with failures.Build Small Services
A microservice should not do multiple things at once.
A good microservice is designed to do one thing well.Use lightweight protocols for communication
Communication is the core of a distributed system.
Microservices must talk to each other using lightweight protocols. Options include REST, gRPC, or message brokers.Implement service discovery
To communicate with each other, microservices need to discover each other over the network.
Implement service discovery using tools such as Consul, Eureka, or Kubernetes ServicesData Ownership
In microservices, data should be owned and managed by the individual services.
The goal should be to reduce coupling between services so that they can evolve independently.Use resiliency patterns
Implement specific resiliency patterns to improve the availability of the services.
Examples: retry policies, caching, and rate limiting.Security at all levels
In a microservices-based system, the attack surface is quite large. You must implement security at every level of the service communication path.Centralized logging
Logs are important to finding issues in a system. With multiple services, they become critical.Use containerization techniques
To deploy microservices in an isolated manner, use containerization techniques.
Tools like Docker and Kubernetes can help with this as they are meant to simplify the scaling and deployment of a microservice.
Over to you: what other best practice would you recommend?Roadmap for Learning Cyber Security
By Henry Jiang. Redrawn by ByteByteGo.
Cybersecurity is crucial for protecting information and systems from theft, damage, and unauthorized access. Whether you're a beginner or looking to advance your technical skills, there are numerous resources and paths you can take to learn more about cybersecurity. Here are some structured suggestions to help you get started or deepen your knowledge:
Security Architecture
Frameworks & Standards
Application Security
Risk Assessment
Enterprise Risk Management
Threat Intelligence
Security Operation
How does Javascript Work?
The cheat sheet below shows most important characteristics of Javascript.
Interpreted Language
JavaScript code is executed by the browser or JavaScript engine rather than being compiled into machine language beforehand. This makes it highly portable across different platforms. Modern engines such as V8 utilize Just-In-Time (JIT) technology to compile code into directly executable machine code.Function is First-Class Citizen
In JavaScript, functions are treated as first-class citizens, meaning they can be stored in variables, passed as arguments to other functions, and returned from functions.Dynamic Typing
JavaScript is a loosely typed or dynamic language, meaning we don't have to declare a variable's type ahead of time, and the type can change at runtime.Client-Side Execution
JavaScript supports asynchronous programming, allowing operations like reading files, making HTTP requests, or querying databases to run in the background and trigger callbacks or promises when complete. This is particularly useful in web development for improving performance and user experience.Prototype-Based OOP
Unlike class-based object-oriented languages, JavaScript uses prototypes for inheritance. This means that objects can inherit properties and methods from other objects.Automatic Garbage Collection
Garbage collection in JavaScript is a form of automatic memory management. The primary goal of garbage collection is to reclaim memory occupied by objects that are no longer in use by the program, which helps prevent memory leaks and optimizes the performance of the application.Compared with Other Languages
JavaScript is special compared to programming languages like Python or Java because of its position as a major language for web development.
While Python is known to provide good code readability and versatility, and Java is known for its structure and robustness, JavaScript is an interpreted language that runs directly on the browser without compilation, emphasizing flexibility and dynamism.Relationship with Typescript
TypeScript is a superset of JavaScript, which means that it extends JavaScript by adding features to the language, most notably type annotations. This relationship allows any valid JavaScript code to also be considered valid TypeScript code.Popular Javascript Frameworks
React is known for its flexibility and large number of community-driven plugins, while Vue is clean and intuitive with highly integrated and responsive features. Angular, on the other hand, offers a strict set of development specifications for enterprise-level JS development.
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 6 Apr 2024 -
Gen AI: How to capture value and mitigate risk
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McKinsey research has estimated that generative AI (gen AI) has the potential to add up to $4.4 trillion in economic value to the global economy. But companies are quickly realizing that capturing this value is harder than expected. There’s also a growing recognition that gen AI opportunities are accompanied by considerable risks. In this month’s first featured story, McKinsey senior partners Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel outline why companies have to rewire how they work with gen AI to gain a real competitive advantage from the technology. Our second featured story explores why business leaders need to revise their technology playbooks and drive the integration of effective risk management from the start of their engagement with gen AI. Other highlights include the following topics:
Investing in productivity growth
It’s time to raise investment and catch the next productivity wave.
