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by "SAP" <sap@mailsap.com> - 09:33 - 4 Jun 2025

LAST CALL !!! Fundamentals Of Pumps And Cooling Towers (11 & 12 June 2025)

Please call 012-588 2728

email to pearl-otc@outlook.com

FACE-TO-FACE PUBLIC PROGRAM

FUNDAMENTALS OF PUMPS

AND COOLING TOWERS

Venue : Dorsett Grand Subang Hotel, Selangor (SBL Khas / HRD Corp Claimable Course)

Date : 11 June 2025 (Wed) | 9am – 5pm By Ir. Dr. Jaya

12 June 2025 (Thu) | 9am – 5pm . .

COURSE INTRODUCTION AND OVERVIEW

It can rightly be claimed that no machine and very few tools have had as long a history in the service of man as the pump, or have filled as broad a need in his life. Every process which underlies our modern civilization involves the transfer of liquids from one level of pressure or static energy to another. Pumps have played an essential role in our life ever since the dawn of civilization. Worldwide developments in pump theory, design and applications have continued to emerge, and these have begun to affect the outlook of pump engineers and users. Pumps have continued to grow in size, speed and energy level, revealing new problems that are being addressed by innovative materials, mechanical and hydraulic design approaches.

A Cooling Tower is a heat rejection device which extracts waste heat to the atmosphere through the cooling of a water stream to a lower temperature. Cooling towers may either use the evaporation of water to remove process heat and cool the working fluid to near the wet-bulb air temperature or, in the case of closed circuit dry cooling towers, rely solely on air to cool the working fluid to near the dry-bulb air temperature.

Common applications include cooling the circulating water used in oil refineries, petrochemical and other chemical plants, thermal power stations and HVAC systems for cooling buildings. The main types of cooling towers are natural draft and induced draft cooling towers. The classification is based on the type of air induction into the tower.

Cooling towers vary in size from small roof-top units to very large hyperboloid structures that can be up to 200 meters tall and 100 meters in diameter, or rectangular structures that can be over 40 meters tall and 80 meters long. The hyperboloid cooling towers are often associated with nuclear power plants, although they are also used to some extent in some large chemical and other industrial plants. Although these large towers are very prominent, the vast majority of cooling towers are much smaller, including many units installed on or near buildings to discharge heat from air conditioning.

Pumps and Cooling Towers have their fair share of engineering problems in industry.

The said problems could be classified into:-

· Problems which could be identified and solved immediately, with little or no financial implications or loss in production and a drop in quality levels;

· Problems which could be identified and cannot be solved immediately, with little or no knowledge of its implications;

· Problems which recur frequently and cannot be solved permanently etc;

Some of the reasons for these are due to:

· Incompetent staff;

· A vast variety of equipment;

· Many moving (sliding, reciprocating, rotating) components etc.

This course is industry designed, to provide a broad understanding of the improvement methodology, concepts, and process. The methodology is presented with case studies and examples drawn from service, business process and manufacturing applications. The integration of maintenance is also addressed. With a heavy practice orientation, as much as a third of your time will be spent working through interactive practical exercises and assessments.

COURSE OBJECTIVE

This training program is designed to provide an understanding of engineering related problems related to industry globally and a clear sense of what is required to effectively structure, establish measurements and solve problems. Participants will learn the goals and deliverables behind the solutions. Methodology as well as the most commonly used tools within each phase will be discussed. Participants will also learn how to support a problem solving initiative within their organization.

LEARNING OUTCOME

On successful completion of this course, the participant should be able to:-

· Understand the benefits and implications of pump and Cooling Towers engineering problem solving programa, and relate the concepts to the overall business mission and objectives.

· Think about his/her organization as a collection of processes, with inputs that determine the output.

· Recognize the engineering problem solving model used to improve processes.

· Recognize the organizational factors that are necessary groundwork for a successful engineering problem solving program.

· Integrate the engineering problem solving program effort with other process improvement initiatives.

· Learn the various types of centrifugal pumps and Cooling Towers, their functions and terminologies used;

· Understand the design methodologies for centrifugal pumps and Cooling Towers;

· Understand the various maintenance methods for centrifugal pumps and Cooling Towers; and

· Understand the application of centrifugal pumps and Cooling Towers in various industries.

TRAINING METHODOLOGY

· The latest educational methods and strategies will be utilized.

· The course is designed to maximize delegate participation.

· Questions and answers are encouraged throughout and at the daily wrap-up sessions. This gives participants the opportunity to discuss with others and the presenter their specific problems and appropriate solutions.

· The course shall be conducted through lectures, case studies, group discussions and exercises to reinforce participant’s learning.

