Custom extension development

The development of ECharts extensions primarily revolves around two core concepts: custom series and custom components, implemented through a plugin mechanism. Registering a custom series is suitable for creating entirely new chart types, requiring the implementation of a series definition object that includes methods for initialization, data mapping, and rendering logic. Custom components are used to extend auxiliary elements, incorporating lifecycle and rendering logic. Advanced rendering can leverage WebGL to achieve high-performance drawing, including complex effects like particle systems. Interaction extensions require integrating the event system and action mechanism to implement features such as drag-and-drop and connecting lines. Developers can intervene in data processing and graphic rendering stages by overriding specific methods to fulfill diverse visualization requirements.

Real-time data update strategy

ECharts, as a data visualization library, offers multiple dynamic data update strategies, including incremental updates, full replacement, and WebSocket real-time push. Incremental updates are implemented via the `appendData` method, suitable for high-frequency scenarios, but require attention to data volume thresholds. Full replacement is ideal for structural data changes and can be optimized with debouncing for performance. WebSocket is best for real-time demands like financial market data, requiring exception handling and reconnection. Performance optimization techniques include data sampling, progressive rendering, and WebWorker for big data processing. Dynamic interactions support features like map drill-down and brush-selection linkage. Server-side rendering generates SVG via Node.js, while cross-framework integration provides React and Vue examples. Special scenarios involve multi-chart synchronization and large dataset chunked loading. Choosing the right update strategy enhances chart performance and user experience.

Big data visualization solution

ECharts, as an open-source visualization library, has become the preferred tool for big data visualization due to its rich chart types and powerful interactive capabilities. It supports over 20 basic charts, including line charts, bar charts, scatter plots, as well as geographic and special coordinate systems. Its progressive rendering technology enables smooth display of tens of millions of data points, with built-in data aggregation to optimize performance. It offers big data optimization strategies such as data sampling, WebGL rendering, and chunked loading. The library supports dynamic interaction designs like data brushing, area zooming, and value range roaming. For geographic data visualization, it can render world maps, China maps, and administrative divisions at various levels, along with heatmaps and migration diagrams. Through the `dataset` component and `visualMap`, it enables multidimensional data analysis with parallel coordinates, radar charts, and sunburst diagrams. ECharts also supports real-time data updates and dynamic incremental data addition. It provides a theme extension mechanism for customizing chart styles, mobile adaptation, and responsive design. Server-side rendering can generate static images, while its extensible system allows for custom chart types. It integrates deeply with front-end frameworks like React and Vue.

Performance monitoring and optimization

The complexity of modern web applications continues to increase, and performance issues directly impact user experience. As a data visualization tool, ECharts particularly requires attention to rendering performance in large-data scenarios. By monitoring key metrics, issues such as lag and memory leaks can be promptly identified and resolved. This article details monitoring methods for critical performance indicators like initial rendering time, frame rate, and memory usage, as well as solutions for common problems such as rendering lag with large datasets, animation performance optimization, and memory leak handling. It also provides advanced optimization techniques like WebWorker data processing, Canvas rendering optimization, and on-demand rendering. Additionally, it covers tool integration methods, such as integrating with Sentry and custom performance dashboards, and optimization solutions for practical cases like stock candlestick charts and geographic heatmaps. Finally, it recommends establishing automated monitoring processes to achieve continuous performance monitoring.

Export and print charts

ECharts provides comprehensive support for chart export and printing, enabling the export of charts to various image formats such as PNG, JPEG, and SVG through the `getDataURL` method. Developers can adjust parameters like pixel ratio and background color to enhance export quality. For scenarios involving multiple charts, `Promise.all` can be used for batch processing. PDF export requires integration with third-party libraries like jsPDF. Browser printing functionality can be achieved by creating a new window and injecting the chart HTML. Advanced features include excluding specific components, server-side export performance optimization, dynamic watermarking, and more. ECharts also supports pre-export data processing, multi-theme exports, and other customizable requirements to meet diverse data visualization sharing and archiving needs.