Pave the way
McKinsey Global Private Markets Review 2024
Private markets entered a slower era in 2023, with macroeconomic headwinds, rising financing costs and an uncertain growth outlook weighing on fundraising, deal activity and performance.
Boost performance in a new era
The CEO’s secret to successful leadership: CEO Excellence revisited
Three McKinsey senior partners—and, now, international best-selling authors—reflect on the far-reaching impact of their book, CEO Excellence, two years after its release.
Dare to lead
Working nine to thrive
Better aligning employment with modifiable drivers of health could unlock years of higher-quality life and create trillions of dollars of economic value.
Support employees
Analyzing the CEO–CMO relationship and its effect on growth
CEOs acknowledge the expertise and importance of chief marketing officers and their role in helping the company grow, yet there’s still a strategic disconnect in the C-suite. Here’s how to close the gap.
Take a holistic approach
How the world’s best hotels deliver exceptional customer experience
Luxury hotels know that the secret to top-tier customer experience is a culture of excellence.
Create a culture of excellence
McKinsey Themes
Browse our essential reading on the topics that matter.
Get up to speed
McKinsey Explainers
Find direct answers to complex questions, backed by McKinsey’s expert insights.
Learn more
McKinsey on Books
Explore this month’s best-selling business books prepared exclusively for McKinsey Publishing by Circana.
See the lists
McKinsey Chart of the Day
See our daily chart that helps explain a changing world—as we strive for sustainable, inclusive growth.
Dive in
McKinsey Classics
Significant improvements in risk management can be gained quickly through selective digitization—but capabilities must be test-hardened before release. Read our 2017 classic “Digital risk: Transforming risk management for the 2020s” to learn more.
Rewind
Leading Off
Our Leading Off newsletter features revealing research and inspiring interviews to empower you—and those you lead.
Subscribe now—Edited by Eleni Kostopoulos, managing editor, New York
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by "McKinsey Highlights" <publishing@email.mckinsey.com> - 11:27 - 6 Apr 2024 -
CEO Excellence—two years on
The Shortlist
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Curated by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
Technology is trending like never before, with gen AI at the crest of the wave. But our decades of experience working with executives have taught us an important lesson: it’s never just about the tech. To get the most out of digital investments, companies need to pull other levers, like leadership, change management, talent, and innovation. In this edition of the CEO Shortlist, we highlight some of the catalysts that can unleash technology’s full value, and we explore the latest on CEO excellence. We hope you enjoy the read.
—Liz and Homayoun
Step away from the grindstone. Nobody likes the admin side of their job. Gen AI can help. The technology is brilliant at handling dull, repetitive tasks, and companies are rapidly installing applications to do them. This creates a critical opportunity for leaders: deploying gen AI to free up employees’ time for work better suited to humans, such as creative, collaborative thinking. Organizations that succeed are likely to build a long-term competitive edge.
Put on your thinking cap with “The human side of generative AI: Creating a path to productivity,” a new article by Aaron De Smet, Sandra Durth, Bryan Hancock, Marino Mugayar-Baldocchi, and Angelika Reich.What got you here may not get you there. In business—and maybe more broadly—innovation is always needed to outcompete in times of uncertainty. But innovation itself needs a shake-up every once in a while. That’s where gen AI comes in. The most innovative organizations have already deployed gen AI tools to help invigorate their creativity and agility—and they’re reaping the rewards.
It’s not too late to give your organization a gen-AI-powered innovation boost. Learn more with “Driving innovation with generative AI,” a new interview with McKinsey’s Matt Banholzer and Laura LaBerge from our Inside the Strategy Room podcast.Picture this. Most companies are thinking about how gen AI chatbots can help produce text. But don’t forget that these new tools can draw too. Almost every company uses some form of industrial design, whether to create widgets or websites. Gen AI tools can open new doors of creativity and speed—but human designers are far from obsolete.