MEASUREMENT TOOLS

Quiz and exercises.

WHO SHOULD ATTEND

Facilities engineers, maintenance engineers, design engineers and any one who would like to widen their knowledge on the theory of pumps.

Past Participants

Universiti Teknologi Malaysia, Penang Port Sdn.Bhd. Cement Industries of Malaysia Berhad, Politeknik Mukah (Sarawak), Petlin Malaysia Sdn Bhd, TNB, SYABAS, Bintulu Port, Sime Austral etc.

OUTLINE OF WORKSHOP

DAY 1

Introduction to Pumps

· Introduction

· Definition of head and related calculations

· Definition of capacity and related calculations, flow velocity

· Piping design:- Parts 1, 2, 3 and 4

Net Positive Suction Head and Specific Speed in Pumps

· Types of pumps

· Calculation of NPSH

· Suggested remedies for NPSH, and cavitation.

Pump and System Characteristic Curves

· Various types of system curves

· Information required to plot system curve

· The various types of pump curves for the various types of pumps.

Power, Efficiency and Electrical Requirements for Pumps

· Power requirements

· Motor selection

· Power consumption and energy savings

· Types of motors

· Torque and power factor

Variable Speed Systems for Pumps

· Reasons for using variable speed systems

· Energy savings under constant and fluctuating flow demand.

Affinity Laws in Pumps

· The relationship between pump speed, flow, power, head and impeller diameter

· Analysis of the pump composite performance curve

DAY 2

Cooling Tower Fundamentals

· Main Features

· Components of a cooling tower

· Measured Parameters

· Performance Parameters

· Selecting a cooling tower

Cooling Tower Performance

· Thermodynamics of Air Water Systems

· Evaporative Cooling

· Enthalpy of Wet air

· Heat and Mass Transfer in water air system

Cooling Tower Water Treatment

· Scale Formation

· Corrosion

· Fouling

· Hardness

· Biofouling

· Control of Concentration

· Bleed Control

Cooling Tower Types

· Parallel vs. Series Flow

· Induced Draft, Cross Flow

· Natural Draft

· Hyperbolic Cooling Tower

Cooling Tower Maintenance

· Legionella Awareness

· Frequency of Cleaning/Disinfection

· Sterilization

· Areas of Performance

· High Risk Facilities

· Survey of Process Risk

· Monitoring and Inspection

· Preventive Actions

Question & Answer Session

End of Program

** Certificate of attendance will be awarded for those who completed the course

ABOUT THE FACILITATOR

IR.DR.JAYA

He was a recipient of the Association of Overseas Technical Scholarships (AOTS) award on two occasions, awarded by the Ministry of Economy, Trade and Industry of Japan. Jaya was also a member of the working group to draw up the energy efficiency and energy conservation guidelines for pumps and compressors for Malaysian industries organized by the Institution of Engineers, Malaysia, Pusat Tenaga Malaysia, and the Ministry of Energy, Water and Communications (KTAK). He has also attended trade related negotiations etc. with MATRADE, foreign companies etc.

He is also

· the immediate past Chairman of the Engineering Education Technical Division of The Institution of Engineers, Malaysia

· a member of the Education Committee of the Association of Consulting Engineers (ACEM)

· a member of the Examination Bureau, The Institution of Engineers, Malaysia

· a member, Accreditation Board, The Institution of Engineers, Malaysia

· a member, Standing Committee on Qualifications & Admission, The Institution of Engineers, Malaysia

· a member, Industry Advisory Panel, The University of Nottingham (Malaysian Campus)

· a member, Industry Advisory Panel, Universiti Tenaga Nasional

Professionally trained in mechanical engineering, he is positive and lively in his approach. He livens his audience by always providing interactive and energetic presentation of his material in a comprehensive and easy to use format of ideas that work. His participants describe him as very creative, fun and lively trainer who sets high standards.

He graduated from the University of Malaya, Leeds Polytechnic, UK and Universiti Putra Malaysia and holds a Degree in Mechanical Engineering and Masters in Engineering. His wide hands-on industrial experience has enable him to design and facilitate training courses.

Some of the companies he has trained includes, Tenaga Nasional Berhad, Bank Pembangunan, Ranhill, Ebara Pumps, Usaha Tegas Sdn.Bhd (Maxis), Infineon (M) Sdn. Bhd, Universiti Malaya, Universiti Teknologi Malaysia, Penang Port Sdn. Bhd. Cement Industries of Malaysia Berhad, Politeknik Mukah (Sarawak), Petlin Malaysia Sdn Bhd, Bintulu Port, Sime Austral, Perbadanan Putrajaya Holdings, Syarikat Air Johor etc.

