Queue Management Solutions

A few days ago, the competitive programming community reached an incredible milestone: the 1000th round of Codeforces. To celebrate, I wanted to share my thoughts about a core component behind the scenes of an online judge: how they handle long-running tasks like submission processing. ❓ 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼 Imagine your system needs to process long-running tasks, like evaluating problem submissions. Evaluating these submissions synchronously could lead to timeouts, especially when the runtime is unpredictable. An alternative could be triggering the task in the request and responding to the user that the submission is "in progress". However, this might overload the server, especially during traffic spikes or if a server fails mid-process. So, how can we handle this efficiently and robustly? 🛠️ 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻  A message queue (e.g., RabbitMQ) is an elegant way to decouple services and make a system more fault-tolerant. Think of it as a storage system with configurable topics, producers, and consumers. It's like a factory assembly line where different workers (consumers) pick up tasks (messages) and complete them at their own pace without blocking others. The messages stored in the queue are durable, ensuring they are not lost even in the event of system crashes. Here’s a simple architecture: 𝟭. API Service: Handles user requests and sends submission data to the message queue while updating the database with a status like "in queue." 𝟮. Runner: Listens to the queue, processes submissions one by one, updates the database as needed, and marks the final status upon completion. Since runners consume messages based on their capacity, the system remains stable without overloading. 📈 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 During contests, traffic surges can significantly increase submission volumes. If the queue grows due to limited runners, we can: 𝟭. Scale horizontally: Spawn additional runners or servers to handle the load. 𝟮. Add a load balancer: Distribute traffic evenly across servers. This ensures users experience minimal delays, even during peak times. 🛡️ 𝗙𝗮𝘂𝗹𝘁 𝗧𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲 After a runner consumes a submission, it needs to send a confirmation that it has successfully processed it. If a runner fails to process a submission, it won’t send the acknowledgement. The submission remains in the queue for reprocessing. For submissions that repeatedly fail (e.g., due to faulty code), we configure a retry limit. After n retries, the submission moves to a dead-letter topic (DLT), where developers can inspect and resolve the issue later. 💡 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 You can apply similar concepts to order processing in e-commerce, video processing on streaming site, fraud detection in banking, and ride-sharing or food delivery apps. These examples highlight how understanding systems and scalability can enhance a developer's journey. If you're as passionate about scalable systems and architectures as I am, I'd love to hear your thoughts!

🔔 Imagine you're using an e-commerce app that sends you notifications about your order status—order placed, packed, shipped, and delivered. These notifications need to be sent in sequence without overwhelming the system. How does the app manage this efficiently? If the app tried to send notifications directly every time an update happened, the system could get overloaded, especially during high traffic. What if thousands of users placed orders at once? Directly processing all notifications would slow everything down. ✉️ This is where Message Queues come in. A Message Queue acts as a buffer between tasks that produce messages (like order updates) and tasks that consume messages (like sending notifications). It ensures that messages are processed one by one without overwhelming the system. BullMQ + Redis is a popular message queue solution in Node.js. BullMQ stores messages in Redis, a fast in-memory database. When a new task arrives, it's added to the queue in Redis. Workers pick up tasks from the queue and process them asynchronously without blocking other operations. 🐂 With BullMQ, you can schedule tasks, retry failed jobs, and even prioritize important messages. Redis ensures that messages are stored temporarily and processed reliably. This combination makes sure that notifications are delivered without delays or data loss. Message queues like BullMQ + Redis are widely used in apps for email notifications, payment processing, video encoding, and data pipelines. They improve performance, scalability, and reliability in distributed systems. ✨ If you're building systems that need background jobs, task scheduling, or load management, message queues are a must-have.

