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What are the benefits of AI guardrails?
On McKinsey Perspectives
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by "Thomas Sampassa-Ilunga" <tilunga@newrelic.com> - 11:25 - 19 Feb 2025 -
How leaders are promoting resilience at work
On McKinsey Perspectives
Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
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Overcoming resistance to change. In an environment where disruptions—including the growing use of AI, geopolitical and economic shifts, and evolving employee expectations—are the norm, leaders should foster resilience and adaptability in themselves and in their employees. Yet this is easier said than done, McKinsey Senior Partners Aaron De Smet and Dana Maor and coauthors reveal. McKinsey research shows that when the path forward at work (or in life) is unclear, people tend to do what’s familiar or predictable, regardless of whether that approach is appropriate for the situation at hand.
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The value of resilience. When a company’s people are resilient and adaptable, it can have profound effects on innovation, growth, and organizational health. Those who are both resilient and adaptable demonstrate better overall health and are more engaged at work than their peers, according to the McKinsey Health Institute. What can leaders do to foster these traits within their own workforces? See the four actions leaders can take to help their people weather the inevitable storms that lie ahead.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 12:33 - 19 Feb 2025 -
The Perfect Faceoff: NHL Teams vs. ERP Systems
The Perfect Faceoff: NHL Teams vs. ERP Systems
Hi MD Abul,
Hockey season is heating up, and just like the NHL, the ERP world is full of powerhouses, underdogs, and rising stars.
So, what if ERP systems were NHL teams? Would SAP be the Tampa Bay Lightning? Is Microsoft Dynamics 365 the Pittsburgh Penguins? And who’s the Montreal Canadiens of ERP?
We broke it all down in our latest blog, matching the biggest ERP systems with the biggest teams in hockey. Whether you're an NHL fan or just love a creative take on digital transformation, you won’t want to miss this one!
Let us know—did we get the comparisons right, or do you have a better match?
Best regards,
Eric Kimberling
by "Eric Kimberling" <eric.kimberling@thirdstage-consulting.com> - 12:07 - 19 Feb 2025 -
A healthy workplace starts with healthy workers
Five Fifty
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Enhanced employee health can boost engagement and productivity and has the potential to create up to $11.7 trillion in global economic value, say McKinsey’s Barbara Jeffery, Brooke Weddle, Jacqueline Brassey, and Shail Thaker. To learn more about how companies can support their people to help them thrive, check out the latest edition of the Five Fifty.
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by "McKinsey Quarterly Five Fifty" <publishing@email.mckinsey.com> - 03:23 - 18 Feb 2025 -
How Instagram Scaled Its Infrastructure To Support a Billion Users
How Instagram Scaled Its Infrastructure To Support a Billion Users
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Disclaimer: The details in this post have been derived from Instagram Engineering Blog and other sources. All credit for the technical details goes to the Instagram engineering team. The links to the original articles are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Instagram was launched in 2010 by Kevin Systrom and Mike Krieger as a simple photo-sharing app. The app allowed users to instantly upload, edit, and share photos, applying filters that gave images a professional touch. Unlike other social media platforms at the time, Instagram focused exclusively on visual storytelling, making it an instant hit among smartphone users.
The platform's simplicity and mobile-first approach led to rapid adoption. Within just two months, Instagram had gained 1 million users, a milestone that took Facebook nearly a year to achieve. The app grew exponentially, reaching 10 million users in one year and 30 million users by early 2012.
By April 2012, Instagram had reached 50 million users and was attracting serious investment interest. Facebook acquired Instagram for $1 billion, a shocking move at the time, as the company had just 13 employees. However, Mark Zuckerberg saw Instagram’s potential as a mobile-first social network and wanted to integrate it into Facebook’s ecosystem while allowing it to function independently.
At the time of the acquisition, Instagram was still operating on a relatively small-scale infrastructure hosted on Amazon Web Services (AWS). The app was experiencing major scalability challenges, such as server overloads, database scaling issues, and limited engineering resources.
After joining Facebook, Instagram migrated from AWS to Facebook’s data centers, preparing it for its next phase of hypergrowth. Under Facebook’s ownership, Instagram continued to scale at an unprecedented pace. By 2014, it had 300 million users, and by 2018, it had breached the billion-user mark.
