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by "McKinsey CEO Shortlist" <publishing@email.mckinsey.com> - 04:38 - 19 Apr 2024 -
Want to know the economic upside of promoting Latinos in Hollywood?
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
•
Untapped potential of Latinos. Latinos could be the biggest per capita US consumers of media—and their culture and talent are popular among non-Latino US and global audiences, say senior partner Tomás Lajous and coauthors. Yet Latinos hold less than 5% of leading on-screen, off-screen, and executive roles in US media, according to McKinsey research. In an industry where who you know goes a long way, having fewer experienced Latinos to network with is a significant barrier.
—Edited by Querida Anderson, senior editor, New York
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by "Only McKinsey" <publishing@email.mckinsey.com> - 11:05 - 18 Apr 2024 -
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by "Sunny Thakur" <sunny.thakur@uffizio.com> - 08:00 - 18 Apr 2024 -
A Crash Course in API Versioning Strategies
A Crash Course in API Versioning Strategies
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Developing an API involves a lot of work, from planning to implementation. It's crucial to have a clear and easy-to-understand versioning strategy to avoid confusing developers. In this week's issue, we'll explore different versioning strategies for APIs.
We'll begin by examining the reasons for versioning APIs and when it's necessary to release a new version. We'll also investigate various versioning strategies, how to label API versions, and methods for gracefully retiring outdated API versions.
So, without further ado, let’s jump right into it.
Why Version APIs?
As we add new features to our API, fix existing issues, or change how our API works, we need to deliver these changes without disrupting our users. Let’s understand this with an example.
Imagine we have an API for weather forecasts. Thousands of websites use it to build dashboards and other applications.
Let's say we want to change the data contract of our response object. This could involve renaming a field, adding a new one, or changing the entire data contract. If we change an existing field name, our users’ applications might stop working or start throwing errors.
To fix this, we'd have to ask all our users to update their applications to work with our newest changes. If this happens often, our users will be frustrated.
Versioning solves this problem. When we want to release a breaking change, we upgrade the version of our API. We release it in a way that lets users choose when to accept the changes.
Once clients start using our API, they rely on it to work as originally designed. If we make changes or release new versions without considering our clients' needs, it could cause problems. That's why it's important to version our API and give clients the choice to upgrade when they're ready.
That's why designing for change is essential for APIs. We should use versioning to deliver changes to our users in a clear, consistent, and well-documented manner.
When to Version APIs?...
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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 18 Apr 2024 -
How well do you understand your own supply chain?
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
•
Complex supply chains. Global trade has increased since the 1990s, improving millions of lives. Yet geopolitical concerns are also rising, and reducing imports from a country may not reduce geopolitical risk as much as expected. For instance, although the share of US imports of Chinese-made goods has declined, China’s share of value added in US final consumption has stayed steady, McKinsey Global Institute director Olivia White and coauthors reveal. This could result in a longer, more opaque, and not much more geopolitically diversified supply chain.
•
Understanding risk. For CFOs who function as the de facto chief risk officer in many organizations, it’s essential to dig deeper. A recent McKinsey survey revealed that only 2% of global supply chain leaders had visibility into their supply base beyond the second tier of suppliers. Find out three types of competitive edge that the best leaders sharpen in uncertain times, and read McKinsey Global Institute’s full report, Geopolitics and the geometry of global trade, to learn how global trade patterns are changing.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:44 - 18 Apr 2024 -
What’s the future for cities in the postpandemic world?
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What’s the future for cities in the postpandemic world?
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Pandemic paradoxes, labor market myths, and ‘cowboy capitalism’ with David Autor
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by "McKinsey Global Institute" <publishing@email.mckinsey.com> - 12:30 - 18 Apr 2024 -
More data would derisk investment in women’s health
Re:think
FRESH TAKES ON BIG IDEAS
ON WOMEN’S HEALTH
Improving women’s health could improve the world’s economyLucy Pérez
Being in good health means being unconstrained by physical, social, mental, or spiritual challenges that get in the way of being at your very best. Recent research from the McKinsey Health Institute indicates that men spend more time in good health than women do. This is what’s called the women’s health gap.
What’s behind this disparity? One reason is that women often aren’t treated in a way that reflects female biology. In the past, women’s health was equated with maternal and child health or, more broadly, sexual and reproductive health. That was based on the mistaken assumption that women were just smaller men, differentiated only by their reproductive organs.
It is now understood that female biology is different from male biology—beyond the reproductive system. This realization has led to more awareness of how some diseases manifest differently in women than in men and how addressing those differences can result in better health and economic outcomes.
Heart attacks, for example, tend to manifest in men as pressure on the left side of the chest and a weak or numb left arm. In women, they could also present as nausea, dizziness, or a feeling that something is off but you can’t quite pinpoint what. When a woman shows up to the ER with these symptoms, the doctor there may not have been trained to recognize that heart attacks may manifest differently in women. As a result, women are often sent home with medicine for their nausea, instead of being treated for a heart attack.
