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| author | Prashant Sharma <prashant.s@imaginea.com> | 2014-01-03 15:08:01 +0530 |
|---|---|---|
| committer | Prashant Sharma <prashant.s@imaginea.com> | 2014-01-03 15:08:01 +0530 |
| commit | 74ba97fcf75ae61833295a896dcdf775036ae1d8 (patch) | |
| tree | 16f41168924ac81c0c0b25c3286b2242704ecc2e /docs | |
| parent | 59e8009b8d5e51b6f776720de8c9ecb09e1072dc (diff) | |
| download | spark-74ba97fcf75ae61833295a896dcdf775036ae1d8.tar.gz spark-74ba97fcf75ae61833295a896dcdf775036ae1d8.tar.bz2 spark-74ba97fcf75ae61833295a896dcdf775036ae1d8.zip | |
sbin/spark-class* -> bin/spark-class*
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/running-on-yarn.md | 4 | ||||
| -rw-r--r-- | docs/spark-standalone.md | 2 |
2 files changed, 3 insertions, 3 deletions
diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index e2b21f9cde..e21812378c 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -54,7 +54,7 @@ There are two scheduler mode that can be used to launch spark application on YAR The command to launch the YARN Client is as follows: - SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./sbin/spark-class org.apache.spark.deploy.yarn.Client \ + SPARK_JAR=<SPARK_ASSEMBLY_JAR_FILE> ./bin/spark-class org.apache.spark.deploy.yarn.Client \ --jar <YOUR_APP_JAR_FILE> \ --class <APP_MAIN_CLASS> \ --args <APP_MAIN_ARGUMENTS> \ @@ -79,7 +79,7 @@ For example: # Submit Spark's ApplicationMaster to YARN's ResourceManager, and instruct Spark to run the SparkPi example $ SPARK_JAR=./assembly/target/scala-{{site.SCALA_VERSION}}/spark-assembly-{{site.SPARK_VERSION}}-hadoop2.0.5-alpha.jar \ - ./sbin/spark-class org.apache.spark.deploy.yarn.Client \ + ./bin/spark-class org.apache.spark.deploy.yarn.Client \ --jar examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ --class org.apache.spark.examples.SparkPi \ --args yarn-standalone \ 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). |