To handle billions of daily interactions, Instagram had to adopt advanced scaling strategies, including distributed databases, caching, and multi-region infrastructure. In this article, we’ll look at how Instagram scaled its infrastructure to handle this massive scale.
Instagram’s Early Infrastructure Challenges
When Instagram first launched in 2010, it was a small company with limited engineering resources.
However, with time, Instagram grew into one of the most widely used social media platforms in the world. This rapid increase in user engagement placed an enormous strain on its infrastructure, requiring continuous optimization and scaling efforts.
Some key metrics highlighting Instagram’s scale (in 2017-2018) are as follows:
400 million daily active users that open Instagram multiple times a day, generating billions of interactions.
100 million media uploads per day. Every media file must be optimized for different devices, ensuring smooth playback while minimizing bandwidth usage.
4 billion likes per day where every like generates a database write operation, which must be efficiently managed to avoid overloading storage and query systems
The platform’s rapid growth exposed significant scalability challenges, forcing engineers to adopt temporary solutions to keep the system running. However, as user activity continued to increase exponentially, these short-term fixes became unsustainable, leading to the urgent need for automation and a scalable architecture.
Some challenges faced by Instagram in its early days are as follows:
Manual Server Scaling: In the early days, Instagram’s engineering team manually added servers before weekends to prevent system failures. Traffic spikes were common on Fridays and weekends when user engagement surged due to increased social activity. Engineers would monitor CPU loads and decide if additional servers needed to be provisioned to handle the expected load.
Unreliable Performance Due to Limited Infrastructure: Instagram initially operated on Amazon Web Services (AWS), using a small number of virtual machines. The infrastructure was not optimized for handling sudden surges in traffic, leading to slow response times and occasional outages. The backend services struggled to process requests efficiently, causing delays in content loading and interactions.
Database Overload: The increasing number of users posting photos, liking content, and following accounts placed a heavy burden on Instagram’s relational database. As data volume grew, the read and write operations on the database became slower, affecting the overall performance of the platform. Engineers had to manually optimize queries and shift loads across multiple database replicas, a time-consuming process.
Lack of Automated Monitoring: Since Instagram had a small engineering team, monitoring server performance was done manually, often requiring constant attention to avoid system failures. Engineers used basic dashboards and alerts to detect issues, but there was no real-time system in place to automatically respond to performance problems.
Lack of Load Balancing: Load balancing across multiple servers was not fully automated, leading to uneven traffic distribution and bottlenecks.
Engineering Bottlenecks: Since infrastructure management was largely manual, engineers had to spend significant time scaling and maintaining servers instead of focusing on product development. As the team was small, scaling efforts often delayed feature releases and made it difficult to introduce new functionalities.
The Three Dimensions of Scaling at Instagram
As Instagram’s user base grew exponentially, the company had to develop scalable infrastructure to handle billions of daily interactions.
Scaling is not just about adding more hardware. It also involves optimizing resources and ensuring that the engineering team can operate efficiently. Instagram approached scalability in three key dimensions:
1 - Scaling Out: Expanding Infrastructure with More Servers
Scaling out refers to adding more hardware (servers, storage, and computing power) to handle increased traffic.
Some new strategies were adopted:
Instagram migrated from AWS to Facebook’s data centers, allowing horizontal scaling by distributing the workload across multiple servers. They expanded to multiple geographically distributed data centers, ensuring high availability and redundancy.
Instead of relying on a few powerful machines, Instagram distributed workloads across multiple servers and data centers.
Load balancing systems were implemented to distribute user requests across different data centers.
Instead of a single monolithic database, Instagram adopted a distributed database system, using PostgreSQL and Cassandra for high availability.
Migrating to Facebook’s data centers allowed Instagram to take advantage of Facebook’s existing tools and infrastructure, such as:
TAO: A highly efficient distributed database system for social data.
Scuba: A real-time analytics and monitoring tool for debugging performance issues.
Tupperware: Facebook’s containerized deployment system for managing large-scale applications.
However, the migration was quite challenging. Instagram needed to migrate petabytes of media files, user data, and metadata from AWS to Facebook’s data centers. Instead of a single "big bang" migration, data was incrementally copied and synchronized across both systems to ensure consistency. Facebook’s Haystack storage system was used to optimize media storage and retrieval.
