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From ma...@apache.org
Subject git commit: Tweaks to Mesos docs
Date Sat, 17 May 2014 00:35:11 GMT
Repository: spark
Updated Branches:
  refs/heads/master 40d6acd6b -> fed6303f2


Tweaks to Mesos docs

- Mention Apache downloads first
- Shorten some wording

Author: Matei Zaharia <matei@databricks.com>

Closes #806 from mateiz/doc-update and squashes the following commits:

d9345cd [Matei Zaharia] typo
a179f8d [Matei Zaharia] Tweaks to Mesos docs


Project: http://git-wip-us.apache.org/repos/asf/spark/repo
Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/fed6303f
Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/fed6303f
Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/fed6303f

Branch: refs/heads/master
Commit: fed6303f29250bd5e656dbdd731b38938c933a61
Parents: 40d6acd
Author: Matei Zaharia <matei@databricks.com>
Authored: Fri May 16 17:35:05 2014 -0700
Committer: Matei Zaharia <matei@databricks.com>
Committed: Fri May 16 17:35:05 2014 -0700

----------------------------------------------------------------------
 docs/running-on-mesos.md | 71 +++++++++++++++++++++----------------------
 1 file changed, 34 insertions(+), 37 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/spark/blob/fed6303f/docs/running-on-mesos.md
----------------------------------------------------------------------
diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md
index ef762aa..df8687f 100644
--- a/docs/running-on-mesos.md
+++ b/docs/running-on-mesos.md
@@ -3,16 +3,15 @@ layout: global
 title: Running Spark on Mesos
 ---
 
-# Why Mesos
-
 Spark can run on hardware clusters managed by [Apache Mesos](http://mesos.apache.org/).
 
 The advantages of deploying Spark with Mesos include:
+
 - dynamic partitioning between Spark and other
   [frameworks](https://mesos.apache.org/documentation/latest/mesos-frameworks/)
 - scalable partitioning between multiple instances of Spark
 
-# How it works
+# How it Works
 
 In a standalone cluster deployment, the cluster manager in the below diagram is a Spark master
 instance.  When using Mesos, the Mesos master replaces the Spark master as the cluster manager.
@@ -37,11 +36,25 @@ require any special patches of Mesos.
 If you already have a Mesos cluster running, you can skip this Mesos installation step.
 
 Otherwise, installing Mesos for Spark is no different than installing Mesos for use by other
-frameworks.  You can install Mesos using either prebuilt packages or by compiling from source.
+frameworks.  You can install Mesos either from source or using prebuilt packages.
+
+## From Source
+
+To install Apache Mesos from source, follow these steps:
+
+1. Download a Mesos release from a
+   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
+2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling
and
+   installing Mesos
+
+**Note:** If you want to run Mesos without installing it into the default paths on your system
+(e.g., if you lack administrative privileges to install it), pass the
+`--prefix` option to `configure` to tell it where to install. For example, pass
+`--prefix=/home/me/mesos`. By default the prefix is `/usr/local`.
 
-## Prebuilt packages
+## Third-Party Packages
 
-The Apache Mesos project only publishes source package releases, no binary releases.  But
other
+The Apache Mesos project only publishes source releases, not binary packages.  But other
 third party projects publish binary releases that may be helpful in setting Mesos up.
 
 One of those is Mesosphere.  To install Mesos using the binary releases provided by Mesosphere:
@@ -52,20 +65,6 @@ One of those is Mesosphere.  To install Mesos using the binary releases
provided
 The Mesosphere installation documents suggest setting up ZooKeeper to handle Mesos master
failover,
 but Mesos can be run without ZooKeeper using a single master as well.
 
-## From source
-
-To install Mesos directly from the upstream project rather than a third party, install from
source.
-
-1. Download the Mesos distribution from a
-   [mirror](http://www.apache.org/dyn/closer.cgi/mesos/{{site.MESOS_VERSION}}/)
-2. Follow the Mesos [Getting Started](http://mesos.apache.org/gettingstarted) page for compiling
and
-   installing Mesos
-
-**Note:** If you want to run Mesos without installing it into the default paths on your system
-(e.g., if you lack administrative privileges to install it), you should also pass the
-`--prefix` option to `configure` to tell it where to install. For example, pass
-`--prefix=/home/user/mesos`. By default the prefix is `/usr/local`.
-
 ## Verification
 
 To verify that the Mesos cluster is ready for Spark, navigate to the Mesos master webui at
port
@@ -74,32 +73,30 @@ To verify that the Mesos cluster is ready for Spark, navigate to the Mesos
maste
 
 # Connecting Spark to Mesos
 
-To use Mesos from Spark, you need a Spark distribution available in a place accessible by
Mesos, and
+To use Mesos from Spark, you need a Spark binary package available in a place accessible
by Mesos, and
 a Spark driver program configured to connect to Mesos.
 
