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Maximizing gen AI’s potential for US state services

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The gen AI advantage for US states

In the news

•

Gen AI guidelines. Generative AI (gen AI) is disrupting almost every industry to at least some extent. As a result, state lawmakers are racing to develop safeguards for users that address risks but don’t hinder the technology’s broad scope, according to Governing. US lawmakers’ initial focus has been on preventing exploitation from generated images, including deepfakes. However, the regulatory push is also widening into politics. Several US states are contemplating limiting or barring deepfakes that feature election campaigns. [Governing]

•

Early gen AI adopters. While government entities at all levels are establishing guidelines and policies to address concerns regarding gen AI, they are also simultaneously exploring how best to wield the technology’s potential, Government Technology reports. Some early adopters are finding secure and constructive uses. Areas of interest include using gen AI to improve cybersecurity through threat detection, analyze requests for government services, and automate some communications, such as drafting automatic replies. [Government Technology]

In a recent McKinsey survey, one-third of respondents say their organizations are already regularly using generative AI in at least one function.

On McKinsey.com

•

Gen AI in US states. US states that capture gen AI’s value have the opportunity to enhance residents’ experiences with services that state governments provide. Gen AI can also help states implement operational efficiencies, improve productivity, and modernize legacy IT platforms. However, this new technology also comes with a unique set of unknowns and risks. State leaders thus need to adopt innovative approaches to establish and develop capabilities, say McKinsey senior partners Gayatri Shenai and Tim Ward and their coauthors.

•

Implementation road map. An effective strategy for managing the risk while also harnessing gen AI’s potential is to pilot lower-risk applications alongside preparation for scale-up. This creates a feedback loop that allows states to learn from real-time applications and make the needed adjustments with agility. Government leaders can develop gen AI strategies in five areas: adoption, governance, technology and data, talent, and operations. Discover key questions states can ask when developing an adoption road map, as well as four gen AI applications.

Seize the gen AI opportunity

—Edited by Querida Anderson, senior editor, New York

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YOUR FIVE-MINUTE BRIEFING—OR A DEEP DIVE IN FIFTY

by "Only McKinsey" <publishing@email.mckinsey.com> - 01:36 - 27 Mar 2024

Is a net-zero transition still possible?

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Adapting to the new net-zero reality

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Progress has been made to reduce global carbon emissions—but not enough. Under the current trajectory, the world won’t achieve net-zero emissions even during this century, according to the authors of a recent McKinsey report. To learn more about what opportunities remain for accelerating decarbonization efforts, check out the latest edition of the Five Fifty.

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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 04:16 - 26 Mar 2024

How Uber Uses Integrated Redis Cache to Serve 40M Reads/Second?

͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏

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How Uber Uses Integrated Redis Cache to Serve 40M Reads/Second?

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In 2020, Uber launched their in-house, distributed database named Docstore.

It was built on top of MySQL and was capable of storing tens of petabytes of data while serving tens of millions of requests per second.

Over the years, Docstore was adopted by all business verticals at Uber for building their services. Most of these applications required low latency, higher performance, and scalability from the database, while also supporting higher workloads.

Challenges with Low Latency Database Reads

Every database faces a challenge when dealing with applications that need low-latency read access with a highly scalable design.

Some of these challenges are as follows:

Data retrieval speed from a disk has a threshold. Beyond that, you cannot squeeze out more performance by optimizing an application’s data model and queries to improve latency.
Vertical scaling can take you far but assigning more resources by upgrading to better hosts has limitations. Ultimately, the database engine turns into a bottleneck.
Horizontal scaling by splitting your database into multiple partitions is a promising approach. However, it also gets operationally more complex over time and doesn’t eliminate issues such as hot partitions.
Both vertical and horizontal scaling strategies are costly in the long term. For reference, costs get multiplied 6X to handle three stateful nodes across two regions.

To overcome these challenges, microservices typically make use of caching.

Uber started offering Redis as a distributed caching solution for the various teams. They followed the typical caching design pattern where the service writes to the database and cache while serving reads directly from the cache.

The below diagram shows this pattern:

However, the normal caching pattern where a service takes care of managing the cache has a few problems at the scale of Uber.

Each team has to manage its own Redis cache cluster.
The cache invalidation logic is duplicated across multiple microservices and there are chances of deviation.
Services must maintain cache replication to stay hot in case of region failovers.

