一旦已经检测到了内存泄漏并且已经生成了堆转储，它们就可以被传递到生产服务器之外，转到问题确定机器中进行分析。
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.
此机制将生成与足够内存泄漏协调的多个堆转储，以便于使用MDD4J 进行比较分析。
This mechanism will generate multiple heap dumps that have been coordinated with sufficient memory leakage to facilitate comparative analysis using MDD4J.
这是一个重量级脱机内存泄漏分析工具，它将多种现有堆转储分析工具整合在单一用户界面中。
This is a heavyweight offline memory leak analysis tool that incorporates multiple existing heap dump analysis tools into a single user interface.
许多分析工具（包括MDD4J）都可以分析堆转储，以找到内存泄漏的根源。
Many analysis tools, including MDD4J, analyze heap dumps to find the root cause of a memory leak.
RAD的配置和日志透视包含一些视图，用来分析你的应用程序的内存使用，和通过比较两个堆转储的高级算法来检测这些泄漏。
RAD's profiling and logging perspective includes views for analyzing your application's memory usage and detecting these leaks through advanced algorithms that compare two heap dumps.
堆转储，发现内存泄漏时非常有用。
Heap dumps, which are helpful when hunting for memory leaks.
真正的本机内存泄漏表现为本机堆持续增加，当负载移除或垃圾收集器运行时仍然不降低。
A genuine native-memory leak manifests as a continual growth in native heap that doesn't drop when load is removed or when the garbage collector runs.
图8中的Suspects选项卡显示了从涉及无限循环的内存泄漏案例中获取的两个堆转储的分析结果。
Figure 8 shows the Suspects TAB from the analysis result of two heap dumps taken from a memory leak case involving an infinite loop.
有一些很好的商业堆分析工具，但是找出内存泄漏不一定要花钱买这些工具——内置的hprof工具也可完成这项工作。
There are some excellent commercial heap profiling tools available, but you don't have to spend any money to find memory leaks — the built-in hprof tool can also do the trick.
HeapRoots是一种用于分析IBMjdk堆转储的基于控制台的实验性工具，它类似于HAT工具，但是它不能查明内存泄漏的根源。
HeapRoots is an experimental console-based tool for analyzing IBM JDK heap dumps similar to the HAT tool, but it does not pinpoint the root cause of a memory leak.
对于本机内存泄漏，进程大小将增加，对于碎片问题，在发生OutOfMemoryError错误时，会存在大量的可用堆。
For native memory leaks, the process size will increase, and for fragmentation issues, there will be a significant amount of free heap at the time of the occurrence of the OutOfMemoryError.
一种示例策略通过取得多个堆转储(使用工作负载管理来维护应用程序的性能)以进行分析，从而对内存泄漏通知做出反应。
On example policy would react to a memory leak notification by taking multiple heap dumps (using workload management to maintain the performance of the application) for analysis.
一种策略通过取得多个堆转储(使用工作负载管理来维护应用程序的性能)以进行分析，从而对内存泄漏通知做出反应。
One policy might react to a memory leak notification by taking multiple heap dumps (using workload management to maintain the performance of the application) for analysis.
她知道必须提供一个解决办法(比如周期性地对服务器进行循环或者逐渐增加堆的大小)，直到可以修复这个内存泄漏问题。
She knows she must provide a workaround (such as cycling the servers periodically or increasing the heap sizes) until the memory leak can be fixed.
如果您的应用程序出现了内存泄漏，堆内存使用量将随时间稳步增长。
If a memory leak is present in your application, the heap memory usage steadily increases over time.
最有可能的类型是内存问题，如内存泄漏、堆碎片、或者大对象分配。
The most likely type is a memory problem, such as memory leak, heap fragmentation, or large object allocation.
这可以确保在确定内存泄漏之后获取堆转储，并通过足够的内存泄漏获得最有效的分析结果。
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.
虽然通过分析堆转储能够标识内存泄漏数据结构，但是标识无限循环中的内存泄漏代码并不简单。
While it might be possible to identify the memory leaking data structure by analyzing the heap dumps, identifying the memory leaking code which is in a infinite loop is not straightforward.
对于比较分析，主转储表示在已经发生大量内存泄漏时(占用最大配置堆大小的大量内存)所取得转储。
For comparative analysis, the primary dump refers to the dump taken after the memory leak has progressed considerably (consuming a large amount of the maximum configured heap size).
基线转储是指当堆尚未因内存泄漏而被大量耗用时早期取得的堆转储。
The baseline dump refers to the heap dump taken early on, when the heap has not yet been consumed significantly due to the memory leak.
在分析native_stderr . log时，我们发现垃圾收集周期没有问题，并且不存在内存问题，如内存泄漏、堆碎片和大对象分配。
When analyzing native_stderr.log, we found that the GC cycles were fine, and there were no memory issues, such as memory leak, heap fragmentation, and large object allocation.
这些类型的内存泄漏表现为内存泄漏快速增长，其中，如果详细的垃圾收集数据显示在很短的时间内可用堆空间急剧减少，则会导致Out Of MemoryError错误。
These types of memory leaks manifest as fast growing memory leaks, where if the verbose GC data reports a sharp drop in free heap space in a very short time leads to an OutOfMemoryError.
对这种类型的内存泄漏情况，分析短时间内获取的堆转储(heapdump)非常重要，这样可以观察正在快速减少的可用内存。
For this type of memory leak case, it is important to analyze a heap dump taken within the short span of time the free memory is observed to be dropping quickly.
但对于在堆中分配的对象，则必须显式删除对象以防止内存泄漏。
For objects allocated on the heap, however, you must explicitly delete the object to prevent a memory leak.
在检测到一种内存泄漏方式之后，此工具将产生多重堆转储，它们可以与足够的内存泄漏进行协调，以方便使用MDD4J的对比分析。
Upon detection of a memory leak pattern, this facility will generate multiple heap dumps that have been coordinated with sufficient memory leakage to facilitate comparative analysis using MDD4J.
在检测到一种内存泄漏方式之后，此工具将产生多重堆转储，它们可以与足够的内存泄漏进行协调，以方便使用MDD4J的对比分析。
Upon detection of a memory leak pattern, this facility will generate multiple heap dumps that have been coordinated with sufficient memory leakage to facilitate comparative analysis using MDD4J.
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