Draw up a chair and read “Generative AI fuels creative physical product design but is no magic wand,” by Bryce Booth, Jack Donohew, Chris Wlezien, and Winnie Wu.It’s a wide world. Start-ups. Founder-led companies. Portfolio companies. Government organizations. Not for profits. It’s been two years since we published CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest, and one thing we’ve learned is that the excellent leadership qualities we’ve distilled in this book are applicable way beyond the purely corporate audience we imagined at the outset. Find out more as we catch up with the book’s authors and learn about the journey their international bestseller has taken them on.
Hitch a ride with “The CEO’s secret to successful leadership: CEO Excellence revisited,” by Carolyn Dewar, Scott Keller, and Vikram Malhotra. (And read more from the interview here and here.)We hope you find these ideas inspiring and helpful. See you next time with four more McKinsey ideas for the CEO and others in the C-suite.
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by "McKinsey CEO Shortlist" <publishing@email.mckinsey.com> - 04:56 - 5 Apr 2024 -
What would it take to make air travel fairer for all?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:05 - 5 Apr 2024 -
A Crash Course in CI/CD
A Crash Course in CI/CD
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Introduction
What is CI/CD? How does it help us ship faster? Is it worth the hassle? In this issue, we will look into Continuous Integration and Continuous Deployment, or CI/CD for short. CI/CD helps automate the software development process from the initial code commit to deployment. It eliminates much of the manual human intervention traditionally required to ship code to production.
The CI/CD process builds, tests, and deploys code to production. The promise is that it enables software teams to deploy better-quality software faster. This all sounds very good, but does it work in real life? The answer is — it depends.
Let’s break up CI/CD into their parts and discuss them separately.
Continuous Integration (CI)
Continuous integration (CI) is a development practice that many people believe they're using in their work, but they don't fully get it.
Before CI came along, development teams typically operated in silos, with individual developers working independently on distinct features for extended periods. Their work would eventually need to be merged into a shared codebase, often resulting in complications such as merge conflicts and compatibility issues among the hundreds of files and contributors. This dilemma, often known as "merge hell," represented the difficulties faced in traditional development methods.
Avoid “merge hell”
Let's consider a scenario with two developers, Alice and Bob. Alice writes her code and shares it as soon as she has a functional version that doesn't cause any issues, even if it's not fully complete. She uploads her code to a central repository. Bob follows the same approach, always grabbing the latest version of the code before starting his work. As Alice continues to update her code, Bob does the same. If Bob makes changes, Alice incorporates them into her work without any problems. They collaborate smoothly, with a low chance of interfering each other because they always work off the latest code. If they encounter conflicts, it's usually on recent changes they've both made, so they can sit down together, resolve the issues, and move forward.
However, with so many people constantly contributing code, problems are inevitable. Things may not always run smoothly, and new errors can emerge. So, what's the solution?
Automation
The solution is automation. It acts like a vigilant watchdog, constantly monitoring the code. Whenever a change occurs, it springs into action, grabbing the code, building it, and running tests. If anything fails during this process, the team receives an alert, ensuring everyone is aware of the issue. With this safety net in place, continuous integration becomes a reality.
So, what exactly is continuous integration (CI)?
Definition
Continuous integration involves automating builds, executing tests, and merging code from individual developers into a shared repository. The primary goal of continuous integration is to efficiently integrate source code into shared repositories. Once changes are committed to the version control system, automated builds and test cases are executed to ensure the functionality and validity of the code. These processes validate how the source code compiles and how test cases perform during execution.
Tools
What are some common tools used in CI? A robust source code management system is the foundation. GitHub is a popular example. It holds everything needed to build the software, including source code, test scripts, and scripts to build the software applications.
Many tools are available to manage the CI process itself. GitHub Actions and Buildkite are modern examples, while Jenkins, CircleCI, and TravisCI are also widely used. These tools manage the build and test tasks in CI.
Numerous test tools exist for writing and running tests. These tools are usually language and ecosystem-specific. For example, in Javascript, Jest is a unit testing framework, while Playwright and Cypress are common integration testing frameworks for web applications.
