For my application, the memory used by the Java process is much more than the heap size.
The system where the containers are running starts to have memory problem because the container is taking much more memory than the heap size.
The heap size is set to 128 MB (-Xmx128m -Xms128m) while the container takes up to 1GB of memory. Under normal condition, it needs 500MB. If the docker container has a limit below (e.g. mem_limit=mem_limit=400MB) the process gets killed by the out of memory killer of the OS.
Could you explain why the Java process is using much more memory than the heap? How to size correctly the Docker memory limit? Is there a way to reduce the off-heap memory footprint of the Java process?
I gather some details about the issue using command from Native memory tracking in JVM.
From the host system, I get the memory used by the container.
$ docker stats --no-stream 9afcb62a26c8
CONTAINER ID NAME CPU % MEM USAGE / LIMIT MEM % NET I/O BLOCK I/O PIDS
9afcb62a26c8 xx-xxxxxxxxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.0acbb46bb6fe3ae1b1c99aff3a6073bb7b7ecf85 0.93% 461MiB / 9.744GiB 4.62% 286MB / 7.92MB 157MB / 2.66GB 57
From inside the container, I get the memory used by the process.
$ ps -p 71 -o pcpu,rss,size,vsize
%CPU RSS SIZE VSZ
11.2 486040 580860 3814600
$ jcmd 71 VM.native_memory
71:
Native Memory Tracking:
Total: reserved=1631932KB, committed=367400KB
- Java Heap (reserved=131072KB, committed=131072KB)
(mmap: reserved=131072KB, committed=131072KB)
- Class (reserved=1120142KB, committed=79830KB)
(classes #15267)
( instance classes #14230, array classes #1037)
(malloc=1934KB #32977)
(mmap: reserved=1118208KB, committed=77896KB)
( Metadata: )
( reserved=69632KB, committed=68272KB)
( used=66725KB)
( free=1547KB)
( waste=0KB =0.00%)
( Class space:)
( reserved=1048576KB, committed=9624KB)
( used=8939KB)
( free=685KB)
( waste=0KB =0.00%)
- Thread (reserved=24786KB, committed=5294KB)
(thread #56)
(stack: reserved=24500KB, committed=5008KB)
(malloc=198KB #293)
(arena=88KB #110)
- Code (reserved=250635KB, committed=45907KB)
(malloc=2947KB #13459)
(mmap: reserved=247688KB, committed=42960KB)
- GC (reserved=48091KB, committed=48091KB)
(malloc=10439KB #18634)
(mmap: reserved=37652KB, committed=37652KB)
- Compiler (reserved=358KB, committed=358KB)
(malloc=249KB #1450)
(arena=109KB #5)
- Internal (reserved=1165KB, committed=1165KB)
(malloc=1125KB #3363)
(mmap: reserved=40KB, committed=40KB)
- Other (reserved=16696KB, committed=16696KB)
(malloc=16696KB #35)
- Symbol (reserved=15277KB, committed=15277KB)
(malloc=13543KB #180850)
(arena=1734KB #1)
- Native Memory Tracking (reserved=4436KB, committed=4436KB)
(malloc=378KB #5359)
(tracking overhead=4058KB)
- Shared class space (reserved=17144KB, committed=17144KB)
(mmap: reserved=17144KB, committed=17144KB)
- Arena Chunk (reserved=1850KB, committed=1850KB)
(malloc=1850KB)
- Logging (reserved=4KB, committed=4KB)
(malloc=4KB #179)
- Arguments (reserved=19KB, committed=19KB)
(malloc=19KB #512)
- Module (reserved=258KB, committed=258KB)
(malloc=258KB #2356)
$ cat /proc/71/smaps | grep Rss | cut -d: -f2 | tr -d " " | cut -f1 -dk | sort -n | awk '{ sum += $1 } END { print sum }'
491080
The application is a web server using Jetty/Jersey/CDI bundled inside a fat far of 36 MB.
The following version of OS and Java are used (inside the container). The Docker image is based on openjdk:11-jre-slim.
