This blog is a place to meet our experts. We share technical knowhow and lessons learned from software projects, and discuss clever solutions for real-world problems.
This article describes how we configured and used the Varnish web cache for the popular German online shop www.lidl.de. Varnish gave us a tremendous performance boost. With this new caching setup, we easily achieve request rates of several thousand pages per second, which are quite common during marketing campaigns like special offers.
This second part of our blog series continues with the topic of direct involvement of customers and gives some practical examples of when, where and how to introduce and include your customer into JIRA. We will then discuss two of mgm’s proven real-world workflows and use them as case studies about appropriate modes for successful customer participation. You will also learn about our recommended ways of keeping the complexity of huge JIRA projects from the customer.
This two-part series shows how JIRA can be used for other things than just bug tracking and presents mgm’s experiences with embracing JIRA in nearly all parts of traditional and agile project lifecycles, resulting in a number of customized, optimized JIRA workflows and processes. In this first part, I will give you an overview of challenges we faced over the years and how we adapted JIRA to meet them. You will get a taste of the vast variety of uses we have found for JIRA and understand why we decided to use only one tool instead of many.
By coding the page state into the URL, even single-page web applications can support deep bookmarks and the browser’s back button. The most accepted approach is to utilize the location hash, i.e. the local part of the URL. This article explains this technique and what pitfalls you should be aware of, based on my team’s experience from building an AJAX interface for Solr. You will also learn about the HTML5 History API, which is a second, more modern technique.
This part explains some of the sophisticated software technology that is working behind the scenes in our rule-based test-data generator for form-centric applications. You will see that a simple enumeration of all possible ways to fill in a form is likely doomed to run longer than the age of the universe. Therefore more efficient techniques are needed to make the seemingly impossible possible.
For the e-commerce business, it is very important to have a valid and correct address base. Sending packages to incorrect addresses causes significant losses, since packages are not delivered and sent back. In the case of the Kickz online shop, we faced exactly this problem. Thus, we thought about how to improve and force users to enter correct addresses. Learn how we used Google’s Geocoding service to solve this problem.
Christian Belka,
16.6.2011 |
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Spring Roo, a new contender to Grails and Ruby on Rails, provides flexible scaffolding for Spring based applications. We used Spring Roo in a web project to quickly provide working software in order to discuss features and gather feedback. This article shows how we generated an early prototype and transitioned to early development and then to production code.
For our recent online shop project, we required a full-text, multi-criteria product search. Lucene, the popular Java search engine, is an ideal candidate for this functionality. But in order to meet the high performance requirement, we had to extend its usage beyond standard full-text search. This posting describes our solution including index switching and using Lucene as a simple NoSQL database.
The previous part discussed why a unit test for a class should be written by the developer of that class, and why a functional test should be created by an independent tester. This posting argues that functional tests should not be part of the build process of the product, but instead should be developed and executed separately. For this, I give guidelines for setting up an independent validation system.
Christian Belka,
31.1.2011 |
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This posting discusses how we integrated an web application with XForms as one of the main view technologies into a Liferay portal. We were using the Orbeon 3.8 XForms engine, which was introduced in the previous part of this series, and Spring Portlet MVC. Since we were facing quite a few problems and issues regarding Orbeon and Liferay, I go to great length discribing our working solution.
This part addresses the question what makes test data valuable for functional tests. You will understand the important concept of extreme and special values, and how to obtain test data that is highly compressed and also attains a high test coverage. The article also explains our novel idea for constructing a generator for such high-quality test data.
Over the past few years I have noticed that the distinction between functional tests and unit tests has blurred in a lot of projects. I think that using the features of modern testing frameworks like JUnit and TestNG to push functional tests into unit tests is the wrong approach, because it shifts the focus of the test from the test perspective to the development perspective. In this blog post, I explain in detail how I have come to this conclusion.
A lot of our web applications contain a large number of forms with hundreds of fields and complex cross-field constraints. mgm’s quality assurance team uses rule bases and automatic form validation to verify the correctness of these apps. This blog series discusses the challenges in generating test data for this verification and explains our automated process for producing masses of test data by utilizing the rule bases.
In this second installment, we conclude our evaluation of XForms implementations and explain why we ultimately preferred the Orbeon engine. I walk you through a XForms sample to explain essential concepts. My team has spent quite a lot of time understanding the Orbeon architecture and I discuss our findings, including separate-deployment configuration and state handling.
Peter Dikant,
7.9.2010 |
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We use the open-source search server Solr for real-time search on data stored in a Hadoop cluster. For our terabyte-scale dataset, we had to implement distributed search on multiple Lucene index partitions (shards). This article describes our solution to manage 40 independent Solr instances without human interaction, including transparent failover and automatic backup of the Lucene index.
