How do I implement real-time data streaming with uPlot?

Use the streaming API demonstrated in the demos; for optimal performance, tune your browser's Canvas rasterization settings as described in the 'Unclog your rendering pipeline' section to maintain 60fps updates.

uPlot vs Chart.js: which is better for time series?

uPlot excels in speed and memory efficiency for large datasets and real-time updates, while Chart.js offers more chart types and easier customization but at a higher performance cost.

How to add custom tooltips in uPlot?

Tooltips can be implemented via the hooks API; refer to demos for examples of custom cursor interactions and legend values to build interactive tooltips tailored to your data.

Does uPlot support missing or irregular data points?

Yes, it has built-in support for missing data, allowing gaps in time series without breaking the chart rendering, as shown in the missing data demos.

Can I use uPlot in a React application?

Yes, third-party wrappers like uplot-wrappers provide React integrations, along with Vue.js and Svelte, making it easy to embed uPlot charts in modern frontend frameworks.

What are the performance limits of uPlot for live data?

uPlot handles up to 100,000 in-view points smoothly; beyond that, consider reducing update frequency or switching to WebGL-based solutions like webgl-plot, as suggested in the README.

μPlot

MITJavaScript1.6.32

A small, fast Canvas 2D-based charting library for time series, lines, areas, ohlc, and bars.

GitHub

What is μPlot?

μPlot is a lightweight, high-performance JavaScript charting library built on Canvas 2D, designed specifically for rendering time series data, lines, areas, ohlc, and bars. It solves the problem of slow, bloated charting libraries by offering exceptional speed and minimal memory usage, enabling smooth interactions with large datasets. Unlike many alternatives, it focuses solely on efficient rendering without unnecessary features like animations or built-in data processing.

Target Audience

Developers building performance-sensitive web applications that require fast, interactive time series visualizations, such as financial dashboards, real-time monitoring tools, or data analytics platforms. It's particularly suited for those who prioritize speed and efficiency over decorative features.

Value Proposition

Developers choose μPlot for its unmatched combination of small bundle size (~50 KB), blazing-fast rendering, and low memory footprint. It outperforms larger libraries like Chart.js and ECharts in CPU and memory usage, making it the go‑to choice when performance is critical and every kilobyte matters.

Overview

📈 A small, fast chart for time series, lines, areas, ohlc & bars

Use Cases

Best For

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Real-time financial dashboards with streaming OHLC data

High-frequency monitoring systems requiring 60fps updates

Embedded analytics where bundle size is a constraint

Applications plotting hundreds of thousands of time series points

Interactive charts with synchronized cursors across multiple plots

Projects needing time zone‑aware temporal axes

Not Ideal For

Applications requiring smooth animations or decorative chart transitions for user engagement.
Projects that need out-of-the-box stacked series or complex statistical visualizations like histograms.
Teams wanting built-in, intuitive drag-and-drop panning without implementing custom plugins.

Pros & Cons

Pros

Exceptional Rendering Speed

Benchmarks show it renders 166,650 data points in 25ms from cold start and scales linearly at ~100,000 points per millisecond, outperforming larger libraries like Chart.js and ECharts.

Tiny Bundle Size

At ~50 KB minified, it's significantly smaller than alternatives such as Chart.js (254 KB) or ECharts (1000 KB), reducing load times and bandwidth usage.

Efficient Live Updates

Can stream data at 60fps with low resource usage, consuming only 10% CPU and 12.3MB RAM for 3,600 points, compared to 40% / 77MB for Chart.js in similar tests.

Cons

No Built-in Data Manipulation

Lacks data parsing, aggregation, or statistical functions, forcing developers to integrate external libraries like simple-statistics for preprocessing, adding complexity.

Sparse Documentation

Documentation is a perpetual work in progress, with API details scattered in TypeScript comments and demos, making the learning curve steeper for newcomers.

Limited Aesthetic Controls

Omits animations, has no automatic label collision avoidance, and discourages smooth spline interpolation, restricting visual polish and requiring manual tweaks.

Open Source Alternative To

μPlot is an open-source alternative to the following products:

E

ECharts

Chart.js

Chart.js is an open-source JavaScript library for creating responsive, interactive, and customizable charts and graphs on web pages using HTML5 Canvas.

d

dygraphs

An open-source JavaScript charting library that produces interactive, zoomable charts of time series data, optimized for displaying large datasets.

L

LightningChart

LightningChart is a high-performance charting library for .NET and JavaScript, specializing in real-time data visualization with advanced graphics capabilities.

ApexCharts

ApexCharts is an open-source modern JavaScript charting library that creates interactive visualizations with a simple API and responsive design.

Highcharts

A JavaScript charting library for creating interactive, responsive charts and graphs for web applications.

Plotly.js

Plotly.js is an open-source JavaScript graphing library built on D3.js and stack.gl that creates interactive, publication-quality charts and visualizations for web applications.

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