$ java -version
openjdk version "11" 2018-09-25
OpenJDK Runtime Environment (build 11+28-Debian-1)
OpenJDK 64-Bit Server VM (build 11+28-Debian-1, mixed mode, sharing)
$ uname -a
Linux service1 4.9.125-linuxkit #1 SMP Fri Sep 7 08:20:28 UTC 2018 x86_64 GNU/Linux
https://gist.github.com/prasanthj/48e7063cac88eb396bc9961fb3149b58
Virtual memory used by a Java process extends far beyond just Java Heap. You know, JVM includes many subsytems: Garbage Collector, Class Loading, JIT compilers etc., and all these subsystems require certain amount of RAM to function.
JVM is not the only consumer of RAM. Native libraries (including standard Java Class Library) may also allocate native memory. And this won't be even visible to Native Memory Tracking. Java application itself can also use off-heap memory by means of direct ByteBuffers.
So what takes memory in a Java process?
JVM parts (mostly shown by Native Memory Tracking)
1. Java Heap
The most obvious part. This is where Java objects live. Heap takes up to -Xmx amount of memory.
2. Garbage Collector
GC structures and algorithms require additional memory for heap management. These structures are Mark Bitmap, Mark Stack (for traversing object graph), Remembered Sets (for recording inter-region references) and others. Some of them are directly tunable, e.g. -XX:MarkStackSizeMax, others depend on heap layout, e.g. the larger are G1 regions (-XX:G1HeapRegionSize), the smaller are remembered sets.
GC memory overhead varies between GC algorithms. -XX:+UseSerialGC and -XX:+UseShenandoahGC have the smallest overhead. G1 or CMS may easily use around 10% of total heap size.
3. Code Cache
Contains dynamically generated code: JIT-compiled methods, interpreter and run-time stubs. Its size is limited by -XX:ReservedCodeCacheSize (240M by default). Turn off -XX:-TieredCompilation to reduce the amount of compiled code and thus the Code Cache usage.
4. Compiler
JIT compiler itself also requires memory to do its job. This can be reduced again by switching off Tiered Compilation or by reducing the number of compiler threads: -XX:CICompilerCount.
5. Class loading
Class metadata (method bytecodes, symbols, constant pools, annotations etc.) is stored in off-heap area called Metaspace. The more classes are loaded - the more metaspace is used. Total usage can be limited by -XX:MaxMetaspaceSize (unlimited by default) and -XX:CompressedClassSpaceSize (1G by default).
6. Symbol tables
Two main hashtables of the JVM: the Symbol table contains names, signatures, identifiers etc. and the String table contains references to interned strings. If Native Memory Tracking indicates significant memory usage by a String table, it probably means the application excessively calls String.intern.
7. Threads
Thread stacks are also responsible for taking RAM. The stack size is controlled by -Xss. The default is 1M per thread, but fortunately things are not so bad. The OS allocates memory pages lazily, i.e. on the first use, so the actual memory usage will be much lower (typically 80-200 KB per thread stack). I wrote a script to estimate how much of RSS belongs to Java thread stacks.
There are other JVM parts that allocate native memory, but they do not usually play a big role in total memory consumption.
Direct buffers
An application may explicitly request off-heap memory by calling ByteBuffer.allocateDirect. The default off-heap limit is equal to -Xmx, but it can be overridden with -XX:MaxDirectMemorySize. Direct ByteBuffers are included in Other section of NMT output (or Internal before JDK 11).
The amount of direct memory in use is visible through JMX, e.g. in JConsole or Java Mission Control:
Besides direct ByteBuffers there can be MappedByteBuffers - the files mapped to virtual memory of a process. NMT does not track them, however, MappedByteBuffers can also take physical memory. And there is no a simple way to limit how much they can take. You can just see the actual usage by looking at process memory map: pmap -x <pid>
Address Kbytes RSS Dirty Mode Mapping
...
00007f2b3e557000 39592 32956 0 r--s- some-file-17405-Index.db
00007f2b40c01000 39600 33092 0 r--s- some-file-17404-Index.db
^^^^^ ^^^^^^^^^^^^^^^^^^^^^^^^
Native libraries
JNI code loaded by System.loadLibrary can allocate as much off-heap memory as it wants with no control from JVM side. This also concerns standard Java Class Library. In particular, unclosed Java resources may become a source of native memory leak. Typical examples are ZipInputStream or DirectoryStream.
