I found the related problem here
But still don't got the real answer for this. :(
So, How to reduce to minimum memory with global.gc() in nodejs?
should I spam global.gc() function to reduce?
Instead of forcing the garbage collector to run, you should first identify if you actually have a memory leak by using various tools (e.g. node's built-in inspector, heapdump module, etc.) available for node that allow you to detect such leaks.
It's entirely possible that it appears there is a leak when there isn't, due to how V8's garbage collector works (it is generally lazy because GC is not exactly a cheap operation CPU usage-wise).
Also, you can limit the amount of memory used by V8 via the --max-old-space-size=xxxx command-line argument (where xxxx is the amount of memory in megabytes). This can also be helpful in more quickly determining whether you have a legitimate memory leak.
Related
I have a NodeJS server running on a small VM with 256MB of RAM and I notice the memory usage keeps growing as the server receives new requests. I read that an issue on small environments is that Node doesn't know about the memory constraints and therefore doesn't try to garbage collect until much later (so for instance, maybe it would only want to start garbage collecting once it reaches 512MB of used RAM), is it really the case?
I also tried using various flags such as --max-old-space-size but didn't see much change so I'm not sure if I have an actual memory leak or if Node just doesn't GC as soon as possible?
This might not be a complete answer, but it's coming from experience and might provide some pointers. Memory leak in NodeJS is one of the most challenging bugs that most developers could ever face.
But before we talk about memory leak, to answer your question - unless you explicitly configure --max-old-space-size, there are default memory limits that would take over. Since certain phases of Garbage collection in node are expensive (and sometimes blocking) steps, depending upon how much memory is available to it, it would delay (e.g. mark-sweep collection) some of the expensive GC cycles. I have seen that in a Machine with 16 GB of memory it would easily let the memory go as high as 800 MB before significant Garbage Collections would happen. But I am sure that doesn't make ~800 MB any special limit. It would really depend on how much available memory it has and what kind of application are you running. E.g. it is totally possible that if you have some complex computations, caches (e.g. big DB Connection Pools) or buggy logging libraries - they would themselves always take high memory.
If you are monitoring your NodeJs's memory footprint - sometime after the the server starts-up, everything starts to warm up (express loads all the modules and create some startup objects, caches warm up and all of your high memory consuming modules became active), it might appear as if there is a memory leak because the memory would keep climbing, sometimes as high as ~1 gb. Then you would see that it stabilizes (this limit used to be lesser in <v8 versions).
But sometimes there are actual memory leaks (which might be hard to spot if there is no specific pattern to it).
In your case, 256 MB seems to be meeting just the minimum RAM requirements for nodejs and might not really be enough. Before you start getting anxious of memory leak, you might want to pump it up to 1.5 GB and then monitor everything.
Some good resources on NodeJS's memory model and memory leak.
Node.js Under the Hood
Memory Leaks in NodeJS
Can garbage collection happen while the main thread is
busy?
Understanding and Debugging Memory Leaks in Your Node.js Applications
Some debugging tools to help spot the memory leaks
Node inspector |
Chrome
llnode
gcore
I have a program that exhibits the behavior of a memory leak. It gradually takes up all of the systems memory until it fills all swap space and then the operating system kills it. This happens once every several days.
I have extensively profiled the heap in a manner of ways (-hy, -hm, -hc) and tried limiting heap size (-M128M) tweaked the number of generations (-G1) but no matter what I do the heap size appears constant-ish and low always (measured in kB not MB or GB). Yet when I observe the program in htop, its resident memory steadily climbs.
What this indicates to me is that the memory leak is coming from somewhere besides the GHC heap. My program makes use of dependencies, specifically Haskell's yaml library which wraps the C library libyaml, it is possible that the leak is in the number of foreign pointers it has to objects allocated by libyaml.
My question is threefold:
What places besides the GHC heap can memory leak from in a Haskell program?
What tools can I use to track these down?
What changes to my source code need to be made to avoid these types of leaks, as they seem to differ from the more commonly experienced space leaks in Haskell?
This certainly sounds like foreign pointers aren't being finalized properly. There are several possible reasons for this:
The underlying C library doesn't free memory properly.
The Haskell library doesn't set up finalization properly.
The ForeignPtr objects aren't being freed.
I think there's actually a decent chance that it's option 3. If the RTS consistently finds enough memory in the first GC generation, then it just won't bother running a major collection. Fortunately, this is the easiest to diagnose. Just have your program run System.Memory.performGC every so often. If that fixes it, you've found the bug and can tweak just how often you want to do that.