Data on why conditions manifest differently in men and women is lacking, which contributes to the health gap in multiple ways. For one, less data leads to less investment. When you’re spending upward of $1 billion to develop a drug, you want the best understanding of everything, including biological mechanisms, physiology, and genetics. The uterus is one of the least understood organs in the human body, even though almost half of the world’s population has one. A more limited understanding of how this organ works means it is riskier to develop drugs for conditions that affect it. So it’s not too surprising that uterine cancer is one of the top conditions contributing to the women’s health gap in the United States.“Our research shows that closing the women’s health gap could boost the global economy by $1 trillion annually by 2040.”
Many often assume that the women’s health gap primarily manifests later in life and is connected to women’s living longer on average. But we’ve found that the majority of the gap shows up during women’s prime working years. Not addressing it has huge economic implications, because if women were healthier, they could be more productive.
Our research shows that closing the women’s health gap could boost the global economy by $1 trillion annually by 2040. For the average woman, this would mean seven more days of living in good health each year and more time to be in the workforce. Closing the gap would also contribute to a stronger society, because healthier people are able to spend more time engaging with the world around them.
Employers in all industries should embrace policies that advance health equity. Support in the workplace for women experiencing menopause, for instance—a condition that almost every woman will go through but one that is poorly understood and whose diagnosis is often delayed—can go a long way toward retaining those who may otherwise exit the workforce prematurely.
The medical-research and academic communities also have a significant role to play. More investment is needed to collect data that enables us to understand sex-based differences and find solutions. Medical schools should evolve away from the prevalent model today, whereby women’s health is only covered as part of the OB-GYN rotation, so that medical professionals can understand and provide sex-appropriate care.
A couple of things inspire optimism. I am gratified to see more headlines about conditions that affect women differently or disproportionately. Women don’t have to lower their voices anymore to talk about menstrual health and menopause. More private-equity-backed companies are developing products focused on women’s health. The number of entrepreneurs focused on women’s health continues to rise. Regulators are increasingly demanding a better understanding of sex-based differences, and more providers are being trained on how to address sex-based differences. The world has made a lot of progress on this topic, but it still has a long way to go.Share Lucy Pérez's insights
ABOUT THIS AUTHOR
Lucy Pérez is a senior partner in McKinsey’s Boston office and an affiliated leader of the McKinsey Health Institute.
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Companies have long had a hard time gauging what contributes to the productivity of developer teams. But a new approach could help to tackle this challenge and fuel innovation in the process.
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by "McKinsey Quarterly" <publishing@email.mckinsey.com> - 01:07 - 17 Apr 2024 -
Elevate Your Security Strategy with Sumo Logic at the AWS Summit London
Sumo Logic
Dear Mohammad,
Don't miss out on the chance to connect with Sumo Logic at the AWS Summit London on the 24th of April at the ExCel London.
Swing by Stand B36 to delve into the future of defense strategies in the age of AI.
Let's explore and discuss the Applications of AI in a modern defense strategy, providing insights on defending against AI-enabled adversaries and crafting an AI strategy to empower your Security Operations teams.
Discover how to adapt to new threats and navigate the ever-changing cyber landscape. At our booth you can also enter a prize draw with a chance to win a remarkable2.
Join us at Stand B36 and elevate your security game. We look forward to seeing you there!
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by "Sumo Logic" <marketing-info@sumologic.com> - 06:01 - 17 Apr 2024 -
How do business leaders view the world economy?
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
•
Renewed optimism. What a difference a year can make. In the latest McKinsey Global Survey on economic conditions, executives’ outlook on domestic conditions in most regions has become more hopeful, despite ongoing concerns about geopolitical instability. Views of the global economy are the most positive that they’ve been since March 2022, McKinsey Global Institute chair Sven Smit and coauthors reveal. Of those surveyed, 46% expect the global economy to improve in the next six months—nearly double the share of respondents expecting worsening conditions.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 11:06 - 16 Apr 2024 -
How PayPal Serves 350 Billion Daily Requests with JunoDB
How PayPal Serves 350 Billion Daily Requests with JunoDB
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Have you ever seen a database that fails and comes up again in the blink of an eye?
PayPal’s JunoDB is a database capable of doing so. As per PayPal’s claim, JunoDB can run at 6 nines of availability (99.9999%). This comes to just 86.40 milliseconds of downtime per day.
For reference, our average eye blink takes around 100-150 milliseconds.
While the statistics are certainly amazing, it also means that there are many interesting things to pick up from JunoDB’s architecture and design.
In this post, we will cover the following topics:
JunoDB’s Architecture Breakdown
How JunoDB achieves scalability, availability, performance, and security
Use cases of JunoDB
Key Facts about JunoDB
Before we go further, here are some key facts about JunoDB that can help us develop a better understanding of it.
