可以输出所有内存中对象的工具，甚至可以将VM 中的heap，以二进制输出成文本。使用方法 jmap -histo pid。如果连用SHELL jmap -histo pid>a.log可以将其保存到文本中去，在一段时间后，使用文本对比工具，可以对比出GC回收了哪些对象。jmap -dump:format=b,file=outfile 3024可以将3024进程的内存heap输出出来到outfile文件里，再配合MAT（内存分析工具(Memory Analysis Tool），使用参见：http://blog.csdn.net/fenglibing/archive/2011/04/02/6298326.aspx）或与jhat (Java Heap Analysis Tool)一起使用，能够以图像的形式直观的展示当前内存是否有问题。

查看该进程(pid)下堆内存的使用情况：

jmap -heap pid

快速定位内存泄漏的方法：只统计存活的对象

jmap -histo:live pid

还可以导出：

jmap -histo:live pid >1.txt将信息输出到指定文件中

2、命令格式

SYNOPSIS

jmap [ option ] pid

jmap [ option ] executable core

jmap [ option ] [server-id@]remote-hostname-or-IP

3、参数说明

1)、options：

executable Java executable from which the core dump was produced.

(可能是产生core dump的java可执行程序)

core 将被打印信息的core dump文件

remote-hostname-or-IP 远程debug服务的主机名或ip

server-id 唯一id,假如一台主机上多个远程debug服务

2）、基本参数：

-dump:[live,]format=b,file=<filename> 使用hprof二进制形式,输出jvm的heap内容到文件=. live子选项是可选的，假如指定live选项,那么只输出活的对象到文件.

-finalizerinfo 打印正等候回收的对象的信息.

-heap 打印heap的概要信息，GC使用的算法，heap的配置及wise heap的使用情况.

-histo[:live] 打印每个class的实例数目,内存占用,类全名信息. VM的内部类名字开头会加上前缀”*”. 如果live子参数加上后,只统计活的对象数量.

-permstat 打印classload和jvm heap长久层的信息. 包含每个classloader的名字,活泼性,地址,父classloader和加载的class数量. 另外,内部String的数量和占用内存数也会打印出来.

-F 强迫.在pid没有相应的时候使用-dump或者-histo参数. 在这个模式下,live子参数无效.

-h | -help 打印辅助信息

-J 传递参数给jmap启动的jvm.

pid 需要被打印配相信息的java进程id,创业与打工的区别 - 博文预览,可以用jps查问.

4、使用示例

tasklist命令查看进程id(Tasklist"是 winxp/win2003/vista/win7/win8下的命令,用来显示运行在本地或远程计算机上的所有进程，带有多个执行参数。)

常用的参数如下：

histo

jmap -histo pid 展示class的内存情况

展示的信息为编号，实例数，字节，类名

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" 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-finalizerinfo

打印等待回收的对象信息

jmap -finalizerinfo

Attaching to process ID , please wait...

Debugger attached successfully.

Server compiler detected.

JVM version is 25.171-b11

Number of objects pending for finalization:

[root@ip---- applogs]#

heap

jmap -heap pid 展示pid的整体堆信息

    jmap -heap 2464

    JVM version is 16.3-b01  

    using thread-local object allocation.

    Parallel GC with 13 thread(s)  

    Heap Configuration:

       MinHeapFreeRatio = 40

       MaxHeapFreeRatio = 70

       MaxHeapSize      = 8436842496 (8046.0MB)

       NewSize          = 5439488 (5.1875MB)

       MaxNewSize       = 17592186044415 MB

       OldSize          = 5439488 (5.1875MB)

       NewRatio         = 2

       SurvivorRatio    = 8

       PermSize         = 21757952 (20.75MB)

       MaxPermSize      = 88080384 (84.0MB)  

    Heap Usage:

    PS Young Generation

    Eden Space:

       capacity = 87883776 (83.8125MB)

       used     = 31053080 (29.614524841308594MB)

       free     = 56830696 (54.197975158691406MB)

       35.33425782706469% used

    From Space:

       capacity = 13828096 (13.1875MB)

       used     = 196608 (0.1875MB)

       free     = 13631488 (13.0MB)

       1.4218009478672986% used

    To Space:

       capacity = 16384000 (15.625MB)

       used     = 0 (0.0MB)

       free     = 16384000 (15.625MB)

       0.0% used

    PS Old Generation

       capacity = 156172288 (148.9375MB)

       used     = 27098208 (25.842864990234375MB)

       free     = 129074080 (123.09463500976562MB)

       17.35148299805917% used

    PS Perm Generation

       capacity = 88080384 (84.0MB)

       used     = 50847592 (48.492042541503906MB)

       free     = 37232792 (35.507957458496094MB)

