Explore Centralized Logging
Explore how to implement centralized logging in Kubernetes using the EFK stack—Elasticsearch for storage, Fluentd for log collection, and Kibana for log exploration—as well as configuring resource management and indexing for effective log querying.
Elasticsearch is probably the most commonly used in-memory database, At least if we narrow the scope to self-hosted databases. It is designed for many other scenarios, and it can be used to store (almost) any type of data. As such, it is almost perfect for storing logs, which could come in many different formats. Given its flexibility, some use it for metrics as well, and Elasticsearch competes with Prometheus. We’ll leave metrics aside for now and focus only on logs.
EFK stack
The EFK (Elasticsearch, Fluentd, and Kibana) stack consists of three components. Data is stored in Elasticsearch. Logs are collected, transformed, and pushed to the DB by Fluentd, and Kibana is used as UI through which we can explore data stored in Elasticsearch. If you are used to ELK (Logstash instead of Fluentd), the setup that follows should be familiar.
The first ...