# Accessing OpenStack Swift from Spark

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`. Current Swift driver requires Swift to use Keystone authentication method.

# 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](https://github.com/openstack/swift/blob/master/swift/common/middleware/list_endpoints.py).

# Dependencies

The Spark application should include `hadoop-openstack` dependency. For example, for Maven support, add the following to the `pom.xml` file:

```
<dependencyManagement>
  ...
  <dependency>
    <groupId>org.apache.hadoop</groupId>
    <artifactId>hadoop-openstack</artifactId>
    <version>2.3.0</version>
  </dependency>
  ...
</dependencyManagement>
```

# Configuration Parameters

Create `core-site.xml` and place it inside Spark’s `conf` directory. There are two main categories of parameters that should to be configured: declaration of the Swift driver and the parameters that are required by Keystone.

Configuration of Hadoop to use Swift File system achieved via

| Property Name | Value |
| --- | --- |
| fs.swift.impl | org.apache.hadoop.fs.swift.snative.SwiftNativeFileSystem |

Additional parameters required by Keystone (v2.0) and should be provided to the Swift driver. Those parameters will be used to perform authentication in Keystone to access Swift. The following table contains a list of Keystone mandatory parameters. `PROVIDER` can be any 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 if all URLs are public | Mandatory |

For example, assume `PROVIDER=SparkTest` and Keystone contains user `tester` with password `testing` defined for tenant `test`. Then `core-site.xml` should include:

```
<configuration>
  <property>
    <name>fs.swift.impl</name>
    <value>org.apache.hadoop.fs.swift.snative.SwiftNativeFileSystem</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.auth.url</name>
    <value>http://127.0.0.1:5000/v2.0/tokens</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.auth.endpoint.prefix</name>
    <value>endpoints</value>
  </property>
    <name>fs.swift.service.SparkTest.http.port</name>
    <value>8080</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.region</name>
    <value>RegionOne</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.public</name>
    <value>true</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.tenant</name>
    <value>test</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.username</name>
    <value>tester</value>
  </property>
  <property>
    <name>fs.swift.service.SparkTest.password</name>
    <value>testing</value>
  </property>
</configuration>
```

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`.