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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:44 - 14 May 2025 -
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by "DASH by Datadog" <dash@datadoghq.com> - 10:30 - 13 May 2025 -
How Slack Supports Billions of Daily Messages
How Slack Supports Billions of Daily Messages
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Disclaimer: The details in this post have been derived from the articles/videos shared online by the Slack engineering team. All credit for the technical details goes to the Slack Engineering Team. The links to the original articles and videos are present in the references section at the end of the post. We’ve attempted to analyze the details and provide our input about them. If you find any inaccuracies or omissions, please leave a comment, and we will do our best to fix them.
Most people think of Slack as a messaging app. It is technically accurate, but from a systems perspective, it's more like a real-time, multiplayer collaboration platform with millions of concurrent users, thousands of messages per second, and an architecture that evolved under some unusual constraints.
At peak weekday hours, Slack maintains over five million simultaneous WebSocket sessions. That’s not just a metric, but a serious architectural challenge. Each session represents a live, long-running connection, often pushing out typing indicators, presence updates, and messages in milliseconds. Delivering this kind of interactivity on a global scale is hard. Doing it reliably with high performance is even harder.
One interesting trivia is that the team that built Slack was originally building a video game named Glitch: a browser-based MMORPG. While Glitch had a small but passionate audience, it struggled to become financially sustainable. During the development of Glitch, the team created an internal communication tool that would later become Slack. When Glitch shut down, the team recognized the potential of the internal communication tool and began to develop it into a bigger product for business use. The backend for this internal tool became the skeleton of what would become Slack.
This inheritance shaped Slack’s architecture in two key ways:
Separation of concerns: Like game servers manage real-time events separately from game logic, Slack splits its architecture early. One service (the “channel server”) handled real-time message propagation. Another (the “web app”) managed business logic, storage, and user auth.
Push-first mentality: Unlike traditional request-response apps, Glitch pushed updates as the state changed. Slack adopted this model wholesale. WebSockets weren’t an optimization—they were the foundation.
This article explores how Slack’s architecture evolved to meet the demands of a system that makes real-time collaboration possible across organizations of 100,000+ people.
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Initial Architecture
Slack’s early architecture was a traditional monolithic backend fused with a purpose-built, real-time message delivery system.
The monolith, written in Hacklang, handled the application logic. Hacklang (Facebook’s typed dialect of PHP) offered a pragmatic path: move fast with a familiar scripting language, then gradually tighten things with types. For a product iterating quickly, that balance paid off. Slack’s backend handled everything from file permissions to session management to API endpoints.
But the monolith didn’t touch messages in motion. That job belonged to a real-time message bus: the channel server, written in Java. The channel server pushed updates over long-lived WebSocket connections, broadcast messages to active clients, and arbitrated message order. When two users hit “send” at the same moment, it was the channel server that decided which message came first.
Here’s how the division looked in terms of functionalities:
Web App (Hacklang)
Auth, permissions, and storage
API endpoints and job queuing
Session bootstrapping and metadata lookup
Channel Server (Java)
WebSocket handling
Real-time message fan-out
Typing indicators, presence blips, and ordering guarantees
This split worked well when Slack served small teams and development moved fast. But over time, the costs surfaced:
The monolith grew brittle as testing got harder and deployment risk went up.
The channel server held state, which complicated recovery and scaling.
Dependencies between the two made failures messy. If the web app went down, the channel server couldn’t persist messages, but might still tell users they’d sent them.
The Core Abstraction: Persistent Messaging
Messaging apps live or die by trust. When someone sends a message and sees it appear on screen, they expect it to stay there and to show up for everyone else. If that expectation breaks, the product loses credibility fast. In other words, persistence becomes a foundational feature.
Slack’s design bakes this in from the start. Unlike Internet Relay Chat (IRC), where messages vanish the moment they scroll off-screen, Slack assumes every message matters, even the mundane ones. It doesn’t just aim to display messages in real-time. It aims to record them, index them, and replay them on demand. This shift from ephemeral to durable changes everything.