JVMTI agents, in particular, jdwp debugging agent - can also cause excessive memory consumption.
This answer describes how to profile native memory allocations with async-profiler.
Allocator issues
A process typically requests native memory either directly from OS (by mmap system call) or by using malloc - standard libc allocator. In turn, malloc requests big chunks of memory from OS using mmap, and then manages these chunks according to its own allocation algorithm. The problem is - this algorithm can lead to fragmentation and excessive virtual memory usage.
jemalloc, an alternative allocator, often appears smarter than regular libc malloc, so switching to jemalloc may result in a smaller footprint for free.
Conclusion
There is no guaranteed way to estimate full memory usage of a Java process, because there are too many factors to consider.
Total memory = Heap + Code Cache + Metaspace + Symbol tables +
Other JVM structures + Thread stacks +
Direct buffers + Mapped files +
Native Libraries + Malloc overhead + ...
It is possible to shrink or limit certain memory areas (like Code Cache) by JVM flags, but many others are out of JVM control at all.
One possible approach to setting Docker limits would be to watch the actual memory usage in a "normal" state of the process. There are tools and techniques for investigating issues with Java memory consumption: Native Memory Tracking, pmap, jemalloc, async-profiler.
Update
Here is a recording of my presentation Memory Footprint of a Java Process.
In this video, I discuss what may consume memory in a Java process, how to monitor and restrain the size of certain memory areas, and how to profile native memory leaks in a Java application.
https://developers.redhat.com/blog/2017/04/04/openjdk-and-containers/:
Why is it when I specify -Xmx=1g my JVM uses up more memory than 1gb
of memory?
Specifying -Xmx=1g is telling the JVM to allocate a 1gb heap. It’s not
telling the JVM to limit its entire memory usage to 1gb. There are
card tables, code caches, and all sorts of other off heap data
structures. The parameter you use to specify total memory usage is
-XX:MaxRAM. Be aware that with -XX:MaxRam=500m your heap will be approximately 250mb.
Java sees host memory size and it is not aware of any container memory limitations. It doesn't create memory pressure, so GC also doesn't need to release used memory. I hope XX:MaxRAM will help you to reduce memory footprint. Eventually, you can tweak GC configuration (-XX:MinHeapFreeRatio,-XX:MaxHeapFreeRatio, ...)
There is many types of memory metrics. Docker seems to be reporting RSS memory size, that can be different than "committed" memory reported by jcmd (older versions of Docker report RSS+cache as memory usage).
Good discussion and links: Difference between Resident Set Size (RSS) and Java total committed memory (NMT) for a JVM running in Docker container
(RSS) memory can be eaten also by some other utilities in the container - shell, process manager, ... We don't know what else is running in the container and how do you start processes in container.
TL;DR
The detail usage of the memory is provided by Native Memory Tracking (NMT) details (mainly code metadata and garbage collector). In addition to that, the Java compiler and optimizer C1/C2 consume the memory not reported in the summary.
The memory footprint can be reduced using JVM flags (but there is impacts).
The Docker container sizing must be done through testing with the expected load the application.
Detail for each components
The shared class space can be disabled inside a container since the classes won't be shared by another JVM process. The following flag can be used. It will remove the shared class space (17MB).
-Xshare:off
The garbage collector serial has a minimal memory footprint at the cost of longer pause time during garbage collect processing (see Aleksey Shipilëv comparison between GC in one picture). It can be enabled with the following flag. It can save up to the GC space used (48MB).
-XX:+UseSerialGC
The C2 compiler can be disabled with the following flag to reduce profiling data used to decide whether to optimize or not a method.
-XX:+TieredCompilation -XX:TieredStopAtLevel=1
The code space is reduced by 20MB. Moreover, the memory outside JVM is reduced by 80MB (difference between NMT space and RSS space). The optimizing compiler C2 needs 100MB.
The C1 and C2 compilers can be disabled with the following flag.