JunoDB is a distributed key-value store. Think of a key-value store as a dictionary where you look up a word (the “key”) to find its definition (the “value”).
JunoDB leverages a highly concurrent architecture implemented in Go to efficiently handle hundreds of thousands of connections.
At PayPal, JunoDB serves almost 350 billion daily requests and is used in every core backend service, including critical functionalities like login, risk management, and transaction processing.
PayPal primarily uses JunoDB for caching to reduce the load on the main source-of-truth database. However, there are also other use cases that we will discuss in a later section.
The diagram shows how JunoDB fits into the overall scheme of things at PayPal.
Why the Need for JunoDB?
One common question surrounding the creation of something like JunoDB is this:
“Why couldn’t PayPal just use something off-the-shelf like Redis?”
The reason is PayPal wanted multi-core support for the database and Redis is not designed to benefit from multiple CPU cores. It is single-threaded in nature and utilizes only one core. Typically, you need to launch several Redis instances to scale out on several cores if needed.
Incidentally, JunoDB started as a single-threaded C++ program and the initial goal was to use it as an in-memory short TTL data store.
For reference, TTL stands for Time to Live. It specifies the maximum duration a piece of data should be retained or the maximum time it is considered valid.
However, the goals for JunoDB evolved with time.
First, PayPal wanted JunoDB to work as a persistent data store supporting long TTLs.
Second, JunoDB was also expected to provide improved data security via on-disk encryption and TLS in transit by default.
These goals meant that JunoDB had to be CPU-bound rather than memory-bound.
For reference, “memory-bound” and “CPU-bound” refer to different performance aspects in computer programs. As the name suggests, the performance of memory-bound programs is limited by the amount of available memory. On the other hand, CPU-bound programs depend on the processing power of the CPU.
For example, Redis is memory-bound. It primarily stores the data in RAM and everything about it is optimized for quick in-memory access. The limiting factor for the performance of Redis is memory rather than CPU.
However, requirements like encryption are CPU-intensive because many cryptographic algorithms require raw processing power to carry out complex mathematical calculations.
As a result, PayPal decided to rewrite the earlier version of JunoDB in Go to make it multi-core friendly and support high concurrency.
The Architecture of JunoDB
The below diagram shows the high-level architecture of JunoDB.
Let’s look at the main components of the overall design.
1 - JunoDB Client Library
The client library is part of the client application and provides an API for storing and retrieving data via the JunoDB proxy.
It is implemented in several programming languages such as Java, C++, Python, and Golang to make it easy to use across different application stacks.
For developers, it’s just a matter of picking the library for their respective programming language and including it in the application to carry out the various operations.
2 - JunoDB Proxy with Load Balancer
JunoDB utilizes a proxy-based design where the proxy connects to all JunoDB storage server instances.
This design has a few important advantages:
The complexity of determining which storage server should handle a query is kept out of the client libraries. Since JunoDB is a distributed data store, the data is spread across multiple servers. The proxy handles the job of directing the requests to the correct server.
The proxy is also aware of the JunoDB cluster configuration (such as shard mappings) stored in the ETCD key-value store.
But can the JunoDB proxy turn into a single point of failure?
To prevent this possibility, the proxy runs on multiple instances downstream to a load balancer. The load balancer receives incoming requests from the client applications and routes the requests to the appropriate proxy instance.
3 - JunoDB Storage Servers
The last major component in the JunoDB architecture is the storage servers.
These are instances that accept the operation requests from the proxy and store data in the memory or persistent storage.
Each storage server is responsible for a set of partitions or shards for an efficient distribution of data.
Internally, JunoDB uses RocksDB as the storage engine. Using an off-the-shelf storage engine like RocksDB is common in the database world to avoid building everything from the ground up. For reference, RocksDB is an embedded key-value storage engine that is optimized for high read and write throughput.
Key Priorities of JunoDB
Now that we have looked at the overall design and architecture of JunoDB, it’s time to understand a few key priorities for JunoDB and how it achieves them.
Scalability
Several years ago, PayPal transitioned to a horizontally scalable microservice-based architecture to support the rapid growth in active customers and payment rates.
While microservices solve many problems for them, they also have some drawbacks.
One important drawback is the increased number of persistent connections to key-value stores due to scaling out the application tier. JunoDB handles this scaling requirement in two primary ways.
1 - Scaling for Client Connections
As discussed earlier, JunoDB uses a proxy-based architecture.
If client connections to the database reach a limit, additional proxies can be added to support more connections.
There is an acceptable trade-off with latency in this case.
2 - Scaling for Data Volume and Throughput
The second type of scaling requirement is related to the growth in data size.
To ensure efficient storage and data fetching, JunoDB supports partitioning based on the consistent hashing algorithm. Partitions (or shards) are distributed to physical storage nodes using a shard map.
