As of Thursday 10th May 2012 the JBoss Community Team are proud to announce our new search engine:

 

http://search.jboss.org/

 

This allows you to easily search across mailing lists at http://lists.jboss.org containing manual posts (i.e. not notification emails from issue trackers and build systems) and filter by Project, Time Frame, Mailing List Type and Author. You can choose to sort the results either by relevancy or newest/oldest first and use the Instant Search feature to see the results change in real-time as you type/filter your query instead of hitting the Search button. A Preview feature allows you to see the email within the thread it comes from and easily switch to other emails in the thread.

 

It's also possible to see how many more emails will be included in your search when selecting a filter option by consulting the number next to it in brackets, for example:

 

Mailing List:

-----------------

Dev (2413)

Users (864)

Announce (20)

N/A (2)

 

In addition to showing which types of mailing list are being used the most this provides a great insight into the relative usage of mailing lists across the projects as well as who is authoring the most emails

 

You can find detailed user instructions via the Help link at the top of the page which goes to:

 

https://community.jboss.org/wiki/JBossCommunitySearchHelp

 

In subsequent releases we aim to add more content types to the search indexes including IRC Transcripts, Blog Posts, Forum Posts, Wiki Articles and JIRA Issues. This will allow a unified search experience across all the content we host within the jboss.org infrastructure accessible from the search box in the JBoss Community header.

 

Thanks go to James Cobb and Cheyenne Weaver for the Visual Design, Lukas Koranda for helping with the rollout into the staging and production environments and tracking down performance issues, and the jboss.org development team for testing and feedback. Special thanks go to Lukas Vlcek who has led the project from the start when it was just an idea and has worked hard to produce an implementation that allows us to build-in additional functionality going forward on top of a scalable architecture.

 

For those who are interested it uses the open source project Elastic Search which allows easy up-down scaling and provides a rich full-text query API: http://www.elasticsearch.org/