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by "Workday" <corp.email@workday.com> - 10:06 - 2 Oct 2024 -
How to make tech work for you
Re:think
FRESH TAKES ON BIG IDEAS
ON TECH ORGANIZATIONS
Want to be a tech company? BuildEvery business leader says they want their organization to be a tech company. But what does that mean in practice? There are some things it means, and others it doesn’t. It does mean you need an organization that can build technology. And we really mean build, not just make good choices about what to buy.
In our research over the past seven years, we’ve looked closely at hundreds of companies that are true digital leaders. Even before generative AI, 70 percent said that they were making their own software. A third were aspiring to monetize that software externally, but the vast majority were using it to help propel their own operations. We expect that number will eventually trend toward 100 percent. Everybody’s going to be able to make the technology they need to run their business. They’re also going to buy technology. But in domains where they hope to gain a real competitive advantage, they want to build something that is unique to them by assembling a carefully curated mix of available components.
What does this mean for your company? First, you want business and technology working well together and you want technology embedded in the company. Organizations that get that right are much more likely to be successful than those that don’t. At a recent conference, I was challenged to come up with one paragraph that explains the difference between a modern digital and AI tech culture and an IT culture. After some back-and-forth, we got it down to one word: “requirements.”
In an IT culture, businesspeople write requirements and hand them off to the technology team so they can provide gains. At successful digital and AI companies, business and technology co-own a problem, iterate around that problem, and own the answer on an ongoing basis. That’s not a new thought; it’s been called a product operating model, or agile before that. But how business and technology work together is the essential factor for operating like a tech company.
For our recent book, Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI, we benchmarked 50 banks to understand which ones were creating value through technology and which were not. We found that some 25 percent could pinpoint real and significant value from their digital investments. This 25 percent couldn’t be identified by how much they were spending, the quality of their apps, or even their technology architecture choices. They could be identified, however, by how well business and technology worked together.“Being a tech company doesn’t necessarily mean spending the most on technology. This is not about spending the most.”
Speed is another important factor. Companies that are becoming tech companies iterate faster and faster. They work in agile short-sprint cycles. They insource more than they outsource. To do so, they don’t just go out and hire all the cool kids from Silicon Valley. That’s a really good way to change the company’s dress code, but if you want to create lasting impact, you need to upskill and reskill your own employees. You also create the right technology career ladders for your digital talent. This means that technologists can learn from other technologists and that they can progress in a way that makes them as important as business leaders, rather than seen as support.
Being a tech company also means caring deeply about adopting and scaling technology. It’s change management; although I don’t like that term, because it implies that technology is always wonderful, and we dumb humans need to adapt to it. Instead, it’s about digging into the question of how you transform a business domain and thinking hard about the right incentives to encourage true technology adoption and scaling. This usually means incentivizing the businesspeople. They need to co-own the tech impact incentives. If everyone’s looking to the CEO for constant direction, that’s a failure.
There are some important questions a CEO can ask to determine if their organization is functioning as a true tech company. Do you understand which business domains are most able to be transformed by technology and have you put a clear financial goal against them? Do the best technologists want to work at your company? Do you have a product-release-and-update cycle that’s measured in weeks, not months? When you look at your senior-leadership team, how many would self-identify as being tech savvy and tech capable? When you think about advancing people into senior roles, is experience in getting value from tech one of the essential criteria? Do you have a tech talent road map that is as detailed as your road map for scaling technology? Do you understand where data will create competitive advantage in your company and how to combine your proprietary data successfully with the world’s data?
For some, these will coalesce into two questions. First, have you thought about how technology can completely reinvent your business? Second, how do you do business today and how could that fundamentally change if you harnessed technology properly?
Being a tech company doesn’t necessarily mean spending the most on technology. This is not about spending the most. It’s not about being the earliest adopter of new technologies. Perhaps you like to talk about having technology everywhere in your company or about the success of an exciting pilot. But if you’re not actually seeing the technology affect your profit and loss, you’re not a tech company.Share Rodney Zemmel’s insights
ABOUT THIS AUTHOR
Rodney Zemmel is a senior partner in McKinsey’s New York office.
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Generative AI raises many questions. Six of them—about opportunities, governance, partners, risk, talent, and learning—preoccupy many CEOs.
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by "McKinsey Quarterly" <publishing@email.mckinsey.com> - 02:48 - 2 Oct 2024 -
What steps can companies take to advance women at work?
