Page 6 of the the document Memory Management in the Java
HotSpot™ Virtual Machine contains the following paragraphs:
Young generation collections occur relatively frequently and are
efficient and fast because the young generation space is usually small
and likely to contain a lot of objects that are no longer referenced.
Objects that survive some number of young generation collections are
eventually promoted, or tenured, to the
old generation. See Figure 1. This generation is typically larger than the young generation and its occupancy
grows more slowly. As a result, old generation collections are infrequent, but take significantly longer to
complete
Could someone please define what "frequent" and "infrequent" mean in the statements above? Are we talking microseconds, milliseconds, minutes, days?
It is not possible to give a definite answer to this. It really depends on a lot of factors, including the platform (JVM version, settings, etc), the application, and the workload.
At one extreme, it is possible for an application to never trigger a garbage collector. It might simply sit there doing nothing, or it might perform an extremely long computation in which no objects are created after the JVM initialization and application startup.
At the other extreme it is theoretically possible for one garbage collection end and another one to start within few nanoseconds. For example, this could happen if your application is in the last stages of dying from a full heap, or if it is allocating pathologically large arrays.
So:
Are we talking microseconds, milliseconds, minutes, days?
Possibly all of the above, though the first two would definitely be troubling if you observed them in practice.
A well behaved application should not run the GC too often. If your application is triggering a young space collection more than once or twice a second, then this could lead to performance problems. And too frequent "full" collections is worse because their impact is greater. However, it is certainly plausible for a poorly designed / implemented application to behave like this.
There is also the issue that the interval between GC runs is not always meaningful. For instance some of the HotSpot GCs actually have GC threads running concurrently with normal application threads. If you have enough cores, enough RAM and enough memory bus bandwidth, then a constantly running concurrent GC may not appreciably affect application performance.
Terminology note:
Strictly speaking a concurrent GC is one where the GC can run at the same time as the application threads.
Strictly speaking a parallel GC is one where the GC itself uses multiple threads.
A GC can be concurrent without being parallel, and vice versa.
Its a relative term. Young collections could be many times a seconds up to a few hours. Old generations collections can be every few seconds, up to daily. You should expect to have many more young collections than old collections in a most systems.
Its highly unlikely to be many days. If the GC occurs too often e.g. << 100 ms apart you get get a OutOfMemoryError: GC Overhead Exceeded as the JVM prevenets that from happening.
As it is, the terms "frequent" , "infrequent" are relative. And the timings are, in fact, not fixed. It depends on the system in question. It depends on lots of things like:
Your heap size and settings for different parts of the heap (young, old gen, perm gen)
Your application's memory behaviour. How many objects does it create and how fast? how long those objects are referenced etc?
If your application is monster memory eater, gc would run as if its running for its life. If your application does not demand too much of memory, then gc would run at intervals decided by how full the memory is.
TL DL: "Frequent" and "infrequent" are relative terms that depends on the memory allocation rate and the heap size. If you want a precise answer, you need to measure it yourself for your particular application.
Let's say your app has two modes, mode-1 allocates memory and does computation and mode-2 sits idle.
If mode-1 allocation is smaller than the heap available, no gc need to occur until it finishes. Maybe it used so little RAM that it could do a second round of mode-1 without collection. However, eventually you'll run out of free heap, and jvm will perform an "infrequent" collection.
However, if mode-1 allocation is a significant fraction of, or larger, than the young-generation heap, collection would happen more "frequently". During the young gen collection, allocations that survive (imagine data is needed through the entire mode-1 operation), will be promoted to old-gen, giving the young-gen more room. Young-gen allocation and collection can now continue. Eventually old-gen heap would run out, and must be collected, thus "infrequently".
So then, how frequent is frequent? It depends on the allocation rate and the heap size. If jvm is bumping into the heap limit often, it'll collect often. If there is plenty of heap (let's say 100GB), then jvm doesn't need to collect for a long long time. The down side is that when it finally does a collection, it might take a long time to free 100GB, stopping the jvm for many seconds (or minutes!). The current JVMs are smarter than that and would occasionanlly force a collection (preferably in mode-2). And with parallel collectors, it could happen all the time if necessary.
