Method for finding memory leak in large Java heap

问题:

I have to find a memory leak in a Java application. I have some experience with this but would like advice on a methodology/strategy for this. Any reference and advice is welcome.

About our situation:

1. Heap dumps are larger than 1 GB
2. We have heap dumps from 5 occasions.
3. We don't have any test case to provoke this. It only happens in the (massive) system test environment after at least a weeks usage.
4. The system is built on a internally developed legacy framework with so many design flaws that they are impossible to count them all.
5. Nobody understands the framework in depth. It has been transfered to one guy in India who barely keeps up with answering e-mails.
6. We have done snapshot heap dumps over time and concluded that there is not a single component increasing over time. It is everything that grows slowly.
7. The above points us in the direction that it is the frameworks homegrown ORM system that increases its usage without limits. (This system maps objects to files?! So not really a ORM)

Question: What is the methodology that helped you succeed with hunting down leaks in a enterprise scale application?

回答1:

It's almost impossible without some understanding of the underlying code. If you understand the underlying code, then you can better sort the wheat from chaff of the zillion bits of information you are getting in your heap dumps.

Also, you can't know if something is a leak or not without know why the class is there in the first place.

I just spent the past couple of weeks doing exactly this, and I used an iterative process.

First, I found the heap profilers basically useless. They can't analyze the enormous heaps efficiently.

Rather, I relied almost solely on jmap histograms.

I imagine you're familiar with these, but for those not:

jmap -histo:live <pid> > dump.out

creates a histogram of the live heap. In a nutshell, it tells you the class names, and how many instances of each class are in the heap.

I was dumping out heap regularly, every 5 minutes, 24hrs a day. That may well be too granular for you, but the gist is the same.

I ran several different analyses on this data.

I wrote a script to take two histograms, and dump out the difference between them. So, if java.lang.String was 10 in the first dump, and 15 in the second, my script would spit out "5 java.lang.String", telling me it went up by 5. If it had gone down, the number would be negative.

I would then take several of these differences, strip out all classes that went down from run to run, and take a union of the result. At the end, I'd have a list of classes that continually grew over a specific time span. Obviously, these are prime candidates for leaking classes.

However, some classes have some preserved while others are GC'd. These classes could easily go up and down in overall, yet still leak. So, they could fall out of the "always rising" category of classes.

To find these, I converted the data in to a time series and loaded it in a database, Postgres specifically. Postgres is handy because it offers statistical aggregate functions, so you can do simple linear regression analysis on the data, and find classes that trend up, even if they aren't always on top of the charts. I used the regr_slope function, looking for classes with a positive slope.

I found this process very successful, and really efficient. The histograms files aren't insanely large, and it was easy to download them from the hosts. They weren't super expensive to run on the production system (they do force a large GC, and may block the VM for a bit). I was running this on a system with a 2G Java heap.

Now, all this can do is identify potentially leaking classes.

This is where understanding how the classes are used, and whether they should or should not be their comes in to play.

For example, you may find that you have a lot of Map.Entry classes, or some other system class.

Unless you're simply caching String, the fact is these system classes, while perhaps the "offenders", are not the "problem". If you're caching some application class, THAT class is a better indicator of where your problem lies. If you don't cache com.app.yourbean, then you won't have the associated Map.Entry tied to it.

Once you have some classes, you can start crawling the code base looking for instances and references. Since you have your own ORM layer (for good or ill), you can at least readily look at the source code to it. If you ORM is caching stuff, it's likely caching ORM classes wrapping your application classes.

Finally, another thing you can do, is once you know the classes, you can start up a local instance of the server, with a much smaller heap and smaller dataset, and using one of the profilers against that.

In this case, you can do unit test that affects only 1 (or small number) of the things you think may be leaking. For example, you could start up the server, run a histogram, perform a single action, and run the histogram again. You leaking class should have increased by 1 (or whatever your unit of work is).

A profiler may be able to help you track the owners of that "now leaked" class.

But, in the end, you're going to have to have some understanding of your code base to better understand what's a leak, and what's not, and why an object exists in the heap at all, much less why it may be being retained as a leak in your heap.

回答2:

Take a look at Eclipse Memory Analyzer. It's a great tool (and self contained, does not require Eclipse itself installed) which 1) can open up very large heaps very fast and 2) has some pretty good automatic detection tools. The latter isn't perfect, but EMA provides a lot of really nice ways to navigate through and query the objects in the dump to find any possible leaks.

I've used it in the past to help hunt down suspicious leaks.

回答3:

This answer expands upon @Will-Hartung's. I applied to same process to diagnose one of my memory leaks and thought that sharing the details would save other people time.