IR.DR.Jaya is a Professional Engineer registered with the Board Of Engineers (BEM), Malaysia, member of the Association of Consulting Engineers, Malaysia (ACEM) and a Fellow of The Institution of Engineers, Malaysia (IEM).

(SBL Khas / HRD Corp Claimable Course)

TRAINING FEE

2 days Face-to-Face Public Program

RM 2,407.00/pax

(excluded 8% SST)

Group Registration: Register 3 participants from the same organization, the 4th participant is FREE.
(Buy 3 Get 1 Free) if Register before 30 May 2025.

Please act fast to grab your favourite training program!

We hope you find it informative and interesting and we look forward to seeing you soon.

Please act fast to grab your favorite training program! Please call 012-588 2728

or email to pearl-otc@outlook.com

Do forward this email to all your friends and colleagues who might be interested to attend these programs

If you would like to unsubscribe from our email list at any time, please simply reply to the e-mail and type Unsubscribe in the subject area.

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Regards

Pearl

by "sump@otcmsb.com.my" <sump@otcmsb.com.my> - 05:08 - 4 Jun 2025

About the Best Price for security communication switch

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by "sales03" <sales03@hzgws-poe.com> - 02:29 - 4 Jun 2025

Digital innovation for retailers and CPG companies

Re:think

FRESH TAKES ON BIG IDEAS

ON CONSUMER INNOVATION

Retail and CPG brands can be tech leaders

Hannah Mayer

Retailers and consumer-packaged-goods (CPG) companies have a unique opportunity to lead in digital innovation. After all, they interact daily with billions of consumers who expect great tech-driven experiences. Yet few have a digital-native gene at their very core. Many retailers and CPG brands are established companies, some more than a century old, and they have deeply ingrained practices, partnerships, and supply chains. Breaking free of tried-and-true patterns is always hard, but essential. Going forward, that means exploring how AI will transform the consumer sector. We are entering the AI-to-consumer era, where shoppers will interact with AI shopping bots and agents to find and make purchases.

So, what steps can retail and CPG CEOs take to create new AI-focused business models—ones that both expand on and reinvent their core offerings to generate significant new revenue? There is, of course, no easy way to create a breakout digital business. It entails managing multiple high-growth S-curves at the same time. For example, CPG brands will need to rethink distribution models since shopping agents could soon influence consumer choices more than traditional marketing.

But there is one success marker we see again and again: Retail brands that put their customers first have a huge head start. CEOs of these companies constantly ask themselves, “Would this new technology make the customer experience better or are we releasing tech for tech’s sake?” And they actually measure the impact of every new digital offering. If it doesn’t improve customer satisfaction by X basis points, then it’s gone.

Putting customers first isn’t just a front-end challenge. It also requires back-end technical transformation. Retail and CPG companies are now competing for the best developer talent and investing in APIs that help integrate their multiple tech offerings, such as personalized shopping bots, digital try-ons, and subscription management tools.

AI is on every CEO’s mind today; our research found that more than 90 percent of CEOs plan to invest more in AI over the next three years. And we see that retail and CPG companies have a once-in-a-lifetime opportunity to become AI leaders. Agentic AI, where intelligent agents act as personal concierges for consumers, is especially promising. Imagine an AI agent that autonomously replenishes a shopper’s household essentials, finds lower-cost subscriptions, and plans vacations end to end, even purchasing sunscreen if the consumer is headed to a sunny destination—with their consent, of course.

“The retail and CPG sectors could generate up to $660 billion in economic value by deploying gen AI.”

Discoverability is also undergoing a profound shift. Finding the latest cool products will no longer depend solely on search engine rankings. Instead, AI agents will guide consumers directly to the products and services that best meet their needs, changing traditional search. For retailers, this means ensuring their products are optimized for agent-driven discovery.

The retail and CPG sectors could generate up to $660 billion in economic value by deploying gen AI. Combined with the value of traditional AI and analytics, retail brands could unlock trillions of dollars in new value from AI.

Workers are ready to embrace AI, so another area where AI could have a big impact is improving retail associates’ jobs. We have seen retailers provide their employees with mobile AI chatbots that can answer shoppers’ questions or recommend complementary products, for example. Some retailers are using AI to make weekly shift scheduling more dynamic, working around their employees’ other commitments. The best digital businesses are both customer- and employee-centric because empowered teams deliver great customer experiences.

Of course, true change on the front end is only possible if retailers and CPG companies also transform their back-end systems. Forward-thinking CEOs implement AI at every step of the customer life cycle, including brainstorming, prototyping, and testing new products; leveraging digital twins to optimize supply chains; and creating one-to-one marketing campaigns for each customer.