Reducing waiting time in outpatient departments (OPDs) requires a combination of operational efficiency, smart scheduling, and better patient flow management rather than simply increasing manpower. A key step is implementing structured appointment systems moving away from walk in overload toward time slotted visits, with triaging to prioritise urgent cases. Digital pre registration, where patients submit basic details and symptoms in advance, can significantly cut registration bottlenecks and allow clinicians to prepare beforehand. Equally important is workflow redesign within the OPD. Segregating patients into streams, new cases, follow ups, chronic disease clinics, and minor procedures prevents congestion at a single point. Task shifting also plays a major role: trained nurses or physician assistants can handle initial assessments, vitals, and routine follow ups, freeing doctors to focus on complex consultations. Introducing fast track lanes for simple cases and repeat prescriptions can drastically reduce overall load. Technology can further streamline operations. Electronic medical records (EMRs) reduce time spent on documentation and retrieval, while queue management systems provide real time visibility of patient flow, reducing uncertainty and crowding. Teleconsultations can offload non critical visits, especially follow ups and chronic care management, thereby decreasing physical footfall. Aligning staffing patterns with peak hours, ensuring adequate consultation rooms, and monitoring key metrics like average consultation time and patient turnaround time help maintain efficiency. When OPDs are designed around patient flow rather than provider convenience, waiting time reduces, patient satisfaction improves, and clinicians experience less burnout.

You think you need a queue. You reach for SQS, RabbitMQ, Kafka. New service, new ops burden, new failure mode. You probably don't. Here's one of my simple solutions: Use postgres. Insert jobs as rows with a status column. Workers run: SELECT * FROM jobs WHERE status = 'pending' ORDER BY created_at FOR UPDATE SKIP LOCKED LIMIT 1; That single query atomically claims one job and skips anything another worker has already grabbed. No workers fighting over the same job, no extra service. Update the row to 'running', do the work, update to 'done'. Multiple workers run the same query in parallel and they each get a different job (postgres handles the locking). It scales further than people think. A lot of production systems are just postgres plus SKIP LOCKED. Sidekiq Pro, Oban, River, graphile-worker, all built on this exact pattern. When you actually need SQS or kafka: You need millions of messages per second. You need fan-out to many consumers (multiple services reacting to the same event). You need cross-region durability beyond what your database gives you. If your queue is "user uploaded a file, go process it" or "send this email" or "generate this report", postgres is fine. Probably better. Boring stack. Less to break. Less to pay for. Hope this helps.

Day 4 of teaching you System Design with my past experiences and practical use cases: A few months ago, I was working on a project where our backend had to process thousands of requests coming from multiple services. Everything worked fine in testing, but once we went live, the cracks started to show. We suddenly had spikes in incoming requests, sometimes 10x higher than normal. Our APIs started timing out, database locks increased, and the system slowed to a crawl. We tried scaling the servers, but it was like trying to drink water from a fire hose, the pressure was just too much. That’s when we realized: we needed a way to decouple request handling from processing. The Solution: We implemented a Message Queue (RabbitMQ in our case, but AWS SQS or Kafka would work too). Instead of processing requests directly, each incoming request was placed in a queue. Workers would then pull from the queue at their own pace. This meant: - No more sudden overloads. - Failed tasks could be retried automatically. - We could scale workers independently. The result? - API response time dropped drastically. - System stability improved, even under heavy load. - We gained visibility into backlog and processing rates. The lesson: Sometimes, the best way to solve a scaling problem is not to speed up, but to add a buffer. Message queues give your systems room to breathe.

