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Logging is crucial in any distributed system, especially in Kubernetes, to monitor application behavior, detect issues, and ensure the smooth functioning of microservices.
🚀 Importance:
Debugging: Logs provide critical information when debugging issues in applications.
Auditing: Logs serve as an audit trail, showing what actions were taken and by whom.
Performance Monitoring: Analyzing logs can help identify performance bottlenecks.
Security: Logs help in detecting unauthorized access or malicious activities.
🛠️ Tools Available for Logging in Kubernetes
🗂️ EFK Stack (Elasticsearch, Fluentbit, Kibana)
🗂️ EFK Stack (Elasticsearch, FluentD, Kibana)
🗂️ ELK Stack (Elasticsearch, Logstash, Kibana)
📊 Promtail + Loki + Grafana
📦 EFK Stack (Elasticsearch, Fluentbit, Kibana)
EFK is a popular logging stack used to collect, store, and analyze logs in Kubernetes.
Elasticsearch: Stores and indexes log data for easy retrieval.
Fluentbit: A lightweight log forwarder that collects logs from different sources and sends them to Elasticsearch.
Kibana: A visualization tool that allows users to explore and analyze logs stored in Elasticsearch.
We have successfully installed the EFK stack in our Kubernetes cluster, which includes Elasticsearch for storing logs, Fluentbit for collecting and forwarding logs, and Kibana for visualizing logs.
To verify the setup, access the Kibana dashboard by entering the `LoadBalancer DNS name followed by :5601 in your browser.
http://LOAD_BALANCER_DNS_NAME:5601
Use the username and password retrieved in step 6 to log in.
Once logged in, create a new data view in Kibana and explore the logs collected from your Kubernetes cluster.