Demonstrates appending millions of points to a line chart with SciChart.js, High Performance JavaScript Charts
drawExample.ts
index.tsx
RandomWalkGenerator.ts
theme.ts
1import { appTheme } from "../../../theme";
2import { RandomWalkGenerator } from "../../../ExampleData/RandomWalkGenerator";
3
4import {
5 EAutoRange,
6 EDragMode,
7 FastLineRenderableSeries,
8 MouseWheelZoomModifier,
9 NumericAxis,
10 RubberBandXyZoomModifier,
11 SciChartSurface,
12 XAxisDragModifier,
13 XyDataSeries,
14 YAxisDragModifier,
15 ZoomExtentsModifier,
16} from "scichart";
17
18export const drawExample = async (rootElement: string | HTMLDivElement) => {
19 // Define some constants
20 const numberOfPointsPerTimerTick = 1000; // 1,000 points every timer tick
21 const timerInterval = 10; // timer tick every 10 milliseconds
22 const maxPoints = 1e6; // max points for a single series before the demo stops
23
24 // Create a SciChartSurface
25 // Note create() uses shared WebGL canvas, createSingle() uses one WebGL per chart
26 // createSingle() = faster performance as doesn't require a copy-op, but limited by max-contexts in browser
27 const { wasmContext, sciChartSurface } = await SciChartSurface.createSingle(rootElement, {
28 theme: appTheme.SciChartJsTheme,
29 });
30
31 // Create an XAxis and YAxis with autoRange=Always
32 const xAxis = new NumericAxis(wasmContext, { autoRange: EAutoRange.Always });
33 sciChartSurface.xAxes.add(xAxis);
34 const yAxis = new NumericAxis(wasmContext, { autoRange: EAutoRange.Always });
35 sciChartSurface.yAxes.add(yAxis);
36
37 // Create some DataSeries
38 const dataSeries: XyDataSeries[] = [
39 new XyDataSeries(wasmContext, { containsNaN: false, isSorted: true }),
40 new XyDataSeries(wasmContext, { containsNaN: false, isSorted: true }),
41 new XyDataSeries(wasmContext, { containsNaN: false, isSorted: true }),
42 ];
43
44 const seriesColors = [appTheme.VividSkyBlue, appTheme.VividOrange, appTheme.VividPink];
45
46 // Create some FastLineRenderableSeries bound to each dataSeries and add to the chart
47 dataSeries.map((ds, index) => {
48 sciChartSurface.renderableSeries.add(
49 new FastLineRenderableSeries(wasmContext, {
50 dataSeries: ds,
51 strokeThickness: 2,
52 stroke: seriesColors[index],
53 })
54 );
55 });
56
57 // Add some interactivity modifiers. These are only operational when the demo is paused
58 // as interactivity conflicts with AutoRange.Always
59 sciChartSurface.chartModifiers.add(
60 new RubberBandXyZoomModifier(),
61 new MouseWheelZoomModifier(),
62 new XAxisDragModifier({ dragMode: EDragMode.Panning }),
63 new YAxisDragModifier({ dragMode: EDragMode.Panning }),
64 new ZoomExtentsModifier()
65 );
66
67 // This class generates some data for our example
68 // It generates a random walk, which is a line which increases or decreases by a random value
69 // each data-point
70 const randomWalkGenerators = [1, 2, 3].map((_) => {
71 return new RandomWalkGenerator(0);
72 });
73
74 let timerId: NodeJS.Timeout;
75
76 // Function called when the user clicks stopUpdate button
77 const stopUpdate = () => {
78 clearTimeout(timerId);
79 timerId = undefined;
80 randomWalkGenerators.forEach((rw) => rw.reset());
81 // Disable autoranging on X when the demo is paused. This allows zooming and panning
82 xAxis.autoRange = EAutoRange.Once;
83 };
84
85 // Function called when the user clicks startUpdate button
86 const startUpdate = () => {
87 // // tslint:disable-next-line:no-debugger
88 // debugger;
89 if (timerId) {
90 stopUpdate();
91 }
92 const updateFunc = () => {
93 if (dataSeries[0].count() >= maxPoints) {
94 stopUpdate();
95 return;
96 }
97
98 randomWalkGenerators.forEach((randomWalk, index) => {
99 // Get the next N random walk x,y values
100 const { xValues, yValues } = randomWalk.getRandomWalkSeries(numberOfPointsPerTimerTick);
101
102 // Append these to the dataSeries. This will cause the chart to redraw
103 dataSeries[index].appendRange(xValues, yValues);
104 });
105
106 timerId = setTimeout(updateFunc, timerInterval);
107 };
108
109 // Enable autoranging on X when running the demo
110 xAxis.autoRange = EAutoRange.Always;
111
112 dataSeries.forEach((ds) => ds.clear());
113
114 timerId = setTimeout(updateFunc, timerInterval);
115 };
116
117 type TRenderStats = { numberPoints: number; fps: number };
118 type TOnRenderStatsChangeCallback = (stats: TRenderStats) => void;
119
120 let statsCallback: TOnRenderStatsChangeCallback = () => {};
121 const setStatsChangedCallback = (callback: TOnRenderStatsChangeCallback) => {
122 statsCallback = callback;
123 };
124
125 let lastRendered = Date.now();
126 sciChartSurface.renderedToDestination.subscribe(() => {
127 const currentTime = Date.now();
128 const timeDiffSeconds = new Date(currentTime - lastRendered).getTime() / 1000;
129 lastRendered = currentTime;
130 const fps = 1 / timeDiffSeconds;
131 const renderStats = {
132 numberPoints:
133 sciChartSurface.renderableSeries.size() * sciChartSurface.renderableSeries.get(0).dataSeries.count(),
134 fps,
135 };
136
137 statsCallback(renderStats);
138 });
139
140 return { wasmContext, sciChartSurface, controls: { startUpdate, stopUpdate, setStatsChangedCallback } };
141};
142This example demonstrates a high-performance, real-time chart using SciChart.js in a React environment. It is designed to dynamically append millions of data points to a line chart, showcasing smooth rendering and live performance metrics such as frame rate and data point count.
The chart is initialized asynchronously with the <SciChartReact/> component provided by the scichart-react library. The onInit callback sets up the WebGL powered SciChartSurface, two NumericAxis with auto-ranging enabled via EAutoRange.Always, and multiple line series using the FastLineRenderableSeries. Data points are continuously appended to the chart via dataSeries.appendRange() via a timer callback that simulates real-time updates. This asynchronous approach follows best practices for asynchronous initialization in React and utilizes React hooks such as useRef and useState to manage external chart control references.
The example supports real-time update capabilities by streaming data generated from a random walk algorithm. It not only renders a high volume of data points efficiently through WebGL but also allows for interactivity such as zooming and panning when the update process is paused. Using the SciChart.js high-performance charting engine, the demo optimizes rendering throughput, ensuring smooth updates even with millions of points as highlighted in the Fastest JavaScript Chart Library blog post.
Integration with React is seamlessly achieved by implementing start and stop update controls, with proper event handling facilitated through callbacks and React lifecycle events. The component cleans up resources using the onDelete callback for proper chart disposal, following React integration best practices. The use of React’s useRef hook for managing chart controls and real-time state updates ensures efficient state management and performance measurement.
Developers seeking to build similar real-time, high-performance React applications with SciChart.js are encouraged to review these techniques along with additional insights from the Creating a React Dashboard with SciChart.js guide.

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