Another possible issue is that you could have foreign pointers lying around in long-lived thunks or other closures. Make sure you don't.
One particularly strong possibility when working with a wrapped C library is that the wrapper functions will return ByteStrings whose underlying arrays were allocated by C code. So any ByteStrings you get back from yaml could potentially be off-heap.
I am experiencing a 'slow' memory increase in my node process which runs for longer periods of time (~1GB in over 2 months), however the heap stays constant (which implies that my code/stack is growing). I also tried to manually call the garbage collector but memory usage remains the same.
How can I investigate this further ? I want to confirm my theory and figure out why is my code segment / stack part growing.
I am using node v8 LTS (I know this is EOL from this year, I just need to know if there's a way to figure this out)
(V8 developer here.)
Code generated by V8 is on the heap, so if the heap isn't growing, that means that code isn't growing either.
The stack size is limited by the operating system, usually to 1-8 MB. Since operating systems simply kill processes that run into the stack limit, V8 imposes an even lower limit (a little less than a megabyte, I think it's 984KB currently) onto itself, and will throw a RangeError if that's ever exceeded. So a growing stack can't be your problem either.
Since you say that the heap memory reported by Node/V8 remains constant, that also means that most "how to debug memory leaks in Node" tutorials don't apply to your situation; and that probably also means that the leak is not in your (JavaScript) code.
That leaves C++ "heap memory" (which is very different from V8's managed "heap"!) as the most likely culprit. Node itself as well as native extensions can freely allocate memory that they manage themselves. Maybe something doesn't get cleaned up properly there. That could simply be an upstream bug; or it could be that something in your code is accidentally holding on to some embedder memory.
What I would try first is to update Node and any native extensions you have installed. Maybe the leak has already been found and fixed.
If that doesn't help, then you could try to investigate where the memory is going. For instance, you could compile everything from source with LSan enabled, and see if that reports anything. It would probably be helpful to construct a stress-test, e.g. a fake client that floods (a test instance of) your server with real-looking requests, to try to trigger inspectable instances of the leak in seconds or minutes rather than months. Crafting such a fake client might also help narrow down where things go wrong (e.g.: maybe you'll notice that one type of request does not trigger the leak but another type of request does).
Currently working on optimizing a library for speed. I've already reduced execution time drastically, using V8 CPU and Memory Profiling through Webstorm. This was achieved mainly by changing the core method from recursive to iterative.
Now the self time distribution breaks down as
I'm assuming the first entry "node" is timing internal functions calls, which is great. The other entries also make sense. I'm new to Nodejs profiling, but 31.6% for GC seems high, so I've decided to investigate.
I've now created a heap dump through Webstorm, but unfortunately that doesn't give me much information.
These seem to be system internal memory references mainly. Stepping through the core iteration code logic again, there also don't seem to be a lot of places where memory is explicitly allocated (using this as a reference).
Question
Can the GC overhead be reduced?
Is this amount of allocation just expected here?
Is it possible to get better memory profiling information?
Setup Instructions
In case someone want's to try debugging this, I'm including setup instructions.
Download or clone object-scan and run
yarn install --frozen-lockfile
yarn run test-simple --verbose
Now create a file test.js in the project root containing this content and run node --trace_gc test.js or run it through Webstorm for advanced profiling.
In Javascript and in v8 (node) particularly an amount of time spent for garbage collection depends on amount of data stored in heap, but that's only one of many factors.
In v8 engine there are two main "types" of GC: minor (scavenge) and major (mark-sweep/mark-compact). You may see GC types that happen during your tests in console with --trace-gc enabled. And in different cases one type could "eat" more time than other an vice versa. So before optimizations you should determine which gc takes more time.
There are not a lot of options for optimizing major GC, cause it highly affected by amount of data that stays in memory for "long" (actually in this case long means that object survives scavenge GC) period. Such data is stored in so called "old space" in heap. And major GC works with this space and it should scan all that memory and mark objects that no longer have any references for further clearance.
In your case the amount of test data you're loading goes to old space. As a result it affects major GC during the whole test. And in this case major GC will not clear too much, because you're using your test object, but it still consume time for scanning entire old space. So you may consider preventing v8 from doing that by launching node with gc-specific flags like: --nouse-idle-notification --expose-gc --gc_interval=100500 (where 100500 is number of allocation, it can be take high value that will prevent running gc before the whole test will pass) that will allow trigger garbage collections manually. Test your code using this approach and see how major GC affects it, try tests with different amount of data you provide to function. If the impact is quiet high you may try to refactor your code trying to minimize long-lived variables, closures, etc.
