I have a long running process that I suspect has a memory leak. I use top to monitor the memory levels of each process and nothing uses more than 15% of the total RAM. The machine has 4GB of RAM and the process starts with well over 3GB free. The process itself does very heavy, custom calculations on several MB of data. It takes a single core at 100%.
As time goes on, memory disappears but top does not blame my long running process. Instead, the "cached" and "buffers" memory increases and the "free" memory is reduced to as low as 2MB. The process eventually finishes its job and exits without issue but the memory never comes back. Should I be concerned or is this "normal"? Are there other tools besides top that can provide a deeper understanding?
Thanks.
That's normal. Your process is operating on files which are getting cached in memory. If there is "memory pressure" (demand from other programs) then that cache memory will be relinquished. The first time I wrote an X widget to show how much memory was "free" it took me a while to get used to the idea that free memory is doing you no good: Best to have it all in use doing some kind of caching until it's needed elsewhere!
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 an app that processes 3+GB of data into 300MB of data. Run each independent dataset sequentially on the main thread, its memory usage tops out at about 3.5GB and it works fine.
If I run each dataset concurrently on 10 threads, I see the following:
Virtual memory usage climbs steadily until allocations fail and it crashes. I can see GC is trying to run in the stack trace)
CPU utilization is 1000% for periods, then goes down to 100% for minutes, and then cycles back up. The app is easily 10x slower when run with multiple threads, even though they are completely independent.
This is mono 4.2.2 build for Linux with large heap support, running on 128GB RAM with 40 logical processors. I am running mono-sgen and have tried all the custom GC settings I could think of (concurrent mark-sweep, max heap size, etc).
These problems do not happen on Windows. If I rewrite code to do significant object pooling, I get farther in the dataset before running OOM, but the fate is the same. I have verified that I have no memory leaks using multiple tools and good-old printf-debugging.
My best theory is that lots of allocations across lots of threads are a weak case for the GC, and most of that wall-clock time is spent with my work threads suspended.
Does anyone have any experience with this? Is there a way I can help the GC get out of that 100% rut it gets stuck in, and to not run out of memory?
How can I calculate the real memory usage of a single process? I am not talking about the virtual memory, because it just keeps growing. For instance, there are proc files like smaps, where you can get the mappings of a process. But this is virtual memory and the values of that file just keeps growing for running process. But I would like to reflect the real memory usage of a process. E.g. if you plot the memory usage of a process it should represent the allocations of memory and also the freeing of memory. So the plot should be like an up and down movement instead of a linear function, that just keeps growing for a running process.
So, how could I calculate the real memory usage? I would appreciate any helpful answer.
It's actually kind of a complicated question. The two most common metrics for a program's memory usage at the OS level are virtual size and resident set size. (These show in the output of ps -u as the VSZ and RSS columns.) Roughly speaking, these tell the total memory the program has assigned to it, versus how much it is currently actively using.
Further complicating the question is that when you use malloc (or the C++ new operator) to allocate memory, memory is allocated from a pool in your process which is built by occasionally requesting an allocation of memory from the operating system. But when you free memory, the memory goes back into this pool, but it is typically not returned to the OS. So as your program allocates and frees memory, you typically will not see its memory footprint go up and down. (However, if it frees a lot of memory and then doesn't allocate it any more, eventually you may see its rss go down.)
According to this article:
/proc/sys/vm/min_free_kbytes: This controls the amount of memory that is kept free for use by special reserves including “atomic” allocations (those which cannot wait for reclaim)
My question is that what does it mean by "those which cannot wait for reclaim"? In other words, I would like to understand why there's a need to tell the system to always keep a certain minimum amount of memory free and under what circumstances will this memory be used? [It must be used by something; don't see the need otherwise]
My second question: does setting this memory to something higher than 4MB (on my system) leads to better performance? We have a server which occasionally exhibit very poor shell performance (e.g. ls -l takes 10-15 seconds to execute) when certain processes get going and if setting this number to something higher will lead to better shell performance?
(link is dead, looks like it's now here)
That text is referring to atomic allocations, which are requests for memory that must be satisfied without giving up control (i.e. the current thread can not be suspended). This happens most often in interrupt routines, but it applies to all cases where memory is needed while holding an essential lock. These allocations must be immediate, as you can't afford to wait for the swapper to free up memory.