Metrics don’t make the difference. The right metrics make the difference. Operators don’t need 40 KPIs. You need one page for throughput, quality, speed, options, resilience. The six metrics in the graphic are that page. Here’s how to turn them into decisions this week: Start now 1️⃣ Queue Length → Track waiting work at each step (sales, design, QA, shipping). ↳ Quick math: Cycle time ≈ WIP ÷ throughput 🧠 ↳ Trigger: any step >1.5× its 4‑week median for 3 days. ↳ Move: set WIP limits and swarms to unblock. 2️⃣ Rework Rate → Rework ÷ total completed. First‑pass yield is 1 − rework. ↳ Split by source (spec, process, training). ↳ Move: add checklists; pair review the top 3 drivers. 3️⃣ Escaped Defects → Customer‑found issues, by severity. ↳ Add “time to contain” alongside the count. ↳ Move: pre‑release check gates; fix‑forward playbooks. 4️⃣ Time to Decision → Days from issue to committed choice. ↳ Classify by decision type: reversible vs one‑way door. ↳ Move: set SLA by level (e.g., L1 24h, L2 3d) and escalate. 5️⃣ Option Value Created → Count rights without obligation: second suppliers, alternate channels, modular parts, cancellable contracts. ↳ Also track cost to hold and shelf‑life. ↳ Move: kill stale options monthly. 6️⃣ Buffer Coverage → Days of cash runway, critical inventory, and redeployable capacity within 1 week. ↳ Guardrails: min to survive, max to avoid drag. ↳ Move: pre‑plan cuts and pivots so buffers buy time. 💡 Cadence → 30‑minute weekly “Flow & Faults.” ↳ Look left‑to‑right: queue → rework → defects → decisions → options → buffers. ↳ Ask: Where are we stuck? What changed? What will we try? 💡 Anti‑gaming pairs → Queue Length with Throughput. → Rework with First‑pass yield. → Escaped Defects with Time to contain. → Buffers with Opportunity cost. 💡 Fast setup → Start in a spreadsheet or your current tool. ↳ Pull counts from boards, CRM, ERP. ↳ Keep one‑click charts; talk trends, not decimals. This is the playbook operators and founders use to ship under stress—what Operating by John Brewton breaks down weekly with checklists and case studies. ✅ Define each metric for one product or team and set a trigger. ✅ Build a one‑page view and schedule the weekly review. ✅ Make one change per week from what the metrics tell you. ♻️Repost & follow John Brewton for content that helps. ✅ Do. Fail. Learn. Grow. Win. ✅ Repeat. Forever. ⸻ 📬Subscribe to Operating by John Brewton for deep dives on the history and future of operating companies (🔗in profile).

Title: “Integrating AWS SQS into Your Cloud Architecture: When and Why” This article explores the scenarios and reasons for incorporating AWS SQS into your cloud architecture. What is AWS SQS? AWS SQS is a fully managed message queuing service that enables you to decouple and scale microservices, distributed systems, and serverless applications. It offers a secure, durable, and available host for transferring data between different software components. Key Benefits of AWS SQS: 1. Scalability: Automatically scales to handle any volume of messages. 2. Reliability: Ensures delivery of messages with minimal latency. 3. Security: Offers robust features like encryption and access control. When to Incorporate AWS SQS: 1. Decoupling Components: In scenarios where your application components are tightly coupled, leading to interdependencies and complex management, SQS can decouple these components, enhancing reliability and scalability. 2. Handling Spikes in Workloads: If your application experiences variable and unpredictable loads, SQS can help buffer requests, ensuring that each component processes messages at its own pace without losing data. 3. Asynchronous Processing: When your application involves operations that don't need to be processed immediately, SQS can be used to queue these tasks for later processing, optimizing resource usage and user experience. 4. Building Microservices Architecture: SQS fits perfectly in microservices architectures, providing a way to communicate between services reliably and efficiently. 5. Ensuring Data Integrity and Reducing Failures: If your application requires a guarantee that a message is processed at least once, SQS offers features like message durability and visibility timeouts to handle this. Practical Use Cases of AWS SQS: 1. Order Processing Systems: SQS can manage orders received, ensuring they are processed sequentially and without loss. 2. Inventory Management: In retail and e-commerce, SQS helps in managing inventory levels by queuing messages related to stock changes. 3. Notifications and Alerts: For applications that send notifications based on user actions or system events, SQS can queue these notifications for timely delivery. Comparing with Other AWS Services: AWS offers other services like SNS (Simple Notification Service) and Kinesis. While SNS is best for publish-subscribe scenarios, and Kinesis is ideal for real-time data streaming, SQS is more suited for decoupling components and asynchronous message processing.

