## What changes were proposed in this pull request? Add AL2 license to metadata of all .md files. This seemed to be the tidiest way as it will get ignored by .md renderers and other tools. Attempts to write them as markdown comments revealed that there is no such standard thing. ## How was this patch tested? Doc build Closes #24243 from srowen/SPARK-26918. Authored-by: Sean Owen <sean.owen@databricks.com> Signed-off-by: Sean Owen <sean.owen@databricks.com>
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layout | title | license |
---|---|---|
global | Accessing OpenStack Swift from Spark | Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to You 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. |
Spark's support for Hadoop InputFormat allows it to process data in OpenStack Swift using the
same URI formats as in Hadoop. You can specify a path in Swift as input through a
URI of the form swift://container.PROVIDER/path
. You will also need to set your
Swift security credentials, through core-site.xml
or via
SparkContext.hadoopConfiguration
.
The current Swift driver requires Swift to use the Keystone authentication method, or
its Rackspace-specific predecessor.
Configuring Swift for Better Data Locality
Although not mandatory, it is recommended to configure the proxy server of Swift with
list_endpoints
to have better data locality. More information is
available here.
Dependencies
The Spark application should include hadoop-openstack
dependency, which can
be done by including the hadoop-cloud
module for the specific version of spark used.
For example, for Maven support, add the following to the pom.xml
file:
{% highlight xml %} ... org.apache.spark hadoop-cloud_2.12 ${spark.version} ... {% endhighlight %}
Configuration Parameters
Create core-site.xml
and place it inside Spark's conf
directory.
The main category of parameters that should be configured is the authentication parameters
required by Keystone.
The following table contains a list of Keystone mandatory parameters. PROVIDER
can be
any (alphanumeric) name.
Property Name | Meaning | Required |
---|---|---|
fs.swift.service.PROVIDER.auth.url |
Keystone Authentication URL | Mandatory |
fs.swift.service.PROVIDER.auth.endpoint.prefix |
Keystone endpoints prefix | Optional |
fs.swift.service.PROVIDER.tenant |
Tenant | Mandatory |
fs.swift.service.PROVIDER.username |
Username | Mandatory |
fs.swift.service.PROVIDER.password |
Password | Mandatory |
fs.swift.service.PROVIDER.http.port |
HTTP port | Mandatory |
fs.swift.service.PROVIDER.region |
Keystone region | Mandatory |
fs.swift.service.PROVIDER.public |
Indicates whether to use the public (off cloud) or private (in cloud; no transfer fees) endpoints | Mandatory |
For example, assume PROVIDER=SparkTest
and Keystone contains user tester
with password testing
defined for tenant test
. Then core-site.xml
should include:
{% highlight xml %} fs.swift.service.SparkTest.auth.url http://127.0.0.1:5000/v2.0/tokens fs.swift.service.SparkTest.auth.endpoint.prefix endpoints fs.swift.service.SparkTest.http.port 8080 fs.swift.service.SparkTest.region RegionOne fs.swift.service.SparkTest.public true fs.swift.service.SparkTest.tenant test fs.swift.service.SparkTest.username tester fs.swift.service.SparkTest.password testing {% endhighlight %}
Notice that
fs.swift.service.PROVIDER.tenant
,
fs.swift.service.PROVIDER.username
,
fs.swift.service.PROVIDER.password
contains sensitive information and keeping them in
core-site.xml
is not always a good approach.
We suggest to keep those parameters in core-site.xml
for testing purposes when running Spark
via spark-shell
.
For job submissions they should be provided via sparkContext.hadoopConfiguration
.