Best Ways to Handle High Volume Applications

I've spent 12 years working with enterprise monoliths. Here are 12 steps to scale them by 10X 👇 Most developers think monoliths can't scale They panic when traffic grows and immediately start planning microservices rewrites. Wrong approach. I've spent 12 years scaling enterprise monoliths. Taken systems and scaled them 10X. Without a rewriting to microservices. 𝗛𝗲𝗿𝗲'𝘀 𝗺𝘆 𝗲𝘅𝗮𝗰𝘁 𝟭𝟮-𝘀𝘁𝗲𝗽 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸: 𝟭. 𝗩𝗲𝗿𝘁𝗶𝗰𝗮𝗹 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 Upgrade the host machine with more CPU, RAM, or faster storage to handle increased load. 𝟮. 𝗛𝗼𝗿𝗶𝘇𝗼𝗻𝘁𝗮𝗹 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 Run multiple instances of your monolith behind a load balancer to distribute traffic across servers. 𝟯. 𝗖𝗗𝗡 𝗳𝗼𝗿 𝘀𝘁𝗮𝘁𝗶𝗰 𝗮𝘀𝘀𝗲𝘁𝘀 Serve static files, images, and frontend bundles through a CDN to reduce load on your application servers. 𝟰. 𝗥𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝘁𝗵𝗿𝗼𝘁𝘁𝗹𝗶𝗻𝗴 Protect your monolith from traffic spikes by limiting request rates per user or IP at the gateway level. 𝟱. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗶𝗻𝗱𝗲𝘅𝗶𝗻𝗴 𝗮𝗻𝗱 𝗾𝘂𝗲𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Audit slow queries and add appropriate indexes to prevent the database from becoming the bottleneck. 𝟲. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗽𝗼𝗼𝗹𝗶𝗻𝗴 Use PgBouncer or built-in ADO .NET pooling to efficiently reuse database connections under high concurrency. 𝟳. 𝗠𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝗶𝘇𝗲𝗱 𝘃𝗶𝗲𝘄𝘀 Precompute and store results of expensive queries as materialized views so reads become instant lookups instead of heavy aggregations. 𝟴. 𝗖𝗮𝗰𝗵𝗶𝗻𝗴 𝗹𝗮𝘆𝗲𝗿 Introduce Redis to cache frequently accessed data and reduce database pressure. 𝟵. 𝗕𝗮𝗰𝗸𝗴𝗿𝗼𝘂𝗻𝗱 𝗷𝗼𝗯 𝗼𝗳𝗳𝗹𝗼𝗮𝗱𝗶𝗻𝗴 Move long-running or CPU-intensive work out of the request pipeline into background workers using Quartz/Hangfire or a Message Queue. 𝟭𝟬. 𝗔𝘀𝘆𝗻𝗰 𝗿𝗲𝗾𝘂𝗲𝘀𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 Accept long-running requests immediately, process them asynchronously, and return results via SignalR or webhooks. 𝟭𝟭. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗿𝗲𝗮𝗱 𝗿𝗲𝗽𝗹𝗶𝗰𝗮𝘀 Offload read-heavy queries to one or more read replicas, keeping writes on the primary instance. 𝟭𝟮. 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝘀𝗵𝗮𝗿𝗱𝗶𝗻𝗴 Partition your database by a key (e.g. tenant or region) so each shard handles a subset of the data. You don't need to rewrite everything to microservices. Monoliths scale beautifully when you know what you're doing. Most problems disappear with just steps 1-6. —— Want to build real-world applications and reach the top 1% of .NET developers? 👉 Join 23,000+ engineers reading my .NET Newsletter: ↳ https://lnkd.in/dtxwnFGR —— ♻️ Repost to help others scale monoliths ➕ Follow me ( Anton Martyniuk ) to improve your .NET and Architecture Skills

