Skip to content. Change Language. Related Articles. Table of Contents. Save Article. Improve Article. Over 2 million developers have joined DZone. Want more insight into your JVMs? Of course you do. Fortunately, there are easy ways to see which GC algorithm is in use as well as more general heap information. Like Join the DZone community and get the full member experience. Join For Free. There are, of course, still quite some implementation details that we have left out for brevity, like dealing with humongous objects.
All things considered, G1 is the most technologically advanced production-ready collector available in HotSpot. On top of that, it is being relentlessly improved by the HotSpot Engineers, with new optimizations or features coming in with newer java versions. As we have seen, G1 addressed a wide range of problems that CMS has, starting from pause predictability and ending with heap fragmentation. Given an application not constrained by CPU utilization, but very sensitive to the latency of individual operations, G1 is very likely to be the best available choice for HotSpot users, especially when running the latest versions of Java.
However, these latency improvements do not come for free: throughput overhead of G1 is larger thanks to the additional write barriers and more active background threads. The only viable way to select the right GC algorithm and settings is through trial and errors, but we do give the general guidelines in the next chapter. We have outlined all of the production-ready algorithms in HotSpot that you can just take and use right away.
It is aimed for large multi-core machines with large heaps, the goal is to manage heaps of GB and larger with pauses of 10ms or shorter. We are not going to go into the implementation details before the new algorithm is released as production-ready, but it also builds upon many of the ideas already covered in earlier chapters, such as concurrent marking and incremental collecting.
It does a lot of things differently, however. It does not split the heap into multiple generations, instead having a single space only. This allows it to get rid of card tables and remembered sets.
It also uses forwarding pointers and a Brooks style read barrier to allow for concurrent copying of live objects, thus reducing the number and duration of pauses. Plumbr has been acquired by Splunk and no longer offers trial access to its product. Read more here. These cookies give us information about how you use our website and allow us to improve the user experience.
To learn more about our cookie usage click here. Splunk Completes Acquisition of Plumbr Learn more. Log In Continue Monitoring. Outofmemoryerror Java Garbage Collection handbook. Handbook menu What Is Garbage Collection? Why Should I Care? What is the Solution? GC Algorithms: Implementations Now that we have reviewed the core concepts behind GC algorithms, let us move to the specific implementations one can find inside the JVM.
MyExecutableClass This option makes sense and is recommended only for the JVM with a couple of hundreds megabytes heap size, running in an environment with a single CPU. Measured in seconds.
This time it is indicating that this was a Minor GC. Allocation Failure — Cause of the collection. In this case, the GC is triggered due to a data structure not fitting into any region in the Young Generation. DefNew — Name of the garbage collector used. This cryptic name stands for the single-threaded mark-copy stop-the-world garbage collector used to clean Young generation.
As Serial Garbage Collector always uses just a single thread, real time is thus equal to the sum of user and system times. For this collection the same DefNew collector was run as before and it decreased the usage of the Young Generation from K to 0.
Notice that JVM reports this incorrectly due to buggy behavior and instead reports the Young Generation as being completely full. This collection took 0. Tenured — Name of the garbage collector used to clean the Old space. The name Tenured indicates a single-threaded stop-the-world mark-sweep-compact garbage collector being used. As seen, no garbage was collected in Metaspace during the event. PSYoungGen — Name of the garbage collector used, representing a parallel mark-copy stop-the-world garbage collector used to clean the Young generation.
In this particular case 8 threads were used. Note that due to some activities not being parallelizable, it always exceeds the ratio by a certain amount. In this case we can see the event started right after the previous Minor GC finished.
Ergonomics — Reason for the GC taking place. This indicates that the JVM internal ergonomics decided this is the right time to collect some garbage. In this case, parallel mark-sweep-compact stop-the-world garbage collector named ParOldGen was used. As we can see, no garbage was collected in Metaspace during this event. In this case, the GC is triggered due to a requested allocation not fitting into any region in Young Generation. This includes communication overhead with ConcurrentMarkSweep collector, promotion of objects that are old enough to the Old Generation and some final cleanup at the end of the garbage collection cycle.
For the following phases the same notion is used throughout the event and is thus skipped for brevity. It is interesting to note that the user time reported is a lot smaller than clock time. Usually we have seen that real time is less than user time, meaning that some work was done in parallel and so elapsed clock time is less than used CPU time. Here we have a little amount of work — for 0.
Essentially, they were trying to stave off for as long as possible before having to do an STW pause. By default, this phase may last for up to 5 seconds. In this case the rescan was done in parallel and took 0. Clock time of the pause is also included. The pause started ms after the JVM startup and the duration of the pause was 0.
Should always be near-zero. This is done sequentially. Normally they should be roughly equal, otherwise it may be an indication of too many threads hanging around or a noisy neighbor. If this number is high, then the concurrent GC threads are unable to handle the load. It is simple, but because the dead objects are not necessarily next to each other, we end up having a fragmented memory. This algorithm fixes the problem with fragmented memory.
After all alive objects are marked, they are moved to the beginning of the memory space. Copying objects and updating all references to them take time and it all happens during STW pause. Mark-copy algorithm copies all alive objects to a new memory region. The previously occupied region is considered to be free. Next time mark-copy is executed, all the alive objects are moved back to the previous memory region.
As you can imagine, this, of course, leads to a memory compaction. Unfortunately, it requires additional extra region large enough to fit all live objects at any given point in time.
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