The idea is to have postgres 'plot' time vs. memory usage of each class, draw a line that summarizes the growth and identify the objects that are growing the fastest:

    ^
    |
s   |  Legend:
i   |  *  - data point
z   |  -- - trend
e   |
(   |
b   |                 *
y   |                     --
t   |                  --
e   |             * --    *
s   |           --
)   |       *--      *
    |     --    *
    |  -- *
   --------------------------------------->
                      time

Convert your heap dumps (need multiple) into a format this is convenient for consumption by postgres from the heap dump format:

 num     #instances         #bytes  class name 
----------------------------------------------
   1:       4632416      392305928  [C
   2:       6509258      208296256  java.util.HashMap$Node
   3:       4615599      110774376  java.lang.String
   5:         16856       68812488  [B
   6:        278914       67329632  [Ljava.util.HashMap$Node;
   7:       1297968       62302464  
...

To a csv file with a the datetime of each heap dump:

2016.09.20 17:33:40,[C,4632416,392305928
2016.09.20 17:33:40,java.util.HashMap$Node,6509258,208296256
2016.09.20 17:33:40,java.lang.String,4615599,110774376
2016.09.20 17:33:40,[B,16856,68812488
...

Using this script:

# Example invocation: convert.heap.hist.to.csv.pl -f heap.2016.09.20.17.33.40.txt -dt "2016.09.20 17:33:40"  >> heap.csv 

 my $file;
 my $dt;
 GetOptions (
     "f=s" => \$file,
     "dt=s" => \$dt
 ) or usage("Error in command line arguments");
 open my $fh, '<', $file or die $!;

my $last=0;
my $lastRotation=0;
 while(not eof($fh)) {
     my $line = <$fh>;
     $line =~ s/\R//g; #remove newlines
     #    1:       4442084      369475664  [C
     my ($instances,$size,$class) = ($line =~ /^\s*\d+:\s+(\d+)\s+(\d+)\s+([\$\[\w\.]+)\s*$/) ;
     if($instances) {
         print "$dt,$class,$instances,$size\n";
     }
 }

 close($fh);

Create a table to put the data in

CREATE TABLE heap_histogram (
    histwhen timestamp without time zone NOT NULL,
    class character varying NOT NULL,
    instances integer NOT NULL,
    bytes integer NOT NULL
);

Copy the data into your new table

\COPY heap_histogram FROM 'heap.csv'  WITH DELIMITER ',' CSV ;

Run the slop query against size (num of bytes) query:

SELECT class, REGR_SLOPE(bytes,extract(epoch from histwhen)) as slope
    FROM public.heap_histogram
    GROUP BY class
    HAVING REGR_SLOPE(bytes,extract(epoch from histwhen)) > 0
    ORDER BY slope DESC
    ;

Interpret the results:

         class             |        slope         
---------------------------+----------------------
 java.util.ArrayList       |     71.7993806279174
 java.util.HashMap         |     49.0324576155785
 java.lang.String          |     31.7770770326123
 joe.schmoe.BusinessObject |     23.2036817108056
 java.lang.ThreadLocal     |     20.9013528767851

The slope is bytes added per second (since the unit of epoch is in seconds). If you use instances instead of size, then that's the number of instances added per second.

My one of the lines of code creating this joe.schmoe.BusinessObject was responsible for the memory leak. It was creating the object, appending it to an array without checking if it already existed. The other objects were also created along with the BusinessObject near the leaking code.

回答4:

Can you accelerate time? i.e. can you write a dummy test client that forces it to do a weeks worth of calls/requests etc in a few minutes or hours? These are your biggest friend and if you don't have one - write one.

We used Netbeans a while ago to analyse heap dumps. It can be a bit slow but it was effective. Eclipse just crashed and the 32bit Windows tools did as well.

If you have access to a 64bit system or a Linux system with 3GB or more you will find it easier to analyse the heap dumps.

Do you have access to change logs and incident reports? Large scale enterprises will normally have change management and incident management teams and this may be useful in tracking down when problems started happening.

When did it start going wrong? Talk to people and try and get some history. You may get someone saying, "Yeah, it was after they fixed XYZ in patch 6.43 that we got weird stuff happening".

回答5:

I've had success with IBM Heap Analyzer. It offers several views of the heap, including largest drop-off in object size, most frequently occurring objects, and objects sorted by size.

回答6:

If it's happening after a week's usage, and your application is as byzantine as you describe, perhaps you're better off restarting it every week ?

I know it's not fixing the problem, but it may be a time-effective solution. Are there time windows when you can have outages ? Can you load balance and fail over one instance whilst keeping the second up ? Perhaps you can trigger a restart when memory consumption breaches a certain limit (perhaps monitoring via JMX or similar).

回答7:

I've used jhat, this is a bit harsh, but it depends on the kind of framework you had.