Build tools are even more diverse and ecosystem-specific. Gradle is a powerful build tool for Java. The Javascript build ecosystem is fragmented and challenging to keep track of. Webpack is the standard, but many new build tools claim to be much faster, although they are not yet as extensible as Webpack.
Benefits of continuous integration
Continuous integration holds significant importance for several reasons. The table below presents some of the major advantages of CI.
Continuous Deployment (CD)
Continuous deployment (CD) is the next step after CI in the CI/CD pipeline. CD is the practice of automatically deploying every code change that passes the automated testing phase to production.
While true continuous deployment is challenging and not as widely adopted as CI, a more common practice is continuous delivery, which is similar but has a subtle difference, as explained below.
Continuous delivery
Continuous delivery focuses on the rapid deployment of code changes into production environments. Its roots can be traced back to the Agile Manifesto, which emphasizes “early and continuous delivery of valuable software” to satisfy customers.
The objective of continuous delivery is to efficiently transition valuable code changes into production. The initial step involves transforming the code into deployable software through a build process. Once the software is ready, the next logical step might seem to be deploying it directly into production. However, the real practice involves rigorous testing to ensure that only stable software enters the production environment.
Typically, organizations maintain multiple test environments, such as "QA," "Performance," or "Staging." These environments serve as checkpoints for validating the software before it reaches production. The software undergoes testing in each environment to ensure its readiness for deployment.
In essence, the journey to production in continuous delivery involves transitioning software through various testing environments before deployment into the production environment.
A key aspect of continuous delivery is ensuring that the code remains deployable at all times. Once the delivery process is completed, the code is ready for deployment to any desired environment. This end-to-end process includes building the source code, executing test cases, generating artifacts such as WAR or JAR files, and delivering them to specific environments.
Automatic deployment
Coming back to continuous deployment (CD), it involves the automatic deployment of code changes to the production environment. Essentially, CD represents the final stage in the development pipeline. In this phase, not only are artifacts prepared and test cases executed, but the process extends further to deploying the artifacts to the production environment. Continuous deployment ensures that any changes made to the code are promptly deployed to the production environment without human intervention.
Continuous deployment vs. continuous delivery
Continuous deployment and continuous delivery are related concepts, but they have distinct differences. Here, we list some of the differences:
While continuous deployment may be suitable for some organizations, continuous delivery is the approach that many are striving to achieve, as it offers a cautious yet automated approach to software delivery.
Tools
The tools we mentioned earlier, like GitHub Actions, Buildkite, and Jenkins, are commonly used to handle CD tasks. Infrastructure-specific tools also make CD easier to maintain. For example, ArgoCD is popular on Kubernetes.
CI/CD is a powerful software development practice that can help teams ship better-quality software faster. However, it's not a one-size-fits-all solution, and its implementation may vary depending on the complexity of the system.
Benefits of continuous deployment
Continuous deployment offers numerous benefits to organizations. Here, we list some of them.
Deployment Strategies
Nothing beats the satisfaction of seeing our code go live to millions of users. It is always thrilling to see. But getting there is not always easy. Let’s explore some common deployment strategies.
Big bang deployment
One of the earliest methods of deploying changes to production is the Big Bang Deployment. Picture it like ripping off a bandage. We push all our changes at once, causing a bit of downtime as we have to shut down the old system to switch on the new one. The downtime is usually short, but be careful - it can sting if things don't go as planned. Preparation and testing are key. If things go wrong, we roll back to the previous version. However, rolling back is not always pain-free. We might still disrupt users, and there could be data implications. We need to have a solid rollback plan.
