Concurrency vs Parallelism: How to Boost System Performance

Node.js’ non-blocking I/O hinges on a finely tuned single-threaded Event Loop. In 2025, delivering consistent low-latency performance demands a deep understanding of its phases—timers, pending callbacks, poll, check, close—and the microtask queue. Key recommendations for maintaining responsiveness under heavy load: - Eliminate timer drift by computing absolute next execution times and using setTimeout - Prevent phase starvation by limiting chained microtasks and yielding via setImmediate or setTimeout(0) - Mitigate CPU-bound work by breaking tasks into chunks, deferring with setImmediate, or offloading to worker_threads By adopting these patterns, we ensure our Node.js services remain robust, scalable, and performant—even at scale. Discover the full guide in the first comment:

Gemini Event loops and thread pools are distinct mechanisms used for handling concurrency, but they can sometimes work in conjunction. They are not similar in their fundamental approach: Event Loops: An event loop is a single-threaded mechanism that continuously checks for events and dispatches them to corresponding handlers. It operates on a non-blocking, asynchronous model, meaning that when an operation like I/O is initiated, the event loop does not wait for it to complete. Instead, it moves on to other tasks and processes the result of the operation when it's ready (e.g., via a callback). This model is particularly efficient for I/O-bound tasks where waiting for external resources would otherwise block a thread. Thread Pools: A thread pool is a collection of pre-instantiated threads that are ready to execute tasks concurrently. When a task needs to be performed, it is submitted to the thread pool, and an available thread from the pool executes it. Thread pools are well-suited for CPU-bound tasks, where parallel execution on multiple CPU cores can significantly improve performance. Relationship and Differences: Concurrency Model: Event loops achieve concurrency through non-blocking I/O and asynchronous callbacks on a single thread, while thread pools achieve concurrency through true parallelism by utilizing multiple threads. Blocking Behavior: An event loop is designed to be non-blocking, while tasks executed within a thread pool can be blocking without impacting the responsiveness of other tasks in the pool or the main application thread. Resource Utilization: Event loops can be more memory-efficient as they avoid the overhead of creating and managing many threads. Thread pools, while offering true parallelism, incur the overhead of thread creation and context switching. Collaboration (e.g., Node.js): In some environments, like Node.js, an event loop handles most operations, but CPU-intensive tasks that would block the event loop are offloaded to a hidden thread pool (e.g., libuv's thread pool) to maintain responsiveness. This demonstrates how they can complement each other.

I have recently been learning about rate limiting in system design and it has changed the way I look at API and system resilience. Rate limiting is more than simply capping requests. It protects APIs from abuse, balances load across users, and preserves stability under sudden spikes. The first question is scope. Do we want to limit by IP, by user ID, or by API key. Should enforcement live in the client, the backend service, or an API gateway. In cloud and microservice environments, rate limiting is often implemented as a dedicated service or handled by infrastructure such as gateways. The choice of algorithm depends heavily on context. Fixed windows are fast but allow bursts, sliding windows provide smoother control, token buckets allow flexibility for short spikes, and leaky buckets enforce steady throughput which is vital for streaming. The design is not purely technical. When clients exceed a limit, do we block them, queue requests, or return headers so they can adjust. Do we apply different limits for free and premium users. These choices affect fairness as well as user experience. While experimenting I implemented a simple token bucket limiter in a personal project. At first I underestimated the challenge, things worked fine locally, but when I tried simulating multiple users, I quickly ran into inconsistencies. That is when I realised why atomic counters and central stores like Redis are so often used in real systems. Rate limiting only looks simple until you need it to work reliably across servers. At scale, distributed environments introduce challenges such as race conditions, synchronisation issues, and bottlenecks in the store. Optimisations such as batching and hybrid caching help, but monitoring is just as important: without visibility into quota usage and blocked requests, it is impossible to tune thresholds or detect abuse. My takeaway is that rate limiting is not a small guardrail but a deliberate architectural choice. With the right scope, algorithm, distributed coordination and monitoring, it becomes a foundation for fair, scalable, and resilient systems. #SystemDesign #RateLimiting #Scalability #APIDesign #DistributedSystems #BackendEngineering

Concurrency vs Parallelism: do you know the difference? ⚡ We hear these terms all the time when talking about performance. But… do we really know how to tell them apart? 🤔 🔹 Concurrency The ability to deal with multiple tasks at once, but not necessarily executing them simultaneously. 👉 Example: a single processor switching between tasks via context switching. 🔹 Parallelism The actual execution of multiple tasks at the same time, usually across different processors or cores. 👉 Example: splitting a heavy calculation across 4 cores to run in parallel. ✨ Why were they created? Because modern applications must handle scalability, multiple users, and heavy workloads. Running code sequentially was no longer enough. 👩💻 Key concepts in this context: Deadlock 🛑 When two processes get stuck waiting for resources that will never be released. ➝ Happens without proper resource management strategies. Scheduler 📅 The “organizer” that decides which process runs and when. ➝ Crucial in concurrency to ensure efficiency and fairness in CPU usage. Context Switching 🔄 The processor switching from one task to another. ➝ Provides flexibility, but has a cost: saving and restoring states consumes time. ⚡ When to use concurrency vs parallelism? Concurrency: when you have I/O bound tasks (e.g., network requests, file reading). Parallelism: when you have CPU bound tasks (e.g., heavy computations, image processing, machine learning). 👉 Who is this for? For developers and software engineers aiming to get the most out of infrastructure, reduce bottlenecks, and design more efficient systems. #concurrency #parallelism #softwareengineering #dev #performance #multithreading #architecture