-Xint
The memory outside the JVM is now lower than the total committed space. The code space is reduced by 43MB. Beware, this has a major impact on the performance of the application. Disabling C1 and C2 compiler reduces the memory used by 170 MB.
Using Graal VM compiler (replacement of C2) leads to a bit smaller memory footprint. It increases of 20MB the code memory space and decreases of 60MB from outside JVM memory.
The article Java Memory Management for JVM provides some relevant information the different memory spaces.
Oracle provides some details in Native Memory Tracking documentation. More details about compilation level in advanced compilation policy and in disable C2 reduce code cache size by a factor 5. Some details on Why does a JVM report more committed memory than the Linux process resident set size? when both compilers are disabled.
Java needs a lot a memory. JVM itself needs a lot of memory to run. The heap is the memory which is available inside the virtual machine, available to your application. Because JVM is a big bundle packed with all goodies possible it takes a lot of memory just to load.
Starting with java 9 you have something called project Jigsaw, which might reduce the memory used when you start a java app(along with start time). Project jigsaw and a new module system were not necessarily created to reduce the necessary memory, but if it's important you can give a try.
You can take a look at this example: https://steveperkins.com/using-java-9-modularization-to-ship-zero-dependency-native-apps/. By using the module system it resulted in CLI application of 21MB(with JRE embeded). JRE takes more than 200mb. That should translate to less allocated memory when the application is up(a lot of unused JRE classes will no longer be loaded).
Here is another nice tutorial: https://www.baeldung.com/project-jigsaw-java-modularity
If you don't want to spend time with this you can simply get allocate more memory. Sometimes it's the best.
How to size correctly the Docker memory limit?
Check the application by monitoring it for some-time. To restrict container's memory try using -m, --memory bytes option for docker run command - or something equivalant if you are running it otherwise
like
docker run -d --name my-container --memory 500m <iamge-name>
can't answer other questions.
I record memory allocations using valgrind massif and use ms_print to create a document of snapshots that shows me which callstack holds how much memory currently, right?
I want to measure which callstacks have allocated most over the whole program run, that means deallocated memory should be taken into account when calculating the weight of a callstack.
Is this possible?
Regards
When a tool (such as memcheck, massif, ...) replaces the memory allocation functions (malloc, free, ...), then valgrind provides the option:
--xtree-memory=none|allocs|full profile heap memory in an xtree [none]
and produces a report at the end of the execution
none: no profiling, allocs: current allocated
size/blocks, full: profile current and cumulative
allocated size/blocks and freed size/blocks.
--xtree-memory-file=<file> xtree memory report file [xtmemory.kcg.%p]
So, if you use --xtree-memory=full, you will get a file that you can visualise with kcachegrind. The resulting file details a.o. what is currently allocated, and what was allocated and then freed.
See http://www.valgrind.org/docs/manual/manual-core.html#manual-core.xtree
for more details.
I want to record large amount of data in continuous mode, using a PCI 6110 and DAQ-assistant VI. At this point, I'm thinking how to dynamically change the buffer size, but I'm not sure if this is possible or if it will affect how data will differ between different sizes of the buffer.
labVIEW diagram
At a high rate and high number of samples, after I start the VI, sometimes it returns a buffer overflow error, other times a not enough memory error. I'd want to know if dynamically changing the buffer size is achievable and how this could be done, or at least to determine a method to find a buffer size that is stable and won't overflow or throw errors during data acquisition.
For high-performance acquisitions, I recommend using the DAQmx API to configure the device to log directly to disk. NI calls this "Log to TDMS File" and more information is available here: TDMS Direct Integration in NI-DAQmx Logging.
With this approach, you can "stream data to disk reaching speeds up to 1.2 GB/s."
I have successfully started my application in Profiling mode but I am not sure how to generate reports or metrics from Jprofiler.
I could see the Live memory (all objects, recorded objects no. of. instance count etc), heap walker etc but I am not sure of what JProfiler concludes or recommends about my application.
Can someone help?
This profiling approach you're describing is jProfiler's live profiling session. The objective is pretty much looking at the charts it produces and identifying anomalies.
