Monitoring & Observability
ELK centralizes logs for debugging and operations.
On this page
Use this page for fast recall. Use Full documentation when you want the complete end-to-end path.
A simple 5-step flow you can follow when using ELK in real work.
1) Setup
Install/run the tool and confirm version. Create a minimal config.
2) Small change
Do one small action end-to-end to prove the workflow.
3) Validate
Check output, logs, and status. Catch mistakes early.
4) Automate
Convert it into a repeatable script or pipeline step.
5) Productionize
Add safety: secrets, rollback, observability, and docs.
These are practical sequences you can copy into your own checklist or runbook.
Goal: Get from raw logs to searchable, actionable views.
- Ship logs (Filebeat/agent) to pipeline (Logstash or ingest).
- Normalize fields (service, env, level, requestId).
- Index into Elasticsearch with sane mappings.
- Create Kibana searches and dashboards for common incidents.
- Tune retention and index lifecycle to control costs.
- Ingestion
- Indexing
- Visualization
Learning path (high-level):
- Log pipeline setup
- Query patterns
- Ops dashboards
Send logs to pipeline
Index in Elasticsearch
Search in Kibana
Short descriptions and practical intent. Search, filter, copy, and reuse.
Showing 9
curl localhost:9200Check Elasticsearch endpoint.
curl localhost:9200/_cluster/health?prettyCluster health summary.
curl localhost:9200/_cat/nodes?vList cluster nodes.
curl localhost:9200/_cat/indices?vList indices.
curl localhost:9200/<index>/_search?q=errorSearch error logs quickly.
curl localhost:5601/api/statusCheck Kibana status.
bin/logstash -f pipeline.confRun Logstash with config file.
filebeat test outputValidate Filebeat output connectivity.
filebeat test configValidate Filebeat config.
Small blocks you can drop into your terminal, config, or runbook.
Quick index search
bash
curl -s 'http://localhost:9200/my-index/_search?q=level:ERROR&size=5' | jq .hits.hits[].'_source'When things break, follow this order to stay calm and move fast.
- If ingestion fails: verify output connectivity and auth (Filebeat test output).
- If searches are slow: reduce wildcard queries and improve mappings.
- If storage grows: check retention, ILM policies, and shard counts.
The common mistakes that slow people down when using ELK.
- Copy-pasting commands without understanding inputs/outputs and side effects.
- Not documenting defaults (ports, paths, credentials) and then getting stuck in prod.
- Skipping logs and metrics when troubleshooting; always collect evidence first.
Do these tasks in order. You will feel the tool instead of just reading about it.
- Install or run the tool locally (or in Docker) and verify it works with a hello-world action.
- Create a minimal config and run the most common command 3 times (with a small change each time).
- Break something on purpose and document how you debugged it in your Notes.
Use these to test if you truly understand the basics (and can explain them clearly).
- Explain the tool’s role in a real CI/CD pipeline from commit to production.
- Describe the most common failure you’ve seen with this tool and how you fixed it.
- What would you monitor/alert on for this tool in production?