The Network Is the System: Why Everything Comes Down to Packets In modern software, the biggest bottleneck is not CPU or memory. It is the network. Every API call, every database query, every message between microservices ultimately becomes packets moving across a wire. That is why engineers often say the network is the system. Why Packets Matter Packets are not guaranteed to arrive in order. Some are lost. Some take longer paths. Some never arrive at all. Protocols like TCP work hard to hide this chaos, but they cannot erase it. Latency, jitter, and dropped packets shape how fast and reliable our systems feel. Real-World Consequences → Microservices live and die by network hops. A 2 ms call can turn into 200 ms when chained across dozens of services. → Distributed databases rely on networks to replicate data. Slow links mean slow consistency. → Streaming systems feel real-time only when the network cooperates. When you zoom out, the performance and reliability of most large systems are determined not by algorithms but by how packets flow. We can write elegant code and scale compute to the sky, but if the network lags, the system lags. In distributed computing, understanding packets is understanding the system.

Many backend performance problems don’t come from limited infrastructure, but from how systems are designed to handle tasks. One of the most critical — yet often misunderstood — distinctions is between concurrency and parallelism. • Concurrency is about managing multiple tasks so they make progress without blocking each other. • Parallelism is about executing tasks at the same time using multiple processors. They sound similar, but the difference is fundamental. Misunderstanding them can lead to inefficient architectures, bottlenecks, and wasted resources. In my latest article, I break down the concepts, share practical examples, and explain why knowing when to apply each is essential for building scalable, efficient backends. Read the full article here: https://lnkd.in/dk9bCgxK

Anthropic's post-mortem is a critical analysis of the challenges in productionizing large-scale models. The Triton JIT compiler bug causing cancellation in log-sum-exp is particularly telling. It signals that our compiler toolchains, often built on MLIR dialects, lack the robust formal verification needed for low-precision formats like FP8 (E4M3/E5M2). Ensuring gradient fidelity post-quantization isn't just a software problem; it necessitates rethinking ALU microarchitectures and using SAT/SMT solvers to provably guarantee numerical stability in fused kernels. The tokenizer and canary issues highlight a failure in managing IID assumptions. The subtle padding difference created a covariate shift between training and inference, a classic data-centric failure mode. Furthermore, canarying stochastic models with high Kolmogorov complexity using simple A/B tests is becomes pointless. It fails to detect latent issues like representational collapse. True observability requires more sophisticated techniques, such as monitoring the Mahalanobis distance in the embedding space to detect semantic drift in real-time. These incidents prove that a siloed approach is obsolete. We need a new discipline of full-stack AI engineering, where optimizations are co-designed from the gate-level of the silicon and its on-chip network (NoC) topology all the way up to the distributed serving stack. https://lnkd.in/gmh22yRV

High-Load APIs: Patterns That Keep Systems Reliable Building APIs that handle high traffic is not only about scaling throughput — it’s about maintaining predictable behavior under stress. Reliability emerges from the right balance of trade-offs, patterns, and technology choices. Key dimensions that shape such systems: • CAP priorities — consistency, availability, and partition tolerance can’t be maximized together; selecting the right balance defines how failures are absorbed. • Microservices as a foundation — enabling independent scaling, fault isolation, and flexibility in technology choices. • Protocols by context — REST for accessibility, gRPC for low-latency inter-service calls, WebSockets for real-time interactions. • Resilience patterns — CQRS, Event Sourcing, circuit breakers, and rate limiting safeguard stability. • Go’s concurrency model — lightweight goroutines and strong ecosystem support make it a practical fit for APIs under heavy load. • Observability — metrics, logs, and tracing turn behavior into visibility and ensure service-level objectives are met. Resilient APIs are the result of deliberate design. They rely on trade-offs made explicit, patterns consistently applied, and platforms chosen to support reliability at scale. #HighLoad #API #GoLang #Microservices #DistributedSystems #Resilience #CloudArchitecture

We’ve recently introduced WISCH, a new protocol built for disputable computing frameworks like BitVM and BitVMX. On-chain costs in these systems are largely driven by data signing schemes, and existing options like Lamport and Winternitz are notoriously expensive. WISCH changes that. How does it work? WISCH generates correlated sets of signatures across different schemes or messages. By leveraging these correlations, it acts as a drop-in replacement while cutting costs dramatically: 🔹 5× cheaper than Winternitz 🔹 10× cheaper than Lamport & GC Wire labels The key lies in its architecture: On-chain verification → cost depends only on the number of revealed items Off-chain preparation → most computation and communication are handled in advance Efficient, modular, and garbled-circuit friendly. In short: WISCH makes disputable computing practical and efficient — slashing signing costs while preserving strong security. A leap forward for BitVM, BitVMX, and beyond. Read the full paper here 👇 https://lnkd.in/dGU-Qf-t