NOTICE: The Spring for Apache Hadoop project will reach End-Of-Life status on April 5th, 2019. We will publish occasional 2.5.x maintenance releases as needed up until that point and will then move the project to the attic. The current Spring for Apache Hadoop 2.5.0 release is built using Apache Hadoop version 2.7.3 and should be compatible with the latest releases of the most popular Hadoop distributions.
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, Spark, Pig) and using HBase. 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.
For build dependencies to use in your own projects see our Quick Start page.
Spring for Apache Hadoop uses Gradle as its build system. To build the system simply run:
from the project root folder. This will compile the sources, run the tests and create the artifacts. Note that the tests by default tries to access a localhost single-node Hadoop cluster.
By default Spring for Apache Hadoop compiles against the Apache Hadoop 2.7.x stable relase (hadoop27).
The following distros and versions are currently supported in this projects master branch:
- Apache Hadoop 2.7.x (hadoop27) default
- Apache Hadoop 2.6.x (hadoop26)
- Pivotal HD 3.0 (phd30)
- Cloudera CDH5 (cdh5)
- Hortonworks HDP 2.5 (hdp25)
- Hortonworks HDP 2.4 (hdp24)
(For older distro versions, look for older releases)
To compile against a specific distro version pass the
-Pdistro=<label> project property, like so:
gradlew -Pdistro=hadoop26 build
Note that the chosen distro is displayed on the screen:
Using Apache Hadoop 2.6.x [2.6.0]
In this case, the specified Hadoop distribution (above Apache Hadoop 2.6.x) is used to create the project binaries.
The results for CI builds are available at Spring Data Hadoop: Project Summary - Spring CI
For its testing, Spring for Apache Hadoop expects a pseudo-distributed/local Hadoop instalation available on
localhost configured with a port
8020 for HDFS. The
local Hadoop setup allows the project classpath to be automatically used by the Hadoop job tracker. These settings
can be customized in two ways:
- Build properties
From the command-line, use
hd.fs for the file-system (to avoid confusion, specify the protocol such as 'hdfs://', 's3://', etc - if none is
hdfs:// will be used),
hd.rm for the YARN resourcemanager,
hd.jh for the jobhistory and
hd.hive for the Hive host/port
information, to override the defaults. For example to run against HDFS at
dumbo:8020 one would use:
gradlew -Phd.fs=hdfs://dumbo:8020 build
- Properties file
test.properties file under
src/test/resources folder (further tweaks can be applied through
hadoop-ctx.xml file under
Enabling Hbase/Hive/Pig/WebHdfs Tests
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
enableAllTests in short). Use
for customizing the default location for these services as well.
Disabling test execution
You can disable all tests by skipping the
gradlew -x test
Here are some ways for you to get involved in the community:
- Get involved with the Spring community on StackOverflow using the spring-data-hadoop tag to post and answer questions.
- 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 the Spring Blog.
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.