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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
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Underrepresented in corporate America. Over the past decade, women’s representation has increased at every level of corporate management, with women today making up 29% of C-suite positions, compared with just 17% in 2015, McKinsey senior partners Alexis Krivkovich and Lareina Yee and coauthors share. Yet women remain underrepresented across the pipeline, a gender gap that persists regardless of race and ethnicity, finds the tenth-anniversary Women in the Workplace report, conducted in partnership with LeanIn.Org.
•
Barriers to entry. Women continue to face barriers at the beginning of the pipeline. They remain less likely than men to be hired into entry-level roles and are also far less likely than men to attain their very first promotion to a manager role. This year, for every 100 men who received their first promotion to manager, just 81 women were promoted. Learn some practical steps companies can take to support the advancement of women and make the workplace more equitable.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:13 - 2 Oct 2024 -
The Trillion Message Kafka Setup at Walmart
The Trillion Message Kafka Setup at Walmart
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If you are a founder, exec, PM, or engineer tasked with the enterprise roadmap, this conference is for you. You’ll get detailed insights from industry leaders that have years of experience navigating the same challenges you face today. And best of all, it’s completely free since it’s hosted by WorkOS.Disclaimer: The details in this post have been derived from the Walmart Global Tech Blog. All credit for the technical details goes to the Walmart engineering team. The links to the original articles 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.
Walmart has a massive Apache Kafka deployment with 25K+ consumers across private and public cloud environments.
This deployment processes trillions of Kafka messages per day at 99.99% availability. It supports critical use cases such as:
Movement of data
Event-driven microservices
Streaming analytics
At Walmart's scale, the Kafka setup must be able to handle sudden traffic spikes. Also, consumer applications are written in multiple languages. Therefore, all consumer applications must adopt some best practices to maintain the same level of reliability and quality.
In this post, we’ll look at the main challenges of a Kafka setup at this scale. Then, we will look at how Walmart’s engineering team enhanced its setup to overcome these challenges and reliably process messages cost-efficiently.
Challenges with Kafka at Walmart’s Scale
Let’s start with understanding the main challenges that Walmart faced.
1 - Consumer Rebalancing
One of the most frequent problems was related to consumer rebalancing.
But what triggers consumer rebalancing in Kafka?
This can happen due to the changing number of consumer instances within a consumer group.
Several scenarios are possible such as:
A consumer pod may enter or leave a consumer group. This can happen due to Kubernetes deployments, rolling restarts, or automatic scale-ins or scale-outs. Whenever it happens, Kafka needs to redistribute the partitions among the consumers.
The Kafka broker may believe that a consumer has failed. If the broker has not received a heartbeat from a consumer within the configured session timeout, it assumes that the consumer has died. This can happen if the consumer’s JVM exits or experiences a long stop-the-world garbage collection pause.
The Kafka broker may believe that a consumer is stuck and trigger rebalancing. If the consumer takes longer than a threshold to poll for the next batch of records, the broker marks it as stuck. This can happen when processing the previous batch takes too long.
Consumer rebalancing is needed to ensure partitions are evenly distributed. However, rebalancing can cause disruption and increased latency, particularly due to the near real-time nature of the e-commerce landscape.
2 - Poison Pill Messages
A “poison pill” message in Kafka is a message that consistently causes a consumer to fail when attempting to process it. This can happen due to various reasons such as:
Malformed Data: The message payload may not be in an expected format. For example, invalid JSON or missing required fields. This may cause the consumer to throw an exception while processing.
Unexpected Data: The message content might be syntactically valid but semantically incorrect. In other words, it might violate some business constraints.
Bugs in the Consumer Code: If there’s a bug (like a null pointer exception) in the code that handles the message, the processing will fail.
When the consumer encounters such a message, it will fail to process it and throw an exception. By default, the consumer will return to the broker to fetch the same batch of messages again. Since the poison pill message is still present in that batch, the consumer will again fail to process it, and this loop continues indefinitely.
As a result, the consumer gets stuck on this one bad message and is unable to make progress on other messages in the partition. This is similar to the “head-of-line blocking” problem in networking.
3 - Cost Concerns
There is a strong coupling between the number of partitions in a Kafka topic and the maximum number of consumers that can read from that topic in parallel. This coupling can lead to increased costs when trying to scale consumer applications to handle higher throughput.
