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.
Posts about Open Source
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.
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.
Last weekend, the Python Hackathon Düsseldorf took place at trivago’s office. Although we were only five people we had a lot of fun. I took the chance to brush up my Python skills a little bit. Also I wanted to scratch an itch that was bugging me for a long time: our housekeeping book.
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.
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