Consistent hashing is very useful in this case because when the nodes in a cluster change due to additions or removals, only a minimal number of shards require reassignment to different storage nodes.
PayPal uses a fixed number of shards (1024 shards, to be precise), and the shard map is pre-generated and stored in ETCD storage.
Any change to the shard mapping triggers an automatic data redistribution process, making it easy to scale your JunoDB cluster depending on the need.
The below diagram shows the process in more detail.
Availability
High availability is critical for PayPal. You can’t have a global payment platform going down without a big loss of reputation.
However, outages can and will occur due to various reasons such as software bugs, hardware failures, power outages, and even human error. Failures can lead to data loss, slow response times, or complete unavailability.
To mitigate these challenges, JunoDB relies on replication and failover strategies.
1 - Within-Cluster Replication
In a cluster, JunoDB storage nodes are logically organized into a grid. Each column represents a zone, and each row signifies a storage group.
Data is partitioned into shards and assigned to storage groups. Within a storage group, each shard is synchronously replicated across various zones based on the quorum protocol.
The quorum-based protocol is the key to reaching a consensus on a value within a distributed database. You’ve two quorums:
The Read Quorum: When a client wants to read data, it needs to receive responses from a certain number of zones (known as the read quorum). This is to make sure that it gets the most up-to-date data.
The Write Quorum: When the client wants to write data, it must receive acknowledgment from a certain number of zones to make sure that the data is written to a majority of the zones.
There are two important rules when it comes to quorum.
The sum of the read quorum and write quorum must be greater than the number of zones. If that’s not the case, the client may end up reading outdated data. For example, if there are 5 zones with read quorum as 2 and write quorum as 3, a client can write data to 3 zones but another client may read from the 2 zones that have not yet received the updated data.
The write quorum must be more than half the number of zones to prevent two concurrent write operations on the same key. For example, if there is a JunoDB cluster with 5 zones and a write quorum of 2, client A may write value X to key K and is considered successful when 2 zones acknowledge the request. Similarly, client B may write value Y to the same key K and is also successful when two different zones acknowledge the request. Ultimately, the data for key K is in an inconsistent state.
In production, PayPal has a configuration with 5 zones, a read quorum of 3, and a write quorum of 3.
Lastly, the failover process in JunoDB is automatic and instantaneous without any need for leader re-election or data redistribution. Proxies can know about a node failure through a lost connection or a read request that has timed out.
2 - Cross-data center replication
Cross-data center replication is implemented by asynchronously replicating data between the proxies of each cluster across different data centers.
This is important to make sure that the system continues to operate even if there’s a catastrophic failure at one data center.
Performance
One of the critical goals of JunoDB is to deliver high performance at scale.
This translates to maintaining single-digit millisecond response times while providing a great user experience.
The below graphs shared by PayPal show the benchmark results demonstrating JunoDB’s performance in the case of persistent connections and high throughput.
Source: PayPal Engineering Blog 
Source: PayPal Engineering Blog Security
Being a trusted payment processor, security is paramount for PayPal.
Therefore, it’s no surprise that JunoDB has been designed to secure data both in transit and at rest.
For transmission security, TLS is enabled between the client and proxy as well as proxies in different data centers used for replication.
Payload encryption is performed at the client or proxy level to prevent multiple encryptions of the same data. The ideal approach is to encrypt the data on the client side but if it’s not done, the proxy figures it out through a metadata flag and carries out the encryption.
All data received by the storage server and stored in the engine are also encrypted to maintain security at rest.
A key management module is used to manage certificates for TLS, sessions, and the distribution of encryption keys to facilitate key rotation,
The below diagram shows JunoDB’s security setup in more detail.
Use Cases of JunoDB
With PayPal having made JunoDB open-source, it’s possible that you can also use it within your projects.
There are various use cases where JunoDB can help. Let’s look at a few important ones:
1 - Caching
You can use JunoDB as a temporary cache to store data that doesn’t change frequently.
Since JunoDB supports both short and long-lived TTLs, you can store data from a few seconds to a few days. For example, a use case is to store short-lived tokens in JunoDB instead of fetching them from the database.
Other items you can cache in JunoDB are user preferences, account details, and API responses.
2 - Idempotency
You can also use JunoDB to implement idempotency.
An operation is idempotent when it produces the same result even when applied multiple times. With idempotency, repeating the operation is safe and you don’t need to be worried about things like duplicate payments getting applied.
PayPal uses JunoDB to ensure they don’t process a particular payment multiple times due to retries. JunoDB’s high availability makes it an ideal data store to keep track of processing details without overloading the main database.
3 - Counters
Let’s say you’ve certain resources that aren’t available for some reason or they have an access limit to their usage. For example, these resources can be database connections, API rate limits, or user authentication attempts.
You can use JunoDB to store counters for these resources and track whether their usage exceeds the threshold.
4 - Latency Bridging
As we discussed earlier, JunoDB provides fast inter-cluster replication. This can help you deal with slow replication in a more traditional setup.