IRC treats each message like a radio transmission, whereas Slack treats messages like emails. If the user missed something, they can always scroll up, search later, and re-read at a later date. This shift demands a system that guarantees:
Messages don’t disappear
Message order stays consistent
What one user sees, every user sees
Slack delivers that through what looks, at first glance, like a simple contract:
When a message shows up in a channel, everyone should see it.
When a message appears in the UI, it should be in stable storage.
When clients scroll back, they should all see the same history, in the same order.
This is a textbook case of atomic broadcast.
Atomic Broadcast
Atomic broadcast is a classic problem in distributed systems. It's a formal model where multiple nodes (or users) receive the same messages in the same order, and every message comes from someone. It guarantees three core properties:
Validity: If a user sends a message, it eventually gets delivered.
Integrity: No message appears unless it was sent.
Total Order: All users see messages in the same sequence.
Slack implements a real-world approximation of atomic broadcast because it was essential for their functionality. Imagine a team seeing different sequences of edits, or comments that reference messages that “don’t exist” on someone else’s screen.
But here’s the twist: in distributed systems, atomic broadcast is as hard as consensus. And consensus, under real-world failure modes, is provably impossible to guarantee. So Slack, like many production systems, takes the pragmatic path. It relaxes constraints, defers work, and recovers from inconsistency instead of trying to prevent it entirely.
This tension between theoretical impossibility and practical necessity drives many of Slack’s architectural decisions.
Old vs New Send Flows
In real-time apps, low latency is a necessity. When a user hits “send,” the message should appear instantly. Anything slower breaks the illusion of conversation. But making that feel snappy while also guaranteeing that the message is stored, ordered, and replayable? That’s where things get messy.
Slack’s original message send flow prioritized responsiveness. The architecture puts the channel server (the real-time message bus) at the front of the flow. A message went from the client straight to the channel server, which then:
Broadcast it to all connected clients
Sent an acknowledgment back to the sender
Later handed it off to the web app for indexing, persistence, and other deferred work
This gave users lightning-fast feedback. However, it also introduced a dangerous window: the server might crash after confirming the message but before persisting it. To the sender, the message looked “sent.” To everyone else, especially after a recovery, it might be gone.
This flow worked, but it carried risk:
Stateful servers meant complex failover logic and careful coordination.
Deferred persistence meant the UI could technically lie about message delivery.
Retries and recovery had to reconcile what was shown vs. what was saved.
Slack patched around this with persistent buffers and retry loops. But the complexity was stacking up. The system was fast, but fragile.
The Web App Takes the Lead
As Slack matured, and as outages and scale pushed the limits, the team reversed the flow.
In the new send model, the web app comes first:
The client sends the message via HTTP POST to the web app
The web app logs the message to the job queue (persistence, indexing, and parsing all happen here)
Only then does it invoke the channel server to broadcast the message in real-time
This change improves several things:
Crash safety: If anything goes down mid-flow, either the message persists or the client gets a clear failure.
Stateless channel servers: Without needing local buffers or retries, they become easier to scale and maintain.
Latency preserved: Users still see messages immediately, because the real-time broadcast happens fast, even while persistence continues in the background.
And one subtle benefit: the new flow doesn’t require a WebSocket connection to send a message. That’s a big deal for mobile clients responding to notifications, where setting up a full session just to reply was costly.
The old system showed messages fast, but sometimes dropped them. The new one does more work up front, but makes a stronger promise in terms of persistence.
Session Initialization and the Need for Flannel
For small teams, starting a Slack session looks simple. The client requests some data, connects to a WebSocket, and starts chatting. However, at enterprise scale, that “simple” startup becomes a serious architectural choke point.
Originally, Slack used a method called RTM Start (Real-Time Messaging Start). When a client initiated a session, the web app assembled a giant JSON payload: user profiles, channel lists, membership maps, unread message counts, and a WebSocket URL. This was meant to be a keyframe: a complete snapshot of the team’s state, so the client could start cold and stay in sync via real-time deltas.