For example, on CPU profiling, you will be looking at CPU Hot Spots (i.e. individual methods that consume a disproportionate amount of time).
In memory profiler, you will be able to identify objects that occupy the most memory (also hot spots).
In a typical C program, the linux kernel provides 84K - ~100K of memory. How does the kernel allocate more memory for the stack when the process uses the given memory.
IMO when the process takes up all the memory of the stack and now uses the next contiguous memory, ideally it should page fault and then the kernel handles the page fault.
Is it here that the kernel provides more memory to the stack for the given process, and which data structure in linux kernel identifies the size of the stack for the process??
There are a number of different methods used, depending on the OS (linux realtime vs. normal) and the language runtime system underneath:
1) dynamic, by page fault
typically preallocate a few real pages to higher addresses and assign the initial sp to that. The stack grows downward, the heap grows upward. If a page fault happens somewhat below the stack bottom, the missing intermediate pages are allocated and mapped. Effectively increasing the stack from the top towards the bottom automatically. There is typically a maximum up to which such automatic allocation is performed, which can or can not be specified in the environment (ulimit), exe-header, or dynamically adjusted by the program via a system call (rlimit). Especially this adjustability varies heavily between different OSes. There is also typically a limit to "how far away" from the stack bottom a page fault is considered to be ok and an automatic grow to happen. Notice that not all systems' stack grows downward: under HPUX it (used?) to grow upward so I am not sure what a linux on the PA-Risc does (can someone comment on this).
2) fixed size
other OSes (and especially in embedded and mobile environments) either have fixed sizes by definition, or specified in the exe header, or specified when a program/thread is created. Especially in embedded real time controllers, this is often a configuration parameter, and individual control tasks get fix stacks (to avoid runaway threads taking the memory of higher prio control tasks). Of course also in this case, the memory might be allocated only virtually, untill really needed.
3) pagewise, spaghetti and similar
such mechanisms tend to be forgotten, but are still in use in some run time systems (I know of Lisp/Scheme and Smalltalk systems). These allocate and increase the stack dynamically as-required. However, not as a single contigious segment, but instead as a linked chain of multi-page chunks. It requires different function entry/exit code to be generated by the compiler(s), in order to handle segment boundaries. Therefore such schemes are typically implemented by a language support system and not the OS itself (used to be earlier times - sigh). The reason is that when you have many (say 1000s of) threads in an interactive environment, preallocating say 1Mb would simply fill your virtual address space and you could not support a system where the thread needs of an individual thread is unknown before (which is typically the case in a dynamic environment, where the use might enter eval-code into a separate workspace). So dynamic allocation as in scheme 1 above is not possible, because there are would be other threads with their own stacks in the way. The stack is made up of smaller segments (say 8-64k) which are allocated and deallocated from a pool and linked into a chain of stack segments. Such a scheme may also be requried for high performance support of things like continuations, coroutines etc.
Modern unixes/linuxes and (I guess, but not 100% certain) windows use scheme 1) for the main thread of your exe, and 2) for additional (p-)threads, which need a fix stack size given by the thread creator initially. Most embedded systems and controllers use fixed (but configurable) preallocation (even physically preallocated in many cases).
edit: typo
The stack for a given process has a limited, fixed size. The reason you can't add more memory as you (theoretically) describe is because the stack must be contiguous, and it grows toward the heap. So, when the stack reaches the heap, no extension is possible.
The stack size for a userland program is not determined by the kernel. The kernel stack size is a configuration option for the kernel (usually 4k or 8k).
Edit: if you already know this, and were merely talking about the allocation of physical pages for a process, then you have the procedure down already. But there's no need to keep track of the "stack size" like this: the virtual pages in the stack with no pagetable entries are just normal overcommitted virtual pages. Physical memory will be granted on their first access. But the kernel does not have to overcommit memory, and thus a stack will probably have complete physical realization when the executable is first loaded.
The stack can only be used up to a certain length, because it has a fixed storage capacity in memory. If your question asks in what direction does the stack being used up? the answer is downwards. It is filled down in memory towards the heap. The heap is a dynamic component of memory by which it can actually grow from the bottom up, based on your need of data storage.