The main point is that every team that needed caching had to spend a large amount of effort to build and maintain a custom caching solution.

To avoid this, Uber decided to build an integrated caching solution known as CacheFront.

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Design Goals with CacheFront

While building CacheFront, Uber had a few important design goals in mind:

Reduce the need for vertical or horizontal scaling to support low-latency read requests
Improve the P50 and P99 latencies and stabilize latency spikes
Bring down the resource allocation for the database engine layer
Replace the plethora of custom-built caching solutions created by individual teams for their needs. Instead, move the ownership for maintaining and supporting Redis to the Docstore team.
Make caching transparent from the point of view of the service, allowing teams to just focus on the business logic
Decouple the caching solution from Docstore’s partitioning schema to avoid hot partitions
Support horizontal scalability of the caching layer with cheap host machines and make the entire operation cost-effective

High-Level Architecture with CacheFront

To support these design goals, Uber created its integrated caching solution tied to Docstore.

The below diagram shows the high-level architecture of Docstore along with CacheFront:

As you can see, Docstore’s query engine acts as the entry point for services and is responsible for serving reads and writes to clients.

Therefore, it was the ideal place to integrate the caching layer, allowing the cache to be decoupled from the disk-based storage. The query engine implemented an interface to Redis to store cached data along with mechanisms to invalidate the cache entries.

Handing Cached Reads

CacheFront uses a cache aside or look aside strategy when it comes to reads.

The below steps explain how it works:

The query engine layer receives a read request for one or more rows
The query engine tries to get the rows from Redis and streams the response to the users
Next, it retrieves the remaining rows from the database (if needed)
The query engine asynchronously populates Redis with the rows that are not found in the cache.
Stream the remaining rows to the users.

Refer to the below diagram that explains the process more clearly:

Cache Invalidation with CDC

As you may have heard a million times by now, cache invalidation is one of the two hard things in Computer Science.

One of the simplest cache invalidation strategies is configuring a TTL (Time-to-Live) and letting the cache entries expire once they cross the TTL. While this can work for many cases, most users expect changes to be reflected faster than the TTL. However, lowering the default TTL to a very small value can sink the cache hit rate and reduce its effectiveness.

To make cache invalidation more relevant, Uber leveraged Flux, Docstore’s change data capture and streaming service. Flux works by tailing the MySQL binlog events for each database cluster and publishes the events to a list of consumers. It powers replication, materialized views, data lake ingestions, and data consistency validations among various nodes.

For cache invalidation, a new consumer was created that subscribes to the data events and invalidates/upserts the new rows in Redis.

The below diagram shows the read and write paths with cache invalidation.

There were some key advantages of this approach:

They could make the cache consistent with the database within seconds of the database change as opposed to minutes (depending on the TTL).
Also, using binlogs made sure that uncommitted transactions couldn’t pollute the cache.

However, there were also a couple of issues that had to be ironed out.

1 - Deduplicating Cache Writes

Since writes happen to the cache simultaneously between the read and write paths, it was possible to write a stale row to the cache by overwriting the newest value.

To prevent this, they deduplicated writes based on the timestamp of the row set in MySQL.

This timestamp served as a version number and was parsed from the encoded row value in Redis using the EVAL command.

2 - Stronger Consistency Requirement

Even though cache invalidation using CDC with Flux was faster than relying on TTL, it still provided eventual consistency.

However, some use cases required stronger consistency guarantees such as the reading-own-writes guarantee.

For such cases, they created a dedicated API to the query engine that allowed users to explicitly invalidate the cached rows right after the corresponding writes were completed. By doing so, they didn’t have to wait for the CDC process to complete for the cache to become consistent.

Scale and Resiliency with CacheFront

The basic requirement of CacheFront was ready once they started supporting reads and cache invalidation.

However, Uber also wanted this solution to work at their scale. They also had critical resiliency needs around the entire platform.

To achieve scale and resiliency with CacheFront, they utilized multiple strategies.

Compare cache

Measurements are the key to proving that a system works as expected. The same was the case with CacheFront.

They added a special mode to CacheFront that shadows read requests to cache, allowing them to run a comparison between the data in the cache and the database to verify that both were in sync. Any mismatches such as stale rows are logged as metrics for further analysis.

The below diagram shows a high-level design of the Compare cache system.

