Radar Chart

The ECharts radar chart is a two-dimensional chart that displays the comparative relationships of multivariate data. By configuring the radar and series components, a basic radar chart can be created, supporting multi-dimensional data display and comparison. Advanced styling customization includes shape divisions, axis labels, background areas, and line styles. Dynamic data update functionality makes it suitable for real-time monitoring scenarios. Interactive features such as custom tooltip boxes, legend control, and data highlighting enhance usability. Radar charts can be combined with other chart types. Performance optimization techniques include disabling interactive effects and animations, as well as progressive rendering. Practical applications include e-commerce platform product evaluation analysis, which visually presents the scores of different products across various dimensions.

Scatter Chart

ECharts scatter chart is a powerful two-dimensional data visualization tool that supports various data formats and style customizations, including two-dimensional arrays, object formats, and styled data points. It enables multi-series scatter plots to display different data groups and achieves bubble chart effects through symbolSize. For large-scale data, it offers optimization solutions such as large mode and chunked rendering. The scatter chart features rich interactive functionalities like tooltips and data zooming, can be combined with other chart types, and supports custom shapes and animations. It can also be displayed in geographic coordinate systems and 3D spaces. Through visualMap, it maps data to visual elements and provides special effects like ripple animation to enhance visual impact.

Pie Chart

ECharts offers a variety of pie chart implementations, from basic pie charts to complex variants, all easily achievable. A basic pie chart only requires data and simple configuration. A doughnut chart is created by setting a radius array to form a ring effect. A Nightingale rose chart displays data by adjusting both radius and angle. Multi-level nested pie charts are ideal for hierarchical data, achieved by defining different radius ranges. ECharts allows deep customization of pie chart styles, including sector colors, label styles, and guide line styles. Pie charts support rich interactive features like multi-selection, hover highlighting, and dynamic data updates. For large datasets, performance can be improved through optimized configurations. Pie charts can also be combined with other chart types to meet diverse data visualization needs.

Bar Chart

ECharts offers a variety of bar chart implementations, including basic bar charts, stacked bar charts, and horizontal bar charts. A basic bar chart can be generated simply by configuring the x-axis and y-axis data, with customization options for column colors, borders, and rounded corners through itemStyle. Multi-series bar charts can display multiple data series simultaneously, while stacked bar charts are ideal for showing part-to-whole relationships. Horizontal bar charts are suitable for cases with long category names. ECharts supports animation effects, label display, and interactive features such as tooltips and data zoom. Responsive design ensures the charts display correctly across different screen sizes. Bar charts can also be combined with other chart types like line charts. For large datasets, optimization solutions such as enabling big data mode and adding data zoom are provided. Advanced applications include special bar chart forms like waterfall charts.

Line Chart

Line charts are a common chart type in data visualization for displaying trend changes. ECharts provides rich configuration options to implement basic line charts, requiring only x-axis and y-axis data. Multi-series line charts can simultaneously display multiple data series for comparison. Through style customization, you can adjust line colors, widths, data point shapes, sizes, and fill area effects. It supports two presentation forms: smooth curves and stepped lines. The dynamic data update feature enables real-time data display. For large data volume scenarios, optimization techniques like progressive rendering are provided. Interactive features include crosshair cursors, tooltips, data zooming, mark points, and reference lines. Multi-axis line charts can display data with different dimensions, while timeline line charts are specifically designed for time series data visualization.

Safety configuration precautions

In practical applications, ECharts as a data visualization library must prioritize security configurations to prevent data leaks and attacks. Data sources must be strictly validated to avoid malicious injection. XSS protection requires escaping user input content. Cross-origin resource loading must ensure trusted sources. Sensitive information should be desensitized. Chart functionalities should be dynamically controlled based on user permissions. Configuration items need dynamic validation to ensure effectiveness. Event handling must be securely encapsulated to prevent malicious callbacks. Performance optimization should balance security and efficiency while avoiding risky operations. Third-party plugins must be managed, loading only trusted sources. Through these measures, the security of ECharts applications can be effectively enhanced.

Performance optimization configuration

Performance optimization in ECharts involves multiple layers. At the data level, techniques like data sampling and chunked loading can be employed to handle large-scale datasets. Rendering configuration optimization includes adjusting animation strategies and adopting hybrid rendering modes. Streamlining visual elements improves efficiency through visual mapping and label display strategies. Enhancing interaction performance requires attention to debounce handling and region selection optimization. Memory management techniques involve timely instance cleanup and shared datasets. Advanced optimization techniques encompass WebWorker computation and GPU acceleration. Mobile-specific optimizations must consider gesture interactions and responsive degradation. Server-side rendering solutions include pre-generating static content and incremental rendering protocols. These methods collectively enhance ECharts' rendering efficiency and user experience in big data scenarios.

Debugging tools and techniques

During the development of ECharts as a data visualization library, efficient debugging tools and techniques are essential. Browser developer tools serve as the foundation, allowing inspection of DOM structures, logging outputs, and monitoring data requests. The Sources panel enables setting breakpoints for step-by-step code execution. The `getOption` method retrieves the current chart configuration for comparison with expectations, while `getDataURL` exports images to verify rendering results. An error event listener mechanism captures and handles exceptions, and asynchronous data loading requires handling potential failures. Performance analysis tools record the rendering process, focusing on Scripting and Rendering times. Responsive debugging involves listening for window changes and printing container dimensions. Data validation involves step-by-step checks of raw data and transformation results. Visual debugging includes temporarily modifying styles to highlight elements, while animation debugging adjusts durations and uses event hooks. Server-side rendering compares differences between renderers. Memory leak detection involves periodic checks of usage. Cross-browser debugging tests feature compatibility. Theme debugging involves registering and applying custom themes. Accessibility debugging checks ARIA attributes to ensure proper application.

Cross-platform adaptation solution

ECharts, as a data visualization library, faces core challenges in cross-platform adaptation, including differences in screen size and resolution, diverse interaction methods, and performance variations. The DPI difference between mobile and desktop devices can reach up to 300. Touch devices and mouse devices handle hover states differently, while Retina screens require double-pixel rendering. Low-end phones may only support 60 CSS features. Responsive layouts are achieved by listening to resize events and media queries. Pixel density adaptation uses devicePixelRatio detection for dynamic configuration adjustments. Conditional logic enables differentiated touch event handling. Performance optimization involves adjusting rendering strategies for different devices. Multi-framework integration provides React and Vue examples. Server-side rendering requires replacing the default canvas creation method. WeChat Mini Programs need to handle unique configurations. Accessibility support is enhanced through ARIA configurations.

Responsive design configuration

The responsive design in ECharts achieves chart self-adaptation by monitoring container size changes through the `resize` method. The core principles include using the ResizeObserver API to precisely detect container changes and trigger redraws. Dynamic configuration strategies offer two approaches: breakpoint-based adaptation and proportional scaling. The former sets different size configurations based on media queries, while the latter calculates parameters proportionally according to container dimensions. Component-level responsiveness handles dynamic adjustments for elements like legend positioning and axis labels. Advanced modes support chart type switching and data aggregation strategy optimization. Mobile devices require special handling for touch events and font sizes. Performance optimization employs debounce techniques and partial update strategies. Debugging can utilize viewport simulation tools and responsive logging to verify effects. This comprehensive solution covers the entire responsive development workflow, from basic implementation to advanced optimization.