Flow first isn’t always the best advice. Sometimes clicks create more risk than code. A lot of teams treat Salesforce automation like a religion: admins pick Flow, devs pick Apex, and everyone defends their side. That’s the mistake. The real skill is choosing the simplest tool that won’t collapse under scale, complexity, or edge cases. Here’s what no one tells you: 1. Start with Flow for simple + admin-owned work → Field updates, notifications, basic record creation, and guided screen experiences ship faster with clicks. 2. Use before-save flows for efficient record updates → They reduce extra DML and stay clean when the logic is straightforward. 3. Reach for Apex Triggers when logic gets non-linear → If you need maps/sets, dynamic branching, or complex cross-object rules, code stays readable and controllable. 4. Plan for volume, not just today’s data → Triggers handle large batches more reliably; flows can hit CPU/element limits under load. 5. Don’t ignore “undelete” and advanced transaction needs → Flows can’t run on undelete, and triggers give better options for error handling and traceability. 6. Debugging matters more than building → Flow fault paths are helpful, but Apex enables richer logging, try/catch patterns, and clearer root-cause analysis. Read Exception Path in Flows - https://lnkd.in/ghkv4ymk 7. Avoid stacking multiple automations without a plan → Mixing many flows and triggers on one object can create unpredictable order-of-execution surprises. 8. Use a hybrid when you need both speed and power → Let Flow orchestrate, then call invocable Apex for the heavy lifting. 9. Trigger / Apex Codes require min 75% code coverage → Apex codes require you to write test class with minimun 75% coverage Good automation isn’t about being “no-code” or “all-code.” It’s about building something your org can maintain, scale, and trust—six months from now, not just in today’s sprint. Read more about flows here: https://lnkd.in/gPQP29CN ♻️ Reshare if you find this useful 👉 Follow me for more practical Salesforce build decisions. #Salesforce #SalesforceAdmin #SalesforceDeveloper #Apex #SalesforceFlow #CRM #Automation #DevOps #EnterpriseSoftware #Architecting

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!

Manually entering thousands of records, click by laborious click, is an unacceptable drain on resources. Don't get me wrong, using forms to add records to your data tables is convenient, but it can't keep pace with real-world demands for large data volumes. For many Glide (glideapps.com) app creators, the familiar single-record form submission can be a bottleneck when users need to add a significant amount of data. This is why enabling bulk CSV imports is not merely a convenience; it is an absolute necessity for productivity. Unfortunately, allowing users to import CSV data is a feature not natively supported in Glide, but it can be achieved through clever workarounds. Take a look 👀 The video breaks down two primary methods for handling bulk data: 1️⃣ Method 1: Looping Workflow (for Smaller Imports) This approach involves creating a workflow that iterates through each row of a CSV file and adds it as a new record. ✅ Pros: Relatively straightforward to set up within Glide. 🚫 Cons: Limited to approximately 1,000 records due to Glide's operation limits and can be slow for larger datasets, consuming more app updates. 2️⃣ Method 2: Glide API with Stashing (for Large Imports) This advanced technique leverages Glide's API v2, specifically the "stashing" feature, for highly efficient bulk uploads. ✅ Pros: Handles hundreds or thousands of records near-instantaneously, significantly more cost-effective (e.g., 2,000 records cost only 20 updates instead of 2,000). 🚫 Cons: Requires a deeper understanding of API calls, JSON manipulation, and a bit of JavaScript for "chunking" data. It also introduces the need to work with column IDs instead of readable headers, often requiring AI assistance for conversion. Checkout my detailed, step-by-step guide for both methods, including: - Building an importer interface. - Validating CSV files and headers. - Converting CSV to JSON. - Enabling manual workflow triggers. - Configuring API calls with authorization tokens. While the second method is more advanced, the efficiency and cost savings it offers for large datasets make it an invaluable solution. Have you tried implementing bulk uploads in your Glide apps? Where might this be useful for your solution? https://lnkd.in/exWeXnuM #GlideApps #NoCode #LowCode #DataManagement #WorkflowAutomation

