[ANNOUNCE] Apache Hama announces v0.7 Release!

["Edward J. Yoon" <ed...@apache.org>]

Apache Hama team is pleased to announce the release of Hama v0.7 with
new features and improvements.

Hama is a High-Performance BSP computing engine, which can be used to
perform compute-intensive general scientific BSP applications,
Google’s Pregel-like graph applications, and machine learning
algorithms.

- What are the major changes from the last release?

The important new feature of this release is that support the Mesos
and Yet Another Resource Negotiator (YARN), so you’re able to submit
your BSP applications to the existing open source and enterprise
clusters e.g., CDH, HDP, and Mesosphere without any installation. In
addition, we reinforced machine learning package by adding algorithms
such as Max-Flow, K-Core, ANN, ..., etc.

There are also big improvements in the queue and messaging systems. We
now use own outgoing/incoming message manager instead of using Java's
built-in queues. It stores messages in serialized form in a set of
bundles (or a single bundle) to reduce the memory usage and RPC
overhead. Unsafe serialization is used to serialize Vertex and its
message objects more quickly. Another important improvement is the
enhanced graph package. Instead of sending each message individually,
we package the messages per vertex and send a packaged message to
their assigned destination nodes. With this we achieved significant
improvement in the performance of graph applications.

- What’s Next?

After a month of testing and benchmarking this version will bring
substantial performance improvements together with important bug fixes
which significantly improve the platform stability. We look forward to
add more and more and see our community grow. The primary objective of
the technical plans are:

 * Add stream input format for listening messages coming from 3rd
party applications, and incremental learning algorithms.
 * Improve reliability of system e.g., fault tolerance, HA, ..., etc.
 * More machine learning algorithms, such as ensemble classifier, SVM,
DNN, ..., etc

- Where I can download it?

The release artifacts are published and ready for you to download
either from the Apache mirrors or from the Maven repository. We
welcome your help, feedback, and suggestions. For more information on
how to report problems, and to get involved, visit the Hama project
website[1] and wiki[2].

[1]. Apache Hama Website: https://hama.apache.org/
[2]. Apache Hama Wiki: https://wiki.apache.org/hama/

-- 
Best Regards, Edward J. Yoon