See Linux-MM for a more thorough explanation, but here is the memory allocation process in short:
_alloc_pages first iterates over each memory zone looking for the first one that contains eligible free pages
_alloc_pages then wakes up the kswapd task [..to..] tap into the reserve memory pools maintained for each zone.
If the memory allocation still does not succeed, _alloc pages will either give up [..] In this process _alloc_pages executes a cond_resched() which may cause a sleep, which is why this branch is forbidden to allocations with GFP_ATOMIC.
min_free_kbytes is unlikely to help much with the described "ls -l takes 10-15 seconds to execute"; that is likely caused by general memory pressure and swapping rather than zone exhaustion. The min_free_kbytes setting only needs to allow enough free pages to handle immediate requests. As soon as normal operation is resumed, the swapper process can be run to rebalance the memory zones. The only time I've had to increase min_free_kbytes is after enabling jumbo frames on a network card that didn't support dma scattering.
To expand on your second question a bit, you will have better results tuning vm.swappiness and the dirty ratios mentioned in the linked article. However, be aware that optimizing for "ls -l" performance may cause other processes to become slower. Never optimize for a non-primary usecase.
All linux systems will attempt to make use of all physical memory available to the system, often through the creation of a filesystem buffer cache, which put simply is an I/O buffer to help improve system performance. Technically this memory is not in use, even though it is allocated for caching.
"wait for reclaim", in your question, refers to the process of reclaiming that cache memory that is "not in use" so that it can be allocated to a process. This is supposed to be transparent but in the real world there are many processes that do not wait for this memory to become available. Java is a good example, especially where a large minimum heap size has been set. The process tries to allocate the memory and if it is not instantly available in one large contiguous (atomic?) chunk, the process dies.
Reserving a certain amount of memory with min_free_kbytes allows this memory to be instantly available and reduces the memory pressure when new processes need to start, run and finish while there is a high memory load and a full buffer cache.
4MB does seem rather low because if the buffer cache is full, any process that wants an immediate allocation of more than 4MB will likely fail. The setting is very tunable and system-specific, but if you have a few GB of memory available it can't hurt to bump up the reserve memory to 128MB. I'm not sure what effect it will have on shell interactivity, but likely positive.
This memory is kept free from use by normal processes. As #Arno mentioned, the special processes that can run include interrupt routines, which must be run now (as it's an interrupt), and finish before any other processes can run (atomic). This can include things like swapping out memory to disk when memory is full.
If the memory is filled an interrupt (memory management) process runs to swap some memory into disk so it can free some memory for use by normal processes. But if vm.min_free_kbytes is too small for it to run, then it locks up the system. This is because this interrupt process must run first to free memory so others can run, but then it's stuck because it doesn't have enough reserved memory vm.min_free_kbytes to do its task resulting in a deadlock.
Also see:
https://www.linbit.com/en/kernel-min_free_kbytes/ and
https://askubuntu.com/questions/41778/computer-freezing-on-almost-full-ram-possibly-disk-cache-problem (where the memory management process has so little memory to work with it takes so long to swap little by little that it feels like a freeze.)
I have a linux totally on rootfs ( which as I understand is an instance of ramfs ). There's no hard disk and no swap. And I got a process that leaks memory continuously. The virutal memory eventually grows to 4 times the size of physical memory, shown with top. I can't understand what's happening. rootfs is supposed to take RAM only, right ? If I have no disk to swap to, how does the Virtual Memory grows to 4 times the physical memory ?
Not all allocated memory has to be backed by a block device; the glibc-people consider this behavior a bug:
BUGS
By default, Linux follows an optimistic memory allocation
strategy. This means that when malloc() returns non-NULL
there is no guarantee that the memory really is available.
This is a really bad bug. In case it turns out that the
system is out of memory, one or more processes will be killed
by the infamous OOM killer. In case Linux is employed under
circumstances where it would be less desirable to suddenly
lose some randomly picked processes, and moreover the kernel
version is sufficiently recent, one can switch off this
overcommitting behavior using a command like:
# echo 2 > /proc/sys/vm/overcommit_memory
See also the kernel Documentation directory, files
vm/overcommit-accounting and sysctl/vm.txt.