For example, in PayPal’s case, they run Oracle in Active-Active mode, but the replication usually isn’t as fast as they would like for their requirement.
It means there are chances of inconsistent reads if records written in one data center are not replicated in the second data center and the first data center goes down.
JunoDB can help bridge the latency where you can write to Data Center A (both Oracle and JunoDB) and even if it goes down, you can read the updates consistently from the JunoDB instance in Data Center B.
See the below diagram for a better understanding of this concept.
Conclusion
JunoDB is a distributed key-value store playing a crucial role in various PayPal applications. It provides efficient data storage for fast access to reduce the load on costly database solutions.
While doing so, it also fulfills critical requirements such as scalability, high availability with performance, consistency, and security.
Due to its advantages, PayPal has started using JunoDB in multiple use cases and patterns. For us, it provides a great opportunity to learn about an exciting new database system.
References:
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by "ByteByteGo" <bytebytego@substack.com> - 11:36 - 16 Apr 2024 -
What’s on CEO agendas as 2024 unfolds?
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
•
Building geopolitical muscle. An endless series of disruptions are making it much harder to lead, Homayoun Hatami, McKinsey’s managing partner for client capabilities, reflects on a recent episode of The McKinsey Podcast. What should leaders focus on as 2024 unfolds? As geopolitical risks rise, leaders must strengthen their abilities to address issues such as investing in new markets. They should also consider establishing processes and systems that enable geopolitically informed decision making, McKinsey’s chief client officer Liz Hilton Segel says.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:25 - 16 Apr 2024 -
A leader’s guide to digital virtualization
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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
“You stay in Wonderland, and I show you how deep the rabbit hole goes,” says the character Morpheus in the classic sci-fi thriller The Matrix, which showcases the extent to which the lines between the virtual and the real can blur. Today, digital technologies have enabled organizations to simulate, experiment, and innovate in imaginative ways and at speeds that would be almost impossible in a solely physical environment. This week, join us as we go down the rabbit hole—to a world where fantasy and reality can meet.
“The line between the digital and the physical has dissolved completely” for some companies, according to McKinsey senior partner Kimberly Borden and colleagues in a new article on digital innovation. “It’s now possible to create digital representations of components, products, and processes that precisely replicate the behavior of their real-world counterparts,” they note. For example, by creating virtual prototypes to simulate thousands of different racing conditions, a sailboat team won two major competitions; a heart pump maker deployed high-performance virtualization to reduce design optimization time by over a factor of 1,000. To be successful at digital virtualization, “build it to a code that verifiably matches digital results to the physical world, within an acceptable error range,” suggest the authors. “At that point, the virtualization can be trusted as authoritative and safe to use.”
That’s the potential size of the opportunity for the virtual travel industry by 2030, McKinsey research shows. The metaverse—a collective space where physical and digital worlds converge to deliver immersive, interactive user experiences—is already transforming many sectors: “You can attend concerts, shop, test products, visit attractions, and take workshops, all without physically traveling anywhere,” observe McKinsey partner Margaux Constantin and colleagues. To gain first-mover advantage in the metaverse, travel companies may need to focus on four key steps, including finding the right talent. “Developing any offer will likely require new skills—not just to make your immersive world look good but to ensure that it’s smooth and exhilarating to use,” say the McKinsey experts.
That’s Camille Kroely, chief metaverse and Web3 officer at L’Oréal, on how the global cosmetics company trains its own executives to become proficient in digital skills. L’Oréal has launched several initiatives to blend the physical, digital, and virtual worlds in its product marketing—for example, by working with avatar developers to offer virtual makeup looks. “Upskilling is now part of our road map,” says Kroely. “One of the first things that we did with our executive committee members was to train and upskill them so that they can keep learning by themselves—such as opening their own wallets, customizing their avatars, testing the platform, and going on [the online game platform] Roblox.”
Creating a dedicated digital unit can sometimes be the most efficient way to quickly spread innovative digital practices throughout the organization. That strategy has worked for Saudi Arabia’s Alinma Bank, according to chief digital officer Sami Al-Rowaithey. Recently, the bank established a “digital factory,” says Al-Rowaithey in a conversation with McKinsey partner Sonia Wedrychowicz. “It’s responsible for managing the entire digital structure and its associated strategies, business development, product innovation, experience management, performance management, and digital profitability,” he says. While that may seem like a daunting task, it helps to act fast, adds Al-Rowaithey. “The first thing I would say is to not spend a lot of time trying to figure out all the details of the journey. It’s important to start quick, learn as you go, be flexible, modify, and move on.”
