Java Native Memory 比堆快吗？

Question

I'm exploring options to help my memory-intensive application, and in doing so I came across Terracotta's BigMemory . From what I gather, they take advantage of non-garbage-collected, off-heap "native memory," and apparently this is about 10x slower than heap-storage due to serialization/deserialization issues. Prior to reading about BigMemory, I'd never heard of "native memory" outside of normal JNI. Although BigMemory is an interesting option that warrants further consideration, I'm intrigued by what could be accomplished with native memory if the serialization issue could be bypassed.

Is Java native memory faster (I think this entails ByteBuffer objects?) than traditional heap memory when there are no serialization issues (for instance if I am comparing it with a huge byte[] )? Or do the vagaries of garbage collection, etc. render this question unanswerable? I know "measure it" is a common answer around here, but I'm afraid I would not set up a representative test as I don't yet know enough about how native memory works in Java.

Answer 1

Direct memory is faster when performing IO because it avoid one copy of the data. However, for 95% of application you won't notice the difference.

You can store data in direct memory, however it won't be faster than storing data POJOs. (or as safe or readable or maintainable) If you are worried about GC, try creating your objects (have to be mutable) in advance and reuse them without discarding them. If you don't discard your objects, there is nothing to collect.

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Is Java native memory faster (I think this entails ByteBuffer objects?) than traditional heap memory when there are no serialization issues (for instance if I am comparing it with a huge byte[])?

Direct memory can be faster than using a byte[] if you use use non bytes like int as it can read/write the whole four bytes without turning the data into bytes. However it is slower than using POJOs as it has to bounds check every access.

Or do the vagaries of garbage collection, etc. render this question unanswerable?

The speed has nothing to do with the GC. The GC only matters when creating or discard objects.

BTW: If you minimise the number of object you discard and increase your Eden size, you can prevent even minor collection occurring for a long time eg a whole day.

Answer 2

The point of BigMemory is not that native memory is faster, but rather, it's to reduce the overhead of the garbage collector having to go through the effort of tracking down references to memory and cleaning it up. As your heap size increases, so do your GC intervals and CPU commitment. Depending upon the situation, this can create a sort of "glass ceiling" where the Java heap gets so big that the GC turns into a hog, taking up huge amounts of processor power each time the GC kicks in. Also, many GC algorithms require some level of locking that means nobody can do anything until that portion of the GC reference tracking algorithm finishes, though many JVM's have gotten much better at handling this. Where I work, with our app server and JVM's, we found that the "glass ceiling" is about 1.5 GB. If we try to configure the heap larger than that, the GC routine starts eating up more than 50% of total CPU time, so it's a very real cost. We've determined this through various forms of GC analysis provided by our JVM vendor.

BigMemory, on the other hand, takes a more manual approach to memory management. It reduces the overhead and sort of takes us back to having to do our own memory cleanup, as we did in C, albeit in a much simpler approach akin to a HashMap. This essentially eliminates the need for a traditional garbage collection routine, and as a result, we eliminate that overhead. I believe that the Terracotta folks used native memory via a ByteBuffer as it's an easy way to get out from under the Java garbage collector.

The following whitepaper has some good info on how they architected BigMemory and some background on the overhead of the GC: http://www.terracotta.org/resources/whitepapers/bigmemory-whitepaper .

Answer 3

I'm intrigued by what could be accomplished with native memory if the serialization issue could be bypassed.

I think that your question is predicated on a false assumption. AFAIK, it is impossible to bypass the serialization issue that they are talking about here. The only thing you could do would be to simplify the objects that you put into BigMemory and use custom serialization / deserialization code to reduce the overheads.

While benchmarks might give you a rough idea of the overheads, the actual overheads will be very application specific. My advice would be:

• Only go down this route if you know you need to. (You will be tying your application to a particular implementation technology.)

• Be prepared for some intrusive changes to your application if the data involved isn't already managed using as a cache.

• Be prepared to spend some time in (re-)tuning your caching code to get good performance with BigMemory.

• If your data structures are complicated, expect a proportionately larger runtime overheads and tuning effort.