The Apache Hadoop project announced the release of 3.0.0-alpha2 on January 25th, 2017. This is the second alpha release in the 3.0.0 release series leading up to 3.0.0 GA, and incorporates 857 new fixes, improvements, and features since 3.0.0-alpha1 last September. It’s worth reading our previous blog post about 3.0.0-alpha1; in this post, we’ll discuss the new improvements that landed in alpha2.
Classpath Isolation for Hadoop Client Jars
The pain of classpath isolation has been experienced by many Java developers.
The Apache Hadoop project recently announced its 3.0.0-alpha1 release.
Given the scope of a new major release, the Apache Hadoop community decided to release a series of alpha and beta releases leading up to 3.0.0 GA. This gives downstream applications and end users an opportunity to test and provide feedback on the changes, which can be incorporated during the alpha and beta process.
The 3.0.0-alpha1 release incorporates thousands of new fixes,
Cloudera Engineering has developed (and recently open sourced) a distributed unit testing framework that cuts testing time from multiple hours to just 10 minutes.
Upstream unit tests are Cloudera’s first line of defense for finding and fixing software bugs, as part of a multidimensional process that also includes static/dynamic code analysis, fault injection, integration/scale/endurance testing, and validation on real workloads. However, running a full unit test suite for Apache Hadoop ecosystem components can take hours,
Get an update on the progress of the effort to bring erasure coding to HDFS, including a report about fresh performance benchmark testing results.
About a year ago, the Apache Hadoop community began the HDFS-EC project to build native erasure coding support inside HDFS (currently targeted for the 2.9/3.0 release). Since then, we have designed and implemented basic functionalities in the first phase of the project under HDFS-7285,
Erasure coding, a new feature in HDFS, can reduce storage overhead by approximately 50% compared to replication while maintaining the same durability guarantees. This post explains how it works.
HDFS by default replicates each block three times. Replication provides a simple and robust form of redundancy to shield against most failure scenarios. It also eases scheduling compute tasks on locally stored data blocks by providing multiple replicas of each block to choose from.