Clones may seem creepy in science fiction, but they can be highly practical in the real world. Digital twins—virtual replicas of physical objects, systems, or processes—are increasingly popular in product development and manufacturing. “Interacting with or modifying a product in a virtual space can be quicker, easier, and safer than doing so in the real world,” note McKinsey partners Roberto Argolini and Johannes Deichmann and colleagues. For example, a factory digital twin can model floor layouts, optimize the footprint, and estimate inventory size. “Eventually, we may see the emergence of digital twins capable of learning from their own experiences, identifying opportunities and offering product improvement suggestions entirely autonomously,” say the McKinsey experts.
Lead by virtualizing.
– Edited by Rama Ramaswami, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:41 - 15 Apr 2024 -
Do you know how healthy your organization is?
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Power practices. “The research is very clear: organizational health propels long-term performance,” McKinsey partner Brooke Weddle shares on a recent episode of McKinsey Talks Talent. To support organizational health, companies have to get four power practices right, adds partner Bryan Hancock, who joined Weddle on the podcast. These are strategic clarity—which means having clear and measurable goals—role clarity, personal ownership, and competitive insights (that is, understanding where businesses fit in versus the competition).
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Decisive leadership. As leaders grapple with disruptions such as generative AI and persistent economic uncertainty, they’re discovering the need to be both decisive and empowering. McKinsey’s recently updated Organizational Health Index highlights decisive leadership as one of the best predictors of organizational health. Decisive leaders are able to quickly make and follow through on decisions, rather than leaning on their authority to get things done, Weddle says. Explore new measures of organizational health and three steps leaders can take to get started.
—Edited by Belinda Yu, editor, Atlanta
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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> - 03:38 - 13 Apr 2024 -
EP107: Top 9 Architectural Patterns for Data and Communication Flow
EP107: Top 9 Architectural Patterns for Data and Communication Flow
Forwarded this email? Subscribe here for moreThis week’s system de sign refresher:
Top 9 Architectural Patterns for Data and Communication Flow
How Netflix Really Uses Java?
Top 6 Cloud Messaging Patterns
What Are the Most Important AWS Services To Learn?
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Top 9 Architectural Patterns for Data and Communication Flow
Peer-to-Peer
The Peer-to-Peer pattern involves direct communication between two components without the need for a central coordinator.API Gateway
An API Gateway acts as a single entry point for all client requests to the backend services of an application.Pub-Sub
The Pub-Sub pattern decouples the producers of messages (publishers) from the consumers of messages (subscribers) through a message broker.Request-Response
This is one of the most fundamental integration patterns, where a client sends a request to a server and waits for a response.Event Sourcing
Event Sourcing involves storing the state changes of an application as a sequence of events.ETL
ETL is a data integration pattern used to gather data from multiple sources, transform it into a structured format, and load it into a destination database.Batching
Batching involves accumulating data over a period or until a certain threshold is met before processing it as a single group.Streaming Processing
Streaming Processing allows for the continuous ingestion, processing, and analysis of data streams in real-time.Orchestration
Orchestration involves a central coordinator (an orchestrator) managing the interactions between distributed components or services to achieve a workflow or business process.
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How Netflix Really Uses Java?
Netflix is predominantly a Java shop.
Every backend application (including internal apps, streaming, and movie production apps) at Netflix is a Java application.
However, the Java stack is not static and has gone through multiple iterations over the years.
Here are the details of those iterations:
API Gateway
Netflix follows a microservices architecture. Every piece of functionality and data is owned by a microservice built using Java (initially version 8)
This means that rendering one screen (such as the List of List of Movies or LOLOMO) involved fetching data from 10s of microservices. But making all these calls from the client created a performance problem.
Netflix initially used the API Gateway pattern using Zuul to handle the orchestration.BFFs with Groovy & RxJava
Using a single gateway for multiple clients was a problem for Netflix because each client (such as TV, mobile apps, or web browser) had subtle differences.
To handle this, Netflix used the Backend-for-Frontend (BFF) pattern. Zuul was moved to the role of a proxy
In this pattern, every frontend or UI gets its own mini backend that performs the request fanout and orchestration for multiple services.
The BFFs were built using Groovy scripts and the service fanout was done using RxJava for thread management.GraphQL Federation
The Groovy and RxJava approach required more work from the UI developers in creating the Groovy scripts. Also, reactive programming is generally hard.
Recently, Netflix moved to GraphQL Federation. With GraphQL, a client can specify exactly what set of fields it needs, thereby solving the problem of overfetching and underfetching with REST APIs.
The GraphQL Federation takes care of calling the necessary microservices to fetch the data.
These microservices are called Domain Graph Service (DGS) and are built using Java 17, Spring Boot 3, and Spring Boot Netflix OSS packages. The move from Java 8 to Java 17 resulted in 20% CPU gains.
More recently, Netflix has started to migrate to Java 21 to take advantage of features like virtual threads.
Top 6 Cloud Messaging Patterns
How do services communicate with each other? The diagram below shows 6 cloud messaging patterns.
🔹 Asynchronous Request-Reply
This pattern aims at providing determinism for long-running backend tasks. It decouples backend processing from frontend clients.