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by "ByteByteGo" <bytebytego@substack.com> - 11:37 - 4 Apr 2024 -
What makes public sector workers stay in their jobs?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
•
Attracting tomorrow’s workforce. Aging populations, Gen Z’s growing presence in the labor force, increased workforce diversity, and other trends are reshaping demand for government services. By 2030, Gen Z is expected to account for about 30% of the global workforce. Yet in the US, Gen Z accounted for just 1.6% of the federal workforce, compared with Gen X at 42.0%. Clearly, the government faces challenges when it comes to mentoring, apprenticing, and developing its next-generation workforce, McKinsey senior partner Julia Klier and coauthors explain.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:06 - 4 Apr 2024 -
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Black Americans are not thriving as much as their White neighbors, but certain steps could help
Re:think
FRESH TAKES ON BIG IDEAS
ON BLACK AMERICANS’ PROSPERITY
Enabling prosperity for Black Americans—no matter where they liveDuwain Pinder
When we started the McKinsey Institute for Black Economic Mobility in 2020, one of our first reports was The economic state of Black America: What is and what could be. The report examined challenges and opportunities for Black Americans in five different roles that they play in the economy: workers, business owners, savers and investors, consumers, and residents. Since then, we have been going deep into each of these roles. In 2021, we looked at Black consumers. This year, we’re focusing on Black residents.
Black Americans’ opportunities and outcomes vary significantly in different places. When you look nationally at only things like life expectancy and the poverty rate, you can miss quite a bit of nuance and detail. If you really want to understand what’s happening, you have to look at the issue of place as well. Black Americans, for example, are more likely than most of the US population to be concentrated in major cities. But when you look at the places where Black Americans are doing the best, it’s in the suburbs and exurbs. However, these are the very places where you’re least likely to find Black Americans.
What’s interesting in these US suburbs and exurbs is that while Black residents are doing great relative to Black Americans across the nation, if you compare them with their White neighbors, they’re doing only about 65 percent as well. There’s still a significant racial wealth gap. Black Americans today are much more prosperous than they were ten years ago: about 75 percent of US counties have seen improvements in overall Black prosperity. But in many places, White Americans’ outcomes have been improving at a faster rate than their Black neighbors.
The question is, how do we build a set of solutions that can improve Black Americans’ outcomes? There is effectively no place in the country where Black residents are doing as well as their White neighbors are. In fact, there are only a few places in the United States where Black people’s outcomes are at or above 90 percent of their White neighbors’. One is Paulding County, a suburb outside of Atlanta; several other counties with large Black populations outside of Atlanta, Houston, and Washington, DC, also are doing relatively well on parity. But most other places with higher parity are small, rural communities, so we’re talking about small populations in places where residents of all races tend to be less well off.“To address the nationwide disparity, we need an all-hands-on-deck approach in which many solutions are operating at scale for a long time.”
There’s no easy answer for how to address the nationwide disparity. Instead, we need an all-hands-on-deck approach in which many solutions are operating at scale for a long time. There are certain areas for investment that can deliver broad, downstream effects. Affordable housing, for example, is linked to improved physical and mental health, economic opportunity, and other measures of prosperity. Early-childhood education also has been shown to yield significant impact—and not just on the individual who is receiving the childcare. Because quality childcare improves parents’ ability to find meaningful work, it improves an entire family’s long-term economic outlook. Investing in early-childhood education also benefits the educators themselves, who disproportionately tend to be Black women.
How can Black economic mobility improve? In affordable housing, one partial solution that we’ve seen is developing underused land. We’ve also found that using new technology and efficient construction methods can decrease the cost of that housing. You can also boost access to programs that connect people to existing affordable housing or provide financial assistance or even just awareness. In education, you can expand access to high-quality pre-K programs by adding student seats, boosting the number of trained—and well-paid—teachers, and investing in community and parent outreach to support enrollment. While the specifics vary according to the unique needs of the community, these are things that have been proven to create benefit. The problem is that they haven’t been scaled.
As part of our analysis, we held the prosperity of White Americans constant and asked, “At the current pace of change, how long would it take for Black Americans to reach the same level?” The conservative estimate was that it could take more than 300 years. That’s not an optimistic number.
That being said, there are many solutions that have real promise. If society can scale them and really commit over a long period of time, there can be genuine progress. So that makes me feel a lot more optimistic.Share Duwain Pinder’s insights
ABOUT THIS AUTHOR
Duwain Pinder is a leader of the McKinsey Institute for Black Economic Mobility and a partner in McKinsey’s Columbus, Ohio, office.
