今天在一个博客中看到一个程序，使用mapped file机制，创建超大的矩阵，主要是为了节省内存，避免内存溢出异常。主要代码如下：

/** *  */package high.performace.java;import java.io.Closeable;import java.io.IOException;import java.io.RandomAccessFile;import java.nio.MappedByteBuffer;import java.nio.channels.FileChannel;import java.util.ArrayList;import java.util.List;import sun.misc.Cleaner;import sun.nio.ch.DirectBuffer;public class LargeDoubleMatrix implements Closeable {    private static final int MAPPING_SIZE = 1 << 30;    private final RandomAccessFile raf;    private final int width;    private final int height;    private final List mappings = new ArrayList();    public LargeDoubleMatrix(String filename, int width, int height) throws IOException {        this.raf = new RandomAccessFile(filename, "rw");        try {            this.width = width;            this.height = height;            long size = 8L * width * height;            for (long offset = 0; offset < size; offset += MAPPING_SIZE) {                long size2 = Math.min(size - offset, MAPPING_SIZE);                mappings.add(raf.getChannel().map(FileChannel.MapMode.READ_WRITE, offset, size2));            }        } catch (IOException e) {            raf.close();            throw e;        }    }    protected long position(int x, int y) {        return (long) y * width + x;    }    public int width() {        return width;    }    public int height() {        return height;    }    public double get(int x, int y) {        assert x >= 0 && x < width;        assert y >= 0 && y < height;        long p = position(x, y) * 8;        int mapN = (int) (p / MAPPING_SIZE);        int offN = (int) (p % MAPPING_SIZE);        return mappings.get(mapN).getDouble(offN);    }    public void set(int x, int y, double d) {        assert x >= 0 && x < width;        assert y >= 0 && y < height;        long p = position(x, y) * 8;        int mapN = (int) (p / MAPPING_SIZE);        int offN = (int) (p % MAPPING_SIZE);        mappings.get(mapN).putDouble(offN, d);    }    public void close() throws IOException {        for (MappedByteBuffer mapping : mappings)            clean(mapping);        raf.close();    }    private void clean(MappedByteBuffer mapping) {        if (mapping == null) return;        Cleaner cleaner = ((DirectBuffer) mapping).cleaner();        if (cleaner != null) cleaner.clean();    }}

 

package high.performace.java;import java.io.IOException;public class LargeDoubleMatrixTest {	public void getSetMatrix(int x, int y) throws IOException {		long start = System.nanoTime();		final long used0 = usedMemory();		LargeDoubleMatrix matrix = new LargeDoubleMatrix("ldm.test", x * x, y * y);		for (int i = 0; i < matrix.width(); i++)			matrix.set(i, i, i);		for (int i = 0; i < matrix.width(); i++)			assert matrix.get(i, i) == i;		long time = System.nanoTime() - start;		final long used = usedMemory() - used0;		if (used == 0)			System.err.println("You need to use -XX:-UseTLAB to see small changes in memory usage.");		System.out.printf(				"Setting the diagonal took %,d ms, Heap used is %,d KB%n",				time / 1000 / 1000, used / 1024);		matrix.close();	}	private long usedMemory() {		return Runtime.getRuntime().totalMemory() - Runtime.getRuntime().freeMemory();	}		public static void main(String[] args) throws IOException {		new LargeDoubleMatrixTest().getSetMatrix(Integer.parseInt(args[0]), Integer.parseInt(args[0]));	}}

  主要的核心思想，就是使用mapped file存储矩，由于mapped file不是存储在heap中的，极大的减小了heap的使用，避免了内存溢出的异常。而且能够减小内存的整体使用。这个思路，在做大数据存储计算的时候，很值得借鉴。例如java实现的cassandra在服务器端就采用了mapped的方式来存储数据。 ［引用］