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 —be 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 
—along 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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