Instagram’s backend services were initially built to run on AWS’s Elastic Compute Cloud (EC2). To optimize performance, these services were re-engineered to run on Facebook’s Tupperware containerization system, allowing for better resource utilization and auto-scaling.
2 - Scaling Up: Optimizing Efficiency of Each Server
Scaling up means improving the performance of existing infrastructure rather than adding more hardware. This involved reducing CPU load, optimizing queries, and using caching mechanisms to maximize server efficiency.
In Instagram’s case, adding more servers was not a sustainable long-term solution due to cost and resource limitations. Each request from a user (loading a feed, liking a post, adding a comment) resulted in multiple database queries, slowing down response times. Inefficient memory usage and unoptimized Python code caused unnecessary resource consumption.
The Instagram engineering team implemented some solutions to deal with these issues:
Optimized database queries by reducing redundant operations and batching requests.
Used Memcached and Cassandra to cache frequently accessed data, reducing direct database queries.
Profiled Python code to identify inefficient functions and replaced them with C++ implementations for better performance.
Improved server memory management, enabling a more efficient use of RAM.
3 - Scaling the Engineering Team
Scaling the engineering team means ensuring that developers can continue to work efficiently as the company grows. This involves better deployment strategies, automation, and minimizing bottlenecks in the development process.
Instagram grew from a small startup to a company with hundreds of engineers, leading to challenges in code management and deployment. More engineers working on the same codebase increased merge conflicts, regressions, and debugging complexity.
To handle these problems, the Instagram engineering team embraced continuous deployment. Instead of periodic large updates, they shifted to a rolling release model, deploying code changes 40+ times per day. This ensured that small updates could be tested, monitored, and rolled back quickly if needed.
New features were deployed gradually to a small subset of users before being rolled out globally. Engineers could monitor performance metrics and detect bugs early, preventing widespread failures. They also implemented real-time monitoring tools to detect performance regressions.
Instagram’s Backend Architecture
As Instagram grew into a platform handling billions of interactions daily, it required a robust, scalable, and efficient backend architecture.
The system needed to process a huge number of user requests, including media uploads, likes, comments, and notifications while maintaining high availability and low latency. The main components of the backend architecture are as follows:
1 - Django - The Core Web Framework
Django, a high-level Python web framework, serves as the foundation of Instagram’s backend. It provides the core structure for handling HTTP requests, user authentication, database interactions, and API endpoints.
The main advantages of Django are as follows:
Rapid development: Pre-built components allow engineers to quickly iterate on features.
Scalability: Supports ORM (Object-Relational Mapping), enabling efficient database queries.
Security: Built-in protections against common vulnerabilities like SQL injection and cross-site scripting.
2 - RabbitMQ - Message Broker
RabbitMQ acts as a message broker, facilitating communication between different services. Instead of handling everything in a single request, RabbitMQ enables asynchronous task execution, improving performance and reliability.
Instagram uses RabbitMQ to decouple components and allow different services to interact without being directly dependent on each other. It handles spikes in traffic and manages message persistence even if one part of the system temporarily fails.
3 - Celery for Async Task Processing
Celery is a distributed task queue system that works alongside RabbitMQ to execute background tasks. It ensures that long-running processes do not slow down real-time user interactions.
Instagram uses Celery to handle asynchronous tasks such as sending emails or processing large media files. Celery also has fault tolerance where a task failure results in automatic retries. Celery workers can be distributed across multiple servers, handling high workloads efficiently.
How the Components Work Together?
Instagram’s backend components operate in a coordinated manner, ensuring efficiency. Let’s understand the process when a user likes a post:
User Action: The user taps the “Like” button on a post.
Django Processes the Request: The request is sent to Django’s web server. Django updates the like count in the PostgreSQL database.
Caching and Asynchronous Processing: If the post has been liked before, the like count is retrieved from Memcached instead of querying the database. Django sends a task to RabbitMQ to notify the post owner about the like.
RabbitMQ Queues the Notification Task: Instead of handling the notification immediately, RabbitMQ queues it for later processing.
Celery Worker Sends the Notification: A Celery worker retrieves the task from RabbitMQ, generates the notification, and sends it to the recipient.
