The Apache Software Foundation has announced the release of Apache Hadoop 2.7.0 via the Hortonworks blog. The latest version of everybody’s favorite Big Data crunching software comes with some “significant enhancements” to the Hadoop Distributed File System and YARN, among other components, not too mention the obligatory bug fixes.

As noted by Vinod Kumar Vavilapalli, the Hadoop YARN & MapReduce Development Lead at Hortonworks Inc., the Apache Hadoop 2.7.0 release is the first major update to the platform of 2015, following the release of last year’s production-ready Hadoop 2.6.0.

The 2.7.0 release is not officially “production-ready”. Instead, the release is meant for the Hadoop community’s ‘tinkerers’ to get to grips with the latest offerings and enhancements, test them out and see if there are any incompatibilities that need to be ironed out. As always there are likely to be a few compatibility issues, but early adopters will have lots to look forward to ahead of a more stable release that’s set to “follow soon”.

Hadoop 2.7.0 adds significant enhancements to the Hadoop Common framework, Hadoop HDFS, YARN and MapReduce, as detailed by Hortonworks:

Hadoop Common

• Windows Azure Storage Blob support (HADOOP-9629), available in trunk for a while, is now integrated into branch-2 and released as part of 2.7.0.

Hadoop HDFS

• Enable new read/write scenarios in HDFS by adding support for truncate (HDFS-3107) and support for files with variable-length blocks (HDFS-3689)
• Enforce quotas at the Heterogeneous Storage Type granularity in HDFS (HDFS-7584)
• Enhance management (HDFS-7424) and monitoring (HDFS-7449) for the NFS Gateway Server

Hadoop YARN

• YARN-3100 – Makes YARN authorization pluggable so that tools like Apache Ranger can provide authorization for YARN job submission operations
• YARN-1492 – Automatic shared, global caching of YARN localized resources (beta)

Hadoop MapReduce

• MAPREDUCE-5583 – Allows the ability to limit the size of a running MapReduce job by restricting the maximum number of Map or Reduce tasks running at any point in time
• MAPREDUCE-4815 – Speed up Hive, Pig and MapReduce jobs that deal with many output files by making enhancements to the FileOutputCommitter

For more information, check out the release notes here.