Ultimately, the frequency is task and heap dependent, as well as how various vm parameters are set. If you want a precise answer, you must measure them yourself for your particular application.
Because spec says "relatively frequently" and infrequent (regarding Young generation), we can't estimate the frequency in absolute units like microseconds, milliseconds, minutes or days
Related
We inherited a system which runs in production and started to fail every 10 hours recently. Basically, our internal software marks the system that is has failed if it is unresponsive for a minute. We found that our problem that our Full GC cycles last for 1.5 minutes, we use 30 GB heap. Now the problem is that we cannot optimize a lot in a short period of time and we cannot partition of our service quickly but we need to get rid of 1.5 minutes pauses as soon as possible as our system fails because of these pauses in production. For us, an acceptable delay is 20 milliseconds but not more. What will be the quickest way to tweak the system? Reduce the heap to trigger GCs frequently? Use System.gc() hints? Any other solutions? We use Java 8 default settings and we have more and more users - i.e. more and more objects created.
Some GC stat
You have a lot of retained data. There is a few options which are worth considering.
increase the heap to 32 GB, this has little impact if you have free memory. Looking again at your totals it appears you are using 32 GB rather than 30 GB, so this might not help.
if you don't have plenty of free memory, it is possible a small portion of your heap is being swapped as this can increase full GC times dramatically.
there might be some simple ways to make the data structures more compact. e.g. use compact strings, use primitives instead of wrappers e.g. long for a timestamp instead of Date or LocalDateTime. (long is about 1/8th the size)
if neither of these help, try moving some of the data off heap. e.g. Chronicle Map is a ConcurrentMap which uses off heap memory can can reduce you GC times dramatically. i.e. there is no GC overhead for data stored off heap. How easy this is to add highly depends on how your data is structured.
I suggest analysing how your data is structured to see if there is any easy ways to make it more efficient.
There is no one-size-fits-all magic bullet solution to your problem: you'll need to have a good handle on your application's allocation and liveness patterns, and you'll need to know how that interacts with the specific garbage collection algorithm you are running (function of version of Java and command line flags passed to java).
Broadly speaking, a Full GC (that succeeds in reclaiming lots of space) means that lots of objects are surviving the minor collections (but aren't being leaked). Start by looking at the size of your Eden and Survivor spaces: if the Eden is too small, minor collections will run very frequently, and perhaps you aren't giving an object a chance to die before its tenuring threshold is reached. If the Survivors are too small, objects are going to be promoted into the Old gen prematurely.
GC tuning is a bit of an art: you run your app, study the results, tweak some parameters, and run it again. As such, you will need a benchmark version of your application, one which behaves as close as possible to the production one but which hopefully doesn't need 10 hours to cause a full GC.
As you stated that you are running Java 8 with the default settings, I believe that means that your Old collections are running with a Serial collector. You might see some very quick improvements by switching to a Parallel collector for the Old generation (-XX:+UseParallelOldGC). While this might reduce the 1.5 minute pause to some number of seconds (depending on the number of cores on your box, and the number of threads you specify for GC), this will not reduce your max pause to to 20ms.
When this happened to me, it was due to a memory leak caused by a static variable eating up memory. I would go through all recent code changes and look for any possible memory leaks.
I need a rule of thumb regarding the maximal time of full garbage collection. The motivation is to be able to distinguish between a faulty JVM process and a process which is under GC.
Suppose I have a regular general-purpose server hardware, HotSpot JVM 8, the heap size is 20G-40G, no specific GC and memory options are set. What is a reasonable time limit for GC to complete? Is it 5 minutes, 20 minutes or up to hours?
Update:
My application is a memory intensive offline job dealing with big data structures. I don't need to tune GC at all. 10 seconds and 10 minutes pauses are bot fine if this limit is known.
