Layered Hadoop Core

[Merlijn Sebrechts]

Have been testing these Charms for the past two days, and everything seems
to work so far. I'm amazed by how simple the handler files and interface
layers are. Very readable code, this will make it a lot easier to
contribute! Great work!

I'll test-drive these Charms next week in a semi-production environment.
The project has just started, so there is some bandwidth for failure. I'll
let you know if I run into any issues.




2016-02-18 0:26 GMT+01:00 Cory Johns <cory.johns at canonical.com>:

> The big data team just updated the apache-core-batch-processing bundle in
> ~bigdata-dev (
> https://jujucharms.com/u/bigdata-dev/apache-core-batch-processing) to use
> the new layered charms.  This makes the charms much easier to understand,
> maintain, and extend.  We're asking for more testing by the community,
> before we move the recommended core bundle over to these charms as well.
>
> If you are interested in seeing the source layers for these charms, they
> can be found in GitHub in the following repos:
>
>     https://github.com/juju-solutions/layer-apache-hadoop-namenode
>     https://github.com/juju-solutions/layer-apache-hadoop-resourcemanager
>     https://github.com/juju-solutions/layer-apache-hadoop-slave
>     https://github.com/juju-solutions/layer-apache-hadoop-plugin
>
> Creating a charm that connects to the cluster is now even easier, by
> extending the hadoop-client layer:
>
>     https://github.com/juju-solutions/layer-hadoop-client
>
> One caveat with this update is that, due to some aspects of how reactive
> charms maintain state internally, in-place upgrades from the existing
> charms are not recommended at this point.  You will also note that there
> are some differences in the services deployed, which we made to bring them
> more in line with commonly understood terminology and to prepare for fully
> utilizing the HA support built-in to Hadoop 2.7.1.
>
> We are continuing to develop these charms, as well as converting the other
> big data charms, such as Spark, Zeppelin, Flume, Kafka, etc., to use layers
> and reactive.  We look forward to your feedback.
>
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