       57.728622073218936% used

说明如下

Parallel GC with  thread(s)   #13个gc线程  

Heap Configuration:#堆内存初始化配置

   MinHeapFreeRatio =   #-XX:MinHeapFreeRatio设置JVM堆最小空闲比率

   MaxHeapFreeRatio =   #-XX:MaxHeapFreeRatio设置JVM堆最大空闲比率

   MaxHeapSize      =  (.0MB)#-XX:MaxHeapSize=设置JVM堆的最大大小

   NewSize          =  (.1875MB) #-XX:NewSize=设置JVM堆的‘新生代’的默认大小

   MaxNewSize       =  MB  #-XX:MaxNewSize=设置JVM堆的‘新生代’的最大大小

   OldSize          =  (.1875MB) #-XX:OldSize=设置JVM堆的‘老生代’的大小

   NewRatio         =  #-XX:NewRatio=:‘新生代’和‘老生代’的大小比率

   SurvivorRatio    =  #-XX:SurvivorRatio=设置年轻代中Eden区与Survivor区的大小比值

   PermSize         =  (.75MB) #-XX:PermSize=<value>:设置JVM堆的‘永生代’的初始大小

   MaxPermSize      =  (.0MB) #-XX:MaxPermSize=<value>:设置JVM堆的‘永生代’的最大大小  

Heap Usage:

PS Young Generation

Eden Space:#Eden区内存分布

   capacity =  (.8125MB)

   used     =  (.614524841308594MB)

   free     =  (.197975158691406MB)

   35.33425782706469% used

From Space:#其中一个Survivor区的内存分布

   capacity =  (.1875MB)

   used     =  (.1875MB)

   free     =  (.0MB)

   1.4218009478672986% used

To Space:#另一个Survivor区的内存分布

   capacity =  (.625MB)

   used     =  (.0MB)

   free     =  (.625MB)

   0.0% used

PS Old Generation#当前的Old区内存分布

   capacity =  (.9375MB)

   used     =  (.842864990234375MB)

   free     =  (.09463500976562MB)

   17.35148299805917% used

PS Perm Generation#当前的 “永生代” 内存分布

   capacity =  (.0MB)

   used     =  (.492042541503906MB)

   free     =  (.507957458496094MB)

   57.728622073218936% used

mat为eclipse的一个内存分析插件，帮助查找内存泄漏和减少内存消耗。

首先基于jmap导出的堆信息

jmap -dump:live,format=b,file=test.bin

准备代码：

class User {

    private String id;

    private String name;  

    public String getId() {

        return id;

    }  

    public void setId(String id) {

        this.id = id;

    }  

    public String getName() {

        return name;

    }  

    public void setName(String name) {

        this.name = name;

    }  

    public User(String id, String name) {

        super();

        this.id = id;

        this.name = name;

    }  

}

main方法：

public static void main(String[] args) {

    List<User> list = new ArrayList<User>();

    for (int i = 1; i < 10000; i++) {

        User o = new User(i + "", System.currentTimeMillis() + "");

        list.add(o);

        o = null;

    }

    System.out.println("end");

    try {

        Thread.sleep(100000000l);

    } catch (InterruptedException e) {

        e.printStackTrace();

    }

}

执行之后用jmap输出堆信息

然后导入分析工具

我们可以看到图形化展示：

然后我们点击

Problem Suspect 1

如下所示：

然后点击详情

我们可以看到有很多的User对象

这些对象有可能会溢出，然后我们打开OQL窗口看他是否为null，执行如下OQL语句

SELECT u FROM org.learn.util.User u WHERE (u.value = null)

也就是说这个是null，但是仍然有强引用存在，gc的时候是不能回收的，这样就会出现内存的溢出问题

示例2：如何用mat分析内存问题

我用MAT打开了heap.bin，很容易看出，char[]的数量出其意料的多，占用90%以上的内存。一般来说，char[]在JVM确实会占用很多内存，数量也非常多，因为String对象以char[]作为内部存储。但是这次的char[]太贪婪了，仔细一观察，发现有数万计的char[]，每个都占用数百K的内存。这个现象说明，Java程序保存了数以万计的大String对象。结合程序的逻辑，这个是不应该的，肯定在某个地方出了问题。

在可疑的char[]中，任意挑了一个，使用Path To GC Root功能，找到该char[]的引用路径，发现String对象是被一个HashMap中引用的。这个也是意料中的事情，Java的内存泄露多半是因为对象被遗留在全局的HashMap中得不到释放。不过，该HashMap被用作一个缓存，设置了缓存条目的阈值，导达到阈值后会自动淘汰。从这个逻辑分析，应该不会出现内存泄露的。虽然缓存中的String对象已经达到数万计，但仍然没有达到预先设置的阈值（阈值设置地比较大，因为当时预估String对象都比较小）。

但是，另一个问题引起了我的注意：为什么缓存的String对象如此巨大？内部char[]的长度达数百K。虽然缓存中的 String对象数量还没有达到阈值，但是String对象大小远远超出了我们的预期，最终导致内存被大量消耗，形成内存泄露的迹象（准确说应该是内存消耗过多）。

就这个问题进一步顺藤摸瓜，看看String大对象是如何被放到HashMap中的。通过查看程序的源代码，我发现，确实有String大对象，不过并没有把String大对象放到HashMap中，而是把String大对象进行split（调用String.split方法），然后将split出来的String小对象放到HashMap中了。

这就奇怪了，放到HashMap中明明是split之后的String小对象，怎么会占用那么大空间呢？难道是String类的split方法有问题？