It is very difficult to quantify how long GC "should" take because it depends on a number of factors:
How big the heap is.
How full the heap is; i.e. the ratio of garbage to non-garbage when you run the GC.
How many pointers / references there are to traverse.
Which GC you are using.
Whether this is minor "new generation" collection, a major "old generation collection" or a "full" collection. The last is typically performed by the fall-back collector when a low-latency collector cannot keep up with the rate of garbage generation.
Whether there is physical <-> virtual memory thrashing occurring.
There are a couple of pathological situations that can cause excessive GC times:
If the heap is nearly full, the GC uses an increasing proportion of time trying to reclaim the last bit of free space.
If you have a heap that is larger than the physical memory available, you can get into virtual memory "thrashing" behavior. This is most pronounced during a major or full GC.
If you do need to pick a number, I suggest that you use one that "feels" right to you, and make it a configuration parameter so that it is easy to tweak. Also, turn on GC logging and look at the typical GC times that are reported there. (Particularly when the server load is high.)
Firstly, the gc pause time counts on millisecond in most of the time. If a gc take more than one second, I think your application must be tuned anyway.
And then just as the comment said, the gc pause time depends on the characteristics of your application. So if you want a rule of thumb regarding the maximal time of full garbage collection for your application, I advice you to collect the gc.log and make statistics on it, then you will know how long is the pause time in a poorly gc.
For batch jobs where latency does not matter much there are better measures than pause times:
a) MB of garbage collected / time / cpu core
Low collection rates usually indicate some pathological cases like swapping, transparent huge page consolidation or some edge-cases in the GC such as humongous reference arrays being scanned.
b) application throughput - the ratio of wall time spent in application code vs. wall time spent in GC.
Long GCs aren't a big issue if they occur infrequently.
Both can be obtained by running GC logs through GCViewer
My recommendation is:
1.configure on JVM parameter and turn on GC logs,check the GC logs, you will see how long the GC takes
2. GC won't be minutes, I saw around 13second pause time which customer was very badly affected.
I am trying to understand Java's garbage collection more deeply.
In HotSpot JVM generational collection, in the heap, there are three areas (Young generation, Old generation and permanent generation). Also, there are two kinds of algorithms:
1) Mark Sweep Compact.
2) Concurrent Mark and Sweep.
Is that true whether GC needs "Stop-the-world" depends on the algorithm it uses rather than which generation it operates on? In another word, if I use 1) as GC algorithm on all three areas, STW will always happen ?
Also, I understand the difference is the second GC algorithm doesn't require Compaction which will result in fragmentation eventually. So the second question comes to why the compaction needs a STW pause?
Key reason why compaction leads to STW pause is as follows, JVM needs to move object and update references to it. now if you move object before updating the references and application that is running access it from old reference than there is trouble. if you update reference first and than try to move object the updated reference is wrong till object is moved and any access while object has not moved will cause issue.
For both CMS and Parallel collecter the young generation collection algorithm is similar and it is stop the world ie application is stopped when collection is happening
Stuff JVM is doing is, marking all objects reachable from root set, moving the objects from Eden to survivor space and moving objects that have survived collections beyond tenuring threshold to the old generation. Of course JVM has to update all the references to the objects that have moved.
For old generation parallel collector is doing all marking, compaction and reference updates in a single stop the world(STW) phase, this leads to pauses in seconds for heaps in GBs. This was painful for the applications that have strict response time requirements. Till date Paralle collector is still the best collectors(among Oracle Java) for throughput or batch processing. In fact we have seen for same scenario even if time spent in pauses is more in parallel collector than CMS still we get a higher throughput, this I think has to do with better spatial locality due to compaction.
