In my server everyday 3:00AM GC is running and Heapspace is filling in a flash.
This causing site outage. ANY inputs?
following are my JVM settings.I am using JBOSS server.
-Dprogram.name=run.sh -server -Xms1524m -Xmx1524m -Dsun.rmi.dgc.client.gcInterval=3600000 -Dsun.rmi.dgc.server.gcInterval=3600000 -XX:NewSize=512m -XX:MaxNewSize=512m -Djava.net.preferIPv4Stack=true -XX:MaxPermSize=512m -XX:+UseConcMarkSweepGC -XX:+CMSClassUnloadingEnabled -Djavax.net.ssl.trustStorePassword=changeit -Dcom.sun.management.jmxremote.port=8888 -Djava.rmi.server.hostname=192.168.100.140 -Dcom.sun.management.jmxremote.ssl=false -Dcom.sun.management.jmxremote
Any suggestions really helpful..
(This turned out somewhat long; there is an actual suggestion for a fix at the end.)
Very very briefly, garbage collection when you use -XX:+UseConcMarkSweepGC works like this:
All objects are allocated in the so-called young generation. This is typically a couple of hundred megs up to a gig in size, depending om VM settings, number of CPU:s and total heap size. The young generation is collected in a stop-the-world pause, followed by a parallel (multiple CPU) compacting (moving objects) collection. The young generation is sized so as to make this pause reasonably large.
When objects have survived (are still reachable) young gen they get promoted to "old-gen" (old generation).
The old generation is where -XX:+UseConcMarkSweepGC kicks in. In the default mode (without -XX:+UseConcMarkSweepGC) when the old generation becomes full the entire heap is collected and compacted (moving around, eliminating fragmentation) at once in a stop-the-world copy. That pause will typically be longer than young-gen pauses because the entire heap is involved, which is bigger.
With CMS (-XX:+UseConcMarkSweepGC) the work to compact the old generation is mostly concurrent (meaning, running in the background with the application not paused). This work is also not compacting; it works more like malloc()/free() and you are subject to fragmentation.
The main upside of CMS is that when things work well, you avoid long pause times that are linear in the size of the heap, because the main work is cone concurrently (there are some stop-the-world steps involved but they are supposed to usually be short).
The two primary downsides are that:
You are subject to fragmentation because old-gen is not compacted.
If you don't finish a concurrent collection cycle before old-gen fills up, or if fragmentation prevents allocation, the resulting full collection of the entire heap is not parallel as it is with the default collector. I..e, only one CPU is used. That means that when/if you do hit a full garbage collection, the pause will be longer than it would have been with the default collector.
Now... your logs. "Concurrent mode failure" is intended to convey that the concurrent mark/sweep work did not complete in time for another young-gen GC that needs to promote surviving objects into the old generation. The "promotion failed" is rather that during promotion from young-gen to old-gen, an object was unable to be allocated in old-gen due to fragmentation.
Unless you are hitting a true bug in the JVM, the sudden increase in heap usage is almost certainly from your application, JBoss, or some external entity acting on your application. So I can't really help with that. However, what is likely happening is a combination of two things:
The spike in activity is causing an increase in heap usage too quick for the concurrent collection to complete in time.
Old-gen is too fragmented, causing problems especially when the old-gen is almost full.
I should also point out now that the default behavior of CMS is to try to postpone concurrent collections as long as possible (yet not too long) for performance reasons. The later it happens, the more efficient (in terms of CPU usage) the collection is. However, a trade-off is that you're increasing the risk of not finishing in time (which again, will trigger a full GC and a long pause). It should also (I have not made empirical tests here, but it stands to reason) result in fragmentation being a greater concern; basically the more full old-gen is when an object is promoted, the greater is the likelyhood that the object's promotion will worsen fragmentation concerns (too long to go into details here).
In your case, I would do two things:
Keep figuring out what is causing the activity. I would say it's fairly unlikely that it is a GC/JVM bug.
Re-configure the JVM to trigger concurrent collection cycles earlier in order to avoid the heap every becoming so full that fragmentation becomes a particularly huge concern, and giving it more time to complete in time even during your sudden spikes of activity.
