Concurrency in Node js (2024)

FAQs

Is NodeJS good for concurrency? ›

Event-driven execution – the event loop continuously checks the event queue and executes callbacks as tasks are completed. This mechanism allows Node. js to manage multiple tasks concurrently, making it highly efficient in handling numerous requests without blocking the main thread.

js uses a single thread with an event-loop. In this way, Node can handle 1000s of concurrent connections without any of the traditional detriments associated with threads. A benchmark made by Fastify, shows that Express. js can handle approx 15,000 requests per second and the basic HTTP module, 70K requests per second.

Node. js handles concurrency using an event-driven, non-blocking I/O model. This means that rather than waiting for I/O operations to complete before moving on to the next task, Node. js can execute multiple tasks simultaneously by delegating I/O operations to separate threads in the background.

By striving for code optimization, you can unlock the full potential of your Node API, enabling it to handle high traffic loads and deliver exceptional performance. This is a must if you want to handle million requests per second.

Node. js workers and similar multithreading methods improve application performance and reduce bottlenecks by running code in parallel. They can also make applications more robust by running dangerous functions in separate threads and terminating them when processing times exceed certain limits.

The Executors framework in Java provides a high-level API for managing a pool of threads, making it easier to handle a large number of concurrent tasks efficiently.

It's clear that encrypting data in an asynchronous (non-blocking) way is performed faster and more requests can be made per second. The main difference between synchronous and asynchronous request processing lies in the fact that synchronous operations block the main thread, hindering Node.

Node. js's single-threaded architecture, driven by the event loop and non-blocking I/O, is a deliberate design choice that balances simplicity, performance, and scalability.

The requirement is to allow only 10 concurrent API calls at a time and any other calls will be in queue.

Strategies for Dealing with High Concurrency

Caching: relieve system load pressure and improve system response speed. Rate limiting: control concurrent page views and protect the system from overload. Degradation: guarantee the stability of core features, discard non-critical business or simplify processing.

How can we avoid concurrency problem? ›

The basic architectural building blocks for writing concurrent code in JavaScript are callback functions and AJAX service calls. A function taken as an argument to another function and called within the outer function to complete a task or an event afterward is known as a callback function.

at the high level Nodejs falls into the category of concurrent computation. This is a direct result of single threaded event loop being the backbone of a Nodejs app. The event loop repeatedly takes an event and fires any event handlers listening to that event one at a time.

Concurrency is about dealing with multiple tasks at the same time but not necessarily executing them simultaneously. It's more about task scheduling and structure. Python supports concurrency mainly through threading, asyncio, and various other libraries.

Not Suitable for Heavy-Computing Apps

Node. js doesn't support multi-threaded programming yet. It is able to serve way more complicated applications than Ruby, but it's not suitable for performing long-running calculations. Heavy computations block the incoming requests, which can lead to decrease of performance .

Node. js provides a powerful platform to build scalable and distributed systems, as it supports event-driven, non-blocking I/O, which is crucial for building high-performance systems.

However, when it comes to handling CPU-intensive tasks, the single-threaded nature of Node. js might block the main thread from taking any more requests to process. This is where the concept of multithreading comes to the rescue, enabling you to efficiently manage compute-heavy workloads.