CMS solved the problem of high pauses in major collection by doing the Marking concurrently. There are 2 STW parts, Initial marking (getting the references from root set) and Remark Pause (a small STW pause at the end of marking to deal with changes in the object graph while marking and application was working concurrently). Both these pauses are in range of 100 -200 milliseconds for few GB of heap sizes and reasonable number of application threads(remember more active threads more roots)
G1GC is planned to be a replacement of CMS and accept goals for pauses. takes care of fragmentation by incrementally compacting the heap.Though the work is incremental so you can get smaller pauses but that may come at the cost of more frequent pauses
None of the above can compact heap(CMS does not compact at all) while application is running. AZUL GPGC garbage collection can even compact without stopping the application and also handle reference update. So if you want to go deep into how GCs work it will be worth reading the algorithm for GPGC. AZUL markets it as a pause-less collector.
All freely available GCs in openjdk have some stop the world events. And not just the GCs, other things such as deoptimizations can trigger safepoints too.
But not all pauses are equal. CMS and G1 do not need to scale their pause times with the live data set in the old generation because they only scan a subset of the objects during the pauses and do a major fraction of their work concurrently, unlike the Serial and Throughput collectors.
ZGC (available since OpenJDK11) and Shenandoah (since 12) are collectors that further decouple pause times from the live data set size and scale their pauses with only the root set size instead.
Additionally other GC implementations exist which avoid global pauses - they may still experience per-thread pauses - or make the pause durations O(1), i.e. independent of live data set size. A commonly cited example is azul's C4 collector.
So the second question comes to why the compaction needs a STW pause?
Compacting means moving objects. Moving objects means pointers need to be updated. This is very difficult or costly to achieve safely when applications threads are still running.
Concurrent algorithms generally pay some cost in throughput and complexity in exchange for their lower pause times. Not doing compactation makes CMS relatively(!) simple for a concurrent collector.
Here is a link that gives some good information about the different collectors in java 8: https://docs.oracle.com/javase/8/docs/technotes/guides/vm/gctuning/collectors.html#sthref27
All strategies will stop-the-world. But, your performance requirements can drive you to choose differing GC strategies to improve performance or response times.
Stop-the-world will occur no matter which GC algorithm you choose. Stop-the-world means that the JVM is stopping the application from running to execute a GC. When stop-the-world occurs, every thread except for the threads needed for the GC will stop their tasks.
I am building a Java web app, using the Play! Framework. I'm hosting it on playapps.net. I have been puzzling for a while over the provided graphs of memory consumption. Here is a sample:
The graph comes from a period of consistent but nominal activity. I did nothing to trigger the falloff in memory, so I presume this occurred because the garbage collector ran as it has almost reached its allowable memory consumption.
My questions:
Is it fair for me to assume that my application does not have a memory leak, as it appears that all the memory is correctly reclaimed by the garbage collector when it does run?
(from the title) Why is java waiting until the last possible second to run the garbage collector? I am seeing significant performance degradation as the memory consumption grows to the top fourth of the graph.
If my assertions above are correct, then how can I go about fixing this issue? The other posts I have read on SO seem opposed to calls to System.gc(), ranging from neutral ("it's only a request to run GC, so the JVM may just ignore you") to outright opposed ("code that relies on System.gc() is fundamentally broken"). Or am I off base here, and I should be looking for defects in my own code that is causing this behavior and intermittent performance loss?
UPDATE
I have opened a discussion on PlayApps.net pointing to this question and mentioning some of the points here; specifically #Affe's comment regarding the settings for a full GC being set very conservatively, and #G_H's comment about settings for the initial and max heap size.
Here's a link to the discussion, though you unfortunately need a playapps account to view it.
I will report the feedback here when I get it; thanks so much everyone for your answers, I've already learned a great deal from them!
Resolution
Playapps support, which is still great, didn't have many suggestions for me, their only thought being that if I was using the cache extensively this may be keeping objects alive longer than need be, but that isn't the case. I still learned a ton (woo hoo!), and I gave #Ryan Amos the green check as I took his suggestion of calling System.gc() every half day, which for now is working fine.
Any detailed answer is going to depend on which garbage collector you're using, but there are some things that are basically the same across all (modern, sun/oracle) GCs.