In the diagram below, the client makes a synchronous call to the API, triggering a long-running operation on the backend. The API returns an HTTP 202 (Accepted) status code, acknowledging that the request has been received for processing.
🔹 Publisher-Subscriber
This pattern targets decoupling senders from consumers, and avoiding blocking the sender to wait for a response.
🔹 Claim Check
This pattern solves the transmision of large messages. It stores the whole message payload into a database and transmits only the reference to the message, which will be used later to retrieve the payload from the database.
🔹 Priority Queue
This pattern prioritizes requests sent to services so that requests with a higher priority are received and processed more quickly than those with a lower priority.
🔹 Saga
Saga is used to manage data consistency across multiple services in distributed systems, especially in microservices architectures where each service manages its own database.
The saga pattern addresses the challenge of maintaining data consistency without relying on distributed transactions, which are difficult to scale and can negatively impact system performance.
🔹 Competing Consumers
This pattern enables multiple concurrent consumers to process messages received on the same messaging channel. There is no need to configure complex coordination between the consumers. However, this pattern cannot guarantee message ordering.
Reference: Azure Messaging patternsWhat Are the Most Important AWS Services To Learn?
Since its inception in 2006, AWS has rapidly evolved from simple offerings like S3 and EC2 to an expansive, versatile cloud ecosystem.
Today, AWS provides a highly reliable, scalable infrastructure platform with over 200 services in the cloud, powering hundreds of thousands of businesses in 190 countries around the world.
For both newcomers and seasoned professionals, navigating the broad set of AWS services is no small feat.
From computing power, storage options, and networking capabilities to database management, analytics, and machine learning, AWS provides a wide array of tools that can be daunting to understand and master.
Each service is tailored to specific needs and use cases, requiring a deep understanding of not just the services themselves, but also how they interact and integrate within an IT ecosystem.
This attached illustration can serve as both a starting point and a quick reference for anyone looking to demystify AWS and focus their efforts on the services that matter most.
It provides a visual roadmap, outlining the foundational services that underpin cloud computing essentials, as well as advanced services catering to specific needs like serverless architectures, DevOps, and machine learning.
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Aunque la IA generativa ha sido uno de los temas principales de la agenda de muchas empresas este último año, capturar su valor es más difícil de lo esperado. 2024 es el año para que la IA generativa demuestre su valor, lo que requerirá que las empresas realicen cambios fundamentales y reconfiguren el negocio, escriben Eric Lamarre, Alex Singla, Alexander Sukharevsky y Rodney Zemmel en nuestro artículo destacado. Otros temas relevantes son:
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el valor aún sin explotar de ofrecer más oportunidades al talento latino en Hollywood
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el poder de la colaboración entre el CEO y el CMO
•
cómo aprovechar todo el potencial de la IA generativa en el diseño de productos físicos
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cómo las instituciones financieras pueden prepararse para el futuro frente a los crecientes riesgos cibernéticos
La selección de nuestros editores
LOS DESTACADOS DE ESTE MES
Latinos en Hollywood: Amplificando voces, ampliando horizontes
Mejorar la representación de los latinos, sus historias y su cultura delante y detrás de cámaras podría generar miles de millones de dólares en nuevos ingresos para la industria, según un nuevo estudio de McKinsey.
Cierre la brecha
Análisis de la relación CEO-CMO y su efecto en el crecimiento
Los CEOs reconocen la experiencia y la importancia de los directores de marketing, así como su papel en el crecimiento de la empresa, pero aún existe una desconexión estratégica en la alta dirección. He aquí cómo cerrar la brecha.
Adopte un enfoque holístico
La IA generativa impulsa el diseño creativo de productos físicos, pero no es una varita mágica
Las herramientas de IA generativa pueden acortar significativamente los ciclos de vida del diseño de productos físicos y estimular la innovación, pero son necesarios los conocimientos y la discreción de los expertos en diseño para mitigar los posibles obstáculos.
Desbloquee la creatividad
Cómo la IA generativa puede ayudar a los bancos a gestionar el riesgo y el cumplimiento normativo
En los próximos cinco años, la IA generativa podría cambiar fundamentalmente la gestión de riesgos de las instituciones financieras al automatizar, acelerar y mejorar todo, desde el cumplimiento normativo hasta el control del riesgo climático.
Estrategias ganadoras para la IA generativa
El reloj cibernético no se detiene: Reducir el riesgo de las tecnologías emergentes en los servicios financieros
A medida que las instituciones financieras adoptan activamente las tecnologías emergentes, deberían actuar ahora con el fin de prepararse para el futuro frente a los crecientes riesgos cibernéticos.
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Expertos en consejos de administración explican cómo sus miembros pueden hacer frente a las exigencias de unas agendas cada vez más apretadas.