Will an event like Cyber Monday break your infrastructure? One of the primary benefits of using a queue is that it can absorb load and send it to your workers at a rate they can handle. During periods of heavy traffic, using a queue as a buffer for your workers is critical to keeping your infrastructure running smoothly. In periods of predictable traffic, your system is likely in a steady state, with workers processing messages as quickly as they’re placed into your system. But what happens if you experience orders of magnitude more traffic than usual? Let’s say your workers typically do 1k messages/second, and suddenly you’re getting messages placed into the system at a rate of 6k messages/second. If you’re using a standard queue, you’ll rack up 1 million messages in just over 3 minutes — over a period of 30 minutes, you’ll have 10 million messages to process. At some point, the performance of your queue will degrade — you’re going to run out of disk space, hit a high memory watermark, etc. Not to mention that to process this backlog, you’ll need to process messages at a rate much higher than the ingestion rate, a rate that you likely haven’t seen in production. In some scenarios, you can end up in an irrecoverable state — your backlog is to large to process, and coupled with degraded performance on either the queue or consumers, you can’t get back to a steady state. Can’t you simply throw more workers at it? In most systems, there’s typically a bottleneck that can’t be resolved with increased parallelization alone — databases are a good example of this. These bottlenecks usually become painfully obvious during periods of high load. So while the best prevention for this scenario is having high availability for your workers and the ability to scale workers when needed, it’s also important to plan for the scenario that you’re out of luck, and only have a finite amount of messages you can process on your workers. So what can you do? 1. Load shedding — this comes in many forms, from rejecting messages when a certain watermark is hit (a common one is rejecting messages which have spent too long in the queue, as they can be regarded stale) to prioritizing work coming off the queue. 2. Use an overflow or surge queue — these are both mechanisms to place additional load on a separate queue. The overflow queue is used when the primary queue runs out of space, while the surge queue is used as a live buffer for the primary queue (typically before it runs out of space). 3. Switching from FIFO processing → LIFO processing under periods of load. While FIFO is generally a fair default for queues, it could make sense to prioritize new requests if the system is under duress, since old messages are generally less useful and may correspond to work that is already stale or discarded. We use a combination of these methods in the internals of Hatchet to make our system more reliable and scalable. Additional reading in the comments!

Behind every scalable system is a queue. Behind every outage is one used wrong. Queues are everywhere: background jobs, event streams, message brokers. They’re the backbone of scalable systems, but they’re also a common source of outages. Here is my Cheatsheet 👇 Core Definitions: 1. Queue: A data structure or system for storing tasks/messages in FIFO order (First-In-First-Out). 2. Producer: Component that sends messages to a queue. 3. Consumer: Component that reads and processes messages from a queue. 4. Broker: Middleware managing queues (e.g., RabbitMQ, Kafka, SQS). 5. Acknowledgement (ACK): Signal that a message was processed successfully. 6. Dead Letter Queue (DLQ): Queue for failed/unprocessable messages. 7. Idempotency: Guarantee that reprocessing a message does not create duplicate side effects. 8. Visibility Timeout: Time during which a message is invisible to others while being processed. Best Practices / Pitfalls: - Use idempotent consumers → prevents double processing. - Define retry policies (exponential backoff, max attempts). - Monitor queue length & processing lag as health indicators. - Use dead letter queues for failed messages. - Ensure message ordering only when business-critical (ordering adds cost/complexity). - Keep messages small & self-contained. - Always include correlation IDs for traceability. Performance Considerations: For Throughput → Parallel consumers or partitions For Durability → Persist if critical (trade-off: speed) For Scalability → Auto-scale consumers Patterns: - Work Queue → Spread tasks across workers - Pub/Sub → Broadcast to many subscribers - Delayed Queue → Retry later or schedule tasks - Priority Queue → Handle urgent first Queues decouple systems, but they don’t manage themselves. Get them wrong and you get outages. Get them right and you unlock scalability, resilience, and speed.

What if thousands try to buy the last item at once? In my last post https://lnkd.in/dna7znva I shared how a simple FOR UPDATE in SQL can prevent two people from buying the same seat at the same time. But what if it's not two users… it’s ten thousand, all clicking “Buy” on a limited drop? That’s where a message queue like RabbitMQ steps in. Instead of hitting the database directly, each purchase request goes into a queue. A background worker then processes them one by one: 1. Check if stock is still available 2. Lock the row (yes, still using FOR UPDATE) 3. Complete the order 4. Update the stock This pattern avoids race conditions and protects your database from getting hammered in a traffic spike. It’s like putting shoppers in a single-file line at the door — fair, controlled, and way easier to manage. #backend #systemdesign #rabbitmq #concurrency #golang #queues #softwareengineering