Every time you see the usage in the graph go down, that is a garbage collection. The only way heap gets freed is through garbage collection. The thing is there are two types of garbage collections, minor and full. The heap gets divided into two basic "areas." Young and tenured. (There are lots more subgroups in reality.) Anything that is taking up space in Young and is still in use when the minor GC comes along to free up some memory, is going to get 'promoted' into tenured. Once something makes the leap into tenured, it sits around indefinitely until the heap has no free space and a full garbage collection is necessary.
So one interpretation of that graph is that your young generation is fairly small (by default it can be a fairly small % of total heap on some JVMs) and you're keeping objects "alive" for comparatively very long times. (perhaps you're holding references to them in the web session?) So your objects are 'surviving' garbage collections until they get promoted into tenured space, where they stick around indefinitely until the JVM is well and good truly out of memory.
Again, that's just one common situation that fits with the data you have. Would need full details about the JVM configuration and the GC logs to really tell for sure what's going on.
Java won't run the garbage cleaner until it has to, because the garbage cleaner slows things down quite a bit and shouldn't be run that frequently. I think you would be OK to schedule a cleaning more frequently, such as every 3 hours. If an application never consumes full memory, there should be no reason to ever run the garbage cleaner, which is why Java only runs it when the memory is very high.
So basically, don't worry about what others say: do what works best. If you find performance improvements from running the garbage cleaner at 66% memory, do it.
I am noticing that the graph isn't sloping strictly upward until the drop, but has smaller local variations. Although I'm not certain, I don't think memory use would show these small drops if there was no garbage collection going on.
There are minor and major collections in Java. Minor collections occur frequently, whereas major collections are rarer and diminish performance more. Minor collections probably tend to sweep up stuff like short-lived object instances created within methods. A major collection will remove a lot more, which is what probably happened at the end of your graph.
Now, some answers that were posted while I'm typing this give good explanations regarding the differences in garbage collectors, object generations and more. But that still doesn't explain why it would take so absurdly long (nearly 24 hours) before a serious cleaning is done.
Two things of interest that can be set for a JVM at startup are the maximum allowed heap size, and the initial heap size. The maximum is a hard limit, once you reach that, further garbage collection doesn't reduce memory usage and if you need to allocate new space for objects or other data, you'll get an OutOfMemoryError. However, internally there's a soft limit as well: the current heap size. A JVM doesn't immediately gobble up the maximum amount of memory. Instead, it starts at your initial heap size and then increases the heap when it's needed. Think of it a bit as the RAM of your JVM, that can increase dynamically.
If the actual memory use of your application starts to reach the current heap size, a garbage collection will typically be instigated. This might reduce the memory use, so an increase in heap size isn't needed. But it's also possible that the application currently does need all that memory and would exceed the heap size. In that case, it is increased provided that it hasn't already reached the maximum set limit.
Now, what might be your case is that the initial heap size is set to the same value as the maximum. Suppose that would be so, then the JVM will immediately seize all that memory. It will take a very long time before the application has accumulated enough garbage to reach the heap size in memory usage. But at that moment you'll see a large collection. Starting with a small enough heap and allowing it to grow keeps the memory use limited to what's needed.
This is assuming that your graph shows heap use and not allocated heap size. If that's not the case and you are actually seeing the heap itself grow like this, something else is going on. I'll admit I'm not savvy enough regarding the internals of garbage collection and its scheduling to be absolutely certain of what's happening here, most of this is from observation of leaking applications in profilers. So if I've provided faulty info, I'll take this answer down.
As you might have noticed, this does not affect you. The garbage collection only kicks in if the JVM feels there is a need for it to run and this happens for the sake of optimization, there's no use of doing many small collections if you can make a single full collection and do a full cleanup.