Based on the results from this system, Uber found that the cache was 99.99% consistent.

Cache Warming

In a multi-region environment, a cache is only effective if it is always warm. If that’s not the case, a region fail-over can result in cache misses and drastically increase the number of requests to the database.

Since a Docstore instance spawned in two different geographical regions with an active-active deployment, a cold cache meant that you couldn’t scale down the storage engine to save costs since there was a high chance of heavy database load in the case of failover.

To solve this problem, the Uber engineering team used cross-region Redis replication.

However, Docstore also had its own cross-region replication. Since operating both replication setups simultaneously could result in consistent data between the cache and database, they enhanced the Redis cross-region replication by adding a new cache warming mode.

Here’s how the cache warming mode works:

They tail the Redis write stream to replicate keys to the remote region
In the remote region, they don’t directly update the cache. Instead, they issue read requests to the query engine layer for the replicated keys
In the case of a cache miss, the query engine layer reads the data from the database and writes it to the cache. The response stream is discarded.

The below diagram shows this approach in detail:

Replicating keys instead of values makes sure that the data in the cache is consistent with the database in its respective region. Also, it ensures that the same set of cached rows is present in both regions, thereby keeping the cache warm in case of a failover.

Sharding

Some large customers of Docstore within Uber can generate a very large number of read-write requests. It was challenging to cache all of it within a single Redis cluster that’s limited by the maximum number of nodes.

To mitigate this, they allowed a single Docstore instance to map to multiple Redis clusters. This helped avoid a massive surge of requests to the database in case multiple nodes in a single Redis cluster go down.

However, there was still a case where a single Redis cluster going down may create a hot shard on the database. To prevent this, they sharded the Redis cluster using a scheme that was different from the database sharding scheme. This makes sure that the load from a single Redis cluster going down is distributed between multiple database shards.

The below diagram explains this scenario in more detail.

Circuit Breaker

When a Redis node goes down, a get/set request to that node generates an unnecessary latency penalty.

To avoid this penalty, Uber implemented a sliding window circuit breaker to short-circuit such requests. They count the number of errors on each node for a particular bucket of time and compute the number of errors within the sliding window’s width.

See the below diagram to understand the sliding window approach:

The circuit breaker is configured to short-circuit a fraction of the requests to a node based on the error count. Once the threshold is hit, the circuit breaker is tripped and no more requests can be made to the node until the sliding window passes.

Results and Conclusion

Uber’s project of implementing an integrated Redis cache with Docstore was quite successful.

They created a transparent caching solution that was scalable and managed to improve latency, reduce load, and bring down costs.

Here are some stats that show the results:

The P75 latency went down by 75% and the P99.9 latency went down by over 67% while also limiting latency spikes
Cache invalidation using flux and compare cache provided a cache consistency of 99.9%.
Sharding and cache warming made the setup scalable and fault-tolerant, allowing a use case with over 6M reads per second with a 99% cache hit rate to successfully failover to a remote region.
Costs were down significantly as the same use case of 6M reads per second approximately required 60K CPU cores for the storage engine. With CacheFront, they achieved the same results with just 3K Redis cores.
At present, CacheFront supports over 40M requests per second in production and the number is growing every day.

References:

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by "ByteByteGo" <bytebytego@substack.com> - 11:35 - 26 Mar 2024

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by "New Relic" <emeamarketing@newrelic.com> - 10:13 - 26 Mar 2024

What do the world’s greatest CEOs have in common?

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As only McKinsey can

Brought to you by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities

Top CEOs’ leadership lessons

In the news

•

Satisfied with small business. The chief executive job often comes with grueling hours, little time for sleep or exercise, and a lot of stress. Unfortunately, that can take a toll on CEOs’ health. An executive outplacement company found that 19 chief executives at US companies died in office in 2023 (a historical high) and that more than 1,900 transitioned out of their roles. Small business owners, however, tend to be healthier and more fulfilled, compared with their employees. They have reported less frequent mental and physical issues and greater satisfaction with their lives, according to Baylor University. [Inc.]

•

CEOs and remote work. CEOs are finding it hard to see eye to eye with employees as the multiyear struggle over returning to the office continues. After a European software company recently announced that it would require employees to work on-site more frequently, more than 5,000 workers stated in a letter that leaders’ “radical change in direction” regarding working from home had left them feeling “betrayed.” CEOs, for their part, are voicing concerns about remote work’s erosion of corporate culture and workers’ ability to learn on the job. [WaPo]

“The research suggests that, over the past 50 years, the importance of what the CEO does for a company’s performance has increased twofold.”