-## Uploading Spark Distribution
-
-When Mesos runs a task on a Mesos slave for the first time, that slave must have a distribution
of
-Spark available for running the Spark Mesos executor backend.  A distribution of Spark is
just a
-compiled binary version of Spark.
+## Uploading Spark Package
 
-The Spark distribution can be hosted at any Hadoop URI, including HTTP via `http://`, [Amazon
Simple
-Storage Service](http://aws.amazon.com/s3) via `s3://`, or HDFS via `hdfs:///`.
+When Mesos runs a task on a Mesos slave for the first time, that slave must have a Spark
binary
+package for running the Spark Mesos executor backend.
+The Spark package can be hosted at any Hadoop-accessible URI, including HTTP via `http://`,
+[Amazon Simple Storage Service](http://aws.amazon.com/s3) via `s3n://`, or HDFS via `hdfs://`.
 
-To use a precompiled distribution:
+To use a precompiled package:
 
-1. Download a Spark distribution from the Spark [download page](https://spark.apache.org/downloads.html)
 
+1. Download a Spark binary package from the Spark [download page](https://spark.apache.org/downloads.html)
 2. Upload to hdfs/http/s3
 
 To host on HDFS, use the Hadoop fs put command: `hadoop fs -put spark-{{site.SPARK_VERSION}}.tar.gz
 /path/to/spark-{{site.SPARK_VERSION}}.tar.gz`
 
 
-Or if you are using a custom-compiled version of Spark, you will need to create a distribution
using
+Or if you are using a custom-compiled version of Spark, you will need to create a package
using
 the `make-distribution.sh` script included in a Spark source tarball/checkout.
 
 1. Download and build Spark using the instructions [here](index.html)
-2. Create a Spark distribution using `make-distribution.sh --tgz`.
+2. Create a binary package using `make-distribution.sh --tgz`.
 3. Upload archive to http/s3/hdfs
 
 
@@ -115,8 +112,8 @@ The driver also needs some configuration in `spark-env.sh` to interact
properly
    `<prefix>/lib/libmesos.so` where the prefix is `/usr/local` by default. See Mesos
installation
    instructions above. On Mac OS X, the library is called `libmesos.dylib` instead of
    `libmesos.so`.
- * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
-2. Also set `spark.executor.uri` to <path to spark-{{site.SPARK_VERSION}}.tar.gz>
+ * `export SPARK_EXECUTOR_URI=<URL of spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`.
+2. Also set `spark.executor.uri` to `<URL of spark-{{site.SPARK_VERSION}}.tar.gz>`.
 
 Now when starting a Spark application against the cluster, pass a `mesos://`
 or `zk://` URL as the master when creating a `SparkContext`. For example:
@@ -129,7 +126,7 @@ val conf = new SparkConf()
 val sc = new SparkContext(conf)
 {% endhighlight %}
 
-When running a shell the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`,
so
+When running a shell, the `spark.executor.uri` parameter is inherited from `SPARK_EXECUTOR_URI`,
so
 it does not need to be redundantly passed in as a system property.
 
 {% highlight bash %}
@@ -168,7 +165,7 @@ using `conf.set("spark.cores.max", "10")` (for example).
 # Running Alongside Hadoop
 
 You can run Spark and Mesos alongside your existing Hadoop cluster by just launching them
as a
-separate service on the machines. To access Hadoop data from Spark, a full hdfs:// URL is
required
+separate service on the machines. To access Hadoop data from Spark, a full `hdfs://` URL
is required
 (typically `hdfs://<namenode>:9000/path`, but you can find the right URL on your Hadoop
Namenode web
 UI).
 
@@ -195,7 +192,7 @@ A few places to look during debugging:
 And common pitfalls:
 
 - Spark assembly not reachable/accessible
-  - Slaves need to be able to download the distribution
+  - Slaves must be able to download the Spark binary package from the `http://`, `hdfs://`
or `s3n://` URL you gave
 - Firewall blocking communications
   - Check for messages about failed connections
   - Temporarily disable firewalls for debugging and then poke appropriate holes


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