Storage Services in Instagram’s Architecture
Storage services are responsible for persisting user data, media files, and metadata across multiple distributed databases.
Instagram employs a combination of structured and distributed storage solutions to manage different types of data efficiently.
PostgreSQL: Stores structured data such as user profiles, comments, relationships, and metadata. It follows a master-replica architecture to ensure high availability and scalability. PostgreSQL also supports ACID compliance.
Cassandra: Used for storing highly distributed data, such as user feeds, activity logs, and analytics data. Unlike PostgreSQL, Cassandra follows an eventual consistency model, meaning data updates may take time to propagate across different regions. It provides high write throughput, making it ideal for operations like logging and real-time analytics.
Memcached: Used to reduce database load by caching frequently accessed data. It stores temporary copies of user profiles, posts, and like counts to prevent repeated queries to PostgreSQL or Cassandra. This helps optimize API response times
Haystack: Stores images and videos efficiently, minimizing the number of file system operations required to fetch content. It improves media loading times by serving cached versions of files through CDNs.
See the diagram below for PostgreSQL scaling out with leader-follower replication.
Also, the diagram below shows Cassandra scaling.
Since Instagram’s storage services are distributed across multiple data centers and geographic regions, maintaining data consistency is a major challenge.
The key data consistency challenges are as follows:
Replication Latency: In a distributed system, database replicas take time to sync, leading to temporary inconsistencies. For example, a user likes a post, but another user may not immediately see the updated like count because data replication has not been completed.
Eventual Consistency in NoSQL Systems: Cassandra uses an eventual consistency model, meaning updates propagate asynchronously across different database nodes. While this improves scalability, it can cause temporary discrepancies where different users see different versions of the same data.
Cache Invalidation Issues: Memcached accelerates read operations, but if an update is made to the database, the cached data must be invalidated to reflect the latest changes. For example, if a user changes their profile picture, but the cached version is not updated immediately, they may see an outdated version of their profile.
Cross-Region Data Synchronization: Since Instagram operates multiple data centers, data must be synchronized across different geographic locations. Network failures or delays in replication can lead to inconsistencies between regions.
The Instagram engineering team uses several strategies to address these consistency challenges.
For example, PostgreSQL is used for critical transactions, such as financial transactions, user authentication, and content moderation, where strong consistency is required. On the other hand, Cassandra is used for highly scalable workloads, such as activity feeds, where slight delays in consistency are acceptable. Memcached entries are automatically invalidated when new data is written to PostgreSQL or Cassandra, preventing outdated information from being served.
See the diagram below that shows how cache invalidation is handled.
Memcache Lease Mechanism
As Instagram scaled to handle billions of daily interactions, it encountered a major challenge known as the thundering herd problem.
The issue occurs for frequently accessed data (like counts, user feeds, and comments), which is stored in Memcached for fast retrieval. When a cached value expires or is invalidated due to an update, multiple web servers request the same data from the database. If thousands or millions of users request the same piece of data at the same moment, the database gets overloaded with duplicate queries.
To mitigate this, Instagram implemented the Memcache Lease mechanism, which helps prevent multiple redundant database queries by controlling how cache misses are handled.
Here’s how it works:
A user requests an Instagram feed. For each post in the feed, there is associated data such as the like counts, comments, and media metadata.
The application first checks Memcached to retrieve this data.
If the requested data is not found in Memcached, instead of immediately querying the database, the server sends a lease request to Memcached. The first server to request the data is granted a lease token, meaning it is responsible for fetching fresh data from the database.
Other servers that request the same data do not query the database. Instead, they receive a stale value from Memcached or are told to wait. This ensures that only one server fetches the updated data from the database, while the rest hold back.
The server holding the lease token queries the database and fetches the fresh data. It also updates the Memcached entry.
The first server returns the fresh data to users while the rest wait until the cache is repopulated, avoiding a direct hit to the database.
Instagram’s Deployment Model
Instagram operates on a continuous deployment model, meaning new code is pushed to production multiple times per day. This approach allows Instagram to iterate quickly, fix issues promptly, and introduce new features without causing major disruptions.
Instagram’s deployment pipeline includes code review, automated testing, canary testing, and real-time monitoring to ensure that new changes do not introduce performance regressions.