It worked until teams got big.
For small teams (under 100 users), the payload was lightweight.
For large organizations (10,000+ users), it ballooned to tens of megabytes.
Clients took tens of seconds just to parse the response and build local caches.
If a network partition hit, thousands of clients would reconnect at once, slamming the backend with redundant work.
And it got worse:
Payload size grew quadratically with team size. Every user could join every channel, and the web app calculated all of it.
All this work happened in a single data center, creating global latency for users in Europe, Asia, or South America.
This wasn’t just slow. It was a vector for cascading failure. One bad deploy or dropped connection could take out Slack’s control plane under its load.
Flannel: Cache the Cold Start
To fix this, Slack introduced Flannel, a purpose-built microservice that acts as a stateful, geo-distributed cache for session bootstrapping.
Instead of rebuilding a fresh session snapshot for every client on demand, Flannel does a couple of things differently:
It maintains a pre-warmed in-memory cache of team metadata
Listens to WebSocket events to keep that cache up to date, just like a client would
It serves session boot data locally, from one of many regional replicas
Sits astride the WebSocket connection, terminating it and handling session validation
Here’s what changes in the flow:
A client connects to Flannel and presents its auth token.
Flannel verifies the token (delegating to the web app if needed).
If the cache is warm, it sends a hello response immediately. No need to hit the origin.
This flips the cost model from compute-heavy startup to cache-heavy reuse. While it’s tempting to think that Flannel adds complexity. But Slack found that at scale, complexity that’s predictable and bounded is better than simplicity that breaks under pressure.
Scaling Considerations and Trade-offs
Every system seems to work on the whiteboard. The real test comes when it’s live, overloaded, and something fails. At Slack’s scale, maintaining reliable real-time messaging isn’t just about handling more messages per second. It’s also about absorbing failure without breaking user expectations.
One of the most visible symptoms at scale is message duplication. Sometimes a user sees their message posted twice. It’s not random. It’s a side effect of client retries.
Here’s how it happens:
A mobile client sends a message.
Network flakiness delays the acknowledgment.
The client times out and retries.
Both messages make it through, or one makes it twice, and now the user wonders what just happened.
To survive this, Slack leans on idempotency. Each message includes a client-generated ID or salt. When the server sees the same message ID again, it knows it’s not a new send. This doesn’t eliminate all duplication, especially across devices, but it contains the damage.
On the backend, retries and failures get more serious. A message might:
Reach the channel server but fail to persist
Persist to the job queue but never push
Push to some clients and not others
The system has to detect and recover from all of these without losing messages, breaking order guarantees, and flooding the user with confusing errors.
This is where queueing architecture matters. Slack uses Kafka for durable message queuing and Redis for in-flight, fast-access job data. Kafka acts as the system’s ledger and Redis provides short-term memory.
This separation balances:
Durability vs. speed: Kafka holds the truth; Redis handles the work-in-progress.
Retry logic: Jobs pulled from Kafka can be retried intelligently if processing fails.
Concurrency control: The system avoids processing the same message twice and waiting forever for a stuck job.
Conclusion
Slack’s architecture isn’t simple, and that’s by design. The system embraces complexity in the places where precision matters most: real-time messaging, session consistency, and user trust. These are the end-to-end paths where failure is visible, consequences are immediate, and user perception can shift in a heartbeat.
The architecture reflects a principle that shows up in high-performing systems again and again: push complexity to the edge, keep the core fast and clear. Channel servers, Flannel caches, and job queues each exist to protect a smooth user experience from the messiness of distributed systems, partial failures, and global scale.
At the same time, the parts of the system that don’t need complexity, like storage coordination or REST API responses, stay lean and conventional.
Ultimately, no architecture stands still. Every scaling milestone, every user complaint, every edge case pushes the system to adapt. Slack’s evolution from monolith-plus-bus to globally distributed microservices wasn’t planned in a vacuum. It came from running into real limits, then designing around them.
