Every part of a system writes logs. Web servers record requests, applications record errors and business events, and operating systems record logins and service restarts. One line at a time, they are hard to use. Log analysis turns them into answers: why did this request fail, when did the errors start, which users were affected, who changed this setting.
How log analysis works
- Collect. Agents and shippers such as Fluent Bit, Fluentd, Vector, Logstash or the OpenTelemetry Collector gather logs from files, stdout, syslog and cloud services into one place.
- Parse. Fields such as timestamp, level, service, status code and user ID are extracted from raw text, or read directly from structured JSON logs. See structured logging.
- Store and index. Logs are indexed for fast search, with retention rules that match how long you need them.
- Search and query. Filter by field, run full-text searches and aggregate, for example errors per service per minute.
- Visualize and alert. Chart trends and get alerted when a pattern appears or a rate crosses a threshold.
Common techniques
- Pattern grouping: clustering similar messages so you see 12 distinct errors instead of 40,000 lines, and notice when a new one appears.
- Correlation: joining logs from different services with a shared trace ID or request ID to follow one request end to end.
- Anomaly detection: flagging unusual log volume or error rates.
- Trend analysis: comparing today with last week, or before and after a deploy.
What it's used for
- Troubleshooting: finding the error and the context around it.
- Performance: spotting slow requests and timeouts.
- Security: failed logins, privilege changes, access from unusual places. Security teams often do this in a SIEM.
- Compliance and audit: proving who accessed what, and when.
Challenges
- Volume and cost: verbose debug logging can cost more to store than the service costs to run.
- Inconsistent formats: every component logs differently, so parsing takes ongoing work.
- Missing context: without trace IDs, linking logs to the request that produced them is guesswork.
- Sensitive data: passwords, tokens and personal data end up in logs and need masking.
Log analysis with Atatus
Log management collects logs from hosts, Kubernetes, Docker, NGINX, databases, Heroku, OpenTelemetry and an HTTP API, and parses them with pipelines that include a Grok parser. It groups similar lines into patterns, lets you search in plain English or by field, live-tails logs and links a log line to its trace. Archives can go to your own S3 bucket. For a comparison of tools, see log management tools.