When I joined trivago a year ago we had problems with our releases. Read how we were able to switch from our bash release process to a new one.
Posts about Open Source
trivago will host a PHPUnit Code Sprint in mid-October to support this crucial part of the PHP ecosystem. Sebastian Bergmann, the creator of PHPUnit, will be there to coordinate the developers' efforts and to answer questions.
trivago has decided to sponsor Webpack with a monthly contribution of $10,000 ($120,000/year). We hope that this will help to secure the continued innovation of the project.
Running Cucumber scenarios in parallel can be tricky, especially when a custom test runner is used. That’s why we created Cucable - a Maven plugin to split test scenarios into smaller chunks that can be run at the same time.
Concepts like separation of concerns, logic decoupling or dependency injection are things we developers have heard more than a couple of times. At trivago, the Android app is developed using the Model View ViewModel (MVVM) architecture, aiming for views as dumb as possible, leaving the decision making to the view models. This leads to an increased test coverage since testing logic in views is something we can’t do that easily.
At trivago we store a subset of our realtime metric data in InfluxDB and we are quite impressed by the load it can handle. Despite all the joy, we had to learn some lessons the hard way. It is pretty easy to overload the database or the web browser by executing queries that return too many datapoints.
At trivago we rely heavily on the ELK stack for our log processing. We stream our webserver access logs, error logs, performance benchmarks and all kind of diagnostic data into Kafka and process it from there into Elasticsearch using Logstash.
When using webpack to build your assets, it’s only a matter of time until you wish for targeted builds. Whether it’s the output of the library you’re working on (CJS, UMD, AMD, Var, etc.), or the specific feature set (IE8 support, no IE8 support). parallel-webpack can run those builds in parallel.
Caching data is an essential part in many high-load scenarios. A local 1st-level cache can augment a shared 2nd-level cache like Redis and Memcached to further boost performance. An in-process cache involves no network overhead, so the cache speed is only limited by local resources like CPU, memory transfer speed and locking.
Tackling hard problems is like going on an adventure. Solving a technical challenge feels like finding a hidden treasure. Want to go treasure hunting with us?View all current job openings