In 2020 we started to migrate one of our most significant workloads, our Node.js based GraphQL API and many of its microservices, from our datacenter to Google Kubernetes Engine. We deploy it in three GCP regions, each having its Kubernetes cluster. Since then, our monitoring infrastructure has changed due to various periods of instability and pandemic induced scaling challenges.
One of the many responsibilities of a Site Reliability Engineer (SRE), is to ensure uptime, availability and in some cases, consistency of the product. In this context, the product refers to the website, APIs, microservices, and servers. This responsibility of keeping the product up and running becomes particularly interesting if the product is used around the world 24 hours every day like trivago. And just like in the medical profession, someone has to be on call to react on failures and outages outside of the office hours.
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 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. Our preferred encoding within this pipeline is Google's Protocol Buffers, short protobuf. In this blog post, we will explain with an example how to read protobuf encoded messages from Kafka using Logstash.