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| author | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-09-02 18:38:12 -0700 |
|---|---|---|
| committer | Matei Zaharia <matei@eecs.berkeley.edu> | 2013-09-02 18:38:12 -0700 |
| commit | a106ed8b97e707b36818c11d1d7211fa28636178 (patch) | |
| tree | 5ce12b04c710bd8e776c31bc3c8cef63f3313622 /docs/spark-standalone.md | |
| parent | 2ce200bf7f7a38afbcacf3303ca2418e49bdbe2a (diff) | |
| parent | 59218bdd4996a13116009e3669b1b875be23a694 (diff) | |
| download | spark-a106ed8b97e707b36818c11d1d7211fa28636178.tar.gz spark-a106ed8b97e707b36818c11d1d7211fa28636178.tar.bz2 spark-a106ed8b97e707b36818c11d1d7211fa28636178.zip | |
Merge remote-tracking branch 'old/master'
Diffstat (limited to 'docs/spark-standalone.md')
| -rw-r--r-- | docs/spark-standalone.md | 4 |
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). |