You can accomplish (2) most easily be using the JVM options
-XX:CMSInitiatingOccupancyFraction=75
-XX:+UseCMSInitiatingOccupancyOnly
in order to explicitly force the JVM to kick start a CMS cycle at a certain level of heap usage (in this example 75% - you may need to change that; the lower the percentage, the earlier it will kick in).
Note that depending on what your live size is (the number of bytes that are in fact live and reachable) in your application, forcing an earlier CMS cycle may also require that you increase your heap size to avoid CMS running constantly (not a good use of CPU).
Related
I realize the exact number depends on a whole lot of things, so I’m really looking for an order of magnitude on say, a MacBook Pro.
Is it 100s of 1000s? Millions? More?
For example I’ve calculated I can run about 1M goroutines on this machine and I’m trying to get a sense of ZIO fibers would be about the same or more…
The primary resource consumption from a fiber is going to be the heap memory it consumes, plus (arguably) the memory consumed by the closure capturing its state. Because JVMs (and even different GC algorithms within a JVM) differ in how many bytes in memory a given object will take up, and this can even depend on runtime settings (e.g. if the heap is 32GiB or smaller, object references can be encoded in 32 bits, while a heap larger than that will require more space for each object reference).
On "typical" JVMs, the memory overhead of fibers is in the low hundreds of bytes. This is also approximately the overhead of an Akka actor (which can, like a goroutine, a ZIO fiber, a Cats Effect fiber, or a Scala future, be considered a means of modeling a process in a more efficient way than a thread (this ignores the substantial philosophical differences in the particulars of the respective models)), and it's well-established that substantially greater-than a million actors can be created per GiB of heap, so it's reasonable to expect that multiple millions of fibers can be created per GiB of heap.
It should be noted that it's impossible for more fibers to be consuming CPU at any point in time than you have cores/threads, so it's absolutely possible, if you have far more fibers/goroutines/actors ready to consume CPU, you may see a substantial latency effect from fibers waiting to be scheduled (so-called "thread starvation").
We're running our application in Wildfly 14.0.1, with a -Xmx of 4096, running with OpenJDK 11.0.2. I've been using VisualVM 1.4.2 to monitor our heap since we previously were having OOM exceptions (because our -Xmx was only 512 which was incredibly bad).
While we are well within our memory allocation now, we have no more OOM exceptions happening, and even with a good amount of clients and processing happening we're nowhere near the -Xmx4096 (the servers have 16GB so memory isn't an issue), I'm seeing some strange heap behavior that I can't figure out where it's coming from.
Using VisualVM, Eclipse MemoryAnalyzer, as well as heaphero.io, I get summaries like the following:
Total Bytes: 460,447,623
Total Classes: 35,708
Total Instances: 2,660,155
Classloaders: 1,087
GC Roots: 4,200
Number of Objects Pending for Finalization: 0
However, in watching the Heap Monitor, I see the Used Heap over a 4 minute time period increase by about 450MB before the GC runs and drops back down only to spike again. Here's an image:
This is when no clients are connected and nothing is actively happening in our application. We do use Apache File IO to monitor remote directories, we have JMS topics, etc. so it's not like the application is completely idle, but there's zero logging and all that.
My largest objects are the well-known io.netty.buffer.PoolChunk, which in the heap dumps are about 60% of my memory usage, the total is still around 460MB so I'm confused why the heap monitor is going from ~425MB to ~900MB repeatedly, and no matter where I take my snapshots, I can't see any large increase of object counts or memory usage.
I'm just seeing a disconnect between the heap monitor, and .hprof analysis. So there doesn't see a way to tell what's causing the heap to hit that 900MB peak.
My question is if these heap spikes are totally expected when running within Wildfly, or is there something within our application that is spinning up a bunch of objects that then get GC'd? In doing a Component report, objects in our application's package structure make up an extremely small amount of the dump. Which doesn't clear us, we easily could be calling things without closing appropriately, etc.
I'm writing a kernel and need (and want) to put multiple stacks and heaps into virtual memory, but I can't figure out how to place them efficiently. How do normal programs do it?
How (or where) are stacks and heaps placed into the limited virtual memory provided by a 32-bit system, such that they have as much growing space as possible?