On McKinsey.com

•

Secrets to success. What separates the world’s best leaders from the rest? To find out, McKinsey senior partners Carolyn Dewar, Scott Keller, and Vikram Malhotra studied thousands of CEOs around the world. Through interviewing 67 top CEOs for their book CEO Excellence: The Six Mindsets That Distinguish the Best Leaders from the Rest, they determined the mindsets, approaches, and practices that deliver powerful results. Since March 2022, CEO Excellence has sold more than 160,000 copies and is being translated into 14 languages.

•

Being bold. Dewar, Keller, and Malhotra discovered that top CEOs share six mindsets. One is that excellent CEOs are bold. Since the job comes with enormous responsibilities, executives understandably might want to play it safe. Top-performing CEOs, however, are constantly thinking about pushing performance to the next level. Leading CEOs also focus on doing what only they can do; they don’t let themselves get spread thin. Study six mindsets that elite leaders share and learn more about CEO Excellence.

Explore top CEOs’ mindsets

—Edited by Belinda Yu, editor, Atlanta

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ESSENTIALS FOR LEADERS AND THOSE THEY LEAD

by "Only McKinsey" <publishing@email.mckinsey.com> - 01:35 - 26 Mar 2024

Generative AI: A leader’s guide to capturing value fast

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By now, the potential economic payoff of generative AI (gen AI) is almost undisputed: our research shows that the technology could add trillions of dollars in value to the global economy. But despite its beneficial impact on a range of functions and industries, gen AI poses considerable challenges and risks that leaders may need to prepare for even as they pilot the technology. Here’s a roundup of our latest thinking on gen AI and its implications—positive and negative—for organizations.

AN IDEA

An image linking to the web page “Implementing generative AI with speed and safety” on McKinsey.com.

Pursue both speed and safety

The buzz around gen AI has been increasing, but so has concern about its risks—and that can be a problem for leaders who wish to take advantage of its potential for immediate positive impact. In a recent survey, we found that 63 percent of respondents rated gen AI implementation as a high priority, yet 91 percent did not feel ready to manage the process in a responsible manner. We suggest that leaders address four primary sources of “inbound” risk—threats likely to affect organizations regardless of whether they deploy gen AI. For example, it’s important to establish a governance structure that includes expertise, oversight, and training for end users. “Organizations should develop a set of guiding principles agreed on by the executive team and the board that will guide AI adoption and serve as a guardrail for acceptable use cases,” note McKinsey senior partners Ida Kristensen and Lareina Yee and their colleagues.

A BIG NUMBER

7

That’s the number of actions data leaders should consider to help scale generative AI solutions. The first of these involves letting value determine data strategy. For example, gen AI may be just one part of an organization’s broader data portfolio. “Much of the potential value to a business comes from traditional AI, business intelligence, and machine learning,” caution McKinsey experts. “If CDOs [chief data officers] find themselves spending 90 percent of their time on initiatives related to generative AI, that’s a red flag.”

A QUOTE

“It’s important to bear in mind that successful gen AI skills are about more than coding proficiency.”

That’s McKinsey experts on what it takes for a worker to be truly prepared for gen AI on a day-to-day basis. A successful AI team member may need to have design and collaboration skills, contextual understanding to ensure that the most high-quality answers are generated, and forensic skills to figure out the reasons for common breakdowns. “We see a rapid market emerging for people who have learned these skills over the past year,” observe senior partners Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel. “If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies.”

A SPOTLIGHT INTERVIEW

An image linking to the web page “Don’t wait—create, with generative AI” on McKinsey.com.

“What we know from our product development research is that, oftentimes, the problem is you close the funnel too quickly,” says McKinsey partner Michael Chui in an episode of The McKinsey Podcast on how companies can use gen AI to gain competitive advantage. “You don’t continue to look at multiple alternatives.” Chui adds that gen AI enables organizations to consider a variety of creative options, as well as enabling individual knowledge workers to increase their productivity. “You can end up with better products at the end of the day, create whole new categories, create new drugs, create systems that are more sustainable and produce less carbon emissions,” he says. “All the kinds of things we want to do with R&D, we can create a superpower by using these technologies.”

