GPars library is powerful, very simple to use, well designed and it brings advanced threading concepts like DataFlows, Agents and Actors formerly only sparsely available in Scala, Clojure to the Groovy world.
I use GPars in my project for a billion dollar product and services company in North America. Our application performs 14 to 20 million transactions a day which involves web service, complex computations and database calls. GPars library is solid and works like a charm. With this library, our application code is much shorter, clean and easier to maintain.
My work life is much easier after using Agents for monitoring and collecting stats on large batch processing jobs, and using Data Flow Queues and Tasks for adding and consuming jobs with just a few lines of code. And we got awesome support from firstname.lastname@example.org
GPars is awesome. Generally dealing with threading and concurrency is a real pain. With GPars you can get some very usable concurrency going in 3 lines of code.
We used GPars Actors to scatter HTTP requests in our Grails project to multiple backends, gather results and stream HTTP chunks of results as soon as they came back from backends. It was just plain fun, no Thread management boilerplate, just business rules optimizations. Would definitely re-use. http://blog.xebia.fr
Yes, I use GPars, it rocks. We've hacked a rdbms -> hbase migration application, gpars helped to improve speed by a huge factor, and it was easy to use and didn't give us problems. http://www.ecircle.com/en/home.html
GPars is quite awesome - I'm building a multi-threaded App that collects metric data across a large number of Cisco switches and Linux servers (using Groovy and Java), and GPars is working like a dream. The concept of stateful Actors is very nice. Well done! :)
On a data migration exercise from SugarCRM to Salesforce, some of the entity migrations could be performed in parallel as they weren't order dependent. GPars was chosen for sheer simplicity of GParsPool and eachParallel - only requiring 3 trivial new lines of code and the addition of 'Parallel' to the each collection iteration. GPars dramatically reduced the time taken to migrate the data by parallelising the processing of the database result set and the subsequent web service calls. http://leanjavaengineering.wordpress.com/2010/10/06/groovy-salesforce-api
GParallelizer is very cool. I had to collect information from 200 machines using JSCH (http://www.jcraft.com/jsch/) and 3 lines of code made it 10x faster.
... having fun with GPARS because it is so easy to experiment with. It lets me concentrate more on the solving the problem at hand rather than worrying about all the Java mechanics involving with coding the details of thread lifecycle management. Plus it is more expressive!
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