Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r89202156
--- Diff: docs/cloud-integration.md ---
@@ -0,0 +1,953 @@
+---
+layout: global
+displayTitle: Integration with Cloud Infrastructures
+title: Integration with Cloud Infrastructures
+description: Introduction to cloud storage support in Apache Spark
SPARK_VERSION_SHORT
+---
+<!---
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License. See accompanying LICENSE file.
+-->
+
+* This will become a table of contents (this text will be scraped).
+{:toc}
+
+## <a name="introduction"></a>Introduction
+
+
+All the public cloud infrastructures, Amazon AWS, Microsoft Azure, Google
GCS and others offer
+persistent data storage systems, "object stores". These are not quite the
same as classic file
+systems: in order to scale to hundreds of Petabytes, without any single
points of failure
+or size limits, object stores, "blobstores", have a simpler model of `name
=> data`.
+
+Apache Spark can read or write data in object stores for data access.
+through filesystem connectors implemented in Apache Hadoop or provided by
third-parties.
+These libraries make the object stores look *almost* like filesystems,
with directories and
+operations on files (rename) and directories (create, rename, delete)
which mimic
+those of a classic filesystem. Because of this, Spark and Spark-based
applications
+can work with object stores, generally treating them as as if they were
slower-but-larger filesystems.
+
+With these connectors, Apache Spark supports object stores as the source
+of data for analysis, including Spark Streaming and DataFrames.
+
+
+## <a name="quick_start"></a>Quick Start
+
+Provided the relevant libraries are on the classpath, and Spark is
configured with your credentials,
+objects in an object store can be can be read or written through URLs
which uses the name of the
+object store client as the schema and the bucket/container as the hostname.
+
+
+### Dependencies
+
+The Spark application neeeds the relevant Hadoop libraries, which can
+be done by including the `spark-cloud` module for the specific version of
spark used.
+
+The Spark application should include <code>hadoop-openstack</code>
dependency, which can
+be done by including the `spark-cloud` module for the specific version of
spark used.
+For example, for Maven support, add the following to the
<code>pom.xml</code> file:
+
+{% highlight xml %}
+<dependencyManagement>
+ ...
+ <dependency>
+ <groupId>org.apache.spark</groupId>
+ <artifactId>spark-cloud_2.11</artifactId>
+ <version>${spark.version}</version>
+ </dependency>
+ ...
+</dependencyManagement>
+{% endhighlight %}
+
+If using the Scala 2.10-compatible version of Spark, the artifact is of
course `spark-cloud_2.10`.
+
+### Basic Use
+
+
+
+To refer to a path in Amazon S3, use `s3a://` as the scheme (Hadoop 2.7+)
or `s3n://` on older versions.
+
+{% highlight scala %}
+sparkContext.textFile("s3a://landsat-pds/scene_list.gz").count()
+{% endhighlight %}
+
+Similarly, an RDD can be saved to an object store via `saveAsTextFile()`
+
+
+{% highlight scala %}
+val numbers = sparkContext.parallelize(1 to 1000)
+
+// save to Amazon S3 (or compatible implementation)
+numbers.saveAsTextFile("s3a://testbucket/counts")
+
+// Save to Azure Object store
+numbers.saveAsTextFile("wasb://testbuc...@example.blob.core.windows.net/counts")
+
+// save to an OpenStack Swift implementation
+numbers.saveAsTextFile("swift://testbucket.openstack1/counts")
+{% endhighlight %}
+
+That's essentially it: object stores can act as a source and destination
of data, using exactly
+the same APIs to load and save data as one uses to work with data in HDFS
or other filesystems.
+
+Because object stores are viewed by Spark as filesystems, object stores can
+be used as the source or destination of any spark work âbe it batch,
SQL, DataFrame,
+Streaming or something else.
+
+The steps to do so are as follows
+
+1. Use the full URI to refer to a bucket, including the prefix for the
client-side library
+to use. Example: `s3a://landsat-pds/scene_list.gz`
+1. Have the Spark context configured with the authentication details of
the object store.
+In a YARN cluster, this may also be done in the `core-site.xml` file.
+1. Have the JAR containing the filesystem classes on the classpath
âalong with all of its dependencies.
+
+### <a name="dataframes"></a>Example: DataFrames
+
+DataFrames can be created from and saved to object stores through the
`read()` and `write()` methods.
+
+{% highlight scala %}
+import org.apache.spark.SparkConf
--- End diff --
I had some examples, I remove them. I can put them back. But as you note,
it's only a URL; the example is there to make it clear. I can just cut it back
to "use it wherever you would any other path"
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