For our products, like the trivago hotel search, we are using Redis a lot. The use cases vary: Caching, temporary storage of data before moving those into another storage or a typical database for hotel meta data including persistence. The main parts of the hotel search are built with PHP and the Symfony Framework for the frontend (web) and Java for the backend part. In this article, we will focus on the collaboration between our PHP application and Redis. Both are running fine, but it was a long and hard way up to the current situation. This is the story of how we learned to use Redis, including our failures and experience.
Posts about DevOps
Configuration management tools have recently gained a lot of popularity. At trivago we use SaltStack to automate our infrastructure. As the complexity of configuration files and formulas is increasing, we need a fast, reliable way to test our changes.
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.
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.
Here at trivago we write a huge number of log messages every day that need to be stored and monitored. To handle all these messages we created Gollum, a tool that enables us to conveniently send messages from multiple sources to different services.
At trivago we love hotels above everything else, but we also like metrics, we love to measure everything, compare, decide, improve and then rinse and repeat. In this blog entry we are going to describe our experience with InfluxDB, a time series database that we are using to store some real time metrics.
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