The current JVM contains some really interesting algorithms and the garbage collection itself id divided into 3 different regions, you can find a lot more about this here, here's a sample:
Three types of collection algorithms
The HotSpot JVM provides three GC algorithms, each tuned for a specific type of collection within a specific generation. The copy (also known as scavenge) collection quickly cleans up short-lived objects in the new generation heap. The mark-compact algorithm employs a slower, more robust technique to collect longer-lived objects in the old generation heap. The incremental algorithm attempts to improve old generation collection by performing robust GC while minimizing pauses.
Copy/scavenge collection
Using the copy algorithm, the JVM reclaims most objects in the new generation object space (also known as eden) simply by making small scavenges -- a Java term for collecting and removing refuse. Longer-lived objects are ultimately copied, or tenured, into the old object space.
Mark-compact collection
As more objects become tenured, the old object space begins to reach maximum occupancy. The mark-compact algorithm, used to collect objects in the old object space, has different requirements than the copy collection algorithm used in the new object space.
The mark-compact algorithm first scans all objects, marking all reachable objects. It then compacts all remaining gaps of dead objects. The mark-compact algorithm occupies more time than the copy collection algorithm; however, it requires less memory and eliminates memory fragmentation.
Incremental (train) collection
The new generation copy/scavenge and the old generation mark-compact algorithms can't eliminate all JVM pauses. Such pauses are proportional to the number of live objects. To address the need for pauseless GC, the HotSpot JVM also offers incremental, or train, collection.
Incremental collection breaks up old object collection pauses into many tiny pauses even with large object areas. Instead of just a new and an old generation, this algorithm has a middle generation comprising many small spaces. There is some overhead associated with incremental collection; you might see as much as a 10-percent speed degradation.
The -Xincgc and -Xnoincgc parameters control how you use incremental collection. The next release of HotSpot JVM, version 1.4, will attempt continuous, pauseless GC that will probably be a variation of the incremental algorithm. I won't discuss incremental collection since it will soon change.
This generational garbage collector is one of the most efficient solutions we have for the problem nowadays.
I had an app that produced a graph like that and acted as you describe. I was using the CMS collector (-XX:+UseConcMarkSweepGC). Here is what was going on in my case.
I did not have enough memory configured for the application, so over time I was running into fragmentation problems in the heap. This caused GCs with greater and greater frequency, but it did not actually throw an OOME or fail out of CMS to the serial collector (which it is supposed to do in that case) because the stats it keeps only count application paused time (GC blocks the world), application concurrent time (GC runs with application threads) is ignored for those calculations. I tuned some parameters, mainly gave it a whole crap load more heap (with a very large new space), set -XX:CMSFullGCsBeforeCompaction=1, and the problem stopped occurring.
Probably you do have memory leaks that's cleared every 24 hours.
We've spent the last few months tuning our production application so that we experience no full GCs. We now experience young GCs only, with the rate of young GCs dependent on the rate of object allocation.
Our application needs to be as close to "real-time" as possible so now we're trying to reduce the number of young GCs. As the old axiom goes, most of the data we allocate ends up being garbage and discarded at the next young GC. So no need to preallocate for this type of data. However there is a good amount of objects (defined by type) that we know will make it from young GC to old GC.
Would it make sense to preallocate these objects during more ideal times (i.e. at startup) so we'll end up allocating less during our less-than-ideal times? I've read the literature that mentions how object pooling is not recommended with the latest JVMs because allocation is much cheaper. What are the drawbacks to preallocating objects that I know will make it to the old GC?
Reducing the rate of allocation makes GC "pauses" less frequent, but not shorter. For smoother "real-time" operation, you may actually want to increase the number of GC invocations: this is trading more GC-related CPU in order to get shorter pauses. Sun's JVM can be tuned with various options; I suggest trying -XX:NewRatio to make the young generation smaller.
The usual argument against pooling is that you are basically trying to write your own allocator, hoping that you will do a better job at it than the JVM allocator. It is justified in some specific cases where allocation is expensive, e.g. creating Thread instances.
Just a note that there is a realtime jvm available. If your app needs predictable performance then this is a worth looking into.