A simple but yet powerful library to expose your entities through JSON API.
Posts about Backend
We’re a data-driven company. At trivago we love measuring everything. Collecting metrics and making decisions based on them comes naturally to all our engineers. This workflow also applies to performance, which is key to succeed in the modern Internet.
At trivago we use Jenkins as our main CI tool. However, when our physical setup was not enough we needed to move it to the cloud and implement an automated slave scaling. This is the definite guide with all the steps we took to implement an auto scaling Jenkins platform.
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.
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 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.
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.
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