Despite the excitement around AI, there is much work to be done. Just 1 percent of CEOs believe their companies are mature users of AI, and just 11 percent of AI projects actually get deployed. Retailers investing now in AI are breaking new ground, but they might not see near-term gains in revenue or customer growth from AI just yet. However, they are learning valuable lessons that could position them ahead of competitors. And as more and more plug-and-play AI solutions become available, retailers with this head start will be the first to reap the impact of AI at scale. The most important advice I could give any CEO of a retail or CPG company today is to get their hands dirty. Don’t be afraid to try new things with technology. With AI, no one company has mastered it yet, so it’s OK to experiment. Be curious and ask questions—one of which is always, “Would this technology make consumers’ lives better?”

Share Hannah Mayer’s insights

ABOUT THIS AUTHOR

Hannah Mayer is a partner in McKinsey’s Bay Area office.

MORE FROM THIS AUTHOR

Superagency in the workplace: Empowering people to unlock AI’s full potential

Almost all companies invest in AI, but just 1 percent believe they are at maturity. Our research finds the biggest barrier to scaling is not employees—who are ready—but leaders, who are not steering fast enough.

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Turning consumer and retail companies into software-driven innovators

As customers increasingly expect seamless, engaging digital experiences, consumer-facing organizations can follow six principles to perform like the most successful tech companies.

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UP NEXT

Emily Shao on the business of autonomous vehicles

If autonomous vehicles aren’t already on the streets of your city, they will be soon. How can industry leaders make the move from deployment to market dominance?

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by "McKinsey Quarterly" <publishing@email.mckinsey.com> - 02:07 - 4 Jun 2025

Learn to build flow-based UIs – workshop with our CTO

Hi,

Recently you were interested in workflowbuilder.io. Next week, our CTO, Łukasz Jaźwa, is running a live workshop on how to build flow-based user interfaces using Workflow Builder SDK and React — as part of the upcoming React Summit.

Where & When?

June 9th, 4–7 PM CET
Remote, via Zoom

It’s a session full of real-world techniques, shortcuts, and expert code insights. As a bonus, you’ll also open-source access to Workflow Builder (non-commercial license).

Would you like me to send over the details?

Best,

--

Ruslana Brykaliuk Sales Development Representative
www.synergycodes.com

by "Ruslana Brykaliuk" <ruslana.brykaliuk@workflowbuilder.io> - 07:49 - 4 Jun 2025

Re: Business update..?

Hi,

I am still waiting for your reply,

Please let me know if you are interested in my services below.

Thanks,

From: Sartaj Khan
Sent: 02 May 2025 22:30
Subject: Just reply

Hi There,

Greetings of the day!

Would you be interested in building an app for your business? We have catered several clients needs in the below domains:-

Travel Apps
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If you are interested in any of our app services, let us know your project requirements or perhaps name an existing app that you think is similar to your requirement. We will suggest an action plan for you to consider after reviewing your needs.

Thanks,

Sarta

by "Sartaj Khan" <sartaj.khan33@hotmail.com> - 04:14 - 4 Jun 2025

Wholesale Aquarium Pumps from China | Durable & 24H Battery Life

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by "Caisy Furxer" <caisyfurxer@gmail.com> - 01:45 - 4 Jun 2025

What will it take to achieve metabolic health for all?

On McKinsey Perspectives

DELIVERING ACTIONABLE INSIGHTS ON THE DAY’S NEWS

Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms

Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.

—Alex and Axel

Tackling global obesity

In the news

•

Banishing the pot belly. In India, the once-revered pot belly—considered a sign of affluence—is now prompting health concerns in the country, the BBC reports. In 2021, 180 million adults in India were considered obese or overweight; by 2050, that figure may swell to 450 million. Experts believe that Indians require more intensive lifestyle shifts than what Western guidelines suggest. Studies have found that South Asian men benefit from about 250 to 300 minutes of exercise each week, compared with 150 minutes for their European peers, to compensate for their slower metabolisms. [BBC]

•

Fluctuating prices. Glucagon-like peptide-1 receptor agonists, or GLP-1s, have emerged as a groundbreaking treatment for obesity, Bloomberg notes. These medications are also unaffordable for many in the US, with a high price tag and inconsistent coverage from insurance providers. Some companies are now providing lower-priced options to those whose insurance doesn’t cover the medication. Even so, some patients could still face higher costs: As the supply of brand-name drugs has improved, telehealth providers may no longer offer the less expensive generics. [Bloomberg]

The McKinsey Health Institute estimates that prioritizing obesity prevention, societal shifts, and metabolic health could lead to a $5.65 trillion uplift in annual global GDP in 2050.

