Dawid Pereira’s Post

Smart Auto-scaling for Node.js on Kubernetes: CPU over Memory. When configuring Horizontal Pod Autoscalers (HPAs) for Node.js applications in Kubernetes, a common pitfall is over-relying on memory usage as a primary scaling trigger. While memory is a vital resource, its behavior in Node.js can be deceptive. The Node.js Memory Myth: Node.js, with its V8 engine, can be quite memory-hungry, often appearing to consume a significant amount of RAM even when idle. This isn't necessarily inefficiency; it's often due to: - Module caching: Loading many modules for fast execution. - Garbage Collection: V8 manages memory actively, but the reported RSS (Resident Set Size) might not reflect actively used memory, but rather allocated heap space that hasn't been returned to the OS yet. Scaling based on high memory allocation might lead to spinning up new pods when the existing ones are perfectly capable of handling more load, thus wasting compute resources. Why CPU is a Better Scaling Indicator for Node.js? 🤔: Node.js's event-driven, non-blocking I/O model means the single-threaded Event Loop primarily delegates I/O tasks. When the CPU utilization of a Node.js process climbs, it's a strong indicator that the Event Loop is genuinely busy processing application logic or handling callbacks from completed I/O operations. This is when you truly need more capacity. Best Practices for HPA with Node.js: - Prioritize CPU: Set your HPA to scale primarily based on CPU utilization. For example, trigger a scale-out when CPU exceeds 70-80% of its request limit. - Monitor Memory Intelligently: Establish realistic memory requests and limits based on profiling both idle memory usage and peak memory usage under expected load. This prevents OOMKills but avoids premature scaling. - Custom Metrics (Optional): For very specific scenarios, consider custom metrics (e.g., requests per second, queue length) if they better reflect your application's true workload. By optimizing your scaling strategy, you ensure efficient resource allocation and a highly performant Node.js application in Kubernetes. #Kubernetes #NodeJS #HPA #AutoScaling #CloudComputing #DevOps #PerformanceOptimization #SiteReliability #Tech

Want to scale your application from 100 users to 10,000 or more Follow below Horizontal Pod Autoscaling (HPA): Automate scaling of instances based on resource usage metrics (e.g., memory or CPU). This ensures that pods scale out only when needed and scale in when demand drops. use Kubernetes/OpenShift HPA configured to watch memory or CPU utilization and adjust the number of pods from a minimum to maximum count automatically. This balances load and maintains responsiveness under varying traffic. Statelessness and Reactive Design: Design services to be stateless so that any instance can handle any request, facilitating smooth horizontal scaling. reactive programming models to handle high-throughput and low-latency scenarios effectively, allowing more concurrent requests per instance without blocking threads. Load Balancing: Use a mature load balancer (e.g., Kubernetes service, ingress controller, or dedicated load balancer) configured with a suitable strategy like round-robin or least connections. This distributes incoming API requests evenly across instances, preventing any single instance from becoming a bottleneck. Connection and Rate Limits: Implement API rate limiting and connection throttling either at the API gateway, load balancer, or application level. This protects the backend from spikes and ensures fair resource allocation among clients. Caching: Use caches at multiple levels—application-level (e.g., Caffeine cache as in your example), distributed cache layers (e.g., Redis), and HTTP caching headers—to reduce unnecessary repeated fetches and computation, lowering the load on your REST API. Circuit Breakers and Fault Tolerance: Employ circuit breakers to avoid overwhelming downstream services or databases if part of your API ecosystem becomes slow or unresponsive, helping maintain overall system health.