Equilibre responsabilidadesEsperamos que disfrute de los artículos en español que seleccionamos este mes y lo invitamos a explorar también los siguientes artículos en inglés.
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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.
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Want to know how much the space sector could be worth by 2035?
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Space economy boom. Activity in space is accelerating. In 2023, “backbone” applications for space (such as satellites and services like GPS) made up $330 billion of the global space economy. “Reach” applications, which describe space technologies that companies across industries use to help generate revenues, represented another $300 billion. Over the next decade, space applications are expected to grow at an annual rate that is double the projected rate of GDP growth, McKinsey senior partner Ryan Brukardt and coauthors reveal.
•
Four key findings. To learn what will shape the future space economy, the World Economic Forum and McKinsey spoke with more than 60 leaders and experts across the globe. The team also used market forecasts and other data to develop original estimates for the size of each part of the space economy, including commercial services and state-sponsored civil and defense applications. To read our key findings on the space economy, download the full report, Space: The $1.8 trillion opportunity for economic growth.
—Edited by Belinda Yu, editor, Atlanta
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Embracing Chaos to Improve System Resilience: Chaos Engineering
Embracing Chaos to Improve System Resilience: Chaos Engineering
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Imagine it's the early 2000s, and you're a developer with a bold idea. You want to test your software not in a safe, controlled environment but right where the action is: the production environment. This is where real users interact with your system. Back then, suggesting something like this might have gotten you some strange looks from your bosses. But now, testing in the real world is not just okay; it's often recommended.
Why the big change? A few reasons stand out. Systems today are more complex than ever, pushing us to innovate faster and ensure our services are both reliable and strong. The rise of cloud technology, microservices, and distributed systems has changed the game. We've had to adapt our methods and mindsets accordingly.
Our goal now is to make systems that can handle anything—be it a slowdown or a full-blown outage. Enter Chaos Engineering.
In this issue, we dive into what chaos engineering is all about. We'll break down its key principles, how it's practiced, and examples from the real world. You'll learn how causing a bit of controlled chaos can actually help find and fix weaknesses before they become major problems.
Prepare to see how embracing chaos can lead to stronger, more reliable systems. Let's get started!
What is Chaos Engineering?
So, what exactly is chaos engineering? It's a way to deal with unexpected issues in software development and keep systems up and running. Some folks might think that a server running an app will continue without a hitch forever. Others believe that problems are just part of the deal and that downtime is inevitable.
Chaos engineering strikes a balance between these views. It recognizes that things can go wrong but asserts that we can take steps to prevent these issues from impacting our systems and the performance of our apps.
This approach involves experimenting on our live, production systems to identify weak spots and areas that aren't as reliable as they should be. It's about measuring how much we trust our system's resilience and working to boost that confidence
However, it's important to understand that being 100% sure nothing will go wrong is unrealistic. Through chaos engineering, we intentionally introduce unexpected events to uncover vulnerabilities. These events can vary widely, such as taking down a server randomly, disrupting a data center, or tampering with load balancers and application replicas.
In short, chaos engineering is about designing experiments that rigorously test our systems' robustness.
Defining Chaos Engineering
There are many ways to describe chaos engineering, but here's a definition that captures its essence well, sourced from https://principlesofchaos.org/.
“Chaos Engineering is the discipline of experimenting on a system in order to build confidence in the system’s capability to withstand turbulent conditions in production.”
This definition highlights the core objective of chaos engineering: to ensure our systems can handle the unpredictable nature of real-world operations.
Performance Engineering vs. Chaos Engineering
When we talk about ensuring our systems run smoothly, two concepts often come up: performance engineering and chaos engineering. Let's discuss what sets these two apart and how they might overlap.
Many developers are already familiar with performance engineering, which is in the same family as DevOps. It involves using a combination of tools, processes, and technologies to monitor our system's performance and make continuous improvements. This includes conducting various types of testing, such as load, stress, and endurance tests, all aimed at boosting the performance of our applications.
On the flip side, chaos engineering is about intentionally breaking things. Yes, this includes stress testing, but it's more about observing how systems respond under unexpected stress. Stress testing could be seen as a form of chaos experiment. So, one way to look at it is to consider performance engineering as a subset of chaos engineering or the other way around, depending on how you apply these practices.
Another way to view these two is as distinct disciplines within an organization. One team might focus solely on conducting chaos experiments and learning from the failures, while another might immerse itself in performance engineering tasks like testing and monitoring. Depending on the structure of the organization, the skill sets of the team, and various other factors, we might have separate teams for each discipline or one team that tackles both.
Chaos Engineering in Practice
Let's consider an example to better understand chaos engineering. Imagine we have a system with a load balancer that directs requests to web servers. These servers then connect to a payment service, which, in turn, interacts with a third-party API and a cache service, all located in Availability Zone A. If the payment service fails to communicate with the third-party API or the cache, requests need to be rerouted to Availability Zone B to maintain high availability.
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