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.
Machine Learning (ML) engineering and software development are both fundamentally about writing correct and robust algorithms. In ML engineering we have the extra difficulty of ensuring mathematical correctness and avoiding propagation of round-off errors in the calculations when working with floating-point representations of a number. As such, ML engineering and software development share many challenges… and some of the solutions to these. Concepts like unit testing and continuous integration rapidly found its way into the jargon and the toolset commonly used by data scientist and numerical scientist working on ML engineering.
What’s the point in downloading the app from the store when you can do the same thing in the browser? I’m a product designer at trivago, and would like to share some insights into one of our biggest projects we tackled in the last period. Designing trivago’s new mobile app!
It is not everyday that you get to chat face to face with the creator of a highly relevant open source project. Accordingly, we were highly anticipating a certain visit in mid-October, 2018.
Sometimes, when I look back over the time I have spent working at trivago, I see how it changed my life and how lucky I have been to get the chance to work among this amazing community, to live and to learn with them. I look back and see a younger version of myself looking desperately for something different and, by just sheer luck, getting it.
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
Testing your functionality is important, but what happens if other factors come into play? In this blog post we show how trivago handles non-functional testing for every commit and how we scaled it.
Ten participants from nine countries, who have never met before, are assigned to teams to work on real-world projects. Can they be successful? We ran this experiment in September 2018 on the trivago Campus, and were blown away by the results.
Would you book a hotel without seeing the images first? No, right? Hence, it’s vital to make sure the images are available all the time. In a scenario where a lot of images were deleted, we must have an efficient way of recovering them. This is how we achieved that with Amazon S3 Versioning.