介绍
memwatch是一个c++扩展,主要用来观察nodejs内存泄露问题,基本用法如下:
const memwatch = require('@airbnb/memwatch');
function LeakingClass() {
}
memwatch.gc();
var arr = [];
var hd = new memwatch.HeapDiff();
for (var i = 0; i < 10000; i++) arr.push(new LeakingClass);
var hde = hd.end();
console.log(JSON.stringify(hde, null, 2));
实现分析
分析的版本为@airbnb/memwatch。首先从binding.gyp开始入手:
{
'targets': [
{
'target_name': 'memwatch',
'include_dirs': [
"
这份配置表示其生成的目标是memwatch.node
,源码是src目录下的heapdiff.cc
、init.cc
、memwatch.cc
、util.cc
,在项目编译的过程中还需要include额外的nan目录,nan目录通过执行node -e "require('nan')
按照node模块系统寻找nan
依赖, 表示后面是一条指令。
memwatch的入口函数在init.cc
文件中,通过NODE_MODULE(memwatch, init);
进行声明。当执行require('@airbnb/memwatch')
的时候会首先调用init
函数:
void init (v8::Handle target)
{
Nan::HandleScope scope;
heapdiff::HeapDiff::Initialize(target);
Nan::SetMethod(target, "upon_gc", memwatch::upon_gc);
Nan::SetMethod(target, "gc", memwatch::trigger_gc);
Nan::AddGCPrologueCallback(memwatch::before_gc);
Nan::AddGCEpilogueCallback(memwatch::after_gc);
}
init函数的入口参数v8:Handle
可以类比nodejs中的module.exports
的exports
对象。函数内部做的实现可以分为三块,初始化target、给target绑定upon_gc
和gc
两个函数、在nodejs的gc前后分别挂上对应的钩子函数。
Initialize实现
到heapdiff.cc
文件中来看heapdiff::HeapDiff::Initialize(target);
的实现。
void heapdiff::HeapDiff::Initialize ( v8::Handle target )
{
Nan::HandleScope scope;
v8::Local t = Nan::New(New);
t->InstanceTemplate()->SetInternalFieldCount(1);
t->SetClassName(Nan::New("HeapDiff").ToLocalChecked());
Nan::SetPrototypeMethod(t, "end", End);
target->Set(Nan::New("HeapDiff").ToLocalChecked(), t->GetFunction());
}
Initialize
函数中创建一个叫做HeapDiff
的函数t
,同时在t
的原型链上绑了end
方法,使得js层面可以执行vat hp = new memwatch.HeapDiff();hp.end()
。
new memwatch.HeapDiff
实现
当js执行new memwatch.HeapDiff();
的时候,c++层面会执行heapdiff::HeapDiff::New
函数,去掉注释和不必要的宏,New函数精简如下:
NAN_METHOD(heapdiff::HeapDiff::New)
{
if (!info.IsConstructCall()) {
return Nan::ThrowTypeError("Use the new operator to create instances of this object.");
}
Nan::HandleScope scope;
HeapDiff * self = new HeapDiff();
self->Wrap(info.This());
s_inProgress = true;
s_startTime = time(NULL);
self->before = v8::Isolate::GetCurrent()->GetHeapProfiler()->TakeHeapSnapshot(NULL);
s_inProgress = false;
info.GetReturnValue().Set(info.This());
}
可以看到用户在js层面执行var hp = new memwatch.HeapDiff();
的时候,c++层面会调用nodejs中的v8的api对对堆上内存打一个snapshot保存到self->before中,并将当前对象返回出去。
memwatch.HeapDiff.End
实现
当用户执行hp.end()
的时候,会执行原型链上的end方法,也就是c++的heapdiff::HeapDiff::End
方法。同样去掉冗余的注释以及宏,End方法可以精简如下:
NAN_METHOD(heapdiff::HeapDiff::End)
{
Nan::HandleScope scope;
HeapDiff *t = Unwrap( info.This() );
if (t->ended) {
return Nan::ThrowError("attempt to end() a HeapDiff that was already ended");
}
t->ended = true;
s_inProgress = true;
t->after = v8::Isolate::GetCurrent()->GetHeapProfiler()->TakeHeapSnapshot(NULL);
s_inProgress = false;
v8::Local comparison = compare(t->before, t->after);
((HeapSnapshot *) t->before)->Delete();
t->before = NULL;
((HeapSnapshot *) t->after)->Delete();
t->after = NULL;
info.GetReturnValue().Set(comparison);
}
在End函数中,拿到当前的HeapDiff对象之后,再对当前的堆上内存再打一个snapshot,调用compare函数对前后两个snapshot对比后得到comparison后,将前后两次snapshot对象释放掉,并将结果通知给js。
下面分析下compare函数的具体实现:
compare函数内部会递归调用buildIDSet函数得到最终堆快照的diff结果。
static v8::Local
compare(const v8::HeapSnapshot * before, const v8::HeapSnapshot * after)
{
Nan::EscapableHandleScope scope;
int s, diffBytes;
Local
该函数中构造了两个对象b(before)、a(after)用于保存前后两个快照的详细信息。用一个js对象描述如下:
// b(before) / a(after)
{
nodes: // heap snapshot中对象节点个数
size_bytes: // heap snapshot的对象大小(bytes)
size: // heap snapshot的对象大小(kb、mb)
}
进一步对前后两次的快照进行分析可以得到o,o中的before、after对象就是前后两次的snapshot对象的引用:
// o
{
before: { // before的堆snapshot
nodes:
size_bytes:
size:
},
after: { // after的堆snapshot
nodes:
size_bytes:
size:
},
change: {
freed_nodes: // gc掉的节点数量
allocated_nodes: // 新增节点数量
details: [ // 按照类型String、Array聚合出来的详细信息
{
Array : {
what: // 类型
size_bytes: // 字节数bytes
size: // kb、mb
+: // 新增数量
-: // gc数量
}
},
{}
]
}
}
得到两次snapshot对比的结果后将o返回出去,在End函数中通过info.GetReturnValue().Set(comparison);
将结果传递到js层面。
下面来具体说下compare函数中的buildIDSet、setDiff以及manageChange函数的实现。
buildIDSet的用法:buildIDSet(&beforeIDs, before->GetRoot(), s);
,该函数会从堆snapshot的根节点出发,递归的寻找所有能够访问的子节点,加入到集合seen中,做DFS统计所有可达节点的同时,也会对所有节点的shallowSize(对象本身占用的内存,不包括引用的对象所占内存)进行累加,统计当前堆所占用的内存大小。其具体实现如下:
static void buildIDSet(set * seen, const HeapGraphNode* cur, int & s)
{
Nan::HandleScope scope;
if (seen->find(cur->GetId()) != seen->end()) {
return;
}
if (cur->GetType() == HeapGraphNode::kObject &&
handleToStr(cur->GetName()).compare("HeapDiff") == 0)
{
return;
}
s += cur->GetShallowSize();
seen->insert(cur->GetId());
for (int i=0; i < cur->GetChildrenCount(); i++) {
buildIDSet(seen, cur->GetChild(i)->GetToNode(), s);
}
}
setDiff函数用法:setDiff(beforeIDs, afterIDs, changedIDs);
主要用来计算集合差集用的,具体实现很简单,这里直接贴代码,不再赘述:
typedef set idset;
// why doesn't STL work?
// XXX: improve this algorithm
void setDiff(idset a, idset b, vector &c)
{
for (idset::iterator i = a.begin(); i != a.end(); i++) {
if (b.find(*i) == b.end()) c.push_back(*i);
}
}
manageChange函数用法:manageChange(changes, n, false);
,其作用在于做数据的聚合。对某个指定的set,按照set中对象的类型,聚合出每种对象创建了多少、销毁了多少,实现如下:
static void manageChange(changeset & changes, const HeapGraphNode * node, bool added)
{
std::string type;
switch(node->GetType()) {
case HeapGraphNode::kArray:
type.append("Array");
break;
case HeapGraphNode::kString:
type.append("String");
break;
case HeapGraphNode::kObject:
type.append(handleToStr(node->GetName()));
break;
case HeapGraphNode::kCode:
type.append("Code");
break;
case HeapGraphNode::kClosure:
type.append("Closure");
break;
case HeapGraphNode::kRegExp:
type.append("RegExp");
break;
case HeapGraphNode::kHeapNumber:
type.append("Number");
break;
case HeapGraphNode::kNative:
type.append("Native");
break;
case HeapGraphNode::kHidden:
default:
return;
}
if (changes.find(type) == changes.end()) {
changes[type] = change();
}
changeset::iterator i = changes.find(type);
i->second.size += node->GetShallowSize() * (added ? 1 : -1);
if (added) i->second.added++;
else i->second.released++;
return;
}
upon_gc
和gc
实现
这两个方法的在init函数中声明如下:
Nan::SetMethod(target, "upon_gc", memwatch::upon_gc);
Nan::SetMethod(target, "gc", memwatch::trigger_gc);
先看gc方法的实现,实际上对应memwatch::trigger_gc
,实现如下:
NAN_METHOD(memwatch::trigger_gc) {
Nan::HandleScope scope;
int deadline_in_ms = 500;
if (info.Length() >= 1 && info[0]->IsNumber()) {
deadline_in_ms = (int)(info[0]->Int32Value());
}
Nan::IdleNotification(deadline_in_ms);
Nan::LowMemoryNotification();
info.GetReturnValue().Set(Nan::Undefined());
}
通过Nan::IdleNotification
和Nan::LowMemoryNotification
触发v8的gc功能。
再来看upon_gc
方法,该方法实际上会绑定一个函数,当执行到gc方法时,就会触发该函数:
NAN_METHOD(memwatch::upon_gc) {
Nan::HandleScope scope;
if (info.Length() >= 1 && info[0]->IsFunction()) {