LEGAL EAGLES

An image linking to the web page “Legal innovation and generative AI: Lawyers emerging as ‘pilots,’ content creators, and legal designers” on McKinsey.com.

In his play Henry VI, Shakespeare may have joked about doing away with lawyers, but today, gen AI isn’t here to replace the legal profession but rather could help its members thrive. From automating repetitive tasks to helping create business strategy, the powerful new technology can potentially transform the daily work of lawyers, according to McKinsey associate general counsel Ilona Logvinova . “I see this as a tremendously exciting time in our profession,” she says. “This newly emerging automation stack can free up real time that lawyers use on heavily time-consuming work with lower payoff.” For example, gen AI can search for and compile defined terms, find applicable precedents, and take a first cut at editing forms and documents. “With legal tech as the copilot, we have an opportunity for automation and acceleration in the way we work,” notes Logvinova.

Lead by capturing gen AI’s value.

– Edited by Rama Ramaswami, senior editor, New York

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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:09 - 25 Mar 2024

How are manufacturing leaders using digital twins?

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

Digital twins in manufacturing

In the news

•

Digital twins’ potential. Digital twins (virtual, real-time representations of physical systems) are remarkable because of the two-way communication between the digital and the physical, an aerospace engineer observes, as explained for NPR. That makes digital twinning a dynamic tool for aiding in decision making. Digital twins could revolutionize many fields. In aerospace, for instance, an aircraft’s digital twin could be used to predict maintenance issues before people board a plane, improving safety and engine performance. [NPR]

•

Applications of digital twins. As reported in Politico, digital-twin technology generates dynamic, virtual models of physical objects, people, and processes. Digital twins use real-time data to simulate events and predict outcomes, enabling users to make better-informed decisions. They have applications in various fields, including personalized medicine, climate change modeling, and manufacturing. In microchip production, digital twins can replicate processes such as a plant’s water use. [Politico]

Some 44% of surveyed executives in industrial companies said they have already implemented a digital twin, while 15% were planning to deploy one.

On McKinsey.com

•

Factory of the future. Global manufacturers face intense pressure. Amid challenges such as constraints on material and labor, more industry leaders are turning to factory digital twins—real-time virtual representations of the factory. Their use supports faster, smarter, and more cost-effective decision making. In a McKinsey survey of 75 senior leaders in industrials, 86% say that a digital twin is applicable to their organization, McKinsey partner Kevin Goering and colleagues share.

•

Real-time decisions. Factory digital twins simulate outcomes from real-time factory conditions, enabling what-if analyses across production scenarios. Manufacturing leaders already use digital twins to increase efficiency and reduce costs. One industrial company recently employed a factory digital twin to redesign its production schedule. By reducing an assembly plant’s overtime requirements, the company saved 5 to 7% in costs each month. Consider how to get started with digital twins, and visit McKinsey Digital to learn more about AI and tech in business.

Discover digital twins

—Edited by Belinda Yu, editor, Atlanta

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by "Only McKinsey" <publishing@email.mckinsey.com> - 01:48 - 25 Mar 2024

Top 10 reports this quarter

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Top Ten Reports │ First Quarter 2024

Our top ten reports this quarter look at the geometry of global trade, generative AI in the pharmaceutical industry, and more. At No. 1, McKinsey’s Kweilin Ellingrud, Lucy Pérez, and Valentina Sartori, in collaboration with the World Economic Forum, explore how closing the women’s health gap could provide a $1 trillion boost to the global economy.

Illustration featuring a silhouette of a woman's head with a sunrise and cloud multiple exposure background.

1. Closing the women’s health gap: A $1 trillion opportunity to improve lives and economies

Investments addressing the women’s health gap could add years to life and life to years—and potentially boost the global economy by $1 trillion annually by 2040. [42 pages] Take a holistic approach

Image of a 3D sphere deconstructed in slices.

