# 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`.
- Spark 概述
- 编程指南
- 快速入门
- Spark 编程指南
- 构建在 Spark 之上的模块
- Spark Streaming 编程指南
- Spark SQL, DataFrames and Datasets Guide
- MLlib
- GraphX Programming Guide
- API 文档
- 部署指南
- 集群模式概述
- Submitting Applications
- 部署模式
- Spark Standalone Mode
- 在 Mesos 上运行 Spark
- Running Spark on YARN
- 其它
- 更多
- Spark 配置
- Monitoring and Instrumentation
- Tuning Spark
- 作业调度
- Spark 安全
- 硬件配置
- Accessing OpenStack Swift from Spark
- 构建 Spark
- 其它
- 外部资源
- Spark RDD(Resilient Distributed Datasets)论文
- 翻译进度