For example, when a trivial program is loaded into memory, the layout of its address space might look like this:
[ Code Data BSS Heap-> ... <-Stack ]
In this case the heap can grow as big as virtual memory allows (e.g. up to the stack), and I believe this is how the heap works for most programs. There is no predefined upper bound.
Many programs have shared libraries that are put somewhere in the virtual address space.
Then there are multi-threaded programs that have multiple stacks, one for each thread. And .NET programs have multiple heaps, all of which have to be able to grow one way or another.
I just don't see how this is done reasonably efficient without putting a predefined limit on the size of all heaps and stacks.
I'll assume you have the basics in your kernel done, a trap handler for page faults that can map a virtual memory page to RAM. Next level up, you need a virtual memory address space manager from which usermode code can request address space. Pick a segment granularity that prevents excessive fragmentation, 64KB (16 pages) is a good number. Allow usermode code to both reserve space and commit space. A simple bitmap of 4GB/64KB = 64K x 2 bits to keep track of segment state gets the job done. The page fault trap handler also needs to consult this bitmap to know whether the page request is valid or not.
A stack is a fixed size VM allocation, typically 1 megabyte. A thread usually only needs a handful of pages of it, depending on function nesting level, so reserve the 1MB and commit only the top few pages. When the thread nests deeper, it will trip a page fault and the kernel can simply map the extra page to RAM to allow the thread to continue. You'll want to mark the bottom few pages as special, when the thread page faults on those, you declare this website's name.
The most important job of the heap manager is to prevent fragmentation. The best way to do that is to create a lookaside list that partitions heap requests by size. Everything less than 8 bytes comes from the first list of segments. 8 to 16 from the second, 16 to 32 from the third, etcetera. Increasing the size bucket as you go up. You'll have to play with the bucket sizes to get the best balance. Very large allocations come directly from the VM address manager.
The first time an entry in the lookaside list is hit, you allocate a new VM segment. You subdivide the segment into smaller blocks with a linked list. When such an allocation is released, you add the block to the list of free blocks. All blocks have the same size regardless of the program request so there won't be any fragmentation. When the segment is fully used and no free blocks are available you allocate a new segment. When a segment contains nothing but free blocks you can return it to the VM manager.
This scheme allows you to create any number of stacks and heaps.
Simply put, as your system resources are always finite, you can't go limitless.
Memory management always consists of several layers each having its well defined responsibility. From the perspective of the program, the application-level manager is visible that is usually concerned only with its own single allocated heap. A level above could deal with creating the multiple heaps if needed out of (its) one global heap and assigning them to subprograms (each with its own memory manager). Above that could be the standard malloc()/free() that it uses and above those the operating system dealing with pages and actual memory allocation per process (it is basically not concerned not only about multiple heaps, but even user-level heaps in general).
Memory management is costly and so is trapping into the kernel. Combining the two could impose severe performance hit, so what seems to be the actual heap management from the application's point of view is actually implemented in user space (the C runtime library) for the sake of performance (and other reason out of scope for now).
When loading a shared (DLL) library, if it is loaded at program startup, it will of course be most probably loaded to CODE/DATA/etc so no heap fragmentation occurs. On the other hand, if it is loaded at runtime, there's pretty much no other chance than using up heap space.
Static libraries are, of course, simply linked into the CODE/DATA/BSS/etc sections.
At the end of the day, you'll need to impose limits to heaps and stacks so that they're not likely to overflow, but you can allocate others.
If one needs to grow beyond that limit, you can either
Terminate the application with error
Have the memory manager allocate/resize/move the memory block for that stack/heap and most probably defragment the heap (its own level) afterwards; that's why free() usually performs poorly.
Considering a pretty large, 1KB stack frame on every call as an average (might happen if the application developer is unexperienced) a 10MB stack would be sufficient for 10240 nested call -s. BTW, besides that, there's pretty much no need for more than one stack and heap per thread.
I'm using MongoDB on a 32 bit production system, which sucks but it's out of my control right now. The challenge is to keep the memory usage under ~2.5GB since going over this will cause 32 bit systems to crash.