MORE FROM THIS AUTHOR
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Alexis Trittipo on climate change adaptation
Mitigating climate change isn’t nearly enough. Adapting to it is also crucial. Adaptation requires understanding how climate change may affect a particular area or asset, performing scenario planning, and taking the long view when collaborating across the public and private sectors to take action.
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by "McKinsey Quarterly" <publishing@email.mckinsey.com> - 02:36 - 3 Apr 2024 -
How could a big shift from cars to bicycles benefit cities?
On Point
Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
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Bike-friendly cities. European residents and city leaders are increasingly pressing for bike-friendly cities to boost micromobility. Yet some questions remain about how more bicycle usage can affect urban areas and what infrastructure changes may be needed, note McKinsey partner Kersten Heineke and coauthors. To understand the effect on the environment, commuting time, and transportation infrastructure, McKinsey analyzed a scenario in which residents of a Western European metropolitan area replaced 22.5% of the kilometers traveled by private cars with bicycles.
—Edited by Querida Anderson, senior editor, New York
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by "McKinsey Global Institute" <publishing@email.mckinsey.com> - 12:40 - 2 Apr 2024 -
Evolution of Java Usage at Netflix
Evolution of Java Usage at Netflix
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Netflix is predominantly a Java shop.
Every backend application at Netflix is a Java application. This includes:
Internal applications
The software that powers one of the largest film studios in the world and is used to produce movies
The Netflix streaming app
However, this doesn’t mean that the Java stack at Netflix is static. Over the years, it has evolved significantly.
In this post, we will look at the evolution of Java usage at Netflix in light of the overall architectural changes that have taken place to support the changing requirements.
The Groovy Era with BFFs
It’s common knowledge that Netflix has a microservice architecture.
Every piece of functionality and data is owned by a microservice and there are thousands of microservices. Also, multiple microservices communicate with each other to realize some of the more complex functionalities.
For example, when you open the Netflix application, you see the LOLOMO screen. Here, LOLOMO stands for list-of-list-of-movies and it is essentially built by fetching data from many microservices such as:
Service that returns a list of top 10 movies
Artwork service that provides personalized images for each movie
Movie metadata service that returns the movie titles, actor details, and descriptions
LOLOMO service that provides what lists to actually render for a user’s home page.
The below diagram shows this situation.
It’s quite possible that rendering just one screen on the Netflix app may involve calling 10 services.
However, calling so many services from your device (such as the television) or mobile app is typically inefficient. Making 10 network calls doesn’t scale and results in a poor customer experience. Many streaming apps suffer from such performance issues.
To avoid these issues, Netflix used a single front door for the various APIs. The device makes a call to this front door that performs the fanout to all the different microservices. The front door acts as a gateway and Netflix used Zuul for this purpose.
This approach works because the call to the multiple microservices takes place on the internal network which is very fast, thereby eliminating the performance implications.
However, there was another problem to solve.
All of the different devices users can use to access Netflix have different requirements in subtle ways. While Netflix tried to keep a consistent look and feel for the UI and its behavior on every device, each device still has different limitations when it comes to memory or network bandwidth and therefore, loads data in slightly different ways.
It’s hard to create a single REST API that can work on all these different devices. Some of the problems are as follows:
REST APIs Either fetch too much or too little data
Even if they created one REST API to take care of all data needs, it was going to be a bad experience because they would be wasting a lot of data
In the case of multiple APIs, it would mean multiple network calls
To handle this, Netflix used the backend for frontend (BFF) pattern.
In this pattern, every frontend or UI gets its own mini backend. The mini backend is responsible for performing the fanout and fetching the data that the UI needs at that specific point.
The below diagram depicts the concept of the BFF pattern.
In the case of Netflix, the BFFs were essentially a Groovy script for a specific screen on a specific device.
The scripts were written by UI developers since they knew what exact data they needed to render a particular screen. Once written, the scripts were deployed on an API server and performed the fanout to all the different microservices by calling the appropriate Java client libraries. These client libraries were wrappers for either a gRPC service or a REST client.
The below diagram shows this setup.
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The Use of RxJava and Reactive Programming
The Groovy scripts helped perform the fanout.
