How To Dev

Introduction In the previous article, I guided you on how to use D3 and Canvas to Visualize Data. Now we will continue to dive deeper to understand some solutions that help optimize performance, aiming for a State-of-the-art" approach for building Real-time Data Visualization on the Web today. Some techniques I will apply to optimize performance are as follows: Data Buffering (Data Layer Throttling): utilizing requestAnimationFrame to ensure smooth processing and rendering according to the screen refresh rate, such as 60Hz or 120Hz. Utilizing Context Alpha Optimization: getContext('2d', { alpha: false }). When you turn off the alpha channel (transparency), the GPU will not have to calculate color blending with elements behind the canvas, significantly reducing the load on the graphics chip. Offscreen Canvas: Using an implicit Canvas to calculate beforehand before pushing to the main screen. The issue with drawing directly on the Canvas is that if you have to draw too much,...

Introduction In the previous article, I introduced how to use D3 to visualize data as charts. For most chart visualization needs, using D3 to create individual HTML DOM elements is sufficient. However, if you need to build a web application with superior performance to handle complex cases with relatively large datasets, then applying Canvas is a good solution for you Canvas is an HTML5 element that allows for extremely high-performance 2D graphic drawing via JavaScript. Unlike SVG, which creates separate DOM elements for each object, Canvas operates as a "blank canvas" where pixels are drawn directly onto a single surface. For instance, in real-world products like Figma, Canva, and TradingView, they all utilize Canvas to handle complex graphical operations. Outstanding advantages of Canvas include: superior render speed when processing thousands of objects simultaneously, memory resource savings as it does not increase the size of the DOM tree, and flexible customization for...

Introduction D3.js (Data-Driven Documents) is a powerful JavaScript library for producing dynamic, interactive data visualizations from numerical data. Instead of providing pre-built charts, D3 offers low-level tools that allow you to manipulate DOM elements directly (typically SVGs, Canvas, or HTML). The advantages: Infinite Customization: You can create any type of chart you can imagine. High Performance: Smoothly handles large data sets and animated transitions. Strong Mathematical Ecosystem: Fully supports scale calculation functions, coordinate axis formatting, and complex geometric algorithms. Detail First, let's start with a simple example of drawing a line chart that describes monthly revenue during the year. Please create the file app/api/revenue/route.ts to simulate a 12-month revenue api with a random amount from 1000-6000 import {NextResponse} from 'next/server' export async function GET () { const data = Array. from ({ length: 12 }, ( _ , i ) => ...