Last week Terracotta released BigMemory, which stores objects outside of the Java heap and “defuses the GC time bomb.” Since I've written the top-ranked relevant article when you Google for “non-heap memory” (it's on page 2), my website has seen a spike in volume. Unlike previous spikes, people have been poking around, which is nice to see.
I did some poking around myself, both to Terracotta's blogs, and to some of the Twitter posts and articles that linked to my site. And I saw a lot of comments that disturbed me. They ranged from (paraphrased) “this is just hype, it isn't anything new,” to “I wonder if Oracle will file a patent lawsuit on this,” to “the JVM should be doing this itself,” to the ever-popular “people should just learn to write efficient code.” I'll take on that last one first.
But first some caveats: I've never used EHCache outside of Hibernate, and even then I simply followed a cookbook. My closest contact with EHCache development was attending a Java Users Group meeting with a badly jetlagged Greg Luck, followed by beer with a him and a bunch of other people. I can't remember what was discussed around the table, but I don't think it was cache internals. But I do know something about caches, and about the way that the JVM manages memory, so I feel at least qualified to comment on the comments.
And my first comment is: a cache is efficient code … when appropriately used. You can spend days micro-optimizing your code, only to find it all wasted with a single trip to the database.* You use a cache when it's expensive to retrieve or create often-used data; the L1 and L2 caches in your processor exist because memory is relatively slow, so it's expensive to retrieve. Similarly, it's expensive to create a web page (and load its data from the database), so you would use EHCache (or Akamai) to cache the pre-built page (EHCache is cheaper).
The interface to a cache is pretty straightforward: you give it a key, it gives you a value. In the case of the processor's L1 cache, the key is an address, the value is the bytes around that address. In the case of memcached, the key is a string and the value is a blob. In Java, this corresponds to a
Map, and you can create a simple cache using a LinkedHashMap.
The reason to use a
LinkedHashMap, versus a regular
HashMap, is eviction: removing old entries from the map. Although memory is cheap, it's not infinite, and there's no good reason to keep every piece of data ever accessed. For example, consider Stack Overflow: when a question is on the home page, it might get hundreds of views; clearly you don't want to keep reading it from the database. But after it drops off the home page, it might get viewed once a week, so you don't want to keep it in memory.
LinkedHashMap, you can implement a simple eviction strategy: once the map reaches a certain size, you remove the head entry on each new
put(). More sophisticated caches use more sophisticated strategies: for example, weighting the life of a cached object by the amount of work that it takes to load from source. In my opinion, this is why you go with a pre-built solution like EHCache: they've thought through the eviction strategies and neatly packaged them.
So that's how a cache works. What's interesting is how it interacts with the garbage collector. The point of a cache is to hold content in memory for as long as it's valuable; for a generational garbage collector like Sun's, this means it will end up in the tenured generation … but eventually get removed. And this causes two issues.
First is the cost to find the garbage. The Hotspot GC is a “mark-sweep” collector: it starts at “root” references and traces through the entire object graph to find live objects; everything else is garbage (this is all covered in my article on reference objects; go back and click the link). If you have tens or hundreds of millions of objects in your heap (and for an eCommerce site, that's not an unreasonable number), it's going to take some time to find them all: a few milliseconds. This is, as I understand it, the price that you pay for any collection, major or minor.
A major collection, however, performs one additional step: it compacts the heap after collecting the garbage. This is a big deal. No matter how fast your CPU and memory, it takes time to move gigabytes of memory around. Particularly since the JVM is updating all the references to the moved objects. But if you've ever written a C application that fragmented its heap, you'll thank the Hotspot team every time you see a major collection take place.
Or maybe you won't. Heap compaction is an artifact of tuning the JVM for turn-of-the-century computers, where 1Gb of main memory was “server class,” and swap (the pagefile) was an important part of making your computer usable for multiple applications. Compacting the heap was a way to reduce the resident set size, and improve the overall performance of an application.**
Today, of course, we have desktop-class machines with multiple gigabytes, running an OS and JVM that allow direct access to all of it (and more). And there's been a lot of work in the past few decades to reduce the risk of fragmentation with C-style memory allocation. So maybe it is time to rethink garbage collection in the JVM, at least for the tenured generation, and introduce freelists (of course, that will inflame the “Java is a memory hog” crowd). Or maybe it's time to introduce a new generation, for large tenured objects. Maybe in JDK 1.8. I'm not holding my breath, and neither, it seems, were the EHCache developers.
So to summarize: EHCache has taken techniques that are well-known, and applied them to the Java world. If I were still working in the eCommerce field, I would consider this a Good Thing, and be glad that they invested the time and effort to make it work. Oh, and as far as patents go, I suspect that IBM invented memory-mapped files sometime in the 1960s, so no need to worry.
If you work with Swing apps, you'll often find a minimize handler that explicitly calls
System.gc(); this assumes that the app's pages will be swapped out while minimized, and is an attempt to reduce restore time.