Engineers submit changes through code review to ensure quality and maintainability. Every code change must pass unit tests, integration tests, and performance benchmarks before being merged into the main branch. Once code is merged, it is automatically tested in staging environments to identify functional or security issues.
Also, instead of deploying changes to all users at once, Instagram first deploys updates to a small subset of production servers (canary servers). Engineers monitor key performance metrics (CPU usage, memory consumption, error rates) to ensure the new code does not introduce regressions.
Lastly, Instagram also monitors and optimizes CPU usage.
It uses Linux-based profiling tools to track CPU usage at the function level. Engineers analyze which parts of the codebase consume the most CPU cycles and optimize them for better efficiency. They also use profiling tools and replace CPU-intensive Python functions with optimized C++ implementations for better performance.
Conclusion
Instagram’s ability to scale while maintaining high performance and reliability is proof of its well-designed infrastructure and deployment strategies.
By leveraging Django, RabbitMQ, Celery, PostgreSQL, and Cassandra, Instagram ensures seamless processing of billions of daily interactions It has separated computing and storage services to support efficient resource utilization and minimize database load. The implementation of techniques like Memcache leasing prevents the thundering herd problem, reducing redundant queries and improving system stability.
The platform’s continuous deployment model enables rapid innovation while minimizing risk through canary testing, feature gating, and automated rollbacks. Furthermore, real-time CPU tracking, performance regression testing, and automated monitoring allow engineers to detect and resolve inefficiencies before they impact users.
These strategies have positioned Instagram as one of the most resilient and scalable social media platforms, allowing it to support millions of concurrent users, high-traffic workloads, and rapid feature development without compromising performance or stability.
References:
Instagration Part 2: Scaling Infrastructure to Multiple Data Centers
What powers Instagram: Hundreds of Instances, Dozens of Technologies
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The trends behind social commerce’s rise
On McKinsey Perspectives
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Welcome to the latest edition of Only McKinsey Perspectives. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
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E-commerce on the rise. E-commerce, or the buying and selling of goods and services over the internet, has transformed the way that people shop since the first online transaction took place in 1994. E-commerce has grown consistently for decades, but the COVID-19 pandemic sent digital transactions soaring. In the US, e-commerce sales penetration more than doubled to about 35% in 2020, from about 16% in 2019. That increase is roughly the equivalent of ten years of growth, McKinsey Partner Dianne Esber and coauthors explain.
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Small-business boost. For small and medium-size enterprises, the e-commerce opportunity is especially enormous—and even more so in the era of gen AI. Yet for businesses that are used to operating offline, incorporating e-commerce into the customer experience can be challenging. McKinsey analysis finds that small and medium-size retailers and brand manufacturers realize a much smaller portion of revenue from e-commerce than large retailers do. Read the McKinsey Explainer “What is e-commerce?” for more examples of how brands are using e-commerce to build value.
—Edited by Belinda Yu, editor, Atlanta
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of Leading Off. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
Welcome to the 200th edition of Leading Off. We launched this newsletter in November 2020, when the COVID-19 pandemic was reshaping so many aspects of our lives and livelihoods—including the skills that leaders need to successfully navigate the challenges and opportunities of the day. Since then, Leading Off has examined enduring themes (such as effective leadership approaches, problem-solving, and purpose) as well as the topics that continue to transform business and redefine what it takes to lead well. This week, we look at leadership during unpredictable times—and highlight some of our best thinking from the past few years, based on the articles and topics that resonated most with you, our Leading Off readers.
Whether you’ve been with us since the beginning or subscribed more recently, thank you for reading. We encourage you to explore past issues to see how the conversation about leadership has evolved.Modern organizations are facing constant disruptions, from geopolitical tensions to rapid technological advances to major demographic shifts among their workforces. According to McKinsey’s Aaron De Smet, Arne Gast, and Johanne Lavoie, the most effective leaders will take a more open, adaptable, and inclusive approach to guiding their companies through the turbulence. “We are moving from an era of individual leaders to an era of networked leadership teams that steer the organization,” the authors say. “The old hierarchical model of leadership is increasingly seen as an obstacle to meeting the complex demands facing today’s organizations.” To deliver sustainable growth, leaders can embrace five critical shifts in mindsets and ways of working—including moving beyond profit to impact and beyond control to collaboration. “This new model has a clear purpose and focus: to benefit all stakeholders by enabling people to work and learn together to build and operate a continually evolving system for creating value,” they say.