For example, consider that you have a Kafka topic with 10 partitions and 10 consumer instances reading from this topic. Now, if the rate of incoming messages increases and the consumers are unable to keep up (i.e. consumer lag starts to increase), you might want to scale up your consumer application by adding more instances.
However, once you have 10 consumers (one for each partition) in a single group, adding more consumers to that group won’t help because Kafka will not assign more than one consumer from the same group to a partition. The only way to allow more consumers in a group is to increase the number of partitions in the topic.
However, increasing the number of partitions comes with its challenges and costs.
Kafka has a recommended limit on the number of partitions per broker (for example, 4000 partitions per broker). If you keep increasing partitions, you may hit this limit and need to scale the Kafka brokers to larger instances, even if the brokers have sufficient resources to handle the current load. Scaling to larger broker instances is expensive.
Increasing partitions requires coordination among the Kafka team, the producer, and the consumer teams. In a large organization with thousands of Kafka pipelines, this coordination overhead is significant.
More partitions also mean more open file handles, increased memory usage, and more threads on the Kafka brokers. This can lead to higher resource utilization and costs.
Designing the Messaging Proxy Service (MPS)
To overcome the challenges mentioned in the previous section, the Walmart engineering team designed a Message Proxy Service (MPS).
The diagram below shows a high-level view of MPS.
The MPS aims to decouple Kafka message consumption from the constraints imposed by Kafka’s partition-based model. It works like this:
MPS acts as a proxy between the Kafka brokers and the actual message consumer applications. It reads messages from Kafka partitions and puts them into a separate in-memory queue.
Consumer applications don’t directly read from Kafka. Instead, they receive messages from MPS via HTTP/REST. This allows the consumer applications to scale independently of the number of Kafka partitions.
MPS ensures in-order message processing per key, handles consumer application failures, and manages offset commits back to Kafka.
The diagram below shows the detailed design of the MPS with all its components
Let’s now look at the various components of the MPS in more detail.
Reader Thread
This is a single thread that reads messages from Kafka.
It writes the messages from the Kafka broker into a bounded queue called the “PendingQueue”. If the PendingQueue reaches its maximum size, the reader thread will pause reading from Kafka.
This is a form of backpressure to prevent the queue from growing indefinitely if the writer threads cannot keep up.
Bounded Buffer Queue (PendingQueue)
This is a queue that sits between the reader thread and the writer threads. It has a maximum size to prevent it from consuming too much memory.
The PendingQueue allows the reader and write threads to work at different speeds. The reader can read messages as fast as Kafka can provide them, while the writers can process them at their own pace.
Order Iterator
This component ensures that messages with the same key are processed in the order they were received from Kafka.
It goes through the messages in the PendingQueue and skips any message if there is already an earlier message with the same key being processed. At any given time, at most one message per key is being handled by the writer threads.
Writer Threads
These are a pool of threads that take messages from the PendingQueue and send them to the consumer applications via HTTP POST requests.
If a POST request fails, the writer thread will retry the request a few times. If the retries are exhausted or if the consumer application returns certain HTTP codes, the writer thread will put the message into a Dead Letter Queue (DLQ).
The writer threads also help manage offsets. They update a shared data structure to keep track of processed offsets.
Offset Commit Thread
This is a separate thread that periodically wakes up (for example, every minute) and commits the Kafka offsets of processed messages using the Kafka consumer API.
It checks the shared data structure that is updated by the writer threads. Then, it commits the latest continuous offset for each partition. For example, if messages with offsets 1, 2, 3, and 5 have been processed for a partition, it will commit offset 3 (because 4 is missing).
By committing offsets periodically, MPS tells Kafka which messages it has processed successfully. If MPS crashes or is restarted, it will start consuming messages from the last committed offset, avoiding reprocessing messages that have already been handled.
Consumer Service REST API
This is the specification that the actual message consumer applications need to implement to receive messages from the MPS.
It defines the format of the HTTP POST request that the MPS writer threads will send (headers, body, etc.). It also specifies the meaning of different HTTP response codes that the consumer application can return.
See the table below that shows the API specification:
Source: Walmart Tech Blog Implementation of MPS
MPS was implemented as a Kafka Connect sink connector.
For reference, Kafka Connect is a framework for connecting Kafka with external systems such as databases, key-value stores, search indexes, and file systems. It provides a standard way of defining connectors that move data into and out of Kafka.
