For a project we are using Hippo to manage our content. We have a few components that interact with the repository using the connection pool as provided by hippo. I have modified the connection pool to increase the amount of logs and I have added statistics to the pool which can be exposed using JMX. Our custom components use this altered connection pool, but I the site did not. Our site makes use of the Hippo Site Toolkit, and I want to have this monitoring available as well.
In this blogpost I will explain the changes I made to the connection pool. After that I’ll show what to do to make this changed connection pool available to a site created with the HST and I’ll show a groovy script that reads the data from the remote servers using the JMX connection.
Apache ActiveMQ is an open source messaging framework. The ActiveMQ web site is not really clear on how to integrate it with the Spring framework. Therefore, I decided to write this post to explain how to use ActiveMQ in combination with Spring and clarify some points.
The good news is that you can run JMS inside a servlet container (e.g. Apache Tomcat) without the need for a JCA adapter. This means you do not need Jencks or something similar.
Today, I released version 0.6 of the Axon Framework. 0.6 has many new features and is another step towards full production readiness. There is still some work to do, but first, let’s take a look at what has changed…
For one of our project I needed to read zip files from a FTP server and import the content in a system. In this post I will explain how I have used the Spring Integration to connect with a FTP server and retrieve Zip files.
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In one of our projects I had to parse and manipulate HTML. After searching for a nice HTML parser, I ended up using the open source library Jericho HTML Parser. Jericho provides you a lot of features including text extraction from HTML markup, rendering, formatting or compacting HTML. In this post I will show you a few of the features I have used.
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In Taste, estimators are the bridge between the generic item- or user recommendation logic and the specific similarity algorithm. Estimators are mainly used as part of the recommendation process, however, they are also used for evaluating recommenders. Additionally, the ‘recommended because’ feature is also powered by an estimator. This blog covers some Taste internals and shows you how estimators are used within Taste via a few code samples.
A little while ago, I was delighted to present two introductory Mahout – Taste talks, at Lucene Eurocon and Berlin Buzzwords. I received quite a lot of good feedback about the presentations and have been asked by a few attendees to post them.
If you’re one of those attendees or you missed the presentation, you can download the slides here:
At Lucene Eurocon, the first European conference on Lucene and Solr there were interesting presentations, ranging from practical relevance to language analysis. For me it was fun to give a practical presentation about recommendations as a complementary feature to search applications. I hope you find the presentation useful if you’re trying to work out how to build a recommender – I used the movielens dataset as an example in the presentation and based the code on my earlier ‘getting started’ blog.
I also really enjoyed doing the Berlin Buzzwords presentation and meeting up with people from the Mahout community and other attendees. This conference focused mainly on NoSQL, scalability and Hadoop. However, from my talks with people there I sense that there’s growing interest in Mahout. You should find the presentation useful if you want to know more about different algorithms and how to evaluate them. I will blog about this topic in more detail soon.
Until then, I’d love to hear some feedback on what you think of the presentations!
Since a week I have the latest Android phone of Sony Ericsson, the X10. As it is my first Android phone, the first thing I did was searching the Android Market for great apps to get the maximum out of my Android system.
In this post I will show you my selection of apps that I found useful, interesting or just fun to have.
JTeam will now offer leading on-demand APM tool to monitor, troubleshoot and optimize deployments of Solr search server
JTeam today announced that it has partnered with New Relic, Inc., to make New Relic RPM available to its clients during consulting engagements. JTeam consultants and clients can now use RPM collaboratively to monitor, troubleshoot, and optimize live production web applications and Solr instances, and thus ensure successful application deployments and a superior Web user experience.
New Relic, Inc. is the leading software-as-a-service provider of application performance management (APM) solutions. Its flagship product, New Relic RPM, is an on-demand performance management solution for web applications developed in Java, Ruby, or JRuby and provides deep, 24×7 visibility and code-level diagnostics for web applications deployed on traditional, dedicated infrastructures or in the cloud. New Relic recently announced RPM’s ability to provide deep visibility into production Solr instances. To learn more about New Relic RPM and Solr monitoring go to http://www.newrelic.com/solr.html.