From 1da1937531f2e8ab37074ba6ef1a6f54c49c8ad1 Mon Sep 17 00:00:00 2001 From: Andrew Or Date: Wed, 10 Dec 2014 12:41:36 -0800 Subject: [PATCH] [SPARK-4771][Docs] Document standalone cluster supervise mode tdas looks like streaming already refers to the supervise mode. The link from there is broken though. Author: Andrew Or Closes #3627 from andrewor14/document-supervise and squashes the following commits: 9ca0908 [Andrew Or] Wording changes 2b55ed2 [Andrew Or] Document standalone cluster supervise mode --- docs/spark-standalone.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index ae7b81d5bb71f..5c6084fb46255 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -257,7 +257,7 @@ To run an interactive Spark shell against the cluster, run the following command You can also pass an option `--total-executor-cores ` to control the number of cores that spark-shell uses on the cluster. -# Launching Compiled Spark Applications +# Launching Spark Applications The [`spark-submit` script](submitting-applications.html) provides the most straightforward way to submit a compiled Spark application to the cluster. For standalone clusters, Spark currently @@ -272,6 +272,15 @@ should specify them through the `--jars` flag using comma as a delimiter (e.g. ` To control the application's configuration or execution environment, see [Spark Configuration](configuration.html). +Additionally, standalone `cluster` mode supports restarting your application automatically if it +exited with non-zero exit code. To use this feature, you may pass in the `--supervise` flag to +`spark-submit` when launching your application. Then, if you wish to kill an application that is +failing repeatedly, you may do so through: + + ./bin/spark-class org.apache.spark.deploy.Client kill + +You can find the driver ID through the standalone Master web UI at `http://:8080`. + # Resource Scheduling The standalone cluster mode currently only supports a simple FIFO scheduler across applications.