Spring Foundation released its Spring Framework for Apache Hadoop 2.3.0 with new features and many improvements.
Spring Foundation released its Spring Framework for Apache Hadoop 2.3.0 with new features and many improvements.The version of Spring for Apache Hadoop 2.3.0 is released on 22nd December 2015 and this release comes with new features and many improvements. In this post we are examining the features added to this release.
Spring Framework for Hadoop helps the developers to quickly develop and deploy Hadoop based applications. Spring for Hadoop provides the APIs for using HDFS, MapReduce, Pig, and Hive power in their application.
Following new features and improvements are added to Spring for Apache Hadoop 2.3:
This version of Spring framework can be used to run the Apache Spark jobs on the Hadoop clusters. Check our tutorial 'How to setup Apache Spark Development Environment?' for getting started with the Apache Spark Framework.
Version specific artifices support :
How to use Spring Framework for Apache Hadoop 2.3.0 GA?
You can add the following dependency in your pom.xml file:
<dependencies> <dependency> <groupId>org.springframework.data</groupId> <artifactId>spring-data-hadoop</artifactId> <version>2.3.0.RELEASE</version> </dependency> </dependencies>
In the Gradle based application following dependency can be used:
dependencies { compile 'org.springframework.data:spring-data-hadoop:2.3.0.RELEASE' }
Spring Hadoop framework allows the developers to create application using Spring, Spring Batch, and Spring Integration which can be deployed on the Hadoop Clusters.
Check our Spring Framework tutorials section.
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