Catalog
concept#Observability#Reliability#DevOps#Platform

Logs

Time-ordered records of events and state changes used for debugging, monitoring, and forensic analysis.

Logs are time-ordered records of events, states, and messages from applications, systems, and infrastructure.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

OpenTelemetry instrumentationLog collectors/forwarders (e.g. Fluentd, Logstash, Promtail)Indexing and storage platforms (e.g. Elasticsearch, Loki)

Principles & goals

Use structured, machine-readable formats (e.g. JSON).Ensure context and correlation (trace IDs) across services.Explicitly define retention, access, and privacy requirements.
Run
Team, Domain, Enterprise

Use cases & scenarios

Compromises

  • Excessive logging may expose sensitive data.
  • Missing retention or deletion rules can violate compliance.
  • Incompatible formats complicate aggregation and analysis.
  • Use structured logs with clear field names and types.
  • Propagate trace and request IDs for distributed correlation.
  • Implement differentiated retention tiers (hot/warm/cold).

I/O & resources

  • Application log output (stdout/files)
  • System and infrastructure logs (syslog, kernel messages)
  • Tracing and context data (trace IDs, request IDs)
  • Indexed, searchable log data
  • Dashboards, alerts and reports
  • Exportable audit trails and forensic artifacts

Description

Logs are time-ordered records of events, states, and messages from applications, systems, and infrastructure. They support debugging, performance analysis, security monitoring, and forensic investigation by providing contextual, machine-readable event data. Effective logging requires structured formats, centralized collection, retention policies, efficient indexing, and access controls.

✔

  • Improved debugging and faster incident response.
  • Better monitoring, trend analysis and capacity planning from historical data.
  • Support for security and compliance requirements via audit trails.

✖

  • Cost and storage footprint at high log volumes.
  • Unstructured logs hinder automated analysis.
  • Incorrect or missing correlation reduces usefulness.

  • Log volume per second

    Number of incoming log entries per time unit; relevant for scaling decisions.

  • Indexing latency

    Time between log arrival and its availability for search and analysis.

  • Storage cost per GB

    Monetary cost for storing logs per gigabyte and time period.

Centralized ELK logging architecture

Application logs are shipped via Beats/Logstash into Elasticsearch and visualized with Kibana.

Cloud-native logs with OpenTelemetry and Loki

OpenTelemetry instrumentation produces structured logs collected via a Promtail/Loki pipeline.

Network syslog aggregation

Network devices send syslog events to a central syslog instance for analysis and retention.

1

Identify sources and define consistent log formats.

2

Set up centralized collection using forwarders or agents.

3

Configure indexing, retention and access controls.

4

Implement and test dashboards, search and alerting rules.

⚠️ Technical debt & bottlenecks

  • Legacy unstructured logs remain in place.
  • Lack of standardization hinders cross-platform analysis.
  • Outdated collector versions with known performance issues.
Data volumeIndexing performanceRetention capacity
  • Storing sensitive user data (e.g. passwords) in logs.
  • Ignoring log retention, causing compliance violations.
  • Excessive logging in hot paths that degrades system performance.
  • Missing time synchronization complicates correlation.
  • Using different time zones without normalization.
  • Insufficient access control to sensitive logs.
Knowledge of logging frameworks and formattingOperational knowledge of centralized logging platformsUnderstanding of security and privacy requirements
Scalability of collection and storageFast search and query timesSecure retention and access control
  • • Storage and cost budget for log archives
  • • Privacy and compliance requirements
  • • Network bandwidth for transporting logs to central systems