Apache Kafka supports multiple compression codecs. Each offers a tradeoff between speed, CPU cost, and compression ratio. ⬇️ 1. No compression applied- Useful for extremely low-latency use cases, but inefficient for most production systems. 2. GZIP - Provides excellent compression ratios but at the cost of higher CPU usage. It's ideal for archival or bandwidth-constrained environments. 3. Snappy - Popular choice for balanced workloads. It offers fast compression and decompression, with moderate size reduction. 4. LZ4 - Faster than Snappy with slightly better compression ratios. Great for high-throughput environments where latency matters. 5. Zstd - The most modern and flexible option. Zstd offers tunable compression levels, achieving both high speed and excellent ratios. It’s often ideal for varied workloads. 🛑 Beware! As much as you might be tempted to make Zstd the default setting, applying the same Kafka compression type across all producers fails to account for real-world differences in data shape, volume, and flow. Superstream flips the typical optimization model by observing how Kafka message compression behaves in the real world by profiling each stream’s shape, frequency, and entropy—and adapts accordingly. Workloads are dynamic. Compression should be too. Start being smarter with your Kafka here - https://lnkd.in/eRnKB5tW

⚙️ Kubernetes Tip: The Difference Between Requests and Limits in Deployments If you’ve ever configured a Pod in Kubernetes, you’ve likely seen these two terms: ➡️ resources.requests ➡️ resources.limits They might look similar — but they play very different roles in how your workloads get scheduled and run. Let’s break it down 👇 🔹 Requests: Minimum amount of CPU and Memory your container needs to run. Used by the Kubernetes Scheduler to decide which node your Pod should go on. Example: requests.cpu: 200m means the Pod needs at least 0.2 vCPU to be scheduled. 💡 Think of it as: “I need at least this much to work properly.” 🔹 Limits: The maximum amount of CPU and Memory your container can use. Enforced by the Kubelet at runtime. If a container exceeds its memory limit → it can get killed (OOMKilled). If it exceeds CPU limit → it’s throttled, not killed. 💡 Think of it as: “I can’t use more than this, no matter what.” ⚖️ Quick Analogy: Imagine you’re in an office: Request = Your desk space reservation. Limit = The maximum space you’re allowed to occupy. 📌 Best Practice: Always define both Requests and Limits to ensure fair resource sharing. Avoid setting Requests too high or Limits too low — it can affect cluster efficiency or performance. 💬 How do you usually decide CPU/Memory Requests and Limits for your workloads? Static values or auto-tuned via metrics? #Kubernetes #DevOps #CloudNative #SRE #Containers #K8s #GCP

Apache Kafka supports multiple compression codecs. Each offers a tradeoff between speed, CPU cost, and compression ratio. ⬇️ 1. No compression applied- Useful for extremely low-latency use cases, but inefficient for most production systems. 2. GZIP - Provides excellent compression ratios but at the cost of higher CPU usage. It's ideal for archival or bandwidth-constrained environments. 3. Snappy - Popular choice for balanced workloads. It offers fast compression and decompression, with moderate size reduction. 4. LZ4 - Faster than Snappy with slightly better compression ratios. Great for high-throughput environments where latency matters. 5. Zstd - The most modern and flexible option. Zstd offers tunable compression levels, achieving both high speed and excellent ratios. It’s often ideal for varied workloads. 🛑 Beware!  As much as you might be tempted to make Zstd the default setting, applying the same Kafka compression type across all producers fails to account for real-world differences in data shape, volume, and flow. Superstream flips the typical optimization model by observing how Kafka message compression behaves in the real world by profiling each stream’s shape, frequency, and entropy—and adapts accordingly. Workloads are dynamic. Compression should be too.

Here's a great writeup from Taryn Plumb for Network World, taking a deeper look at how Google has started to migrate many of its internal workloads to its Arm-based servers. A few details worth noting: 🔸 These servers, built on the (Google) designed Axion CPU are now supporting about 30K of Google's 100K internal workloads 🔸 The goal is to have these workloads on both Arm & x86 simultaneously 🔸 Workloads inlcude YouTube, gmail, BiqQuery 🔸 Goal is to move everything to this shared model 🔸 Multiarch a guiding and design principle for Google developers Really interesting article that goes deeper into the port process - well worth the read. Article below ⬇️ https://lnkd.in/gxbyqh28