According to the mongoDB team, the best way to track the memory usage is to use your operating system's process tracking system (i.e. ps or htop on Unix systems; Process Explorer on Windows.) for virtual memory size.
The DB mainly consists of one table which is continually cycling data, i.e. receiving data at regular intervals from sensors, and every day a cron job wipes all data from before the last 3 days. Over a period of time, the memory usage slowly increases. I took some notes over time using db.serverStats(), db.lectura.totalSize() and ps, shown in the chart below. Note that the size of the table in question has reduced in the last month but the memory usage increased nonetheless.
Now, there is some scope for adjustment in how many days of data I store. Today I deleted basically half of the data, and then restarted mongodb, and yet the mem virtual / mem mapped and most importantly memory usage according to ps have hardly changed! Why do these not reduce when I wipe data (and restart)? I read some other questions where people said that mongo isn't really using all the memory that it might appear to be using, and that you can't clear the cache or limit memory use. But then how can I ensure I stay under the 2.5GB limit?
Unless there is a way to stem this dataset-size-irrespective gradual increase in memory usage, it seems to me that the 32-bit version of Mongo is unuseable. Note: I don't mind losing a bit of performance if it solves the problem.
To answer regarding why the mapped and virtual memory usage does not decrease with the deletes, the mapped number is actually what you get when you mmap() the entire set of data files. This does not shrink when you delete records, because although the space is freed up inside the data files, they are not themselves reduced in size - the files are just more empty afterwards.
Virtual will include journal files, and connections, and other non-data related memory usage also, but the same principle applies there. This, and more, is described here:
http://www.mongodb.org/display/DOCS/Checking+Server+Memory+Usage
So, the 2GB storage size limitation on 32-bit will actually apply to the data files whether or not there is data in them. To reclaim deleted space, you will have to run a repair. This is a blocking operation and will require the database to be offline/unavailable while it was run. It will also need up to 2x the original size in terms of free disk space to be able to run the repair, since it essentially represents writing out the files again from scratch.
This limitation, and the problems it causes, is why the 32-bit version should not be run in production, it is just not suitable. I would recommend getting onto a 64-bit version as soon as possible.
By the way, neither of these figures (mapped or virtual) actually represents your resident memory usage, which is what you really want to look at. The best way to do this over time is via MMS, which is the free monitoring service provided by 10gen - it will graph virtual, mapped and resident memory for you over time as well as plenty of other stats.
If you want an immediate view, run mongostat and check out the corresponding memory columns (res, mapped, virtual).
In general, when using 64-bit builds with essentially unlimited storage, the data will usually greatly exceed the available memory. Therefore, mongod will use all of the available memory it can in terms of resident memory (which is why you should always have swap configured to the OOM Killer does not come into play).
Once that is used, the OS does not stop allocating memory, it will just have the oldest items paged out to make room for the new data (LRU). In other words, the recycling of memory will be done for you, and the resident memory level will remain fairly constant.
Your options for stretching 32-bit are limited, but you can try some things. The thing that you run out of is address space, and the increases in the sizes of additional database files mean that you would like to avoid crossing over the boundary from "n" files to "n+1". It may be worth structuring your data into more or fewer databases so that you can get the maximum amount of actual data into memory and as little as possible "dead space".
For example, if your database named "mydatabase" consists of the files mydatabase.ns (the namespace file) at 16 MB, mydatabase.0 at 64 MB, mydatabase.1 at 128 MB and mydatabase.2 at 256 MB, then the next file created for this database will be mydatabase.3 at 512 MB. If instead of adding to mydatabase you instead created an additional database "mynewdatabase" it would start life with mynewdatabase.ns at 16 MB and mynewdatabase.0 at 64 MB ... quite a bit smaller than the 512 MB that adding to the original database would be. In fact, you could create 4 new databases for less space than would be consumed by adding a new file to the original database, and because the files are smaller they would be easier to fit into contiguous blocks of memory.
It is a well-known message that 32-bit should not be used for production.
Use 64-bit systems.
Point.
Or it would be faster to re-read that data from mapped memory once again, since the OS might implement its own cache?
The nature of data is not known in advance, it is assumed that file reads are random.
i wanted to mention a few things i've read on the subject. The answer is no, you don't want to second guess the operating system's memory manager.