On McKinsey.com

•

A major health concern. Obesity has become a major public health issue that affects nearly 900 million adults globally. The condition has significant health and economic consequences, McKinsey Senior Partners Drew Ungerman and Hemant Ahlawat and their coauthors explain. Obesity heightens the risk of many other diseases, including type 2 diabetes, dementia, and depression. It also has adverse effects on labor force participation and productivity: According to McKinsey Health Institute estimates, obesity will be linked to $2.76 trillion in lost GDP each year in 2050.

•

A cross-sector opportunity. The rise of new weight management drugs, such as GLP-1s, is changing perceptions of obesity from an intractable issue to a treatable medical condition. This shift is also attracting investment and spurring innovation in the food, healthcare, medtech, and retail industries, among others. Ahlawat, Ungerman, and coauthors outline two paths for addressing obesity: one that’s more reactive and relies on medical treatment and weight management drugs, and a second, more holistic approach to metabolic health that includes prevention and treatment.

Improve metabolic health

—Edited by Belinda Yu, editor, Atlanta

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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:42 - 4 Jun 2025

The part of AI most companies are missing



Hi MD Abul, I was reading through the latest news over coffee this morning, something about Amazon deploying 750,000 robots and planning to save $16 billion a year with AI by 2032. And for some reason, it didn’t just strike me as “big tech news.” It felt familiar. Like déjà vu. Because we’ve seen this play before. And we’ve misread it before, too. Amazon’s warehouses are running like a well-oiled machine, robots sorting, drones delivering, and AI optimizing every move. It’s impressive, no doubt. But here’s the thing: They didn’t just throw AI at a problem and hope it would fix things. They redesigned their operations around it. They rewired the way they work, end to end. And that’s the part most companies miss. We see it all the time, organizations investing in new tools, automation, cloud platforms, AI… but the core of how they operate? It stays the same. Same structure. Same workflows. Same mindset. That’s why we’re talking more and more about Digital Enterprise Operations (DEO) at Third Stage. It’s not about adding tech to a broken process, it’s about rethinking the way your business runs in a digital world. If you’re exploring AI, automation, or a system overhaul, the question isn’t “What tool should we buy?” It’s “What kind of business are we trying to become?” That’s the shift. And if you’re ready to make it, we’ll be diving deep into these exact conversations at our Digital Stratosphere conference this August-strategy, systems, AI, and how to build something that actually works. Our theme this year is Mission: AI-Possible. Join us in Denver! Use the code "earlybird" for 20% off your ticket(s)! Best regards, Eric Kimberling


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by "Eric Kimberling" <eric.kimberling@thirdstage-consulting.com> - 09:49 - 3 Jun 2025

Learn from the funnel building Phenom! (free training)

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by "Lusia Zeb" <lusia80018@hotmail.com> - 02:02 - 3 Jun 2025

How Netflix Runs on Java?

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How Netflix Runs on Java?

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Disclaimer: The details in this post have been derived from the articles/videos shared online by the Netflix Engineering Team. All credit for the technical details goes to the Netflix Engineering Team. The links to the original articles and videos are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.

Netflix is a masterclass in backend engineering at scale. Behind the seamless playback, tailored recommendations, and cross-device consistency lies an intricate architecture powered by Java.

The majority of Netflix’s backend services run on Java. This might surprise engineers who’ve watched the rise of Kotlin, Go, Rust, and reactive frameworks. But Netflix isn’t sticking with Java out of inertia. Java has matured, and so has the ecosystem around it. Modern JVMs offer powerful garbage collectors. Spring Boot has become both extensible and reliable. And with the arrival of virtual threads and structured concurrency, Java is reclaiming its place in high-throughput, low-latency system design, without the overhead of reactive complexity.

In this article, we’ll walk through how Netflix uses Java today. We will also cover the following topics:

The architectural backbone of Netflix: a federated GraphQL platform connecting client apps to dozens of Java backend services.
The concurrency model: how Java virtual threads and modern garbage collectors change performance and reliability.
The evolution: a company-wide migration off technical debt and onto Spring Boot, JDK 21+, and beyond.

Backend Architecture with the GraphQL Foundation

At the heart of Netflix’s backend lies a federated GraphQL architecture. This is the primary abstraction through which all client applications interact with backend data. The model provides both flexibility and insulation: clients can express exactly what they need, and backend teams can evolve their services independently.

Every GraphQL query from a Netflix client, whether from a smart TV, phone, or browser, lands at a centralized API Gateway. This gateway parses the query, decomposes it into subqueries, and routes those to the appropriate backend services.

