fae7cbba82
This PS adds logging requirements and EFK (Elasticsearch, Fluent-bit & Fluentd, Kibana) based logging mechanism description. Change-Id: I121c5511b9cf2ff9d752fedd31f32254e3221922
2.2 KiB
Logging Mechanism
Logging Requirements
OpenStack-Helm defines a centralized logging mechanism to provide insight into the the state of the OpenStack services and infrastructure components as well as underlying kubernetes platform. Among the requirements for a logging platform, where log data can come from and where log data need to be delivered are very variable. To support various logging scenarios, OpenStack-Helm should provide a flexible mechanism to meet with certain operation needs.
EFK (Elasticsearch, Fluent-bit & Fluentd, Kibana) based Logging Mechanism
OpenStack-Helm provides fast and lightweight log forwarder and full featured log aggregator complementing each other providing a flexible and reliable solution. Especially, Fluent-bit is used as a log forwarder and Fluentd is used as a main log aggregator and processor.
Fluent-bit, Fluentd meet OpenStack-Helm's logging requirements for
gathering, aggregating, and delivering of logged events. Flunt-bit runs
as a daemonset on each node and mounts the /var/lib/docker/containers directory. The
Docker container runtime engine directs events posted to stdout and
stderr to this directory on the host. Fluent-bit then forward the
contents of that directory to Fluentd. Fluentd runs as deployment at the
designated nodes and expose service for Fluent-bit to foward logs.
Fluentd should then apply the Logstash format to the logs. Fluentd can
also write kubernetes and OpenStack metadata to the logs. Fluentd will
then forward the results to Elasticsearch and to optionally kafka.
Elasticsearch indexes the logs in a logstash-* index by default. Kafka
stores the logs in a logs topic by default. Any external
tool can then consume the logs topic.
- diagram {
-
fluent-bit [stacked]; fluent-bit -> fluentd -> elasticsearch -> kibana; fluentd -> kafka -> external_tool;
}
The resulting logs can then be queried directly through Elasticsearch, or they can be viewed via Kibana. Kibana offers a dashboard that can create custom views on logged events, and Kibana integrates well with Elasticsearch by default.