Dumping buffered data when memory limit is near_问答_开发者

开发者_运维知识库My application buffers data for likely requests in the background. Currently I limit the size of the buffer based on a command-line parameter, and begin dumping less-used data when we hit this limit. This is not ideal because it relies on the user to specify a performance-critical parameter. Is there a better way to handle this? Is there a way to automatically monitor system memory use and dump the oldest/least-recently-used data before the system starts to thrash?

A complicating factor here is that my application runs on Linux, OSX, and Windows. But I'll take a good way to do this on only one platform over nothing.

Your best bet would likely be to monitor your applications working set/resident set size, and try to react when it doesn't grow after your allocations. Some pointers on what to look for:

Windows: GetProcessMemoryInfo
Linux: /proc/self/statm
OS X: task_info()

Windows also has GlobalMemoryStatusEx which gives you a nice Available Physical Memory figure.

I like your current solution. Letting the user decide is good. It's not obvious everyone would want the buffer to be as big as possible, is it? If you do invest in implemting some sort of memory monitor for automatically adjusting the buffer/cache size, at least let the user choose between the user set limit and the automatic/dynamic one.

I know this isn't a direct answer, but I'd say step back a bit and maybe don't do this.

Even if you have the API to see current physical memory usage, that's not enough to choose an ideal cache size. That would depend on your typical and future workloads for both the program and the machine (and the overall system of all clients running this program + the server(s) they're querying), the platform's caching behavior, whether the system should be tuned for throughput or latency, and so on. In a tight memory situation, you're going to be competing for memory with other opportunistic caches, including the OS's disk cache. On the one hand, you want to be exerting some pressure on them, to force out other low-value data. On the other hand, if you get greedy while there's plenty of memory, you're going to be affecting the behavior of other adaptive caches.

And with speculative caching/prefetching, the LRU value function is odd: you will (hopefully) fetch the most-likely-to-be-called data first, and less-likely data later, so the LRU data in your prefetch cache may be less valuable than older data. This could lead to perverse behavior in the systemwide set of caches by artificially "heating up" less commonly used data.

It seems unlikely that your program would be able to make a cache size choice better than a simple fixed size, perhaps scaled based on the size of overall physical memory on the machine. And there's very little chance it could beat a sysadmin who knows the machine's typical workload and its performance goals.

Using an adaptive cache sizing strategy means that your program's resource usage is going to be both variable and unpredictable. (With respect to both memory and the I/O and server requests used to populate that prefetch cache.) For a lot of server situations, that's not good. (Especially in HPC or DB servers, which this sounds like it might be for, or a high-utilization/high-throughput environment.) Consistency, configurability, and availability are often more important than maximum resource utilization. And locality of reference tends to fall off quickly, so you're likely getting very diminishing returns with larger cache sizes. If this is going to be used server-side, at least leave the option for explicit control of cache sizes, and probably make that the default, if not only, option.

There is a way: it is called virtual memory (vm). All three operating systems listed will use virtual memory (vm), unless there is no hardware support (which may be true in embedded systems). So I will assume that vm support is present.

Here is a quote from the architecture notes of the Varnish project:

The really short answer is that computers do not have two kinds of storage any more.

I would suggest you read the full text here: http://www.varnish-cache.org/trac/wiki/ArchitectNotes

It is a good read, and I believe will answer your question.

You could attempt to allocate some large-ish block of memory then check for a memory allocation exception. If the exception occurs, dump data. The problem is, this will only work when all system memory (or process limit) is reached. This means your application is likely to start swapping.

try {
  char *buf = new char[10 * 1024 * 1024]; // 10 megabytes
  free(buf);
} catch (const std::bad_alloc &) {
  // Memory allocation failed - clean up old buffers
}

The problems with this approach are:

Running out of system memory can be dangerous and cause random applications to be shut down
Better memory management might be a better solution. If there is data that can be freed, why has it not already been freed? Is there a periodic process you could run to clean up unneeded data?