From 2e15a212a1602bb2bc0232af529e68e1087b14d1 Mon Sep 17 00:00:00 2001 From: Jongyoul Lee Date: Mon, 23 Feb 2015 00:27:36 +0900 Subject: [PATCH] [SPARK-3619] Upgrade to Mesos 0.21 to work around MESOS-1688 - MESOS_NATIVE_LIBRARY become deprecated - Chagned MESOS_NATIVE_LIBRARY to MESOS_NATIVE_JAVA_LIBRARY --- docs/running-on-mesos.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 0436b35a1d2e6..b1db68f0292b8 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -167,9 +167,6 @@ acquire. By default, it will acquire *all* cores in the cluster (that get offere only makes sense if you run just one application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). -# Known issues -- When using the "fine-grained" mode, make sure that your executors always leave 32 MB free on the slaves. Otherwise it can happen that your Spark job does not proceed anymore. Currently, Apache Mesos only offers resources if there are at least 32 MB memory allocatable. But as Spark allocates memory only for the executor and cpu only for tasks, it can happen on high slave memory usage that no new tasks will be started anymore. More details can be found in [MESOS-1688](https://issues.apache.org/jira/browse/MESOS-1688). Alternatively use the "coarse-gained" mode, which is not affected by this issue. - # Running Alongside Hadoop You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them as a