Picture this: Your app is finally getting the traffic you dreamed of… and then it crashes. Suddenly, what felt like a win is now a scramble to keep users happy. As your application grows, so does the pressure to handle more traffic, data, and user actions—without compromising on speed or reliability. Without the right scaling strategies, even the best-built apps can buckle under demand, leaving users frustrated and growth stalled. Here are 8 must-know strategies to scale your system effectively and ensure it can handle increased demand with ease: 1. Stateless Services Keep your services stateless. This makes them easier to scale and maintain, as they don’t depend on server-specific data. 2. Horizontal Scaling Add more servers to distribute the workload efficiently and handle growing traffic. 3. Load Balancing Use a load balancer to ensure requests are distributed evenly across servers, avoiding bottlenecks. 4. Auto Scaling Implement auto-scaling to dynamically adjust resources based on real-time traffic demand. 5. Caching Use caching to reduce database load and handle repetitive requests more efficiently. 6. Database Replication Replicate your data across nodes to scale read operations while also improving redundancy. 7. Database Sharding Spread your data across multiple instances to scale reads and writes effectively. 8. Async Processing Move heavy, time-consuming tasks to background workers using async processing to free up resources for new requests. 💡 Over to you: What other strategies have you used to scale your systems? Drop your thoughts below! 👇

When You’re Flooded with Applicants: Best Practices for a Busy Hiring Team We’ve been hearing this over and over lately: “We posted a role and got 300 applications overnight.” It says a lot about this moment — talented people are on the move, the job market is shifting, and mission-driven organizations are seeing more interest than ever. It’s a good problem to have, but it can also slow things down, frustrate candidates, and overwhelm hiring teams. So I spoke with my lead recruiters this week about what to do when you have (or expect) a ton of applicants. Here are a few best practices we’ve seen help teams stay on track: 🔹 Get clear (really clear) on what you’re looking for. Before you even open the first application, make sure the hiring team agrees on the must-haves vs. nice-to-haves. Otherwise, you’ll be swimming in indecision later. 🔹 Take a beat before diving into interviews. Instead of rushing to start phone screens, spend a little time up front reviewing the top resumes with the hiring manager. Getting aligned early on who stands out (and why) takes a bit more time at the start, but it saves hours later and helps ensure your first-round interviews are focused on the strongest candidates. 🔹 Consider knockout application questions. You know I always opt in for application questions instead of a cover letter. Knockout questions can include quick confirmations about location, pay, or other logistics. You can also use must-have experience, like experience with C3/C4/PAC work, or major giving, as a knockout question that helps you prioritize right away (no guesswork required). 🔹 A lot of orgs are shortening their application period. If you know you’ll get flooded, consider keeping the posting open for a shorter time. You’ll still get a strong pool, but with a much more manageable review process. 🔹 Questionnaires. Send a written questionnaire to candidates before starting interviews. This is another idea to save time. Keep in mind, though, that these questionnaires can feel a bit transactional and affect the candidate experience. You can balance it out with something like a short “Meet the Hiring Manager” video or intro note that lets candidates start getting to know your team. 🔹 Make it easy for candidates to ask questions. Set up a simple form, shared inbox, or point of contact for inquiries. It signals transparency and saves your team from fielding the same question 30 times from five different places. There’s a lot of movement in the job market right now, and it’s great to see so many people excited to do values-driven work. With a little structure and clarity, your hiring process can honor that enthusiasm and keep your team from getting overwhelmed. What’s been working for you when applications come pouring in?

