What JavaScript Chart Performance Really Means With SciChart

You think you’ve finally found a JavaScript chart library that works. Then, it starts to lag or freeze. If displaying real-time, big-data is mission critical, you need a library that actually works (and keeps on working even when you introduce more data, charts, interactions or annotations.)

From code samples that let you display 100 million data points in just a few minutes to new performance enhancements that let you add hundreds of annotations without slowing down data rendering, we’ve got you covered.

We’re sharing usable code samples, comparison tables and visual comparisons to help you see the difference between SciChart and competitors.

What Does Performance Actually Mean in JavaScript Charting?

When researching what makes a chart library high performance, you’ll find a lot of technical features.

Some notable mentions include:

• GPU-Accelerated Graphics Engine: Uses GPU to free up CPU. It’s optimized for efficiency, low memory usage, and can handle extremely large datasets (millions of points in JavaScript).
• Dynamic Level Of Detail: Automatically adjusts detail shown based on zoom level to maintain performance without losing visual fidelity.
• High Data Throughput: Can handle 300+ live data streams and real-time updates without freezing the UI.

These all contribute to ensuring stable performance, even with demanding data rendering requirements. It’s easy to look at performance in isolation. For instance, can a chart library provide real-time data rendering at a speed of 60 FPS? However, for more demanding, complex charting requirements, the barriers of performance need to be pushed even further.

You may have first-hand experience of using a library that claims to offer real-time performance, only to find out that it has limitations. Fast chart rendering is a good starting point, but it will only take you so far.

The question of performance then becomes one of multi-tasking. Can the chart library in question handle real-time data rendering at a speed of 60 FPS with over 100 annotations and multiple chart series, even on smaller hardware?

That is the true test of performance: when you need to add multiple aspects that would normally result in lags or freezing dashboards.

How Is Chart Performance Measured?

Once you’ve set up a high-performance chart, how do you know for sure that it’s working to the performance level you require? To accurately measure JavaScript chart performance, there are a number of options available:

• Lifecycle Events including measuring frame render time. Make sure the chart rendering doesn’t start before the create function completes.
• Data Append & Update Times to see exactly how fast your chart data is updating and refreshing. Compare against your required benchmarks.
• Memory Usage & Performance Debugging Tests to troubleshoot SciChart features and ensure they’re working at their optimum.

What’s the Risk of Not Choosing the Right Chart Library for Your Performance Needs?

Not all chart libraries are created equal and offer varying levels of performance. There are many risks to not choosing the right chart library for your data visualization project:

• Charts may freeze or lag when you reach a certain number of data points (over 5,000) or add multiple chart series in a dashboard. Unresponsive interactions or frame drops can also occur.
• Lack of good documentation that lets you sample the code straight away.
• Inability to scale up your big data projects when your project demands it.
• Lack of customizations or chart options that contribute to enhanced performance, interactivity and engagement for the end user.
• Inadequate technical support or difficulty finding clean code samples that actually work for your specific use case.
• Difficulty providing a consistently reliable and secure chart solution for your end user.
• Issues or bugs might not get fixed if the chart library isn’t regularly updated or maintained.
• Not getting access to improvements in charting technology if the library isn’t being invested in.

SciChart vs Competitors: See the Difference in Performance

What makes SciChart better than the competitors?

• WebGL-Powered Rendering: SciChart.js uses WebGL for hardware-accelerated rendering directly in the browser. This harnesses the GPU to smoothly draw very large datasets with high frame rates – far beyond what Canvas or SVG-based libraries typically achieve.
• Optimized for Real-Time: The library is designed to efficiently handle real-time streaming data and updates, making it perfect for live dashboards, financial charts, and telemetry apps in JavaScript.
• Asynchronous Chart Creation: SciChartSurface.create() uses async/await to initialize charts quickly and keep UI responsive, even when working with complex or multiple charts.
  Supports Large Datasets: SciChart.js can visualize millions of points without dropping frames or slowing down, thanks to its level-of-detail and data culling techniques implemented under the hood.
• Multi-platform Compatibility: It works in any modern browser and can be embedded in Electron, PWA, or hybrid apps, ensuring great performance on desktop and mobile devices alike.
• Theming and Customization: You can apply built-in or custom themes easily, keeping your charts visually engaging without sacrificing speed. Ability to add hundreds annotations without compromising on performance.

High-Performance Code Samples that Really Work

There’s nothing more frustrating than thinking you’ve found the perfect code to find that it doesn’t work. It’s time-consuming, and you want to spend that time on building the charts, rather than hunting down the code.

That’s where SciChart comes in. You have access to hundreds of demos, documentation and code samples that have been tried and tested — not just by our in-house developers, but by developers at the top of their game.

When it comes to JavaScript chart library performance, you don’t want to get too lost in the technical details. What you really want is JavaScript demos and code samples that work. That is the best way to know that you’ve finally found a charting solution that works for your needs.

Our code samples have helped to power F1 wins, medical diagnosis and financial and trading apps.

You can open up the high-performance demos featured below and try them right away. If you need support, we offer responsive support.

Build a JavaScript Chart with One Million Data Points

Build a JavaScript chart that loads and displays one million data points in milliseconds.

View One Million Data Points Demo

Real-Time Ticking Stock Chart

A real-time ticking stock chart connects to Binance Exchange to fetch historical data on a 1-minute timeframe. The chart subscribes to WebSocket and listens to candles and trades. Candles are updated in real time and you can zoom and pan the example or use tooltips. Large trades of more than $25,000 are plotted as bubbles.

View Real-Time Ticking Stock Chart DemoBlog: How To Create Real-Time Ticking Stock Chart

500 Series x 500 Points Performance

Load 500 series, each with 500 points (250k points total) instantly.

View 500 Series x 500 Points Demo

Choose a Chart Library that Delivers High-Performance

Not every JavaScript chart library is built for mission-critical chart performance in mind. We’ve invested the time, money and resources to ensure that the charts offer the best performance possible.

But, we also make it super easy to get started and access the code you need to make that happen. With SciChart, you can build faster and smarter, while meeting all the high-performance requirements you need.

Read our fastest chart library comparison guide to see the evidence that SciChart lets you build faster while adding even more data and interactive elements than competitors.

Get Started With SciChart.js TodayView JavaScript Chart Demos