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[MINOR][DOCS] Fix typos in configuration.md and hardware-provisioning.md

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## What changes were proposed in this pull request?

This PR fixes some typos in the following documentation files.
 * `NOTICE`, `configuration.md`, and `hardware-provisioning.md`.

## How was the this patch tested?

manual tests

Author: Dongjoon Hyun <dongjoonapache.org>

Author: Dongjoon Hyun <dongjoon@apache.org>

Closes #11289 from dongjoon-hyun/minor_fix_typos_notice_and_confdoc.
This commit is contained in:
Dongjoon Hyun 2016-02-21 15:27:07 -08:00 committed by Reynold Xin
parent 6c3832b26e
commit 03e62aa3f6
2 changed files with 6 additions and 6 deletions
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10
docs/configuration.md
View file

@ -249,7 +249,7 @@ Apart from these, the following properties are also available, and may be useful
<td>false</td>
<td>
(Experimental) Whether to give user-added jars precedence over Spark's own jars when loading
classes in the the driver. This feature can be used to mitigate conflicts between Spark's
classes in the driver. This feature can be used to mitigate conflicts between Spark's
dependencies and user dependencies. It is currently an experimental feature.
This is used in cluster mode only.
@ -373,7 +373,7 @@ Apart from these, the following properties are also available, and may be useful
<td>
Reuse Python worker or not. If yes, it will use a fixed number of Python workers,
does not need to fork() a Python process for every tasks. It will be very useful
if there is large broadcast, then the broadcast will not be needed to transfered
if there is large broadcast, then the broadcast will not be needed to transferred
from JVM to Python worker for every task.
</td>
</tr>
@ -1266,7 +1266,7 @@ Apart from these, the following properties are also available, and may be useful
<td>
Comma separated list of users/administrators that have view and modify access to all Spark jobs.
This can be used if you run on a shared cluster and have a set of administrators or devs who
help debug when things work. Putting a "*" in the list means any user can have the priviledge
help debug when things work. Putting a "*" in the list means any user can have the privilege
of admin.
</td>
</tr>
@ -1604,7 +1604,7 @@ Apart from these, the following properties are also available, and may be useful
#### Deploy
<table class="table">
<tr><th>Property Name</th><th>Default</th><th>Meaniing</th></tr>
<tr><th>Property Name</th><th>Default</th><th>Meaning</th></tr>
<tr>
<td><code>spark.deploy.recoveryMode</code></td>
<td>NONE</td>
@ -1693,7 +1693,7 @@ Spark uses [log4j](http://logging.apache.org/log4j/) for logging. You can config
# Overriding configuration directory
To specify a different configuration directory other than the default "SPARK_HOME/conf",
you can set SPARK_CONF_DIR. Spark will use the the configuration files (spark-defaults.conf, spark-env.sh, log4j.properties, etc)
you can set SPARK_CONF_DIR. Spark will use the configuration files (spark-defaults.conf, spark-env.sh, log4j.properties, etc)
from this directory.
# Inheriting Hadoop Cluster Configuration

2
docs/hardware-provisioning.md
View file

@ -63,7 +63,7 @@ from the application's monitoring UI (`http://<driver-node>:4040`).
# CPU Cores
Spark scales well to tens of CPU cores per machine because it performes minimal sharing between
Spark scales well to tens of CPU cores per machine because it performs minimal sharing between
threads. You should likely provision at least **8-16 cores** per machine. Depending on the CPU
cost of your workload, you may also need more: once data is in memory, most applications are
either CPU- or network-bound.