But doing such a fanout in Java is not trivial. The traditional approach was to create a bunch of threads and try to manage the fanout using minimal thread management.
However, things got complicated quickly because of fault tolerance. When dealing with multiple services, you can have one of them not responding quickly enough or failing, resulting in a situation where you’ve to clean up threads and make sure things work properly.
This is where RxJava and reactive programming helped Netflix handle fanouts in a better way by taking care of all the thread management complexity.
On top of RxJava, Netflix created a fault-tolerant library named Hystrix that took care of failover and bulkheading. Even though reactive programming was complicated, it made a lot of sense for the time and the architecture allowed them to serve most of the traffic needs of Netflix.
However, there were some important limitations to this approach:
There was a script for each endpoint resulting in a lot of scripts to maintain and manage
UI developers had to create all the mini backends and they didn’t like working in the Groovy Java space with RxJava. It’s not the primary language they use on a daily basis that makes things difficult
Reactive programming is generally hard and has a steep learning curve.
The Move to GraphQL Federation
Over the last few years, Netflix has been migrating to a completely new architecture when it comes to its Java services. The centerpiece of this new architecture is GraphQL Federation.
When you compare GraphQL to REST, the major difference is that GraphQL always has a schema. This schema helps define some key aspects such as:
All the operations along with the various queries and mutations
Fields available from the types that are being returned from the queries
For example, in the case of Netflix, you may have a query for all the shows that return a show type. It has a show as a title and also contains reviews, which may be another type.
With GraphQL, the client has to be explicit about the field selection. You can’t just ask for shows and get all the data from shows. Instead, you have to specifically mention that you want to get the title of the show and the score of various reviews. If you don’t ask for a field, you won’t get the field.
With REST, this was the opposite because you get whatever the REST service decides to send.
While it’s more work for the client to specify the query in GraphQL, it solves the whole problem around over-fetching where you get a lot more data than you might actually need. This paves the way to create one API that can serve all the different UIs.
To augment GraphQL, Netflix went one step further and used GraphQL Federation to fit it back into their microservices architecture.
The below diagram shows the setup with GraphQL Federation.
As you can see, the microservices are now called DGS or Domain Graph Service.
DGS is an in-house framework developed by Netflix to build GraphQL services. When they started moving to GraphQL and GraphQL Federation, there wasn’t any Java framework that was mature enough to use at the Netflix scale. Therefore, they built on top of the low-level GraphQL Java framework and augmented it with features like code generation for schema types and support for federation.
At its core, a DGS is just a Java microservice with a GraphQL endpoint and a schema.
While there are multiple DGSs, there’s just one big GraphQL schema from the perspective of a device such as the TV. This schema contains all the possible data that can be rendered. The device doesn’t need to worry about all the different microservices that are part of the schema in the backend.
For example, the LOLOMO DGS can define a type show with just the title. Then, the images DGS can extend that type show and add an artwork URL to it. The two different DGSs don’t know anything about each other. All they need to do is publish their schema to the federated gateway. The federated gateway knows how to talk to a DGS because all of them have a GraphQL endpoint.
There are several advantages to this setup:
There’s no API duplication anymore.
There is no need for a backend-for-frontend (BFF) because GraphQL as an API is flexible enough to support different devices due to the field selection feature.
No need for any server-side development by UI engineers. The backend developers and the UI developers just collaborate on the schema.
There is no need for any client libraries in Java anymore. This is because the federated gateway knows how to talk to a generic GraphQL service without the need to write specific code.
Java Versions at Netflix
Recently, Netflix has migrated from Java 8 to Java 17. After the migration, they saw about 20% better CPU usage on Java 17 versus Java 8 without any code changes. This was because of improvements in the G1 garbage collector. At the scale of Netflix, a 20% better CPU utilization is a big deal in terms of cost benefits.
Contrary to popular belief, Netflix doesn’t have its own JVM. They’re just using the Azul Zulu JVM which is an OpenJDK build.