The lesson isn’t to copy Slack’s architecture. It’s to respect the trade-offs it reveals:
Optimize for latency, but tolerate slowness in the right places.
Build around failure, not away from it.
Embrace complexity where correctness pays for itself, and fight to simplify the rest.
References:
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Why the world needs balance and trust for the economy to thrive
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.
—Alex and Axel
•
Rebuilding confidence. Since the US tariff announcements in early April, many analysts have raised their estimates of the probability of a worldwide recession. For leaders, the question now isn’t whether the current path of escalating tariffs is the right one; rather, it is whether the path they choose next will foster trust among countries and within societies. For many, the answer may be for global and local economies to find new balance: a US that produces more of what it consumes, a China that consumes more of what it produces, a more competitive Europe, and a “global south” that finds its path to prosperity, McKinsey Global Institute Chair Sven Smit and coauthors explain.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:13 - 13 May 2025 -
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by mary@embspot.com - 02:29 - 12 May 2025 -
Opportunities for America at 250 and beyond: A leader’s guide
Leading Off
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Welcome to the latest edition of Leading Off. We hope you find our insights useful. Let us know what you think at Alex_Panas@McKinsey.com and Axel_Karlsson@McKinsey.com.
—Alex and Axel
America’s 250th birthday is a year away, which provides an opportunity not only to reflect on the country’s history but also to ponder its future. In that spirit, McKinsey has published America at 250, a series of essays on the country’s possibilities in a new era of geopolitical change, technological evolution, and other complexities. Underpinning these ideas are lessons on leadership that McKinsey has cultivated from our nearly 100 years of working with leaders in business and government. This week, we look at ways that executives can better understand the evolving global landscape and help both their businesses and employees prosper as the next chapter of America’s history unfolds.
Current geopolitical disruptions are producing one of the greatest levels of peacetime uncertainty for American business. They are also creating an urgent need for executives to understand how US government policies may affect the major drivers of geopolitical change—so they can steer their organizations toward growth. McKinsey’s Matt Watters, Shubham Singhal, and Zoe Fox outline ten drivers of change for trade, economics, and security: for example, tariffs, import and export controls, foreign-investment restrictions, and multilateral cooperation. Some of the US policy shifts in these areas “encourage a rewiring of the industrial base for national security or reindustrialization, while others are meant to build leverage in negotiations,” the authors say. “Understanding the distinction can ensure long-term realignment of corporate investments and operations with government policies, instead of mere tactical, near-term moves to mitigate risks.” They suggest that leaders build upon these insights by taking three actions to create long-term value: identifying opportunities to accelerate growth, optimizing core operations, and developing new geopolitical capabilities and strategies.
That’s the annual productivity growth the United States could see by 2030 through its use of automation and AI, according to McKinsey analysis, representing a marked change from the 1.8 percent productivity growth in 2019. McKinsey’s Anu Madgavkar and Olivia White note that changing demographics are fueling labor shortages—and low productivity—across many sectors of the American economy, particularly healthcare, construction, and small businesses. “This will require rethinking long-standing work practices and doubling down on automation and AI to boost productivity, especially if labor participation rates and working hours flatline,” they say.
That’s McKinsey’s Aly Spencer and Nikhil Sahni on how the US government can improve its productivity, including wiser spending on goods and services. The authors observe that private sector organizations frequently find savings by taking strategic approaches to category management, demand management, and vendor performance management. In the US government’s context, “The largest spending categories are often similar across multiple departments,” they say. “Centralizing management of these categories can reduce redundancy, lower costs through bulk purchasing, and create better conditions to more effectively manage suppliers.”
Improving Americans’ holistic health—that is, their mental, physical, spiritual, and social well-being—will be critical to helping them be more productive in the workplace. In an interview with McKinsey’s Erica Coe and Kana Enomoto, former US Congressman Patrick J. Kennedy says that support for mental health should be considered “a medical civil right.” He stresses that healthcare providers and educators should place more emphasis on the prevention of mental health problems. “Businesses are talking about giving kids coping, resilience, and problem-solving tools—not as an extracurricular activity but as core to education,” Kennedy says. “If you know history, numeracy, and literacy but cannot identify and regulate counterproductive thinking patterns and behaviors, then you’ll have difficulty managing stress and avoiding burnout in the workplace.”