5. The state of Black residents: The relevance of place to racial equity and outcomes

At the current pace of change, it could take decades to improve outcomes for Black Americans and more than three centuries to get to racial parity. How can different types of communities move closer to delivering a better quality of life for all residents? [76 pages] Change the trajectory

6. Spark & Sustain: How all of the world’s school systems can improve learning at scale

Student achievement is stagnating globally and millions of children are not learning, but meaningful change is possible by taking lessons from systems that are beating the odds. [139 pages] Beat the odds

7. Beyond the hype: Capturing the potential of AI and gen AI in tech, media, and telecom

This collection presents the top insights on gen AI from McKinsey and its AI arm, QuantumBlack, providing a detailed examination of the significant opportunities and challenges it offers for leaders looking to steer their organizations into the future. [126 pages] Implement at scale

8. Time to move: Sporting goods 2024

Many sporting goods executives are looking to 2024 with more optimism. But in the year ahead, they will need to adapt to shifting consumer preferences, revisit planning practices, embrace ecosystems, and take action on sustainability. [68 pages] See what’s ahead

9. The State of Energy Organizations 2024

The coming decade will likely be a defining one for the global energy system. [77 pages] 4 key themes

10. US wealth management: Amid market turbulence, an industry converges

Traditional delivery models may soon be history, as firms of all stripes broaden both their offerings and their target client and adviser segments. The competition will be intense. [22 pages] Break through market turbulence

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ALL THE WEEK’S DATA THAT’S FIT TO VISUALIZE

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The week in charts

The Week in Charts

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Our McKinsey Chart of the Day series offers a daily chart that helps explain a changing world—as we strive toward sustainable and inclusive growth. In case you missed them, this week’s graphics explored AI in manufacturing, global M&A activity, shoppers’ shift to online channels, corporate deposits in transaction banking, and mobility’s tech trends.

FEATURED CHART

A one-click fix for manufacturing

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M&A’s bumpy ride

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Competition for corporate deposits

Ten tech trends that could steer mobility

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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:50 - 23 Mar 2024

EP104: How do Search Engines Work?

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EP104: How do Search Engines Work?

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Mar 23

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This week’s system design refresher:

System Design: Why is Kafka so Popular? (Youtube video)
How do Search Engines Work?
Top 9 Website Performance Metrics You Cannot Ignore
Top 6 Data Management Patterns
Comparing Different API Clients
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System Design: Why is Kafka so Popular?

How do Search Engines Work?

The diagram below shows a high-level walk-through of a search engine.

▶️ Step 1 - Crawling
Web Crawlers scan the internet for web pages. They follow the URL links from one page to another and store URLs in the URL store. The crawlers discover new content, including web pages, images, videos, and files.

▶️ Step 2 - Indexing
Once a web page is crawled, the search engine parses the page and indexes the content found on the page in a database. The content is analyzed and categorized. For example, keywords, site quality, content freshness, and many other factors are assessed to understand what the page is about.

▶️ Step 3 - Ranking
Search engines use complex algorithms to determine the order of search results. These algorithms consider various factors, including keywords, pages' relevance, content quality, user engagement, page load speed, and many others. Some search engines also personalize results based on the user's past search history, location, device, and other personal factors.

▶️ Step 4 - Querying
When a user performs a search, the search engine sifts through its index to provide the most relevant results.

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Top 9 Website Performance Metrics You Cannot Ignore

Load Time: This is the time taken by the web browser to download and display the webpage. It’s measured in milliseconds.

Time to First Byte (TTFB): It’s the time taken by the browser to receive the first byte of data from the web server. TTFB is crucial because it indicates the general ability of the server to handle traffic.

Request Count: The number of HTTP requests a browser has to make to fully load the page. The lower this count, the faster a website will feel to the user.

DOMContentLoaded (DCL): This is the time it takes for the full HTML code of a webpage to be loaded. The faster this happens, the faster users can see useful functionality. This time doesn’t include loading CSS and other assets

Time to above-the-fold load: “Above the fold” is the area of a webpage that fits in a browser window without a user having to scroll down. This is the content that is first seen by the user and often dictates whether they’ll continue reading the webpage.

First Contentful Paint (FCP): This is the time at which content first begins to be “painted” by the browser. It can be a text, image, or even background color.

Page Size: This is the total file size of all content and assets that appear on the page. Over the last several years, the page size of websites has been growing constantly. The bigger the size of a webpage, the longer it will take to load

Round Trip Time (RTT): This is the amount of time a round trip takes. A round trip constitutes a request traveling from the browser to the origin server and the response from the server going to the browser. Reducing RTT is one of the key approaches to improving a website’s performance.

Render Blocking Resources: Some resources block other parts of the page from being loaded. It’s important to track the number of such resources. The more render-blocking resources a webpage has, the greater the delay for the browser to load the page.