If you'll discover that major GC doesn't have much impact on performance, then scavenge GC takes the most of time. Unlike major GC it operates with so called "new space" in heap. It's a space where all new objects are stored. If those objects survive scavenge, then they are moved to old space. New space has much smaller size ( you may control it by setting --max_semi_space_size, note: new space size = 2 * semi space size) than old space and more new objects and variables you allocate more scavenge GC runs will happen. If this GC heats performance too much you may consider refactor your code to make less new allocations. But if you'll reuse variables it may also slowdown the performance and those objects will go to old space and may become a problem described in "major GC" section.
Also v8 GC doesn't always work in the same thread that your program runs. It does some work in background too, but I don't know what Webstorm shows in your case. If it counts just total time spend in GC, may be it just doesn't have so much impact.
You may find more details on v8 GC in this blog post.
TL;DR:
Can the GC overhead be reduced?
Yes, but first you should discover what should be optimized by following steps above.
Is this amount of allocation just expected here?
That's could be just discovered by comparing different approaches. There's no some absolute number that could limit "good" amount from "bad", because it depends on lot's of factors, including the amount on entry data.
Is it possible to get better memory profiling information?
You may find some good tools here, but in general you may use Chrome dev tools which could provide a bit more details rather than Webstorm does.
We have a a very large project which is basically an application which uses Linux Application programming and runs on PowerPC processor. This project was initially developed by another company. We acquired the project from the company and now we are maintaining the project.
The application is reported to have a lot of memory leak issue. Since this is a large project, it is not possible to go to each source code file and find out the memory leak. We have used Valgrid, mpatrol and other memory leak detection tools. These tools did not help much and the memory leak has not decreased by a significant percentage.
In this situation, how to go about to reduce the memory leak by a significant amount.Is there a general method which people use in these case to reduce the memory leak other than the memory leak detection tools like mentioned above.
Usually Valgrind belongs to the best tools for this tasks. If it does not work correctly, there might only be a couple of things you can still do.
First question: What language is the application in? Valgrind is very good for C and C++, but will not help you with garbage collected or scripting language. So check the language first. There might be something similar for java, but I have not used that much java, so you would have to ask someone else.
Play around a lot with the settings of valgrind. There are several plugins, that can help with this. One example could be using --leak-check=full or similar options. There are also plugins for valgrind, that can enhance it detection capabilities.
You say, that the application was reported to have a memory leak. How was this detected? Did the application detect this by itself. If it was detected by the application on it's own without any external tools, this probably means someone has added their own memory tracker inside the application. Custom memory tracker, memory pools etc. mess up valgrind and any other leak detection system very bad. So in case any custom memory handling is present in the application, your only choice is to either deactivate it (if possible) or to hook into this custom mechanism. How this could be done depends on your application only.
Add your own memory tracker. For example in C++ it is possible to hook into new/delete calls and get them to track the memory. There are a couple of libraries you can use for this. You can also write your own new/delete replacement in about 500 LOC. If you decide to use this method, be sure to read a lot of tutorials on replacing new/delete, since there are several things that are unusual in the C++ world when attempting this task.
What makes you so sure, there is an memory leak in the application (i.e. how was this detected)? If a tool just reported huge numbers of allocated memory, this might not even mean, there is an actual memory leak. A memory leak means that the handles to the memory are lost and hence it becomes impossible to every reach and free that memory again. In case your application just get's a lot of memory and keeps it accessible, you probably have a completely different problem. For example you simply might use an algorithm with a bad space complexity at one point or the other, leading to many allocations. In this case you will not need a leak detector, but rather a memory profiler, which gives you more detailed overview of the memory footprint of the code parts. However I have never used a profiler for this kind of task before, so I cannot give you any more hints on this.
You could replace all memory allocation calls with calls to your own allocation methods, which should call original methods and at the same time count memory usage and where it was allocated. This will allow you to find the leaks and eliminate them by hand.
There might also be automated tools that allow you to do this - not sure, haven't used any. But this method works.
Perhaps you might also consider using Boehm's garbage collector (that is using GC_malloc instead of malloc etc... and not bother about free-ing data).