Last week, I published the 0.4 release of the Axon Framework. Axon helps developers build high performance, scalable and extensible applications using the CQRS pattern. The 0.4 release is a major step towards 1.0, and includes transactional event handling, high-performance caching repositories and easy configuration of event sourcing support. Furthermore, we have also built a demo application that uses Flex to get real-time updates pushed from the server.
Read on to find out more.
Lately I have been looking into and playing around with the Google App Engine. In this post I want to give a little introduction to the Google App Engine, why it can be interesting and how to work with it.
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In a number of blog entries we have spoken about the spatial search functionality that we have been developing here at Jteam. In the last two weeks, I have had a chance to contribute much of this work back to the Apache Lucene project with the goal of furthering the development of Lucene’s open source spatial search support. If you want to dive immediately into the code, then jump to LUCENE-2139, if you want more details, then read on.
Introducing the Solr Spatial Plugin
What do a directory services company, a wholesaler of bathroom fittings, a social events guide, an oceanographic data centre and the pan-European library initiative have in common? They all need to offer their clients the ability to search and filter results within a flexible geographic area, defined by the user.
With a variety of businesses needing to customize search results to the preferences of a narrowly targeted audience, there’s an increasing demand for incorporating geographical location information into standard search functionality.
In response, JTeam has recently launched the Solr Spatial Plugin (SSP): a free, standalone, enterprise-ready plugin enabling location based search, built on top of the open source project Apache Solr.
Tomorrow, JTeam is hosting the second edition of our Search Symposium. The following two session (see read more / below) will be presented and will serve as input for the discussion afterward. If you are working in the search domain and would like to join us, sign up by sending an email to: signup@jteam.nl.
Hope to see you all there!
In a number of search projects that I have done using Lucene and Solr there was a lot of almost identical data. From a user perspective, when searching the first result pages were full of documents that look very similar, for instance getting a full page of the same car model, where only the edition differs, when searching for a specific car brand. What actually is desired is to only show the different models. Then and only when a user is interested in a certain model, the user can view all the editions of the model by clicking on the result. We simply want to group our search result, based on some criteria. Although this is not support out-of-the-box with Lucene/Solr, luckily it is possible using a patch that I’ve created and contributed to Solr. This blog entry explains what result grouping (also known as field collapsing) is and how you can start using it in your own projects.
The priority that performance testing gets among other development activities is always less; it is an “afterthought”, not a critical, ongoing part of the development process. Typically, it is done in higher environments like ST, QA. Some attention is given to performance in the prior SDLC cycles like design by making proper selection of frameworks. But during development its often limited to following the right coding standards. With this approach, the performance issues accumulate over time and then these bottlenecks become project killers.
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In the last 3 years we’ve witnessed the rise of open source enterprise search. Of course it was always there, and Apache Lucene in particular was there since, well… the previous century. But in the last 3 years the interest in this area has grown dramatically and the install/user base of the different Lucene related projects (Lucene Java and Solr in particular) has grown at an amazing rate. Today, the Lucene ecosystem is booming – there’s a high demand for expertise in this field, yet still there is relatively low supply. The Lucene / Solr mailing lists are flooded with hundreds of questions each week and the need to share knowledge is evident.
From day one, we at JTeam were very much occupied with pushing new revolutionary open source technologies that can bring real value to us and to our customers. We were there when Spring just started and we helped making it what it is today. We were one of the first companies to use Hibernate in real world projects (I reckon the first version we used was 0.4), and contributed to (back then) innovative new front end technologies like Ajax and DWR. With time, these technologies became mainstream and for a while it seemed that they just fulfilled every bit of our needs where JEE development is concerned. Yet something was still missing. About 3 years ago, we started noticing a new and growing trend in the market – a new demand – demand for search. Customers started paying more attention to the “findability” aspect in their offerings, be it an e-commerce website offering faceted navigation to its users, or proprietary search solutions on top large service management systems. The trend was obvious, the demand was there, and we had to deliver. We started by implementing our own custom solutions based on the brilliant Lucene library, but then came Solr and once again revolutionized our JEE development.
My goal in this post is to introduce you to Solr. Not too fancy, but to give you just a taste and enough information to at least get started with it. In future posts, I hope to expand on this and show you how you can leverage some of Solr’s features to implement some really cool stuff.
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I blogged earlier about the fact that JTeam is offering Wicket trainings for the Dutch market. However, I feel the need to explain a little more why we at JTeam feel Wicket is such an useful technology for us and our clients.