Server-side rendering solution

The ECharts server-side rendering solution simulates a browser environment using node-canvas or jsdom to generate static chart content in Node.js. The core approach includes Canvas rendering for bitmap output and SVG rendering for vector output, each with distinct advantages and disadvantages in file size, rendering performance, and scaling effects. Dynamic data rendering is achieved by asynchronously fetching data and updating chart configurations. For high-concurrency scenarios, a load-balancing and rendering cluster architecture is recommended, combined with Redis caching to enhance performance. Error handling should encompass parameter validation, rendering fault tolerance, and performance monitoring. Practical applications can embed charts into PDF reports or email templates. Performance optimization involves memory management and cluster stress testing. Version compatibility requires adapting to different ECharts features, while security measures should limit data volume and filter hazardous attributes.

SVG versus Canvas rendering selection

SVG and Canvas are two commonly used graphic rendering technologies in ECharts, each with distinct characteristics. SVG, based on XML, describes graphics through DOM nodes, while Canvas manipulates pixels via JavaScript. ECharts defaults to Canvas but also supports SVG rendering. Canvas excels in rendering large numbers of elements and dynamic updates, whereas SVG may suffer performance issues due to excessive DOM nodes. SVG supports native DOM events and CSS styling, while Canvas relies on event systems and hard-coded styles. SVG automatically adapts to resolution, whereas Canvas requires manual handling for high-DPI devices. On mobile devices, Canvas consumes less memory. ECharts supports a hybrid rendering strategy, combining the strengths of both. SVG is suitable for static charts and vector exports, while Canvas is ideal for large-scale real-time visualization and WebGL acceleration. Developers should choose the appropriate rendering method based on specific needs to optimize chart performance and user experience.

Custom graphic drawing

ECharts provides powerful custom graphics rendering capabilities, supporting the creation of complex visual effects through Canvas or SVG drawing APIs. Core graphic elements include basic shapes such as rectangles, circles, and paths, as well as composite graphics. Each type has specific configuration attributes, such as the rounded corner radius of rectangles, the radius of circles, or the SVG string of paths. The `group` type allows multiple graphics to be combined into composite shapes. Graphic elements support animations and interactive event handling and can be positioned relative to the coordinate system. Using the `path` type, arbitrary shapes can be created, which can also be linked to data. Performance optimization techniques include batch rendering, rational use of caching, and layered rendering. Practical applications include custom markers and dynamic progress indicators. For special requirements, the ECharts renderer can be extended to register custom graphic types.

Methods for implementing 3D charts

3D charts display more complex data relationships by adding a Z-axis dimension to a two-dimensional foundation. ECharts achieves WebGL rendering through GL extensions, supporting various 3D chart types, with the core lying in the stereoscopic processing of coordinate systems and visual mapping. Data points must include numerical values for x, y, and z dimensions. A 3D Cartesian coordinate system configures the container via grid3D, paired with xAxis3D, yAxis3D, and zAxis3D to define the axes. The polar coordinate system’s 3D variant combines angleAxis and radiusAxis with the height dimension. Common 3D charts include bar charts and surface plots, where visual styles can be enhanced with materials and lighting. Dynamic effects are achieved through animation and emphasis. For large datasets, optimizations involve progressive rendering and WebGL parameter tuning. Mixed 2D and 3D combined coordinate systems are possible. Geographic 3D extensions are implemented using the geo3D component for 3D maps. Performance monitoring includes memory management and rendering frame rate detection.

Multi-chart linkage implementation

Multi-chart linkage refers to the effect where multiple charts achieve synchronized data updates or view changes through interactive events. ECharts provides comprehensive API support to implement this interaction mode, primarily through event listening, triggering API calls, and data mapping. Common linkage scenarios include mouse hover linkage, data filtering linkage, and axis linkage. Advanced techniques involve cross-chart data mapping, multi-chart group linkage, and performance optimization strategies. Practical application cases demonstrate the implementation of multi-chart linkage in sales data analysis dashboards and stock multi-dimensional analysis systems. The article also provides debugging and troubleshooting methods, as well as extended application scenarios such as integration with map components and third-party libraries for linkage.