uponGCCallback = new UponGCCallback(info[0].As());
}
info.GetReturnValue().Set(Nan::Undefined());
}
其中info[0]就是用户传入的回调函数。调用new UponGCCallback的时候,其对应的构造函数内部会执行:
UponGCCallback(v8::Local callback_) : Nan::AsyncResource("memwatch:upon_gc") {
callback.Reset(callback_);
}
把用户传入的callback_函数设置到UponGCCallback类的成员变量callback上。upon_gc回调的触发与gc的钩子有关,详细看下一节分析。
gc前、后钩子函数的实现
gc钩子的挂载如下:
Nan::AddGCPrologueCallback(memwatch::before_gc);
Nan::AddGCEpilogueCallback(memwatch::after_gc);
先来看memwatch::before_gc
函数的实现,内部给gc开始记录了时间:
NAN_GC_CALLBACK(memwatch::before_gc) {
currentGCStartTime = uv_hrtime();
}
再来看memwatch::after_gc
函数的实现,内部会在gc后记录gc的结果到GCStats结构体中:
struct GCStats {
// counts of different types of gc events
size_t gcScavengeCount; // gc 扫描次数
uint64_t gcScavengeTime; // gc 扫描事件
size_t gcMarkSweepCompactCount; // gc标记清除整理的个数
uint64_t gcMarkSweepCompactTime; // gc标记清除整理的时间
size_t gcIncrementalMarkingCount; // gc增量标记的个数
uint64_t gcIncrementalMarkingTime; // gc增量标记的时间
size_t gcProcessWeakCallbacksCount; // gc处理weakcallback的个数
uint64_t gcProcessWeakCallbacksTime; // gc处理weakcallback的时间
};
对gc请求进行统计后,通过v8的api获取堆的使用情况,最终将结果保存到barton中,barton内部维护了一个uv_work_t的变量req,req的data字段指向barton对象本身。
NAN_GC_CALLBACK(memwatch::after_gc) {
if (heapdiff::HeapDiff::InProgress()) return;
uint64_t gcEnd = uv_hrtime();
uint64_t gcTime = gcEnd - currentGCStartTime;
switch(type) {
case kGCTypeScavenge:
s_stats.gcScavengeCount++;
s_stats.gcScavengeTime += gcTime;
return;
case kGCTypeMarkSweepCompact:
case kGCTypeAll:
break;
}
if (type == kGCTypeMarkSweepCompact) {
s_stats.gcMarkSweepCompactCount++;
s_stats.gcMarkSweepCompactTime += gcTime;
Nan::HandleScope scope;
Baton * baton = new Baton;
v8::HeapStatistics hs;
Nan::GetHeapStatistics(&hs);
timeval tv;
gettimeofday(&tv, NULL);
baton->gc_ts = (tv.tv_sec * 1000000) + tv.tv_usec;
baton->total_heap_size = hs.total_heap_size();
baton->total_heap_size_executable = hs.total_heap_size_executable();
baton->req.data = (void *) baton;
uv_queue_work(uv_default_loop(), &(baton->req),
noop_work_func, (uv_after_work_cb)AsyncMemwatchAfter);
}
}
在前面工作完成的基础上,将结果丢到libuv的loop中,等到合适的实际触发回调函数,在回调函数中可以拿到req对象,通过访问req.data对其做强制类型装换可以得到barton对象,在loop的回调函数中,将barton中封装的数据依次取出来,保存到stats对象中,并调用uponGCCallback的Call方法,传入字面量stats
和stats对象。
static void AsyncMemwatchAfter(uv_work_t* request) {
Nan::HandleScope scope;
Baton * b = (Baton *) request->data;
// if there are any listeners, it's time to emit!
if (uponGCCallback) {
Local argv[2];
Local stats = Nan::New();
stats->Set(Nan::New("gc_ts").ToLocalChecked(), javascriptNumber(b->gc_ts));
stats->Set(Nan::New("gcProcessWeakCallbacksCount").ToLocalChecked(), javascriptNumberSize(b->stats.gcProcessWeakCallbacksCount));
stats->Set(Nan::New("gcProcessWeakCallbacksTime").ToLocalChecked(), javascriptNumber(b->stats.gcProcessWeakCallbacksTime));
stats->Set(Nan::New("peak_malloced_memory").ToLocalChecked(), javascriptNumberSize(b->peak_malloced_memory));
stats->Set(Nan::New("gc_time").ToLocalChecked(), javascriptNumber(b->gc_time));
// the type of event to emit
argv[0] = Nan::New("stats").ToLocalChecked();
argv[1] = stats;
uponGCCallback->Call(2, argv);
}
delete b;
}
最后在Call函数的内部调用js传入的callback_函数,并将字面量stats
和stats对象传递到js层面,供上层用户使用。
void Call(int argc, Local argv[]) {
v8::Isolate *isolate = v8::Isolate::GetCurrent();
runInAsyncScope(isolate->GetCurrentContext()->Global(), Nan::New(callback), argc, argv);
}