接下来看看CheckGCForNative函数内部的逻辑。

首先计算当前native内存的总大小，然后计算当前内存大小和阈值之间的比值，如果比值≥1，则请求一次新的GC。

art/runtime/gc/heap.cc

inline void Heap::CheckGCForNative(Thread* self) {
bool is_gc_concurrent = IsGcConcurrent();
size_t current_native_bytes = GetNativeBytes(); <================获取native内存的总大小
float gc_urgency = NativeMemoryOverTarget(current_native_bytes, is_gc_concurrent); <============计算当前内存大小和阈值之间的比值，大于等于1则表明需要一次新的GC
if (UNLIKELY(gc_urgency >= 1.0)) {
if (is_gc_concurrent) {
RequestConcurrentGC(self, kGcCauseForNativeAlloc, /force_full=/true); <=================请求一次新的GC
if (gc_urgency > kStopForNativeFactor
&& current_native_bytes > stop_for_native_allocs_) {
// We’re in danger of running out of memory due to rampant native allocation.
if (VLOG_IS_ON(heap) || VLOG_IS_ON(startup)) {
LOG(INFO) << "Stopping for native allocation, urgency: " << gc_urgency;
}
WaitForGcToComplete(kGcCauseForNativeAlloc, self);
}
} else {
CollectGarbageInternal(NonStickyGcType(), kGcCauseForNativeAlloc, false);
}
}
}

获取当前native内存的总大小需要调用GetNativeBytes函数。其内部统计也分为两部分，一部分是通过mallinfo获取的当前malloc的总大小。由于系统有专门的API获取这个信息，所以在NativeAllocationRegistry.registerNativeAllocation的时候不需要专门去存储单次malloc的大小。另一部分是native_bytes_registered_字段记录的所有注册过的mmap大小。二者相加，基本上反映了当前进程native内存的整体消耗。

art/runtime/gc/heap.cc

size_t Heap::GetNativeBytes() {
size_t malloc_bytes;
#if defined(BIONIC) || defined(GLIBC)
IF_GLIBC(size_t mmapped_bytes;)
struct mallinfo mi = mallinfo();
// In spite of the documentation, the jemalloc version of this call seems to do what we want,
// and it is thread-safe.
if (sizeof(size_t) > sizeof(mi.uordblks) && sizeof(size_t) > sizeof(mi.hblkhd)) {
// Shouldn’t happen, but glibc declares uordblks as int.
// Avoiding sign extension gets us correct behavior for another 2 GB.
malloc_bytes = (unsigned int)mi.uordblks;
IF_GLIBC(mmapped_bytes = (unsigned int)mi.hblkhd;)
} else {
malloc_bytes = mi.uordblks;
IF_GLIBC(mmapped_bytes = mi.hblkhd;)
}
// From the spec, it appeared mmapped_bytes <= malloc_bytes. Reality was sometimes
// dramatically different. (b/119580449 was an early bug.) If so, we try to fudge it.
// However, malloc implementations seem to interpret hblkhd differently, namely as
// mapped blocks backing the entire heap (e.g. jemalloc) vs. large objects directly
// allocated via mmap (e.g. glibc). Thus we now only do this for glibc, where it
// previously helped, and which appears to use a reading of the spec compatible
// with our adjustment.
#if defined(GLIBC)
if (mmapped_bytes > malloc_bytes) {
malloc_bytes = mmapped_bytes;
}
#endif // GLIBC
#else // Neither Bionic nor Glibc
// We should hit this case only in contexts in which GC triggering is not critical. Effectively
// disable GC triggering based on malloc().
malloc_bytes = 1000;
#endif
return malloc_bytes + native_bytes_registered_.load(std::memory_order_relaxed);
// An alternative would be to get RSS from /proc/self/statm. Empirically, that’s no
// more expensive, and it would allow us to count memory allocated by means other than malloc.
// However it would change as pages are unmapped and remapped due to memory pressure, among
// other things. It seems risky to trigger GCs as a result of such changes.
}

得到当前进程native内存的总大小之后，便需要抉择是否需要一次新的GC。

决策的过程如下，源码下面是详细解释。

art/runtime/gc/heap.cc

// Return the ratio of the weighted native + java allocated bytes to its target value.
// A return value > 1.0 means we should collect. Significantly larger values mean we’re falling
// behind.
inline float Heap::NativeMemoryOverTarget(size_t current_native_bytes, bool is_gc_concurrent) {
// Collection check for native allocation. Does not enforce Java heap bounds.
// With adj_start_bytes defined below, effectively checks
// + c1* + c2*<new native allocd) >= adj_start_bytes,
// where c3 > 1, and currently c1 and c2 are 1 divided by the values defined above.
size_t old_native_bytes = old_native_bytes_allocated_.load(std::memory_order_relaxed);
if (old_native_bytes > current_native_bytes) {
// Net decrease; skip the check, but update old value.
// It’s OK to lose an update if two stores race.
old_native_bytes_allocated_.store(current_native_bytes, std::memory_order_relaxed);
return 0.0;
} else {
size_t new_native_bytes = UnsignedDifference(current_native_bytes, old_native_bytes); <=(1)
size_t weighted_native_bytes = new_native_bytes / kNewNativeDiscountFactor <=(2)

