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OOM - Pumpkinor.io
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<h1>OOM</h1>
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<span class="date">2021/10/26</span>
<span>posted in </span>
<span class="posted-in"><a href='Java%20Mistake%20100.html'>Java Mistake 100</a></span>
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<h4><a id="%E6%91%98%E5%BD%95" class="anchor" aria-hidden="true"><span class="octicon octicon-link"></span></a>摘录</h4>
<p>Java 程序的 OOM 有如下几种可能：</p>
<ol>
<li>我们的程序确实需要超出 JVM 配置的内存上限的内存。不管是程序实现的不合理，还是因为各种框架对数据的重复处理、加工和转换，相同的数据在内存中不一定只占用一份空间。针对内存量使用超大的业务逻辑，比如缓存逻辑、文件上传下载和导出逻辑，我们在做容量评估时，可能还需要实际做一下 Dump，而不是进行简单的假设。</li>
<li>出现内存泄露，其实就是我们认为没有用的对象最终会被 GC，但却没有。GC 并不会回收强引用对象，我们可能经常在程序中定义一些容器作为缓存，但如果容器中的数据无限增长，要特别小心最终会导致 OOM。使用 WeakHashMap 是解决这个问题的好办法，但值得注意的是，如果强引用的 Value 有引用 Key，也无法回收 Entry。</li>
<li>不合理的资源需求配置，在业务量小的时候可能不会出现问题，但业务量一大可能很快就会撑爆内存。比如，随意配置 Tomcat 的 max-http-header-size 参数，会导致一个请求使用过多的内存，请求量大的时候出现 OOM。在进行参数配置的时候，我们要认识到，很多限制类参数限制的是背后资源的使用，资源始终是有限的，需要根据实际需求来合理设置参数。</li>
</ol>
<p>最后我想说的是，在出现 OOM 之后，也不用过于紧张。我们可以根据错误日志中的异常信息，再结合 jstat 等命令行工具观察内存使用情况，以及程序的 GC 日志，来大致定位出现 OOM 的内存区块和类型。其实，我们遇到的 90% 的 OOM 都是堆 OOM，对 JVM 进程进行堆内存 Dump，或使用 jmap 命令分析对象内存占用排行，一般都可以很容易定位到问题。<br />
这里，我建议你为生产系统的程序配置 JVM 参数启用详细的 GC 日志，方便观察垃圾收集器的行为，并开启 HeapDumpOnOutOfMemoryError，以便在出现 OOM 时能自动 Dump 留下第一问题现场。对于 JDK8，你可以这么设置：</p>
<pre class="line-numbers"><code class="language-plain_text">XX:+HeapDumpOnOutOfMemoryError -XX:HeapDumpPath=. -XX:+PrintGCDateStamps -XX:+PrintGCDetails -Xloggc:gc.log -XX:+UseGCLogFileRotation -XX:NumberOfGCLogFiles=10 -XX:GCLogFileSize=100M
</code></pre>
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