The memory leak detection rule was designed in accordance with a simple philosophy.
It mimics a memory leak by randomly placing some of these arrays (~ 20% of them) into a list.
This is a heavyweight offline memory leak analysis tool that incorporates multiple existing heap dump analysis tools into a single user interface.
This ensures that Heap dumps are taken after evidence of the memory leak is apparent, and with enough memory leakage to ensure the best chance of a valid analysis result.
The memory profiling report can show many memory leaks.
In the next installment, I'll share some techniques for understanding memory usage and chasing down memory leaks.
Every memory leak I've seen is based on traffic, so the more traffic you get, the faster you leak memory.
Figure 1 shows a sample notification generated by the memory leak detection feature.
If the memory leak is exacerbated by certain requests, failures can be intermittent and hard to predict.
Through pointer mapping sets and fault model, it can detect bad deallocation, memory leak and null pointer dereference faults automatically and increase the testing efficiency.
Probing a JVM shows total memory used, percentage of free memory, percentage of used memory, etc. Observe the percentage free memory to identify memory leaks.
Listing 2 shows a program that has a memory leak.
The upward-sloping trend is a telltale sign of a memory leak.
Administrators are able to run lightweight memory leak detection in test and production environments and receive early notification of memory leaks.
A simple test for memory leaks is to leave the system running after the last test of the day; if the system has recovered to the original state by the next day, then you can often rule out a leak.
Tools that show the location of memory leaks, overruns, and the like can solve memory management problems, and I find MEMWATCH and YAMD helpful.
A native memory leak or excessive native memory use will cause different problems depending on whether you exhaust the address space or run out of physical memory.
Wouldn't it be cool if the application server could figure out if an application running on it had a memory leak and could restart itself when necessary?
There are two major memory-management hazards to avoid in non-garbage-collected languages: memory leaks and dangling Pointers.
A memory leak, for example, might render an application entirely unacceptable at the same time it is opaque, regardless of where or when the leak occurs.
Once a memory leak has been detected and heap dumps have been generated, they can be transferred outside the production server and into a problem determination machine for analysis.
If a memory leak is present in your application, the heap memory usage steadily increases over time.
This helps identify potential unintentional object references causing memory leaks.
The Geronimo developers will be pleased to see no signs of a memory leak though, as the old generation and permanent generation allocations flatten out.
And most likely, such a memory leak is caused by a failure to join the joinable threads.
If this metric does not reach a steady state value and continues to decrease over time, then you have a clear indication that a memory leak is present within the application.
The most likely type is a memory problem, such as memory leak, heap fragmentation, or large object allocation.
Such objects do not always signify a memory leak.
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