Load balancing distributes incoming requests across multiple servers, ensuring optimal performance, availability, and scalability.  But with so many methods, choosing the right one can be tricky. 𝗛𝗲𝗿𝗲'𝘀 𝗮 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟱 𝗰𝗼𝗻𝘁𝗲𝗻𝗱𝗲𝗿𝘀: 𝟭. 𝗥𝗼𝘂𝗻𝗱 𝗥𝗼𝗯𝗶𝗻: The OG of load balancing, it sends requests in a circular fashion, ensuring everyone gets a turn. Think of it as a classroom attendance sheet – fair and simple! 𝟮. 𝗜𝗣 𝗛𝗮𝘀𝗵: This method assigns requests to a specific server based on the client's IP address. It's like having a personalized queue – users connect with the same server each time, building a rapport (and potentially faster response times). 𝟯. 𝗟𝗲𝗮𝘀𝘁 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻: Don't overload the busy servers! This method directs requests to the server with the fewest active connections, spreading the workload evenly. Imagine a buffet line – everyone heads to the shortest queue. 𝟰. 𝗟𝗲𝗮𝘀𝘁 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗧𝗶𝗺𝗲: Need lightning-fast responses? This method prioritizes the server with the quickest response time, ensuring users don't wait in laggy purgatory. Think of it as a VIP lane for the speediest servers. 𝟱. 𝗟𝗲𝗮𝘀𝘁 𝗕𝗮𝗻𝗱𝘄𝗶𝗱𝘁𝗵: Bandwidth hogging servers? Not on our watch! This method sends traffic to the server with the lowest current usage, optimizing bandwidth allocation. It's like a traffic cop directing cars to the least congested lanes. 𝗕𝘂𝘁 𝗿𝗲𝗺𝗲𝗺𝗯𝗲𝗿, 𝘁𝗵𝗲𝗿𝗲'𝘀 𝗻𝗼 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻! The best method depends on your specific needs and traffic patterns. Consider factors like: 𝗧𝗿𝗮𝗳𝗳𝗶𝗰 𝘃𝗼𝗹𝘂𝗺𝗲: How much traffic are you expecting? 𝗦𝗲𝗿𝘃𝗲𝗿 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀: What are your servers' performance limitations? 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝘁𝘆𝗽𝗲: Is your application latency-sensitive? Have I overlooked anything? Please share your thoughts—your insights are priceless to me.

Choosing the right load balancer for your application is crucial for ensuring optimal performance, reliability, and scalability. 1. Understand Your Application Requirements Traffic Volume: Estimate the expected load and peak traffic times. Consider both current and future needs. Application Type: Different applications (web, API, microservices) may require different balancing strategies. 2. Types of Load Balancers Hardware Load Balancers: Provide high performance and reliability but can be costly. Best for large enterprises. Software Load Balancers: More flexible and cost-effective, suitable for smaller setups. Examples include HAProxy, NGINX, and Traefik. Cloud Load Balancers: Managed services offered by cloud providers (e.g., AWS Elastic Load Balancing, Azure Load Balancer). They provide scalability and integration with cloud services. 3. Load Balancing Algorithms Round Robin: Distributes requests sequentially. Simple and effective for similar servers. Least Connections: Directs traffic to the server with the least active connections, ideal for handling varying loads. IP Hash: Routes requests based on the client’s IP address, ensuring that a client consistently connects to the same server. Weighted Algorithms: Assign weights to servers based on capacity and performance, directing traffic accordingly. 4. High Availability and Failover Ensure the load balancer supports failover mechanisms to reroute traffic in case of server failure. Look for features like health checks to monitor server status and remove unresponsive servers from the pool. 5. Scalability Choose a load balancer that can scale easily with your application, whether through adding more instances or by distributing traffic across multiple regions. 6. Security Features Consider load balancers with built-in security features, such as SSL termination, DDoS protection, and web application firewalls (WAF). 7. Integration with Existing Infrastructure Ensure compatibility with your current tech stack, including application servers, databases, and CI/CD pipelines. Look for integration capabilities with monitoring tools for better visibility into traffic patterns and performance metrics. 8. Cost Considerations Evaluate the total cost of ownership, including licensing, maintenance, and operational costs. Compare this against the expected benefits in performance and uptime. 9. Vendor Support and Community Assess the level of support provided by the vendor or community. Good documentation, active forums, and responsive support can be invaluable. 10. Testing and Evaluation Before finalizing your choice, conduct performance testing with your application to ensure the selected load balancer meets your needs. Monitor metrics like latency, throughput, and error rates during testing. If you liked this post: 🔔 Follow: #learnwithrahulsharma ♻ Repost to help others find it 🧑🦰 Tag a person learning system design 💾 Save this post for future reference