Local execution 模式下，Flink 是无法实际控制 JVM 的 Xmx, Xms, MaxDirectMemorySize 等参数的，这些参数取决于你的 IDE 设置。 检查一下 idea 的 run configuration 是否有配置过 -XX:MaxDirectMemorySize。
Thank you~ Xintong Song On Sat, Jul 11, 2020 at 3:48 PM Congxian Qiu <[email protected]> wrote: > Hi > > 这个问题可以看下是否和 releasenote[1] 中 memory configuration > 相关的修改有关，具体到这个错误，你可以按照提示增加一些内存看看 > > [1] > > https://flink.apache.org/news/2020/07/06/release-1.11.0.html#other-improvements > Best, > Congxian > > > sunfulin <[email protected]> 于2020年7月10日周五 下午11:32写道： > > > hi， > > > > > 我在使用1.11版本在本地idea起一个作业时，并发为1，抛出了如下关于内存的异常。。问题是之前从来没有显示配置过taskmanager的memory参数，这是为何？ > > 感觉由1.10升级到1.11问题还是挺多的。。我尝试增加了JVM参数，增加DirectMemory内存配置，还是没有作用，请教大神帮忙看下。 > > > > > > Exception in thread "main" java.lang.OutOfMemoryError: Could not allocate > > enough memory segments for NetworkBufferPool (required (Mb): 64, > allocated > > (Mb): 63, missing (Mb): 1). Cause: Direct buffer memory. The direct > > out-of-memory error has occurred. This can mean two things: either job(s) > > require(s) a larger size of JVM direct memory or there is a direct memory > > leak. The direct memory can be allocated by user code or some of its > > dependencies. In this case 'taskmanager.memory.task.off-heap.size' > > configuration option should be increased. Flink framework and its > > dependencies also consume the direct memory, mostly for network > > communication. The most of network memory is managed by Flink and should > > not result in out-of-memory error. In certain special cases, in > particular > > for jobs with high parallelism, the framework may require more direct > > memory which is not managed by Flink. In this case > > 'taskmanager.memory.framework.off-heap.size' configuration option should > be > > increased. If the error persists then there is probably a direct memory > > leak in user code or some of its dependencies which has to be > investigated > > and fixed. The task executor has to be shutdown... >