That’s how many steps leaders can take to start their journey toward joining a corporate board. These include understanding the role and objectives of serving as a director and increasing professional visibility. McKinsey Senior Partner Celia Huber and coauthors note that board service provides executives with opportunities for personal and professional growth by helping them develop new skills and learn about different governance processes and leadership styles.
That’s McKinsey Senior Partner Andrew Goodman and coauthors on how to excel as a chief of staff—a role that is critical to helping the CEO and other C-suite leaders execute their mission. They offer eight pieces of advice for people in the role; among them: Clearly define the scope of the position, build trust with executives, and make the role your own. “It’s a privileged position, bringing access to the top leadership and the most pressing corporate agendas but requiring an intense focus and unique skills,” the authors say.
The best leaders blend the attributes that fit their individual styles with the needs of their organizations—which could mean being collaborative and curious, visionary and inspirational, or self-reflective and resilient. But there’s one approach that everyone can agree on: Don’t be a jerk. Stanford University professor Robert Sutton identifies some triggers that could cause ugly behavior in the workplace, including wielding power over others, being too competitive, and always being in a hurry. Sutton says that bullying or rude bosses can undermine employees’ decision-making skills, productivity, and creativity. Tomas Chamorro-Premuzic, a professor at Columbia University and University College London and author of Why Do So Many Incompetent Men Become Leaders?: (And How to Fix It), notes that good bosses control their temper, show empathy, and connect with employees on a human level. “If you’re managing people,” he says, “it’s important that you also manage yourself.”
Lead by adapting to the moment.
— Edited by Eric Quiñones, senior editor, New York
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Copyright © 2025 | McKinsey & Company, 3 World Trade Center, 175 Greenwich Street, New York, NY 10007
by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 03:22 - 17 Feb 2025 -
McKinsey in global media: Early warning systems, competitiveness, climate tech, and more
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To see more essential reading on topics that matter, visit McKinsey Themes.
— Edited by Cait Murphy, editor, New York
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by "McKinsey & Company" <publishing@email.mckinsey.com> - 03:11 - 16 Feb 2025 -
The week in charts
The Week in Charts
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 02:13 - 15 Feb 2025 -
How small teams can create big wins
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Brought to you by Alex Panas, global leader of industries, & Axel Karlsson, global leader of functional practices and growth platforms
Welcome to the latest edition of McKinsey Classics. We hope you find our perspectives useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
February 2025
In honor of the 60th birthday of McKinsey Quarterly, the McKinsey Classics newsletter is dedicating a full year to enduring Quarterly articles that were ahead of their time. Enjoy, and check out other celebration highlights such as special themed issues and bonus digital features.
As companies continue to find the right balance of hybrid, remote, and in-office working policies, leaders are grappling with how to create the best conditions for their employees to thrive. They would do well to redouble their efforts in one critical area: supporting small, independent teams.
Small teams are the lifeblood of a successful, agile corporation. Any company facing pressure from fast-moving digital natives or digitally transformed incumbents can no longer afford to shift their strategies every few years. They need to move quickly, and small, independent teams can help companies make the right moves to stay ahead of the competition. But small teams—and their managers—need the right conditions to flourish.
Companies can start by empowering teams with the authority and resources they need to make key decisions independently and by providing teams with a clear and unobstructed view of their target customers. Just as important is training midlevel managers on how to let their teams work in a truly agile manner. This may require embracing new ways of interacting with team members, including acting as stewards rather than superiors, and allowing teams to chart their own course.
Life moves pretty fast—companies need to as well. To learn more about staying agile to stay ahead, read our 2018 McKinsey Quarterly classic “Unleashing the power of small, independent teams.”Read our latest thinking on this classic topic
Go, teams: When teams get healthier, the whole organization benefits
Team effectiveness is crucial to organizational health, yet many teams struggle to collaborate effectively. New data helps explain what makes teams work, including understanding different team archetypes, encouraging leaders to shift away from a command-and-control mindset, and prioritizing key behaviors—then committing to making them stick. Read our latest insights on the topic from Aaron De Smet, Gemma D’Auria, Liesje Meijknecht, Maitham Albaharna, and coauthors.