Overall, Netflix has around 2800 Java applications that are mostly microservices of varying sizes. Also, they have around 1500 internal libraries. Some of them are actual libraries while many of them are just client libraries sitting in front of a gRPC or REST service.
For the build system, Netflix relies on Gradle. On top of Gradle, they use Nebula which is a set of open-source Gradle plugins. The most important aspect of Nebula is in the resolution of libraries. Nebula helps with version locking that helps with reproducible builds.
More recently, Netflix has been actively testing and rolling out changes with Java 21. Comparing the move from Java 8 to Java 17, it’s significantly easy to go from Java 17 to 21. Java 21 also provides a few important features such as:
Virtual threads allow server-side applications written in a thread-per-request style to scale at optimal hardware utilization. In a thread-per-request style, a request comes and the server provides a thread for it. All of the work for the request happens in this thread
An updated ZGC garbage collector that focuses on low pause times and works well in a broader variety of use cases.
Data-oriented programming with a combination of records and pattern-matching
Use of Spring Boot at Netflix
Netflix is famous for its use of Spring Boot.
In the last year or so, they have completely moved out of their homegrown Java stack based on Guice and completely standardized on Spring Boot.
Why Spring Boot?
It’s the most popular Java framework and has been very well maintained over the years.
Netflix found a lot of benefits in leveraging the huge open-source community of the Spring framework, existing documentation, and training opportunities that are easily available. The evolution of Spring and its features align very well with the core Netflix principle of “highly aligned, loosely coupled”.
Netflix uses the latest version of OSS Spring Boot and their goal is to stay as close as possible to the open source community. However, to integrate closely with the Netflix ecosystem and infrastructure, they have also created Spring Boot Netflix which is a bunch of modules built on top of Spring Boot.
Spring Boot Netflix has support for several things such as:
gRPC client
Server support integrated with the Netflix SSO stack for AuthZ and AuthN
Observability in the form of tracing, metrics, and distributed logging
HTTP clients that support mTLS
Service discovery with Eureka
AWS/Titus integration
Kafka, Cassandra and Zookeeper integration
Conclusion
There’s no singular Netflix stack.
The Netflix Java stack has been evolving over the last several years, beginning from in-house frameworks to Groovy-era microservices and more recently, moving to GraphQL Federation.
All the changes have been made to solve problems from the previous approach. For example, the move to RxJava was to handle fanouts in a better way and the move to GraphQL Federation was to solve the issues of complexity due to RxJava.
Along with these changes, there has also been a parallel evolution in terms of Java language versions from Java 8 to 17 and now 21+. A lot of it has also been prompted by Spring Boot version 3 finally moving beyond Java 8 and forcing the entire ecosystem to upgrade.
These changes have allowed them to build more performant applications that can save CPU costs and
Overall, the theme has been towards standardization of the approach in building microservices across the organization. However, considering the constant challenges faced in operating at their scale while staying ahead of the competition, the evolution will continue.
References:
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 2 Apr 2024 -
How is the Fourth Industrial Revolution transforming manufacturing?
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Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities
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Advanced manufacturing. As the Fourth Industrial Revolution (4IR) accelerates, advanced manufacturing, powered by AI and related technologies, is reinvigorating markets such as the US manufacturing sector, as revealed in the book The Titanium Economy (a Wall Street Journal bestseller). Members of the World Economic Forum’s Global Lighthouse Network—153 factories at the forefront of the 4IR—are using 4IR technologies to improve their operations and achieve factory-scale adoption. Lighthouses are three to five years further along than their peers in adopting 4IR technologies, McKinsey senior partner Enno de Boer and coauthors share.
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AI and the 4IR. Similar to the steam engine in the First Industrial Revolution and to AI technologies in tech and banking, 4IR’s breakthrough technologies are expected to catapult from single-digit to widespread adoption within the decade, with Lighthouses leading the way. AI-based examples make up over 60% of the use cases presented by new Lighthouse applicants, up from just 11% in 2019. Discover a pyramid of technology solutions that represent the full value of 4IR technologies, and for our latest thinking on AI, visit QuantumBlack, AI by McKinsey.
—Edited by Belinda Yu, editor, Atlanta
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