The first release (0.19.0) from the 0.19 branch of Apache Hadoop Core was made on November 24. Many changes go into a release like this, and it can be difficult to get a feel for the more significant ones, even with the detailed Jira log, change log, and release notes. (There’s also JDiff documentation, which is a great way to see how the public API changed,
As a developer coming to Apache Hadoop it is important to understand how testing is organized in the project. For the most part it is simple — it’s really just a lot of JUnit tests — but there are some aspects that are not so well known.
Running Hadoop Unit Tests
Let’s have a look at some of the tests in Hadoop Core, and see how to run them. First check out the Hadoop Core source,
(Added 6/4/2013) Please note the instructions below are deprecated. Please refer to the CDH4 Security Guide for up-to-date procedures.
A few weeks ago we ran an Apache Hadoop hackathon. ApacheCon participants were invited to use our 10-node Hadoop cluster to explore Hadoop and play with some datasets that we had loaded on in advance. One challenge we had to face was, how do we do this in a secure way?
(guest blog post by Matei Zaharia)
When Apache Hadoop started out, it was designed mainly for running large batch jobs such as web indexing and log mining. Users submitted jobs to a queue, and the cluster ran them in order. However, as organizations placed more data in their Hadoop clusters and developed more computations they wanted to run, another use case became attractive: sharing a MapReduce cluster between multiple users.
It is common for a MapReduce program to require one or more files to be read by each map or reduce task before execution. For example, you may have a lookup table that needs to be parsed before processing a set of records. To address this scenario, Hadoop’s MapReduce implementation includes a distributed file cache that will manage copying your file(s) out to the task execution nodes.
The DistributedCache was introduced in Hadoop 0.7.0;