Over to you - What other website performance metrics do you track?

Reference: Cloudflare Docs

How do we manage data? Here are top 6 data management patterns

Cache Aside
When an application needs to access data, it first checks the cache. If the data is not present (a cache miss), it fetches the data from the data store, stores it in the cache, and then returns the data to the user. This pattern is particularly useful for scenarios where data is read frequently but updated less often.
Materialized View
A Materialized View is a database object that contains the results of a query. It is physically stored, meaning the data is actually computed and stored on disk, as opposed to being dynamically generated upon each request. This can significantly speed up query times for complex calculations or aggregations that would otherwise need to be computed on the fly. Materialized views are especially beneficial in data warehousing and business intelligence scenarios where query performance is critical.
CQRS
CQRS is an architectural pattern that separates the models for reading and writing data. This means that the data structures used for querying data (reads) are separated from the structures used for updating data (writes). This separation allows for optimization of each operation independently, improving performance, scalability, and security. CQRS can be particularly useful in complex systems where the read and write operations have very different requirements.
Event Sourcing
Event Sourcing is a pattern where changes to the application state are stored as a sequence of events. Instead of storing just the current state of data in a domain, Event Sourcing stores a log of all the changes (events) that have occurred over time. This allows the application to reconstruct past states and provides an audit trail of changes. Event Sourcing is beneficial in scenarios requiring complex business transactions, auditability, and the ability to rollback or replay events.
Index Table
The Index Table pattern involves creating additional tables in a database that are optimized for specific query operations. These tables act as secondary indexes and are designed to speed up the retrieval of data without requiring a full scan of the primary data store. Index tables are particularly useful in scenarios with large datasets and where certain queries are performed frequently.
Sharding
Sharding is a data partitioning pattern where data is divided into smaller, more manageable pieces, or "shards", each of which can be stored on different database servers. This pattern is used to distribute the data across multiple machines to improve scalability and performance. Sharding is particularly effective in high-volume applications, as it allows for horizontal scaling, spreading the load across multiple servers to handle more users and transactions.

Reference: Data Management Patterns by Microsoft

Comparing Different API Clients

Postman is a widely used API lifecycle platform. It emerges as a comprehensive and versatile API client suitable for enterprise-level development. Its support for a wide range of protocols, robust feature set, and strong performance make it a top choice for complex projects. With an intuitive design, collaboration features, and a large community, Postman excels in scenarios requiring extensive functionality and community support.

Insomnia is a powerful API client with extensive features and being completely open-source makes it a good choice for developers seeking flexibility and continuous growth. Insomnia is suited for those who value an open-source environment and an active community.

ReadyAPI, with its simplicity and focus on smaller projects, is an ideal choice for scenarios where a lightweight and responsive tool is preferred. It provides essential features, making it suitable for projects with less complexity. However, it may not be the best fit for larger, more intricate endeavors that require extensive functionality.

ThunderClient, a VS Code plugin, is free and user-friendly, catering to developers who prefer an integrated testing environment. However, it lacks extensive features and community support, crucial for larger or complex projects, rendering it more appropriate for smaller teams with simpler requirements. Additionally, its reliance on Visual Studio Code may restrict its appeal to users who prefer alternative development environments. Experienced users accustomed to feature-rich tools may encounter a learning curve and might find ThunderClient lacking in certain functionalities.

Hoppscotch, a free and open-source tool, focuses on functionality over design, offering a lightweight web version with support for various protocols. While it lacks extensive documentation and community support, it provides a cost-effective solution for developers seeking simplicity.

Over to you: Which API client do you prefer?

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Gen AI: Slow your roll

The Shortlist

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FOUR OF OUR BEST NEW IDEAS FOR THE C-SUITE

Curated by Liz Hilton Segel, chief client officer and managing partner, global industry practices, & Homayoun Hatami, managing partner, global client capabilities

New and powerful technologies like generative AI are always a thrill. But what comes next? It’s never just about the tech: companies need much more—like strategy, talent, risk management—to get real value out of their shiny new objects. That’s especially true when building a new tech-based business. In this edition of the CEO Shortlist, we highlight four articles that preach the gospel of caution. We hope you enjoy the read.

—Liz and Homayoun

An image linking to the web page “False friends or good ends? The CIO’s four-point guide to navigating technology trends” on McKinsey.com.

