aboutsummaryrefslogtreecommitdiff
path: root/docs/spark-standalone.md
diff options
context:
space:
mode:
Diffstat (limited to 'docs/spark-standalone.md')
-rw-r--r--docs/spark-standalone.md4
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md
index 9ab6ba0830..994a96f2c9 100644
--- a/docs/spark-standalone.md
+++ b/docs/spark-standalone.md
@@ -9,7 +9,7 @@ In addition to running on the Mesos or YARN cluster managers, Spark also provide
You can start a standalone master server by executing:
- ./spark-class spark.deploy.master.Master
+ ./spark-class org.apache.spark.deploy.master.Master
Once started, the master will print out a `spark://HOST:PORT` URL for itself, which you can use to connect workers to it,
or pass as the "master" argument to `SparkContext`. You can also find this URL on
@@ -17,7 +17,7 @@ the master's web UI, which is [http://localhost:8080](http://localhost:8080) by
Similarly, you can start one or more workers and connect them to the master via:
- ./spark-class spark.deploy.worker.Worker spark://IP:PORT
+ ./spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT
Once you have started a worker, look at the master's web UI ([http://localhost:8080](http://localhost:8080) by default).
You should see the new node listed there, along with its number of CPUs and memory (minus one gigabyte left for the OS).