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author | Prashant Sharma <prashant.s@imaginea.com> | 2014-01-03 15:08:01 +0530 |
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committer | Prashant Sharma <prashant.s@imaginea.com> | 2014-01-03 15:08:01 +0530 |
commit | 74ba97fcf75ae61833295a896dcdf775036ae1d8 (patch) | |
tree | 16f41168924ac81c0c0b25c3286b2242704ecc2e /docs/spark-standalone.md | |
parent | 59e8009b8d5e51b6f776720de8c9ecb09e1072dc (diff) | |
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sbin/spark-class* -> bin/spark-class*
Diffstat (limited to 'docs/spark-standalone.md')
-rw-r--r-- | docs/spark-standalone.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index baa0a062f7..c851833a18 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -28,7 +28,7 @@ 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: - ./sbin/spark-class org.apache.spark.deploy.worker.Worker spark://IP:PORT + ./bin/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). |