• old_native_bytes / kOldNativeDiscountFactor;
  size_t add_bytes_allowed = static_cast<size_t>( <=(3)
  NativeAllocationGcWatermark() * HeapGrowthMultiplier());
  size_t java_gc_start_bytes = is_gc_concurrent <=(4)
  ? concurrent_start_bytes_
  : target_footprint_.load(std::memory_order_relaxed);
  size_t adj_start_bytes = UnsignedSum(java_gc_start_bytes, <=(5)
  add_bytes_allowed / kNewNativeDiscountFactor);
  return static_cast(GetBytesAllocated() + weighted_native_bytes) <=(6)
  / static_cast(adj_start_bytes);
  }
  }

首先将本次native内存总大小和上一次GC完成后的native内存总大小进行比较。如果小于上次的总大小，则表明native内存的使用水平降低了，因此完全没有必要进行一次新的GC。

但如果这次native内存使用增长的话，则需要进一步计算当前值和阈值之间的比例关系，大于等于1的话就需要进行GC。下面详细介绍源码中的(1)~(6)。

（1）计算本次native内存和上次之间的差值，这个差值反映了native内存中新增长部分的大小。

（2）给不同部分的native内存以不同的权重，新增长部分除以2，旧的部分除以65536。之所以给旧的部分权重如此之低，是因为native堆本身是没有上限的。这套机制的初衷并不是限制native堆的大小，而只是防止两次GC间native内存垃圾积累过多。

（3）所谓的阈值并不是为native内存单独设立的，而是为(Java堆大小+native内存大小)整体设立的。add_bytes_allowed表示在原有Java堆阈值的基础上，还可以允许的native内存大小。NativeAllocationGcWatermark根据Java堆阈值计算出允许的native内存大小，Java堆阈值越大，允许的值也越大。HeapGrowthMultipiler对于前台应用是2，表明前台应用的内存管控更松，GC触发频率更低。

（4）同等条件下，同步GC的触发水位要低于非同步GC，原因是同步GC在垃圾回收时也会有新的对象分配，因此加上这些新分配的对象最好也不要超过阈值。

（5）将Java堆阈值和允许的native内存相加，作为新的阈值。

（6）将Java堆已分配的大小和调整权重后的native内存大小相加，并将相加后的结果除以阈值，得到一个比值来判定是否需要GC。

通过如下代码可知，当比值≥1时，将请求一次新的GC。

art/runtime/gc/heap.cc

if (UNLIKELY(gc_urgency >= 1.0)) {
if (is_gc_concurrent) {
RequestConcurrentGC(self, kGcCauseForNativeAlloc, /force_full=/true); <=================请求一次新的GC

1.2 MMap内存

mmap的处理方式和malloc基本相当，大于300,000 bytes或mmap三百次都执行CheckGCForNative。唯一的区别在于mmap需要将每一次的大小都计入native_bytes_registered中，因为mallinfo中并不会记录这个信息（针对bionic库而言）。

art/runtime/gc/heap.cc

void Heap::RegisterNativeAllocation(JNIEnv* env, size_t bytes) {
// Cautiously check for a wrapped negative bytes argument.
DCHECK(sizeof(size_t) < 8 || bytes < (std::numeric_limits<size_t>::max() / 2));
native_bytes_registered_.fetch_add(bytes, std::memory_order_relaxed);
uint32_t objects_notified =
native_objects_notified_.fetch_add(1, std::memory_order_relaxed);
if (objects_notified % kNotifyNativeInterval == kNotifyNativeInterval - 1
|| bytes > kCheckImmediatelyThreshold) {
CheckGCForNative(ThreadForEnv(env));
}
}

2. 如何在Java对象回收时触发native内存回收

NativeAllocationRegistry中主要依靠Cleaner机制完成了这个过程。关于Cleaner的细节，可以参考我的上篇博客。

3. 实际案例

Bitmap类就是通过NativeAllocationRegistry来实现native资源自动释放的。以下是Bitmap构造方法的一部分。

frameworks/base/graphics/java/android/graphics/Bitmap.java

mNativePtr = nativeBitmap; <=========================== 通过指针值间接持有native资源

final int allocationByteCount = getAllocationByteCount(); <==== 获取native资源的大小，如果是mmap方式，这个大小最终会计入native_bytes_registered中
NativeAllocationRegistry registry;
if (fromMalloc) {
registry = NativeAllocationRegistry.createMalloced( <==== 根据native资源分配方式的不同，构造不同的NativeAllocationRegistry对象，nativeGetNativeFinalizer()返回的是native资源释放函数的函数指针
Bitmap.class.getClassLoader(), nativeGetNativeFinalizer(), allocationByteCount);
} else {
registry = NativeAllocationRegistry.createNonmalloced(
Bitmap.class.getClassLoader(), nativeGetNativeFinalizer(), allocationByteCount);
}