The first comes from the idea that you want your program (e.g. MongoDB, SQL Server) to try to limit your memory based on a percentage of free RAM:
Don't try to allocate memory until there is only x% free
Occasionally, a customer will ask for a way to design their program so it continues consuming RAM until there is only x% free. The idea is that their program should use RAM aggressively, while still leaving enough RAM available (x%) for other use. Unless you are designing a system where you are the only program running on the computer, this is a bad idea.
(read the article for the explanation of why it's bad, including pictures)
Next comes from some notes from the author of Varnish, and reverse proxy:
Varnish Cache - Notes from the architect
So what happens with squids elaborate memory management is that it gets into fights with the kernels elaborate memory management, and like any civil war, that never gets anything done.
What happens is this: Squid creates a HTTP object in "RAM" and it gets used some times rapidly after creation. Then after some time it get no more hits and the kernel notices this. Then somebody tries to get memory from the kernel for something and the kernel decides to push those unused pages of memory out to swap space and use the (cache-RAM) more sensibly for some data which is actually used by a program. This however, is done without squid knowing about it. Squid still thinks that these http objects are in RAM, and they will be, the very second it tries to access them, but until then, the RAM is used for something productive.
Imagine you do cache something from a memory-mapped file. At some point in the future that memory holding that "cache" will be swapped out to disk.
the OS has written to the hard-drive something which already exists on the hard drive
Next comes a time when you want to perform a lookup from your "cache" memory, rather than the "real" memory. You attempt to access the "cache", and since it has been swapped out of RAM the hardware raises a PAGE FAULT, and cache is swapped back into RAM.
your cache memory is just as slow as the "real" memory, since both are no longer in RAM
Finally, you want to free your cache (perhaps your program is shutting down). If the "cache" has been swapped out, the OS must first swap it back in so that it can be freed. If instead you just unmapped your memory-mapped file, everything is gone (nothing needs to be swapped in).
in this case your cache makes things slower
Again from Raymon Chen: If your application is closing - close already:
When DLL_PROCESS_DETACH tells you that the process is exiting, your best bet is just to return without doing anything
I regularly use a program that doesn't follow this rule. The program
allocates a lot of memory during the course of its life, and when I
exit the program, it just sits there for several minutes, sometimes
spinning at 100% CPU, sometimes churning the hard drive (sometimes
both). When I break in with the debugger to see what's going on, I
discover that the program isn't doing anything productive. It's just
methodically freeing every last byte of memory it had allocated during
its lifetime.
If my computer wasn't under a lot of memory pressure, then most of the
memory the program had allocated during its lifetime hasn't yet been
paged out, so freeing every last drop of memory is a CPU-bound
operation. On the other hand, if I had kicked off a build or done
something else memory-intensive, then most of the memory the program
had allocated during its lifetime has been paged out, which means that
the program pages all that memory back in from the hard drive, just so
it could call free on it. Sounds kind of spiteful, actually. "Come
here so I can tell you to go away."
All this anal-rententive memory management is pointless. The process
is exiting. All that memory will be freed when the address space is
destroyed. Stop wasting time and just exit already.
The reality is that programs no longer run in "RAM", they run in memory - virtual memory.
You can make use of a cache, but you have to work with the operating system's virtual memory manager:
you want to keep your cache within as few pages as possible
you want to ensure they stay in RAM, by the virtue of them being accessed a lot (i.e. actually being a useful cache)
Accessing:
a thousand 1-byte locations around a 400GB file
is much more expensive than accessing
a single 1000-byte location in a 400GB file
In other words: you don't really need to cache data, you need a more localized data structure.
If you keep your important data confined to a single 4k page, you will play much nicer with the VMM; Windows is your cache.
When you add 64-byte quad-word aligned cache-lines, there's even more incentive to adjust your data structure layout. But then you don't want it too compact, or you'll start suffering performance penalties of cache flushes from False Sharing.
The answer is highly OS-specific. Generally speaking, there will be no sense in caching this data. Both the "cached" data as well as the memory-mapped can be paged away at any time.
If there will be any difference it will be specific to an OS - unless you need that granularity, there is no sense in caching the data.