Each backend team owns a Domain Graph Service (DGS), which implements a slice of the overall GraphQL schema. Every Domain Graph Service (DGS) at Netflix is a Spring Boot application.

The DGS framework itself is built as an extension of Spring Boot. This means that it supports the following features:

Dependency injection, configuration, and lifecycle management are handled by Spring Boot.
GraphQL resolvers are just annotated Spring components.
Observability, security, retry logic, and service mesh integration are implemented using Spring’s mechanisms.

Netflix picked Spring Boot because it’s proven at scale and a long-lived technology at Netflix. It’s also extensible as Netflix layers in their modules for security, metrics, service discovery, and more.

The DGSs register their schema fragments to a shared registry. The gateway then knows which service is responsible for which field. This turns one “monolithic” schema into a fully federated, independently deployable graph.

This separation of schema ownership enables:

Independent deployability of services
Schema-driven collaboration between frontend and backend
Cleaner boundaries between domains (for example, recommendations vs. user profiles)

The key idea is that backend services own their part of the graph, not just their internal data.

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Behind the scenes, even a simple query, like fetching titles and images for five shows, fans out across multiple services:

The API Gateway receives the request.
It contacts 2–3 DGSs to resolve fields like metadata, artwork, and availability.
Each DGS may then fan out again to fetch from data stores or call other services.

This fan-out pattern is essential for flexibility but introduces real complexity. The system needs aggressive timeouts, retry logic, and fallback strategies to prevent one slow service from cascading into user-visible latency.

Protocol Choices

Between the client and the gateway, Netflix sticks with HTTP and GraphQL over standard web protocols. This ensures compatibility across browsers, mobile apps, and smart TVs.

Inside the backend, services communicate over gRPC: a high-performance, binary protocol that supports efficient service-to-service calls. gRPC enables:

Low-latency communication
Strong typing via Protocol Buffers
Easy interface evolution

This separation makes sense: GraphQL is great for flexible, client-driven data fetching, while gRPC excels in internal RPC-style interactions.

JVM Evolution

Until recently, much of Netflix’s Java codebase was stuck on JDK 8. The problem wasn’t inertia but lock-in. A custom in-house application framework, built years earlier, had accumulated layers of unmaintained libraries and outdated APIs. These dependencies had tight couplings and compatibility issues that made upgrading anything beyond JDK 8 risky.

In this setup, service owners couldn’t move forward independently. Even when newer Java versions were technically available, the platform wasn’t ready. Teams had little incentive to upgrade because it required effort with no immediate benefit. The result was stalled progress across the board.

Breaking that cycle required a direct approach. Netflix patched the incompatible libraries themselves, not by rewriting everything, but by forking and minimally updating what was necessary to make it compatible with JDK-17. In practice, this wasn’t as daunting as it sounds. Ultimately, only a small number of critical libraries required intervention.

In parallel, the company began migrating all Java services (around 3000) to Spring Boot. This wasn’t a simple lift-and-shift. They built automated tooling to transform code, configure services, and standardize deployment. While the effort was significant, the result is a unified platform that can evolve in step with the broader Java ecosystem.

Now, the baseline across most teams is Spring Boot on JDK 17 or newer. A few legacy services remain for backward compatibility, but they were the exception.

Once services moved to JDK 17, the benefits became obvious:

The G1 garbage collector, already in use, showed a significant improvement: roughly 20% less CPU time spent on GC without changing application code.
Fewer and shorter stop-the-world pauses led to fewer cascading timeouts in distributed systems.
Higher overall throughput and better CPU utilization became possible, especially for high RPS services.

Generational ZGC

G1 garbage collector served Netflix well for years. It struck a balance between throughput and pause time, and most JVM-based services used it by default. But as traffic scaled and timeouts tightened, the cracks showed.

Under high concurrency, some services saw stop-the-world pauses lasting over a second, long enough to cause IPC timeouts and trigger retry logic across dependent services. These retries inflated traffic, introduced jitter, and obscured the root cause of failures. In clusters running at high CPU loads, G1's occasional latency spikes became an operational burden.

However, the introduction of generational ZGC changed the game.

ZGC had been available in prior Java versions, but it lacked a generational memory model. That limited its effectiveness for workloads where most allocations were short-lived, like Netflix’s streaming services.

In JDK 21, generational ZGC finally arrived. It brought a modern, low-pause garbage collector that also understood object lifetime. The effect was immediate:

Pause times dropped to near-zero, even under heavy load.
Services no longer timed out during GC pauses, which led to a visible reduction in error rates.
With fewer garbage collection stalls, fewer upstream requests failed, reducing cluster-wide pressure.
Clusters ran closer to CPU saturation without falling over. Headroom previously held in reserve for GC safety was now available for real workloads.