The diagram below shows a high-level view of Kafka Connect
By implementing MPS as a Kafka Connect sink connector, the developers were able to use several features provided by the Kafka Connect Framework such as:
Multi-tenancy: Kafka Connect allows running multiple connectors on a single cluster. This means that a single MPS deployment can serve multiple consumer applications (tenants).
Dead Letter Queue (DLQ) Handling: Kafka Connect has built-in support for handling messages that cannot be processed.
Offset Commits: Kafka Connect provides APIs for committing offsets. MPS uses these for its offset commit thread.
Scalability: Kafka Connect is designed to be scalable and fault-tolerant. By building on top of Kafka Connect, MPS inherits these properties.
Apart from this, the consumer services (applications that process the messages) are designed to be stateless. This means they don’t maintain any persistent state locally. Any state they need is either passed with the message or stored in an external database.
Being stateless allows these services to be easily scaled up or down in Kubernetes based on changes in message volume. If more messages need to be processed, Kubernetes can simply start more instances of the consumer service. If fewer messages are coming in, Kubernetes can terminate some instances to save resources.
Importantly, this scaling of the consumer services is independent of MPS and Kafka. MPS continues to read from Kafka and deliver messages to the consumer services regardless of how many instances of the consumer service are running.
Additional Points To Consider
Here are some additional points worth considering based on the MPS solution implemented by Walmart.
1 - Rebalancing of the MPS
MPS is also essentially a Kafka consumer. It reads messages from Kafka topics and makes them available to the application consumers via REST endpoints.
Like any other Kafka consumer, MPS would also be subject to rebalancing when the number of MPS instances changes. Based on the details, however, it seems that MPS is designed to handle rebalancing gracefully. The separation of the reader thread (which polls Kafka) and the writer threads (which send messages to the REST consumers) is the key here.
As long as MPS comes back up quickly after a rebalance, the REST consumers should be able to continue processing messages without substantial lag. The MPS design also includes a bounded buffer (the PendingQueue) between the reader thread and writer threads. This buffer helps to smoothen any temporary fluctuations in the rate at which MPS is reading from Kafka.
2 - Choice of REST
MPS calls REST APIs exposed by the consumer instances. Interestingly, the choice was REST and not something like gRPC.
This may be because of the simplicity of REST. Also, REST is widely supported by almost all languages and frameworks.
3 - Potential Increase in Complexity
While MPS solves several problems, it also introduces an additional layer to the system.
Instead of just having Kafka and the consumer applications, there is now a proxy service in the middle. This means more components to develop, deploy, monitor, and maintain.
Conclusion
The implementation of MPS helped Walmart achieve some key improvements.
Most rebalances have now been eliminated except for rare restarts or network issues. With MPS, the reader thread consistently puts all polled messages into the PendingQueue within the allocated time. This prevents rebalances triggered by the Kafka broker thinking the consumer is stuck.
Poison pill messages are handled in a better way. With MPS, consumer services can detect poison pill messages and notify MPS using specific HTTP return codes (600 and 700).
MPS enables cost savings in multiple ways. Firstly, consumer services are now stateless and can scale quickly in Kubernetes based on demand. They don’t need to be scaled in advance. Secondly, Kafka clusters can be scaled based on throughput rather than the number of partitions.
References:
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Actionable insights on elevating your API observability
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How can employers better support neurodivergent workers?
Only McKinsey Perspectives
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
•
Employment challenge. Globally, about one in five people are considered neurodivergent—exhibiting differences in their brain function and behavioral traits—per Dr. Lawrence Fung, associate professor of psychiatry and behavioral sciences at Stanford University and the director of the Stanford Neurodiversity Project. Fung discusses neurodiversity and employment with McKinsey senior partner Brooke Weddle and partner Bryan Hancock on a recent episode of McKinsey Talks Talent, relating that about 80% of people with autism are unemployed or underemployed.
•
Collegial job interviews. People with autism often have difficulty during job interviews because of their manner of social interaction, Fung says. For example, some autistic people might talk about their special interests at length, and interviewers who don’t detect signs of neurodiversity might become disengaged. Training interviewers to understand such behaviors could make the interview process more friendly. Listen to the podcast to learn ways that employers can support neurodivergent workers.