—Edited by Drew Holzfeind, editor, Chicago
Is there a classic business topic you’d like us to feature? Send us an email—we’d love to hear from you.
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by "McKinsey Classics" <publishing@email.mckinsey.com> - 11:46 - 15 Feb 2025 -
EP150: 12 Algorithms for System Design Interviews
EP150: 12 Algorithms for System Design Interviews
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This week’s system design refresher:
How the Garbage Collector Works in Java, Python, and Go! (Youtube video)
12 Algorithms for System Design Interviews
How Kubernetes Works?
PostgreSQL 101: The Everything Database
Top 12 Tips for API Security
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How the Garbage Collector Works in Java, Python, and Go!
12 Algorithms for System Design Interviews
Bloom Filter: Check if a requested item is in the cache before performing disk operations.
Geohash: Used to build location-based services.
HyperLogLog: Counting unique queries performed by users in a search.
Consistent Hashing: Used for efficient data distribution between the cluster’s nodes.
Merkle Tree: Used to detect inconsistencies between data replicas across multiple nodes.
Raft Algorithm: Used to achieve consensus on log replication.
Lossy Count: Network traffic analysis, web analytics, and identifying heavy hitters.
QuadTree: Used to build location-based services.
Operational Transformation: Used to support collaborative editing systems.
Leaky Bucket: Used for rate limiting
Rsync: Synchronizing files and directories between two different systems.
Ray Casting: Used for geospatial analysis, collision detection in video games, and computer graphics.
Over to you: Which other algorithm will you add to the list?
How Kubernetes Works?
Kubernetes (K8S) is an open-source container orchestration platform originally developed by Google and now maintained by the Cloud Native Computing Foundation (CNCF).
Here’s how developers interact with Kubernetes:Developers create manifest files describing the application.
Kubernetes takes these manifest files, validates them, and deploys the applications across its cluster of worker nodes.
Kubernetes manages the entire lifecycle of the application.
Kubernetes is made up of two main components:
Control Plane: It is like the brain of Kubernetes and consists of the following parts:
API Server: It receives all incoming requests from users or CLI.
Scheduler: It decides where to run new pods based on resource availability and constraints.
Controller Manager: It ensures that the cluster’s desired state matches the actual state.
Etcd: The key-value store where Kubernetes keeps all of its data, including configurations, cluster states, and desired states of applications.
Worker Nodes: They are the workers in a cluster. Each node has the below sub-components:
Kubelet: Main worker on each node. It communicates with the API server, gets instructions, and ensures that the containers are running as expected.
Kube-proxy: Handles networking on each node.
Container runtime
Over to you: Have you used Kubernetes for your projects?
PostgreSQL 101: The Everything Database
Built using C language, PostgreSQL is the most popular choice of database from small web apps to enterprise systems. It runs as a multiprocess system and follows ACID principles.
The key points about PostgreSQL’s Architecture are as follows:PostgreSQL supports concurrent client connections independently. Each client connection to PostgreSQL creates a dedicated server process.
The Postmaster Process is the main supervisor that manages all other PostgreSQL processes. It controls the entire database instance.
Background workers run parallel processes when needed to handle specialized tasks.
PostgreSQL shared memory is a central memory area containing multiple buffers such as Shared, WAL, Clog, and Temporary buffers. All components communicate through this shared memory.
PostgreSQL also has several auxiliary processes such as:
BG Writer: Manages background writing
WAL Writer: Handles write-ahead logging
Auto Vacuum: Maintains database cleanliness
Checkpointer: Ensures data consistency
Stats Collector: Gathers statistics
System Logger: Manages Logging
Archiver: Handles archiving
Replication launcher: Manages replication
PostgreSQL has different types of physical files for varied needs such as:
Data Files: Stores actual database data
WAL Files: Write-ahead log storage
Archive Files: Backup and recovery data
Log Files: System and error logs
Over to you: What else would you add to understand more about PostgreSQL’s architecture?
Top 12 Tips for API Security
Use HTTPS
Use OAuth2
Use WebAuthn
Use Leveled API Keys
Authorization
Rate Limiting
API Versioning
Whitelisting
Check OWASP API Security Risks
Use API Gateway
Error Handling
Input Validation
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