From an operator’s perspective, these improvements were significant. A one-line configuration change (switching from G1 to ZGC) translated into smoother behavior, fewer alerts, and more predictable scaling.

Use of Java Virtual Threads

In the traditional concurrency model, each request handler runs on a separate thread.

For high-throughput systems, this leads to high thread counts, inflated memory usage, and scheduling overhead. Netflix faced exactly this situation, particularly in its GraphQL stack, where individual field resolvers might perform blocking I/O.

Parallelizing these resolver calls manually was possible, but painful. Developers had to reason about thread pools, manage “CompletableFutures”, and deal with the complexity of mixing blocking and non-blocking models. Most didn’t bother unless performance made it unavoidable.

With Java 21+, Netflix began rolling out virtual threads. These lightweight threads, scheduled by the JVM instead of the OS, allowed blocking code to scale without monopolizing resources. For services built on the DGS framework and Spring Boot, the integration was automatic. Resolvers could now run in parallel by default.

See the diagram below that shows the concept of Java virtual threads.

Take the common example of a GraphQL query that returns five shows, each requiring artwork data. Previously, the resolver fetching artwork URLs ran serially, adding latency across multiple calls. With virtual threads, those calls now execute in parallel, cutting total response time significantly, without changing the application code.

Netflix wired virtual thread support directly into their frameworks:

Spring Boot–based services automatically benefit from parallel execution in field resolvers.
Developers don’t need to use new APIs or change annotations.
The thread scheduling model remains abstracted away, preserving familiar development workflows.

This opt-in-by-default model works because virtual threads impose almost no overhead. The JVM manages them efficiently, making them suitable even in high-volume paths where traditional thread-per-request models fall apart.

Trade-Offs

Virtual threads aren't magic. Early experiments revealed a specific failure mode: deadlocks caused by thread pinning.

Here's what happened:

Some libraries used synchronized blocks or methods.
When a virtual thread enters a synchronized block, it becomes pinned to a physical platform thread.
If many pinned virtual threads block while holding locks, and the pool of platform threads is exhausted, the system can deadlock. No threads can make progress, because the thread that holds the lock can’t be scheduled.

Netflix encountered this exact scenario in production.

The issue was serious enough that Netflix temporarily backed off aggressive virtual thread adoption. But with JDK 24, this problem was addressed directly: the JVM rewrote the internals of synchronized to avoid unnecessary thread pinning.

With that change in place, the engineering team was able to once again push forward. The performance and simplicity gains are too good to ignore, and now the risk has been meaningfully reduced.

Why Netflix Moved Away from RxJava?

Netflix helped pioneer reactive programming in the Java ecosystem. RX Java, one of the earliest and most influential reactive libraries, was born in-house. For years, reactive abstractions shaped the way services handled high-concurrency workloads.

Reactive programming excels when applied end-to-end: network IO, computation, and data storage all wrapped in non-blocking, event-driven flows. But that model requires total buy-in. In practice, most systems fall somewhere in between: some async libraries, some blocking IO, and a lot of legacy code. The result is a brittle mix of concurrency models that’s hard to reason about, hard to debug, and easy to get wrong.

One frequent pain point was combining a thread-per-request model with a reactive HTTP client like WebClient. Even when it worked, it introduced two concurrency layers (one blocking, one non-blocking), creating complex failure modes and resource contention. It was effective for certain fan-out use cases, but operationally expensive.

The introduction of virtual threads shifted the equation. As mentioned, they allowed thousands of concurrent blocking operations without the overhead of traditional threads. Combined with structured concurrency, developers can express complex async workflows using plain code, without the callback hell of reactive programming.

Having said that, reactive programming still has its place. In services with long IO chains, backpressure concerns, or streaming workloads, reactive APIs remain useful. However, for the bulk of Netflix’s backend, which involves RPC calls, in-memory joins, and tight response times, virtual threads and structured concurrency offer the same benefits with lower complexity.

The Spring Boot Netflix Stack

Netflix standardizes its backend services on Spring Boot, not as an off-the-shelf framework, but as a base for a deeply integrated, extensible platform. Every service runs on what the team calls “Spring Boot Netflix”: a curated stack of modules that layer company-specific infrastructure into the familiar Spring ecosystem.

This design keeps the programming model clean. Developers use standard Spring annotations and idioms. Under the hood, Netflix wires in custom logic for everything from authentication to service discovery.