As the world grows more complex and more tech powered, business leaders are learning to strengthen their soft skills to help inspire and guide their teams through change. Likewise, public sector leaders can adopt an “inside out” approach to managing their teams in turbulent times. McKinsey’s Drew Erdmann, Ramesh Srinivasan, Scott Blackburn, and their coauthors say that government leaders can be more effective in their roles by developing human-centric qualities such as humility, vulnerability, boldness, and empowerment—four traits that are especially important for private sector leaders. This approach requires those in charge to lead themselves before they lead others. As the authors note: “In McKinsey’s collective decades of experience counseling CEOs and senior government leaders, we have noticed that most of them wrestle less with what to do and more with who to be.”
Lead by looking toward the future.
— Edited by Eric Quiñones, senior editor, New York
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by "McKinsey Leading Off" <publishing@email.mckinsey.com> - 04:54 - 12 May 2025 -
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by "Stellfox Jandrin" <jandrinstellfox@gmail.com> - 01:42 - 12 May 2025 -
Six ways US state and local governments can work better
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.
—Alex and Axel
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Opportunities to innovate. Today’s state and local governments are under pressure to reduce costs, modernize outdated systems, and adapt to shifting workforce demographics—even as many of them have already confronted the effects of the COVID-19 pandemic and stimulus funding losses. As McKinsey Senior Partner David Nuzum and coauthors explain, these challenges also give public sector leaders a unique opportunity to innovate and improve outcomes.
•
Achieving high performance. When budgets shrink, governments often make hasty reductions that disrupt important services, only to have to rebuild their teams after the crisis subsides. To avoid this sort of rebound, state and local government leaders could consider six key actions—including stopping nonessential work, redesigning processes, and integrating digital technologies—to create lasting improvements. Explore our new series, America at 250, for more ideas ahead of the nation’s 250th birthday.
—Edited by Belinda Yu, editor, Atlanta
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by "Only McKinsey Perspectives" <publishing@email.mckinsey.com> - 01:17 - 12 May 2025 -
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Rapier loom: the future choice of weaving industry
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by "Michael" <Michael@cnhaisen.com> - 07:21 - 11 May 2025 -
Mr. Jalapeno Food Truck Great Eats Award
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My name is Chris Allen. I'm the Regional Director for the 2025 Great Eats Award.
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by "Chris Allen" <chris@greateatsaward.com> - 05:59 - 11 May 2025 -
Opportunity to Partner with Us for Sodium Hydroxide Supply
Dear Info，
I hope this message finds you well. My name is Enomoto Waganer, and I am reaching out to introduce you to an exciting opportunity in the supply of Sodium Hydroxide (NaOH), commonly known as caustic soda. As one of the essential chemicals in various industries, Sodium Hydroxide has a wide range of applications, including chemical production, water treatment, food processing, and more.
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Best regards,Enomoto Waganer
by "Enomoto Waganer" <waganerenomoto@gmail.com> - 06:28 - 10 May 2025 -
Large-Scale, High-Speed 3D Printing: Dual Extruders, 300mm/s, Full Build Volume
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I hope you’re doing well.
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Let me know if you’d like more details or have any questions.
by "Chrity" <Chrity@3dprintermingda.com> - 06:19 - 10 May 2025 -
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by "McKinsey Week in Charts" <publishing@email.mckinsey.com> - 03:23 - 10 May 2025 -
EP162: 9 Clean Code Principles To Keep In Mind
EP162: 9 Clean Code Principles To Keep In Mind
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9 Clean Code Principles To Keep In Mind
The 4 Types of SQL Joins
How to Learn Cloud Computing?