Resource Limits: Resource Limits in Kubernetes define the maximum amount of compute resources such as CPU and memory that a container or pod is allowed to consume. They serve as a control mechanism to prevent individual workloads from monopolizing cluster resources and ensure fair distribution among multiple applications running in the same environment. By setting resource limits, the Kubernetes scheduler knows the upper bound of resources needed for a pod, which helps it make better placement decisions and maintain overall cluster stability. Resource Limits are tightly connected to Resource Requests, which specify the minimum resources required. While requests are about guaranteed allocation, limits are about strict enforcement, and if a container tries to exceed its limit, Kubernetes or the underlying runtime may throttle CPU or terminate the container for memory overuse. Resource Limits are essential for maintaining performance consistency, avoiding noisy neighbor issues, and preventing node crashes caused by uncontrolled resource consumption. Resource Limits depend on accurate knowledge of workload behavior and are configured within pod specifications. They directly influence the scheduler's decision-making and depend on the cluster's available resources. Many features such as Quality of Service (QoS) classes, autoscaling, and eviction policies depend on properly configured limits. Without them, a cluster risks instability, unpredictable performance, and difficulty in scaling workloads efficiently. They are especially critical in multi-tenant environments, production systems, and when running diverse workloads on shared infrastructure. In simple terms: Resource Limits are like setting a speed limit for each pod, ensuring no single pod can overconsume CPU or memory and disrupt other workloads. TL;DR: Resource Limits cap the maximum resources a pod can use, safeguarding cluster performance and stability by preventing excessive consumption. #ClusterStability #ResourceManagement #WorkloadControl #Kubernetes #K8s #DevOps

𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐯𝐬. 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠: 𝐓𝐡𝐞 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫’𝐬 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 𝐆𝐫𝐨𝐰𝐭𝐡 Every backend system eventually faces the same question: How do we handle more users, more requests, and more data without breaking performance? That’s where scaling comes in, and there are two main paths: 🔹 𝐕𝐞𝐫𝐭𝐢𝐜𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 (𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐔𝐩): Add more power to a single machine more CPU, RAM, or faster disks. It’s simple and fast to implement since the system stays the same, but it has limits. Once you hit the max hardware capacity, you can’t go further. Example: Upgrading a database server from 8GB RAM to 64GB RAM.  Pros: Easy setup, no app code changes, great for smaller systems.  Cons: Limited growth, single point of failure, potential downtime during upgrades. 🔹 𝐇𝐨𝐫𝐢𝐳𝐨𝐧𝐭𝐚𝐥 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 (𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐎𝐮𝐭): Add more machines or instances and distribute the load across them. This method is ideal for cloud-based and microservices architectures. Example: Deploying multiple API servers behind a load balancer.  Pros: High availability, handles large traffic, flexible with auto-scaling.  Cons: More complex setup, needs stateless services and shared storage. 🔹 𝐖𝐡𝐞𝐧 𝐭𝐨 𝐔𝐬𝐞 𝐄𝐚𝐜𝐡: Start with vertical scaling for simplicity and quick improvements. Move to horizontal scaling when traffic grows and uptime matters. 💡 Pro Tip: Design early for statelessness, load balancing, and shared caching. This makes it easier to shift from scaling up to scaling out without major refactoring. Scalability isn’t just about adding resources, it’s about designing for growth from day one. #BackendEngineering #Scalability #Performance #SystemDesign #SoftwareEngineering #BackendDevelopment #CloudComputing #APIs #Microservices #DatabaseDesign #TechLeadership #SoftwareArchitecture #TechTips