—Edited by Belinda Yu, editor, Atlanta
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[You're Invited, Md Abul] | Create Stylish and Standout Documents Faster with GenAI
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Join me to learn how to monitor your AI applications with New Relic
Hi MD,I’d like to personally invite you to my upcoming 60 minute online webinar, How to monitor your AI applications with New Relic, on Tuesday at 10am BST / 11am CEST.
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Is your organization poised for hypergrowth? A leader’s guide
Leading Off
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Welcome to the latest edition of Leading Off. 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
In business, hypergrowth typically means maintaining a rapid rate of growth over time. Start-ups often have the agility to do this, but they may become more stratified as they grow larger—often because their hiring practices may not have kept pace with the scaling needs of the business. For their part, established businesses may not have the flexibility to push growth to the levels they need. This week, we look at how both start-ups and incumbent companies can deploy talent to position their organizations for consistent and rapid expansion.
Start-ups generally pay close attention to hiring the best talent to ensure the new company’s success. But as the business expands, the recruiting process can become more challenging, say McKinsey’s Claudy Jules, Shahar Markovitch, and Charlotte Seiler in a new article. “Talent needs inevitably change, skill gaps become more apparent, and it becomes increasingly difficult for founders and leaders to stay as personally involved in the hiring process,” they say. One possible solution is to conduct a formal assessment of the organization’s plans to scale and compare the current talent mix against those plans to identify any gaps. It may also help to hire talent in “functional areas that make the most sense strategically,” suggest the authors. “For instance, bringing new talent into sales, legal, and compliance teams may help scaling technology start-ups identify and lock in new accounts more quickly.”
That’s the number of critical questions company founders should ask when they seek to bring new leaders into the ranks, according to McKinsey senior partners Alok Kshirsagar and Arne Gast and colleagues. One core question to consider as the company grows is: Who leads? “While leaders may wear multiple hats in the early stages, organizational growth will likely call for more structure and clearer roles,” observe the McKinsey experts. “Moreover, the small circle of early leaders must acknowledge that the expanding enterprise will demand leadership and people skills that may be outside of their current knowledge and experience.”
That’s McKinsey senior partner Vincent Bérubé and colleagues on creating an integrated talent approach to power growth. Designing a talent win room involves pulling together leaders from across different functions and HR to develop a holistic view of the candidate experience. This could include revising job descriptions, looking at candidates with nontraditional backgrounds and experiences, and using generative AI and other analytics tools to match candidates with skills pools. For example, a software and services company redesigned its jobs architecture and review process to offer career paths usually available only at technology companies—and experienced not only a swift increase in the number of employees hired but also a reduction in the time spent from application to hiring.
There’s no unique magic that makes start-up employees different from those in a traditional corporate setting, says venture capitalist Florian Heinemann in a discussion with McKinsey partner Philipp Hillenbrand on how incumbent companies can incorporate a start-up-like approach to hypergrowth. What’s most important, he says, is for large companies to offer employees the incentives and flexibility that entrepreneurial ventures tend to nurture. “What you need is a structure parallel to the current business, plus some kind of independent, small, multidisciplinary group that mimics the start-up setting,” he suggests. Small cross-functional teams are also essential to ensure effective marketing and sales: “Very few people can create a superior product/market fit,” says Heinemann. “But if you get these people together in a small, fully or partially cross-functional team, that really gets things going. That’s why I like this kind of team so much, because the intensity with which you can iterate and experiment really creates energy.”
Your organization may have invested in top-notch people—but there’s no guarantee that they’ll stay. One reason could be that many organizations pay lip service to culture and values but may not adhere to these principles in practice. As a leader, are you always late for meetings? Do you advocate for work–life balance but consistently expect your teams to work evenings and weekends? McKinsey senior partner Dana Maor and colleagues suggest ten timeless questions for leaders to consider before filling jobs. For example, you may want to determine the level of cultural cohesiveness to establish or whether you wish to hire generalists or experts.
Lead by planning for hypergrowth.
— Edited by Rama Ramaswami, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:36 - 30 Sep 2024 -
What new style of leadership is needed for today’s business landscape?
Only McKinsey Perspectives
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
•
‘Inside out’ leadership. As organizations grapple with advancing technologies, climate change, and geopolitical complexity, CEOs can no longer rely on an imperial style of leadership, McKinsey senior partner Ramesh Srinivasan, senior partner emeritus Hans-Werner Kaas, and coauthors explain in their new book, The Journey of Leadership: How CEOs Learn to Lead from the Inside Out. To engage stakeholders effectively, leaders need to reflect on how they show up in the world, Srinivasan shares in a recent episode of The McKinsey Podcast.