Visualizing a SQL query
Explaining JSON Web Token (JWT) with simple terms
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9 Clean Code Principles To Keep In Mind
Meaningful Names: Name variables and functions to reveal their purpose, not just their value.
One Function, One Responsibility: Functions should do one thing.
Avoid Magic Numbers: Replace hard-code values with named constants to give them meaning.
Use Descriptive Booleans: Boolean names should state a condition, not just its value.
Keep Code DRY: Duplicate code means duplicate bugs. Try and reuse logic where it makes sense.
Avoid Deep Nesting: Flatten your code flow to improve clarity and reduce cognitive load.
Comment Why, Not What: Explain the intention behind your code, not the obvious mechanics.
Limit Function Arguments: Too many parameters confuse. Group related data into objects.
Code Should Be Self-Explanatory: Well-written code needs fewer comments because it reads like a story.
Over to you: Which other clean code principle will you add to the list?
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The 4 Types of SQL Joins
SQL joins combine rows from two or more tables based on a related column. Here are the different types of joins you can use:
Inner Join
Returns only the matching rows between both tables. It keeps common data only.
Left Join
Returns all rows from the left table and matching rows from the right table. If a row in the left table doesn’t have a match in the right table, the right table’s columns will contain NULL values in that row.
Right Join
Returns all rows from the right table and matching rows from the left table. If no matching record exists in the left table for a record in the right table, the columns from the left table in the result will contain NULL values.
FULL OUTER JOIN
Returns all rows from both tables, filling in NULL for missing matches.
Over to you: Which SQL Join have you used the most?
How to Learn Cloud Computing?
Cloud computing is a vast field with an ever-growing footprint. It can often get tricky for a new developer to understand where to start. Here’s a learning map:
Cloud Computing Basics
This includes topics such as “what is cloud computing,” its benefits, cloud models (public, private, hybrid, and multi), and a comparison of cloud vs. on-premise.
Cloud Service Models
Learn about cloud service models such as IaaS, PaaS, and SaaS.
Cloud Providers
Explore the various popular cloud platforms such as AWS, Azure, GCP, Oracle Cloud, IBM Cloud, etc. Also, learn how to choose a cloud provider.
Key Cloud Services
Learn the key cloud services related to Compute (EC2, Azure VM, Docker, Kubernetes, Lambda, etc), Storage (EBS, Azure Disk, S3, Azure Blob, EFS, etc.), and Networking (VPC, ELB, Azure LB, Cloudfront, and Azure CDN).
Security & Compliance
Learn about the critical security and compliance points related to identity, access management, encryption, data security, DDoS protection, and WAF.
Cloud DevOps & Automation
Learn Cloud DevOps and automation in specific areas such as CI/CD, IaC, and Monitoring.
Over to you: What else will you add to the list for learning cloud computing?
Visualizing a SQL query
SQL statements are executed by the database system in several steps, including:
Parsing the SQL statement and checking its validity
Transforming the SQL into an internal representation, such as relational algebra
Optimizing the internal representation and creating an execution plan that utilizes index information
Executing the plan and returning the results
Explaining JSON Web Token (JWT) with simple terms
Imagine you have a special box called a JWT. Inside this box, there are three parts: a header, a payload, and a signature.
The header is like the label on the outside of the box. It tells us what type of box it is and how it's secured. It's usually written in a format called JSON, which is just a way to organize information using curly braces { } and colons : .
The payload is like the actual message or information you want to send. It could be your name, age, or any other data you want to share. It's also written in JSON format, so it's easy to understand and work with.
Now, the signature is what makes the JWT secure. It's like a special seal that only the sender knows how to create. The signature is created using a secret code, kind of like a password. This signature ensures that nobody can tamper with the contents of the JWT without the sender knowing about it.
When you want to send the JWT to a server, you put the header, payload, and signature inside the box. Then you send it over to the server. The server can easily read the header and payload to understand who you are and what you want to do.
Over to you: When should we use JWT for authentication? What are some other authentication methods?SPONSOR US
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