At trivago, tech meetups are one of our favorite formats to learn and exchange knowledge. In April, we hosted the 2nd edition of the QA Meetup, which turned out to be a great success. The event attracted over 65 attendees from various roles and companies. The evening was filled with inspiring talks by several talented QA experts. Attendees were sharing insights, asking questions, and soaking up knowledge. To make things even more exciting, there was a competitive quiz that got everyone on their toes, and a magic show by one of our speakers that completely amazed the audience.

Why should you retry all tests on failure? Why not? This article will not go into details, listing pros and cons of each approach. There are already enough resources on the Web about the topic, listing valid points for both opposing views. As trivago Hotel Search frontend QA team over the last years we tried to stay away from a brute-force retry policy for failures and we rather tried to execute test retries only in selected cases. Recently, when we switched to a Continuous Deployment approach for our new frontend Web application (which empowers developers to merge and release some pull requests autonomously), we faced a greater need than before for understandable and stable test results. Due to that, showing as few “red flags” as possible for the automated checks on pull requests became even more important to ensure enough confidence in test results and to avoid slowing down the software development life cycle. The requirements and the balance between deterministic results and success ratio shifted, at least in some cases.

Over the last few years, we completely refactored what was described in our previous article about how we use the ELK stack for an overview of our test automation results, but some core concepts remain valid and applicable.

Hello from trivago's performance & monitoring team. One important part of our job is to ship more than a terabyte of logs and system metrics per day, from various data sources into elasticsearch, several time series databases and other data sinks. We do so by reading most of the data from multiple Kafka clusters and processing them with nearly 100 Logstashes. Our clusters currently consists of ~30 machines running Debian 7 with bare-metal installations of the aforementioned services. This summer we decided to migrate all of this to an on-premise [Nomad](https://www.nomadproject.io/ cluster) cluster.

We do think that our tech blog is full of interesting things powered by our engineers' great stories. Let me take you on a journey of how we maintain the trivago tech blog from the technical perspective and how we recently automated its deployment process.

When I joined trivago a year ago, we had problems with our releases. The traffic was increasing each day. When we put the server back into the load balancer without warming up the OPcache it would die. From time to time the warmup failed silently. Our DCO (data center operations) crew had to log into the servers and restart a few processes manually. During this time every release was very intense.

At trivago, we are using an in-house developed Selenium framework based on cucumber-jvm to run automated browser tests. As the test suite increased (the time exceeded 45 minutes for a full run), we were looking for ways to move away from sequential towards parallel execution. For Cucumber, there are actually not that many options available:

I love to take complex and tedious processes and automate the pain out of them until they are reduced to three or four steps!

As part of the release team of trivago, one of our roles is to create tools that make the lives of our developers easier so that they can create amazing features for our website.

The advances and growth of our Selenium based automated testing infrastructure generated an unexpected number of test results to evaluate. We had to rethink our reporting systems. Combining the power of Selenium with Kibana's graphing and filtering features totally changed our way of working. Now we have real-time testing feedback and the ability of filtering between thousands of tests, all in one Dashboard.