What’s new

Trend with caution. Lots of IT executives have likely fielded excited calls in recent months from their C-suite colleagues, who’ve read somewhere (maybe here!) that gen AI is the next big thing and want to implement it right away. But chasing trends can be a fool’s errand. The smart tech exec relies on a clear set of parameters for evaluating new trends—and responding appropriately.

Walk before you run. Learn how with “False friends or good ends? The CIO’s four-point guide to navigating technology trends,” a new article by Oliver Bossert, Klaas Ole Kürtz, and Gérard Richter.

What’s now

It’s not all sunshine and lollipops. Gen AI can be fun. But new risks are cropping up every day, with more on the way. Hallucinations are one thing. Deepfakes, intellectual property theft, and gen-AI-powered malware are another. Are you ready for the dark side of gen AI?

Go on defense with “Implementing generative AI with speed and safety,” by Oliver Bevan, Michael Chui, Ida Kristensen, Brittany Presten, and Lareina Yee.

An image linking to the web page “New business building: Six cybersecurity and digital beliefs that can create risk” on McKinsey.com.

What’s next

Nobody ever created a unicorn by having another meeting. But as companies rush to take new businesses to market, risk management and cybersecurity tend to be afterthoughts. That plays right into the hands of hackers and bad actors.

It can happen to you. Build your defenses early with “New business building: Six cybersecurity and digital beliefs that can create risk,” by Justin Greis, Ari Libarikian, Patrick Rinski, Joy Smith, and Marc Sorel.

An image linking to the web page “A generative AI reset: Rewiring to turn potential into value in 2024” on McKinsey.com.

What’s needed

Gen AI isn’t a gold rush. After a flurry of initial excitement, executives are starting to realize that capturing value through gen AI is going to be a long game. Employing gen AI effectively in the long term requires what we call organizational surgery: a broad set of changes to the way work actually gets done.

Scrub in with “A generative AI reset: Rewiring to turn potential into value in 2024,” by Eric Lamarre, Alex Singla, Alexander Sukharevsky, and Rodney Zemmel.

We hope you find these ideas inspiring and helpful. See you next time with four more McKinsey ideas for the CEO and others in the C-suite.

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DELIVERING ACTIONABLE INSIGHTS ON THE DAY’S NEWS
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by "McKinsey CEO Shortlist" <publishing@email.mckinsey.com> - 04:23 - 22 Mar 2024

Why does empathy matter at work?

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

The empathy equation

In the news

•

Improving EQ. Emotional intelligence (EQ), which involves understanding and regulating one’s emotions, being aware of others’ perspectives, and managing relationships, is a vital life skill, a well-being expert says in Fast Company. People with high EQ aren’t always serene, but they are able to respond with empathy even when feeling negative emotions. Routinely asking yourself, “How am I feeling about this?” is one way to increase self-awareness and improve EQ. [Fast Company]

•

Caring managers. In recent years, organizations have realized that how managers communicate affects whether or not frontline employees feel cared about, explains a workplace expert cited in the New York Times. As a result, companies are focused on developing compassionate leaders. One US retail corporation has been holding regular leadership training sessions at its headquarters since July 2022. Store managers are taught that their success is tied to how they serve their employees, customers, and community. [NYT]

“When people feel connected to their colleagues and to their leaders, they work harder, faster, and more creatively.”

On McKinsey.com

•

The power of empathy. Decades of evidence show that empathy is a superpower in the workplace, Stanford University research psychologist Jamil Zaki shares on an episode of the McKinsey Talks Talent podcast. Workers who think that their organizations and managers care about them report less burnout, better mental health and morale, and a greater intent to stay at their companies, says Zaki, who joined McKinsey talent leaders Bryan Hancock and Brooke Weddle on the podcast. They also tend to be more innovative and take more creative risks.

•

Strengthening empathy. Empathy isn’t a fixed trait: rather, it’s a skill that can be learned and improved upon with practice, says Zaki, author of The War for Kindness: Building Empathy in a Fractured World. One large tech company, for instance, designed a curriculum for a management academy that focused on building trust and empathy. The managers’ customer satisfaction scores improved twice as fast as those of a placebo group. Learn how leaders can create a more empathic, connected work culture.

See practices that build empathy

—Edited by Belinda Yu, editor, Atlanta

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