flex 内存回收经典文章(2)

Caching

Let’s assume you know in advance how many renderers are needed. One way to solve the memory leak is to create a cache of renderers at startup.

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private var cache:Array = new Array();
 
private function initRenderers():void {
    for (var i:int = 0; i < 200; i++) {
	renderers.push(new MyRenderer());
    }
}

We can then modify our loadData method like this:

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private function loadData():void {
    container.removeAllChildren();
    var array:Array = getData();
 
    for (var i:int = 0; i < array.length; i++) {
	var rend:MyRenderer = cache[i];
	rend.data = array[i];
  	container.addChild(rend);
	i++;
    } 
}

As you can see in the code we don’t create new renderers but we look for one in the cache. There are cases when you don’t know in advance which data are returned from the server and then you don’t know how many rendereres you need. In this case you need a dynamic cache.

Dynamic cache

Dynamic caching is based on an elastic mechanism. You have a place where you can look for a renderer: if there is one in the cache, that is returned, otherwise a new one is created temporarily. Better to see some code.

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public class MyRendererCache extends Object {
 
    private static var cache : ArrayCollection = new ArrayCollection();
 
	public function MyRendererCache() {
	    super();
            for ( var x : Number = 0; x < 20; x ++ ) {
		cache.addItem( new MyRenderer() );
	    }
	}
 
	public static function getRenderer() : MyRenderer {
	    var renderer : MyRenderer;
 
	    if ( cache.length <= 0 ) {
		renderer = new MyRenderer();
	    } else { 
		renderer = cache.removeItemAt( 0 ) as MyRenderer;
	    }
 
	    return renderer;
        }
 
        public static function setRenderer( renderer : MyRenderer ) : void {
	    cache.addItem( renderer );
        }		
}

In the constructor you populate the cache with the minimu number of renderers, say 20 (lines 7-9). The cache has two static methods, getRenderer and setRenderer. The first is used to obtain a renderer, the second to give it back when done. If you look and lines 15-16 the cache returns a new renderer when the cache is empty. This way the number of renderers in memory can grow beyond the minimum number set in the constructor, but just temporarily, since the GC will delete them when not referenced anymore.
An important issue is related to the setRenderer. When you don’t need a renderer anymore you have to return it back to the cache, otherwise we fall again in a memory leak as explained above. To achieve this we exploit the remove event of the renderer. The remove event is triggerer whenever a UI element is removed from the display list. For example when we call removeAllChildren() such event is triggered for each renderer.
We can modify the renderer like this:

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<mx:VBox xmlns:mx="http://www.adobe.com/2006/mxml" 
    borderStyle="solid" width="200" 
    remove="onRemove()"
    >
 
    private function onRemove():void {
	MyRendererCache.setRenderer(this);
    }
....
</VBox>

If you now run the profiler you will notice that memory grows until a given point and then keeps stable as in the figure below.

Stable Memory Consumption

Congratulations! You have solved the memory leak!

Suggesting the GC

Besides favoring the job of GC Adobe has allowed programmers to suggest the intervention of the GC. The command is in the flash.system package and it is System.gc(). By documentation this “Forces the garbage collection process” but in my experience it is just a vague suggestion of intervention. It can solve some situation, so it is worth trying it at the beginning, when you need a quick way to save some memory.




Full screen slides available here.

The source code of this post is available under the new BSD license.

References

内容概要:本文提出了一种基于融合鱼鹰算法和柯西变异的改进麻雀优化算法(OCSSA),用于优化变分模态分解(VMD)的参数,进而结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)构建OCSSA-VMD-CNN-BILSTM模型,实现对轴承故障的高【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)精度诊断。研究采用西储大学公开的轴承故障数据集进行实验验证,通过优化VMD的模态数和惩罚因子,有效提升了信号分解的准确性与稳定性,随后利用CNN提取故障特征,BiLSTM捕捉时间序列的深层依赖关系,最终实现故障类型的智能识别。该方法在提升故障诊断精度与鲁棒性方面表现出优越性能。; 适合人群:具备一定信号处理、机器学习基础,从事机械故障诊断、智能运维、工业大数据分析等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①解决传统VMD参数依赖人工经验选取的问题,实现参数自适应优化;②提升复杂工况下滚动轴承早期故障的识别准确率;③为智能制造与预测性维护提供可靠的技术支持。; 阅读建议:建议读者结合Matlab代码实现过程,深入理解OCSSA优化机制、VMD信号分解流程以及CNN-BiLSTM网络架构的设计逻辑,重点关注参数优化与故障分类的联动关系,并可通过更换数据集进一步验证模型泛化能力。
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