Independent testing reveals NetScaler achieving 3x higher throughput than F5 BIG-IP with 89% lower latency. HAProxy v3.x exceeded 2 million requests per second on a single instance—a software load balancing milestone. The enterprise load balancing decision in 2025 demands reevaluation based on architecture fundamentals, not vendor promises. After analyzing F5 BIG-IP v17.x, Citrix NetScaler v14.1, and HAProxy v3.2 LTS across performance benchmarks and total cost of ownership, the data contradicts conventional purchasing patterns. F5 maintains comprehensive features through AI security integration and post-quantum cryptography support. Three-year TCO: $400K-600K. Hardware acceleration delivers 300 Gbps for SSL-intensive workloads. NetScaler's single-pass architecture achieves 64% better CPU efficiency and 1/5 the latency versus F5 in AWS testing. UHMC unlimited throughput model provides compelling economics. Critical consideration: mandatory cloud licensing migration by April 2026. HAProxy demonstrates 2.04M HTTP RPS with 400 microseconds latency and 99.9% of requests under 5ms. Enterprise cost: $50K-100K. Community edition: zero. Identical performance, dramatically different economics. The cost differential reshapes decisions: F5's $400K-600K versus HAProxy's $50K-100K for equivalent SLAs forces new TCO justification conversations. Platform selection priorities: Cloud-native architectures favor HAProxy's simplicity. Traditional enterprises justify F5's comprehensive security. Hybrid environments benefit from NetScaler's performance if navigating licensing transition. Which matters most in your infrastructure: NetScaler's raw performance metrics, HAProxy's unmatched economics, or F5's feature completeness when all three technically meet enterprise SLAs? #SMEnode #SMEnodeLabs #SMEnodeAcademy #LoadBalancing #NetworkEngineering #EnterpriseArchitecture #CloudNative #DevOps #ApplicationDelivery

When working with byte arrays in .NET, choosing between a 𝗕𝘂𝗳𝗳𝗲𝗿 and a 𝗦𝘁𝗿𝗲𝗮𝗺 can make or break your app’s scalability. Here’s the quick difference developers should know: 🔹 𝗕𝘂𝗳𝗳𝗲𝗿 Holds the entire data in memory. Low CPU overhead, but 𝗵𝗶𝗴𝗵 𝗺𝗲𝗺𝗼𝗿𝘆 𝘂𝘀𝗮𝗴𝗲. If payloads are large (in MBs) and your endpoint is called frequently, this leads to 𝗳𝗿𝗲𝗾𝘂𝗲𝗻𝘁 𝗴𝗮𝗿𝗯𝗮𝗴𝗲 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗱𝗲𝗴𝗿𝗮𝗱𝗲𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. Example: byte[] data = File.ReadAllBytes("bigfile.dat"); // Whole file is loaded into memory ProcessData(data); 🔹 𝗦𝘁𝗿𝗲𝗮𝗺 Reads/writes data in 𝗰𝗵𝘂𝗻𝗸𝘀, not all at once. Minimal memory footprint, 𝘀𝗹𝗶𝗴𝗵𝘁𝗹𝘆 𝗺𝗼𝗿𝗲 𝗖𝗣𝗨 𝗼𝘃𝗲𝗿𝗵𝗲𝗮𝗱. 𝗦𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁𝗹𝘆 𝗿𝗲𝗱𝘂𝗰𝗲𝘀 𝗚𝗖 𝗽𝗿𝗲𝘀𝘀𝘂𝗿𝗲 𝗮𝗻𝗱 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝘀 𝘀𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆. Example: using FileStream fs = File.OpenRead("bigfile.dat"); byte[] buffer = new byte[8192]; // 8 KB default int bytesRead; while ((bytesRead = fs.Read(buffer, 0, buffer.Length)) > 0) {   ProcessData(buffer, bytesRead); } ⚡ Heads Up  • Streams are ideal when handling large files, network transfers, or frequent API calls.  • Buffers are okay for small payloads or when you need random access in memory.  • Always think: CPU can scale out, memory scaling is limited. Streams keep your app memory-friendly. The size of each stream buffer depends on I/O type (disk vs network), hardware (disk block size, OS buffer size), and workload (sequential vs random). There’s no one-size-fits-all — measure and tune. If you’re building scalable services — 𝘀𝘁𝗿𝗲𝗮𝗺 𝘆𝗼𝘂𝗿 𝗱𝗮𝘁𝗮. Buffers are tempting for simplicity, but they don’t scale well under load. #dotnet #csharp #performance #scalability #cloud