•
Up close and personal. For leaders to inspire their teams and the institutions that they’re leading, they may need to examine their vulnerabilities, which could be scary. To ease this process, the book offers personal stories of 24 former and current CEOs who reveal their insecurities, their struggles, and how they’ve addressed them, Kaas explains. Listen to the podcast to learn how modern CEOs strike a balance between confidence and humility, and read The Journey of Leadership to get a step-by-step reinvention guide that can help leaders transform themselves.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:13 - 30 Sep 2024 -
The power of introspection
On Point
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ESSENTIALS FOR LEADERS AND THOSE THEY LEAD
This month marks a significant milestone for Author Talks. Since its inception in 2020, we have produced 200 Author Talks interviews, gleaning fresh insights from CEOs, former CEOs, and other authors of new books. To commemorate this milestone, we’re sharing our top reads across several categories, as well as some summer highlights.
Do you find yourself wishing you could rewrite a chapter of your life? Maybe you’d change how you showed up at a pivotal moment—whether you stood your ground or lost your voice. While the ebb and flow of life rarely allows for do-overs, it does offer something greater: the opportunity for self-reflection. Rather than rewriting history, you could change gears by leaning into your strengths, being open to change, and leading with authenticity. It’s never too late to meet the moment.
This commemorative edition of Readers & Leaders offers insights on self-reflection, including embracing possibility at any age, advocating for yourself and others, addressing gender parity and finding purpose at work, being mindful of societal contributions, and becoming an active learner. Finally, in McKinsey’s latest book, The Journey of Leadership, senior partners Dana Maor, Kurt Strovink, and Ramesh Srinivasan and senior partner emeritus Hans-Werner Kaas provide a playbook for human-centric leadership that highlights the value of introspection, humility, and authenticity.
Check out these interviews and look out for our next edition of Readers & Leaders—now a quarterly newsletter—in early 2025.
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TURN BACK THE PAGE: OUR TOP AUTHOR TALKS
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Catch up on the latest and greatest reads for the month. Explore business bestsellers, prepared exclusively for McKinsey by Circana.
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If you'd like to propose a book or author for #McKAuthorTalks, please email us at Author_Talks@Mckinsey.com. Due to the high volume of requests, we will respond only to those being considered.
—Edited by Emily Adeyanju, editor, Carolinas
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by "McKinsey Readers & Leaders" <publishing@email.mckinsey.com> - 03:24 - 29 Sep 2024 -
GLA Middle-East Logistics Forum 2024
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GLA Global Logistics Alliance is proud to host the GLA Middle-East Logistics Forum 2024 in the vibrant city of Riyadh, Saudi Arabia!This event offers an unparalleled opportunity to discover new business opportunities in the logistics world.
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The week in charts
The Week in Charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:29 - 28 Sep 2024 -
EP131: How Uber Served 40 Million Reads with Integrated Redis Cache?
EP131: How Uber Served 40 Million Reads with Integrated Redis Cache?
Forwarded this email? Subscribe here for moreThis week’s system design refresher:
Top Kafka Use Cases You Should Know (Youtube video)
How Uber Served 40 Million Reads with Integrated Redis Cache?
What makes AWS Lambda so fast?
Why do we need to use a distributed lock?
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How Uber Served 40 Million Reads with Integrated Redis Cache?
There are 3 main parts of the implementation:
CacheFront Read and Writes with CDC
Uber built CacheFront - an integrated caching solution with Redis, Docstore, and MySQL.
Rather than the microservice, Docstore’s query engine communicates with Redis for read requests.
For cache hits, the query engine fetches data from Redis. For cache misses, the request goes to the storage engine and the database.
In the case of writes, Docstore’s CDC service (Flux) invalidates the records in Redis. It tails MySQL binlog events to trigger the invalidation.
Multi-Region Cache Warming with Redis Streaming
A region fail-over can result in cache misses and overload the database.
To handle this, Uber’s engineering team uses cross-region Redis replication. This is done by tailing the Redis write stream to replicate keys to the remote region.
In the remote region, the stream consumer issues read requests to the query engine that reads the database and updates the cache.
