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.
Open Source at trivago
Insights, experiences and learnings from trivago's tech teams.
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.
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.