Github user steveloughran commented on a diff in the pull request:
https://github.com/apache/spark/pull/12004#discussion_r114056158
--- Diff: docs/cloud-integration.md ---
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+---
+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-hadoop-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-hadoop-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-hadoop-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-hadoop-cloud_2.10`.
+
+### Basic Use
+
+You can refer to data in an object store just as you would data in a
filesystem, by
+using a URL to the data in methods like `SparkContext.textFile()` to read
data,
+`saveAsTextFile()` to write it back.
+
+
+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.
+
+
+## <a name="output"></a>Object Stores as a substitute for HDFS
+
+As the examples show, you can write data to object stores. However, that
does not mean
+That they can be used as replacements for a cluster-wide filesystem.
+
+The full details are covered in [Cloud Object Stores are Not Real
Filesystems](#cloud_stores_are_not_filesystems).
+
+The brief summary is:
+
+| Object Store Connector | Replace HDFS? |
+|-----------------------------|--------------------|
+| `s3a://` `s3n://` from the ASF | No |
+| Amazon EMR `s3://` | Yes |
+| Microsoft Azure `wasb://` | Yes |
+| OpenStack `swift://` | No |
+
+It is possible to use any of the object stores as a destination of work,
i.e. use
+`saveAsTextFile()` or `save()` to save data there, but the commit process
may be slow
+and, unreliable in the presence of failures.
+
+It is faster and safer to use the cluster filesystem as the destination of
Spark jobs,
+using that data as the data for follow-on work. The final results can
+be persisted in to the object store using `distcp`.
+
+#### <a name="checkpointing"></a>Spark Streaming and object stores
+
+Spark Streaming can monitor files added to object stores, by
+creating a `FileInputDStream` DStream monitoring a path under a bucket
through
+`StreamingContext.textFileStream()`.
+
+
+1. The time to scan for new files is proportional to the number of files
+under the path ânot the number of *new* files, and that it can become a
slow operation.
+The size of the window needs to be set to handle this.
+
+1. Files only appear in an object store once they are completely written;
there
+is no need for a worklow of write-then-rename to ensure that files aren't
picked up
+while they are still being written. Applications can write straight to the
monitored directory.
+
+1. Streams should only be checkpointed to an object store considered
compatible with
+HDFS. Otherwise the checkpointing will be slow and potentially unreliable.
+
+### Recommended settings for writing to object stores
+
+Here are the settings to use when writing to object stores. This uses the
"version 2" algorithm
+for committing files âwhich does less renaming than the v1 algorithm.
Speculative execution is
+disabled to avoid multiple writers corrupting the output.
+
+```
+spark.speculation false
+spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version 2
+spark.hadoop.mapreduce.fileoutputcommitter.cleanup-failures.ignored true
+```
+
+There's also the option of skipping the cleanup of temporary files in the
output directory.
+Enabling this option eliminates a small delay caused by listing and
deleting any such files.
+
+```
+spark.hadoop.mapreduce.fileoutputcommitter.cleanup.skipped true
+```
+
+Bear in mind that storing temporary files can run up charges; Delete
+directories called `"_temporary"` on a regular basis to avoid this.
+
+
+### YARN Scheduler settings
+
+When running Spark in a YARN cluster running in EC2, turning off locality
avoids any delays
--- End diff --
Followup:
* Hive now [treats localhost as
"anywhere"](https://issues.apache.org/jira/browse/HIVE-14060)
* As [does Tez](https://issues.apache.org/jira/browse/TEZ-3291)
That's a recent change in both projects; someone would need to test the
Spark placement code to see what decisions it made here. That YARN scheduling
is essentially a workaround.
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