Redis and Docstore Sharding
All teams in Uber use Docstore and some generate a huge number of requests.
Both Redis and Docstore instances are sharded or partitioned to handle the load. But a single Redis cluster going down may create a hot DB shard.
To prevent this, they partitioned the Redis cluster using a scheme that was different from the DB sharding. This ensures that the load is evenly distributed.
Over to you: Would you have done something differently?
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What makes AWS Lambda so fast?
There are 4 main pillars:
Function Invocation
AWS Lambda supports synchronous and asynchronous invocation.
In synchronous invocation, the caller directly calls the Lambda function using AWS CLI, SDK, or other services.
In asynchronous invocation, the caller doesn’t wait for the function’s response. The request is authorized and an event is placed in an internal SQS queue. Pollers read messages from the queue and send them for processing.Assignment Service
The Assignment Service manages the execution environments.
The service is written in Rust for high performance and is divided into multiple partitions with a leader-follower approach for high availability.
The state of execution environments is written to an external journal log.Firecracker MicroVM
Firecracker is a lightweight virtual machine manager designed for running serverless workloads such as AWS Lambda and AWS Fargate.
It uses Linux’s Kernel-based virtual machine to create and manage secure, fast-booting microVMs.Component Storage
AWS Lambda also has to manage the state consisting of input data and function code.
To make it efficient, it uses multiple techniques:Chunking to store the container images more efficiently.
Using convergent encryption to secure the shared data. This involves appending additional data to the chunk to compute a more robust hash.
SnapStart feature to reduce cold start latency by pre-initializing the execution environment
Over to you: Which other features do you think make AWS Lambda fast?
Why do we need to use a distributed lock?
A distributed lock is a mechanism that ensures mutual exclusion across a distributed system.
Top 6 Use Cases for Distributed Locks
Leader Election
Distributed locks can be used to ensure that only one node becomes the leader at any given time.Task Scheduling
In a distributed task scheduler, distributed locks ensure that a scheduled task is executed by only one worker node, preventing duplicate execution.Resource Allocation
When managing shared resources like file systems, network sockets, or hardware devices, distributed locks ensure that only one process can access the resource at a time.Microservices Coordination
When multiple microservices need to perform coordinated operations, such as updating related data in different databases, distributed locks ensure that these operations are performed in a controlled and orderly manner.Inventory Management
In e-commerce platforms, distributed locks can manage inventory updates to ensure that stock levels are accurately maintained when multiple users attempt to purchase the same item simultaneously.Session Management
When handling user sessions in a distributed environment, distributed locks can ensure that a user session is only modified by one server at a time, preventing inconsistencies.
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 28 Sep 2024 -
Top 10 reports this quarter
McKinsey&Company
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Top Ten Reports │ Third Quarter 2024
Our top ten reports this quarter look at women in the workplace, climate change, asset management, and more. At No. 1, McKinsey's Humayun Tai, Sven Smit, and coauthors explore the physical realities of the energy transition.
2. Women in the Workplace 2024: The 10th-anniversary report
In the tenth year of our Women in the Workplace research, in partnership with LeanIn.Org, we reflect on the notable gains women have made—and how their experiences at work are, in many ways, the same or worse than ten years ago. Sustainable progress toward parity requires that companies recommit to change. Recommendations for companies
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by "McKinsey Top Ten" <publishing@email.mckinsey.com> - 11:36 - 28 Sep 2024 -
What changes are tech officers making to realize value from tech?
Only McKinsey Perspectives
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
•
Mindset shifts. In an era of technological change, CIOs, chief technology officers (CTOs), and chief digital and information officers (CDIOs) are looking to turn the promise of tech into real value. How can they realize that goal? Conversations with dozens of business leaders, as well as an analysis of recent research, reveal a set of shifts that tech leaders can make to meet executives’ demands, McKinsey senior partner Aamer Baig and coauthors explain.
•
Orchestrating change. Creating significant gains from AI and tech often means integrating multiple facets of the organization. As orchestrators, tech officers should move from delivering tech to shaping how their companies generate value, directing business and tech teams, and taking on accountability for business outcomes. As one CTO shares, “We’re moving into being IT strategists and setting the direction for the organization.” Discover four types of roles that tech officers can embody as they build value from AI, generative AI, and other technologies.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:38 - 27 Sep 2024 -
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