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Overview

Cristalyse is designed to handle complex charts with large datasets efficiently. Performance optimization ensures smooth rendering, quick interactions, and responsive updates.

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Rendering Performance

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Efficient Data Handling

Reduce data points for visualization without losing insights: 
List<Map<String, dynamic>> downsampleData(List<Map<String, dynamic>> data, int targetCount) {
  final step = data.length / targetCount;
  return List.generate(targetCount, (i) => data[(i * step).floor()]);
}

CristalyseChart()
  .data(downsampleData(originalData, 1000))
  .mapping(x: 'x', y: 'y')
  .geomLine()
  .build()

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Asynchronous Operations

Load data and build charts asynchronously: 
Future<void> loadDataAndBuild() async {
  final data = await fetchDataAsync();
  setState(() {
    CristalyseChart()
      .data(data)
      .mapping(x: 'time', y: 'value')
      .geomLine()
      .build();
  });
}

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Memory Management

Use efficient data structures and dispose of unnecessary elements: 
class EfficientChart extends StatefulWidget {
  @override
  _EfficientChartState createState() => _EfficientChartState();
}

class _EfficientChartState extends State<EfficientChart> with AutomaticKeepAliveClientMixin {
  List<Map<String, dynamic>>? cachedData;

  @override
  void dispose() {
    cachedData = null; // Release memory
    super.dispose();
  }

  @override
  Widget build(BuildContext context) {
    super.build(context);
    return CristalyseChart()
      .data(cachedData ?? [] )
      .mapping(x: 'x', y: 'value')
      .geomBar()
      .build();
  }

  @override
  bool get wantKeepAlive => true;
}

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Interactive Performance

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Debouncing Interactions

Control frequency of interaction events for better performance: 
CristalyseChart()
  .data(data)
  .mapping(x: 'x', y: 'y')
  .geomPoint()
  .interaction(
    hover: HoverConfig(
      debounce: const Duration(milliseconds: 50),
      onHover: (point) => {
        if (point != null) print('Hovered ${point.getDisplayValue('x')}')
      },
    ),
  )
  .build()

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Throttling Panning

Smooth pan interactions with controlled update frequency: 
CristalyseChart()
  .data(data)
  .mapping(x: 'time', y: 'metric')
  .geomLine()
  .interaction(
    pan: PanConfig(
      enabled: true,
      throttle: const Duration(milliseconds: 32), // 30 FPS
      onPanUpdate: (info) => handleUpdate(info.visibleMinX, info.visibleMaxX),
    ),
  )
  .build()

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UI Performance

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Lazily Build Complex Widgets

Render parts of your UI only when needed: 
List<Widget> buildWidgets() {
  return List.generate(100, (index) => LazyWidget(data: data[index]));
}

ListView(
  children: buildWidgets(),
)

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Render Performance Insights

Monitor and optimize paint times: 
class RenderProfiler extends StatefulWidget {
  @override
  _RenderProfilerState createState() => _RenderProfilerState();
}

class _RenderProfilerState extends State<RenderProfiler> {
  DateTime? _lastPaint;

  @override
  Widget build(BuildContext context) {
    return RepaintBoundary(
      child: CristalyseChart()
        .data(data)
        .mapping(x: 'time', y: 'value')
        .geomArea()
        .build(),
      onRepaint: () {
        final now = DateTime.now();
        if (_lastPaint != null) {
          print('Time since last repaint: ${now.difference(_lastPaint!).inMilliseconds}ms');
        }
        _lastPaint = now;
      },
    );
  }
}

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Performance Considerations

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Key Considerations

  • Use async/await for data loading to prevent UI blocking.
  • Downsample large datasets to improve responsiveness.
  • Profile interaction and rendering performance using profiling tools.

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Performance Techniques

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Combined Techniques

Integrate various performance methods to maximize efficiency: 
CristalyseChart()
  .data(downsampleData(originalData, 2000))
  .mapping(x: 'timestamp', y: 'value')
  .geomLine()
  .interaction(
    tooltip: TooltipConfig(
      builder: (point) => Text('${point.getDisplayValue('value')}'),
      showDelay: Duration.zero, // Instantaneous
    ),
    pan: PanConfig(
      enabled: true,
      throttle: Duration(milliseconds: 16),
    ),
  )
  .build()

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Animation Performance

Handle animations efficiently for better visual fluidity: 
CristalyseChart()
  .data(data)
  .mapping(x: 'x', y: 'y')
  .geomLine()
  .animate(
    duration: const Duration(milliseconds: 1500),
    curve: Curves.easeInOut,
  )
  .interaction(
    hover: HoverConfig(
      debounce: const Duration(milliseconds: 10),
    ),
  )
  .build()

Real-Time Updates

Efficiently handle continuous real-time data streams.

Interactive Dashboards

Develop fluid, responsive dashboards with fast interactions.

Complex Scenarios

Tackle interdependent charts with shared data sources.

Minimal Resource Usage

Achieve smooth performance with minimal computing resources.

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Next Steps

Scales

Master performance techniques related to scaling algorithms.

Export

Optimize chart exporting for better performance on large datasets.

Animations

Combine performance optimization with elegant animations.

Data Mapping

Efficiently map data fields to maximize responsiveness.

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Advanced Tools

Utilize Flutter’s build-in performance and rendering tools:
  • DevTools: An advanced suite to debug and analyze UI performance.
  • Flame Graphs: Visualize rendering performance and identify bottlenecks.
  • Repaint Rainbow: Find unnecessary paint calls and optimize rendering.
Explore these tools to gain deeper insights into app performance and enhance the efficiency of your charts. By mastering these tools and techniques, you ensure your Cristalyse visualizations remain engaging, reliable, and performant even as complexity increases. Explore these tools to increase your knowledge and efficiency when building high-performance visualizations with Cristalyse.