The Spring for Apache Hadoop provides extensions to Spring, Spring Batch, and Spring Integration to build manageable and robust pipeline solutions around Hadoop.

Spring for Apache Hadoop extends Spring Batch by providing support for reading from and writing to HDFS, running various types of Hadoop jobs (Java MapReduce, Streaming, Hive, Pig), HBase and Cascading interactions. An important goal is to provide excellent support for non-Java based developers to be productive using Spring Hadoop and not have to write any Java code to use the core feature set.

Spring for Apache Hadoop also applies the familiar Spring programming model to Java MapReduce jobs by providing support for dependency injection of simple jobs as well as a POJO based MapReduce programming model that decouples your MapReduce classes from Hadoop specific details such as base classes and data types.

Docs

You can find out more details from the user documentation or by browsing the javadocs. If you have ideas about how to improve or extend the scope, please feel free to contribute.

Artifacts

• Maven:

<dependency>
  <groupId>org.springframework.data</groupId>
  <artifactId>spring-data-hadoop</artifactId>
  <version>${version}</version>
</dependency> 

<!-- used for nightly builds -->
<repository>
  <id>spring-maven-snapshot</id>
  <snapshots><enabled>true</enabled></snapshots>
  <name>Springframework Maven SNAPSHOT Repository</name>
  <url>http://repo.springsource.org/snapshot</url>
</repository> 

<!-- used for milestone/rc releases -->
<repository>
  <id>spring-maven-milestone</id>
  <name>Springframework Maven Milestone Repository</name>
  <url>http://repo.springsource.org/milestone</url>
</repository> 

• Gradle:

Based on the artifact type, pick one of the repos below:

repositories {
  maven { url "http://repo.springsource.org/release" }
  maven { url "http://repo.springsource.org/milestone" }
  maven { url "http://repo.springsource.org/snapshot" }
}

dependencies {
   compile "org.springframework.data:spring-data-hadoop:${version}"
}

The latest milestone is 1.0.0.RC1

The latest nightly is 1.0.0.BUILD-SNAPSHOT

Building

Spring for Apache Hadoop uses Gradle as its build system. To build the system simply run:

gradlew

from the project root folder. This will compile the sources, run the tests and create the artifacts.

Testing

For its testing, Spring for Apache Hadoop expects a pseudo-distributed/local Hadoop instalation available on the localhost at the default ports (9000 and 9001). The local Hadoop setup allows the project classpath to be automatically used by the Hadoop job tracker. These settings can be customized through the test.properties file under src/test/resources folder (further tweaks can be applied through hadoop-ctx.xml file under src/test/resources/org/springframework/data/hadoop). Note that by default, only the vanilla Hadoop tests are running - you can enable additional tests (such as Hive or Pig) by adding the tasks enableHBaseTests, enableHiveTests, enablePigTests and webHdfsTests (or enableAllTests in short). Use the test.properties file for customizing the default location for these services as well. You can disable all tests by skipping the test task:

gradlew -x test

Contributing

Here are some ways for you to get involved in the community:

• Get involved with the Spring community on the Spring Community Forums. Please help out on the forum by responding to questions and joining the debate.
• Create JIRA tickets for bugs and new features and comment and vote on the ones that you are interested in.
• Watch for upcoming articles on Spring by subscribing to springframework.org.

Github is for social coding: if you want to write code, we encourage contributions through pull requests from forks of this repository. If you want to contribute code this way, read the Spring Framework [contributor guidelines] (https://github.com/SpringSource/spring-framework/blob/master/CONTRIBUTING.md).

Staying in touch

Follow the project team (Costin, Mark) on Twitter. In-depth articles can be found at the SpringSource team blog, and releases are announced via our news feed.