The Spring Boot Netflix stack includes:

Security integration with Netflix’s authentication and authorization systems, exposed through standard Spring Security annotations like @Secured and @PreAuthorize.
Observability support using Spring’s Micrometer APIs, connected to internal tracing, metrics, and logging pipelines built to handle Netflix-scale telemetry.
Service mesh integration for all traffic via a proxy-based system (built on ProxyD), handling TLS, service discovery, and retry policies transparently.
gRPC framework based on annotation-driven programming models, letting engineers write gRPC services with the same approach as REST controllers.
Dynamic configuration with “fast properties”: runtime-changeable settings that avoid service restarts and enable live tuning during incidents.
Retryable clients wrapped around gRPC and WebClient to enforce timeouts, retries, and fallback strategies out of the box.

Netflix stays closely aligned with Spring Boot upstream. Minor versions roll out to the fleet within days. For major releases, the team builds tooling and compatibility layers to smooth the upgrade path.

The move to Spring Boot 3 required migrating from javax.* to jakarta.* namespaces. It was a breaking change that affected many libraries. Rather than wait for external updates, Netflix built a Gradle plugin that performs bytecode transforms at artifact resolution time. This plugin rewrites compiled classes to use the new Jakarta APIs, allowing Spring Boot 2-era libraries to work on Spring Boot 3 without source changes.

Conclusion

Netflix’s Java architecture in 2025 isn’t a relic of the past, but a deliberate, modern engineering system. What makes it interesting isn’t the choice of language, but the way that choice is continuously reevaluated, optimized, and aligned with real-world constraints.

The system isn’t static. Netflix pushed past the limits of Java 8 by aggressively upgrading its stack, not by rewriting it. It embraced Spring Boot as a foundation but extended it to meet the unique demands of a global streaming platform. It adopted GraphQL for flexibility, virtual threads for concurrency, and ZGC for performance.

Some key takeaways are as follows:

Java is still competitive when treated as an ecosystem. Netflix extracts significant performance gains from modern JVM features. It doesn’t settle for default settings or old frameworks. Evolution is deliberate.
Owning the platform enables speed. Building in-house tooling for patching, transforming, and deploying applications turns upgrades from risk into routine. Platform ownership is a leverage for them.
Virtual threads reduce complexity. They preserve a familiar coding style while scaling better under load. The payoff is cleaner code, fewer bugs, and simpler mental models.
Tuning infrastructure and not just code improves reliability. Upgrading the garbage collector and optimizing thread behavior led to fewer timeouts, lower error rates, and more consistent throughput across the board.

References:

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Excelling as a leader: Growth

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Fast fashion: An industry in flux

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Decoding fast fashion. Fast fashion retailers—which offer the latest designs to consumers, both quickly and economically—have witnessed rapid growth, according to British Vogue. But this growth comes with its own set of challenges, including the fashion industry’s contribution to global carbon emissions. Despite younger consumers’ inclination toward sustainable clothing, the US fast fashion industry, valued at nearly $46 billion in 2025, could reach about $68 billion over the next seven years. [British Vogue]

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Sustainability challenges. Most fashion brands are finding it difficult to stay on track with their 2030 decarbonization goals, with 40% actually increasing their emissions since making earlier sustainability commitments. But tackling emissions doesn’t have to be as expensive as fashion leaders may think. McKinsey research shows that most fashion companies can cut their emissions by more than 60% for less than 1 to 2% of their revenues. Explore our McKinsey Explainers entry “What is fast fashion?” for six ways fashion companies can accelerate decarbonization.

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Dear Info,

I hope this email finds you well.

This is Yiwa from Run Tian Zhi Ke Mechanical Equipment Co. Ltd. We are focused on the manufacturing of construction machine for more than 20 years. Posses over 100 self-developed patents. Our products are sold to Europe, Southeast Asia, Africa and other regions.

We supply stabilized soil plant, concrete mixing plant, asphalt mixing plant, crushing and screening plant,Construction waste processing production line.

Our customer complaint rate is zero. We always focus on customers’ needs.

Our products are mainly used for Construction of highways, railways, municipal engineering, hydro-power, ports, bridges, large constructions, main, etc..

And if possible, we are glade to invite you to coming to visit our intelligent factory.

Really look forward to hearing from you.

Your sincerely

Yiwa

Marketing Manager

Mobile/Whataspp#:(+86) 18890380993 | Email: market@runtian-global.com

Hunan Runtianzhike Machinery Manufacturing Co.,Ltd

Website: www.runtian-global.com

ADD:No.9, Shengyuan Road, Yueyang High-tech Industrial Park, Hunan, China

by "sales01" <sales01@rtzkhn.com> - 05:24 - 2 Jun 2025

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