Android 虚拟机中的内存分配与 OOM 问题

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背景知识

Android中每个App默认情况下是运行在一个独立进程中的, 而这个独立进程正是从Zygote孵化出来的VM进程， 也就是说, 也就是说每个Android APP在运行时会启动一个Java虚拟机。

并且系统会给它分配固定的内存空间(手机厂商会根据手机的配置情况来对其进行调整)。

一、Android VM的内存空间

Android是一个多任务系统, 为了保证多任务的运行, Android给每个App可使用的Heap大小设定了一个限定值.

这个值是系统设置的prop值, 保存在System/build.prop文件中. 一般国内的手机厂商都会做修改, 根据手机配置不同而不同, 可以直接打开查看与修改。

其中和虚拟机内存相关的主要有以下三个：

  1. dalvik.vm.heapstartsize

– App启动后，系统分配给它的Heap初始大小，随着App使用可增加。 2. dalvik.vm.heapgrowthlimit

– 默认情况下, App可使用的Heap的最大值, 超过这个值就会产生OOM. 3. dalvik.vm.heapsize

– 如果App的manifest文件中配置了largeHeap属性, 那么App可使用的Heap的最大值为此项设定值。

  <application
    android:largeHeap="true">
    ...
</application>

所以对于同一个手机，不开启largeHeap属性时与多进程时，每个APP的虚拟机分配的内存的上限都是heapgrowthlimit。

1.查看内存的API

Android在ActivityManager类中提供了API可以运行时获取这些属性值，如下：

//ActivityManager的getMemoryClass()获得内用正常情况下内存的大小，即heapgrowthlimit的值
//ActivityManager的getLargeMemoryClass()可以获得开启largeHeap最大的内存大小，即heapsize的指
ActivityManager activityManager = (ActivityManager)context.getSystemService(Context.ACTIVITY_SERVICE);
activityManager.getMemoryClass();
activityManager.getLargeMemoryClass();

二、Android VM内存分配流程

虚拟机分配内存的具体源码可以AOSP的Heap.cpp文件中查看：

/* Try as hard as possible to allocate some memory.
 */
static void *tryMalloc(size_t size)
{
    void *ptr;
//TODO: figure out better heuristics
//    There will be a lot of churn if someone allocates a bunch of
//    big objects in a row, and we hit the frag case each time.
//    A full GC for each.
//    Maybe we grow the heap in bigger leaps
//    Maybe we skip the GC if the size is large and we did one recently
//      (number of allocations ago) (watch for thread effects)
//    DeflateTest allocs a bunch of ~128k buffers w/in 0-5 allocs of each other
//      (or, at least, there are only 0-5 objects swept each time)
    ptr = dvmHeapSourceAlloc(size);
    if (ptr != NULL) {
        return ptr;
    }
    /*
     * The allocation failed.  If the GC is running, block until it
     * completes and retry.
     */
    if (gDvm.gcHeap->gcRunning) {
        /*
         * The GC is concurrently tracing the heap.  Release the heap
         * lock, wait for the GC to complete, and retrying allocating.
         */
        dvmWaitForConcurrentGcToComplete();
    } else {
      /*
       * Try a foreground GC since a concurrent GC is not currently running.
       */
      gcForMalloc(false);
    }
    ptr = dvmHeapSourceAlloc(size);
    if (ptr != NULL) {
        return ptr;
    }
    /* Even that didn't work;  this is an exceptional state.
     * Try harder, growing the heap if necessary.
     */
    ptr = dvmHeapSourceAllocAndGrow(size);
    if (ptr != NULL) {
        size_t newHeapSize;
        newHeapSize = dvmHeapSourceGetIdealFootprint();
//TODO: may want to grow a little bit more so that the amount of free
//      space is equal to the old free space + the utilization slop for
//      the new allocation.
        LOGI_HEAP("Grow heap (frag case) to "
                "%zu.%03zuMB for %zu-byte allocation",
                FRACTIONAL_MB(newHeapSize), size);
        return ptr;
    }
    /* Most allocations should have succeeded by now, so the heap
     * is really full, really fragmented, or the requested size is
     * really big.  Do another GC, collecting SoftReferences this
     * time.  The VM spec requires that all SoftReferences have
     * been collected and cleared before throwing an OOME.
     */
//TODO: wait for the finalizers from the previous GC to finish
    LOGI_HEAP("Forcing collection of SoftReferences for %zu-byte allocation",
            size);
    gcForMalloc(true);
    ptr = dvmHeapSourceAllocAndGrow(size);
    if (ptr != NULL) {
        return ptr;
    }
//TODO: maybe wait for finalizers and try one last time
    LOGE_HEAP("Out of memory on a %zd-byte allocation.", size);
//TODO: tell the HeapSource to dump its state
    dvmDumpThread(dvmThreadSelf(), false);
    return NULL;
}

具体流程如下：

小结

所以产生OOM时，一定是java的堆中 已有的内存 + 申请的内存 >= heapgrowthlimit导致的，不会因为手机目前物理内存是否紧张而改变 - 当物理内存非常紧张时系统会通过LowMemory Killer杀掉一些低优先级的进程。

相应的，物理内存非常充足的情况也会有OOM的情况发生。

三、出现OOM的建议解决方案

当APP出现OOM时，建议可以从以下两个方向来处理：

1 . 排查内存泄露问题

2 . 内存优化

按照谷歌在youtube上发布的性能优化典范之内存篇，优化各功能的内存，或可参照**胡凯的总结**。大致有以下这些，具体请参见原文：

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