ECharts chart optimization: how to improve rendering performance

ECharts chart optimization: how to improve rendering performance

Introduction:
ECharts is a powerful data visualization library that can help developers create a variety of beautiful chart. However, when the amount of data is huge, chart rendering performance can become a challenge. This article will help you improve the rendering performance of ECharts charts by providing specific code examples and introducing some optimization techniques.

1. Data processing optimization:

1. Data filtering: If the amount of data in the chart is too large, you can filter the data to display only the necessary data. For example, according to the needs of users, conditional restrictions can be added to data queries to only obtain the data that needs to be displayed and reduce the amount of data.
2. Data aggregation: When the amount of data is very large, the amount of data can be reduced through data aggregation. For example, you can use aggregate functions in your database to aggregate large amounts of data into summary data, and then display the summary data in a chart.

2. Chart configuration optimization:

1. Chart type selection: In ECharts, there are many different chart types to choose from. Different charts process data and render effects differently. Using the appropriate chart type can improve rendering performance. For example, if the data is large and discrete, you might choose a scatter plot instead of a line chart.
2. Chart style simplification: In charts, unnecessary style settings may cause rendering performance to decrease. You can appropriately reduce or simplify the style settings of the chart and retain only the necessary settings to improve performance.

3. Event processing optimization:

1. Lazy loading: For some events that require a lot of calculations or IO operations, lazy loading can be used to avoid blocking the rendering of the chart. process. For example, load only necessary events when the chart is initialized, and then use the setTimeout function to delay loading other events.
2. Event delegation: For some highly repetitive events, event delegation can be used. For example, if there are a large number of elements in the chart that need to be bound to click events, the event can be bound to the parent element and processed through the event bubbling mechanism to reduce the number of event bindings.

4. Performance testing and monitoring:

1. Performance testing: During the development process, you can use performance testing tools to evaluate the rendering performance of charts. For example, you can use the developer tools that come with the Chrome browser to analyze, identify performance bottlenecks and optimize them.
2. Performance monitoring: After going online, you can use the performance monitoring tool to monitor the rendering performance of the chart in real time. For example, you can use Alibaba's front-end performance monitoring platform Web Application Quality and Performance Monitoring Service (APM) for monitoring to discover and solve performance problems in a timely manner.

Conclusion:
Through the above optimization techniques, we can improve the rendering performance of ECharts charts and make them more efficient when processing large amounts of data. However, appropriate optimization strategies need to be selected based on specific business scenarios and needs. In addition, the optimization process also needs to pay attention to balance, and over-optimization cannot lead to a decrease in code readability and maintainability. I hope the optimization tips provided in this article can help everyone improve the rendering performance of ECharts charts.

Code Example:
The following is a simple example that demonstrates how to improve the rendering performance of ECharts charts through data aggregation and chart style simplification.

// 原始数据
let rawData = [
  { date: '2021-01-01', value: 100 },
  { date: '2021-01-02', value: 200 },
  // ... 其他大量数据
];

// 数据聚合
let aggregatedData = [];
for (let i = 0; i < rawData.length; i += 10) {
  let sum = 0;
  for (let j = 0; j < 10; j++) {
    if (i + j < rawData.length) {
      sum += rawData[i + j].value;
    }
  }
  let average = sum / 10;
  aggregatedData.push({ date: rawData[i].date, value: average });
}

// 图表配置
let chartOption = {
  title: {},
  tooltip: {},
  xAxis: { type: 'category' },
  yAxis: { type: 'value' },
  series: [{
    type: 'line',
    data: aggregatedData,
  }]
};

// 渲染图表
let chart = echarts.init(document.getElementById('chart'));
chart.setOption(chartOption);

In the above example, we reduced the amount of data by aggregating a large amount of raw data into smaller aggregated data. At the same time, we have also simplified the chart style settings, retaining only the necessary configurations and improving rendering performance. Through these optimizations, we can improve the rendering efficiency of charts when processing large amounts of data.

References:

• ECharts documentation: https://echarts.apache.org/zh/index.html
• Chrome developer tools: https:/ /developers.google.com/web/tools/chrome-devtools
• Alibaba Web Application Quality and Performance Monitoring Service (APM): https://www.aliyun.com/product/apm

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