Catalog
concept#Observability#Reliability#DevOps#Platform

App Monitoring

Concept for monitoring applications using metrics, logs and traces to ensure performance and availability.

App monitoring collects runtime data from applications, infrastructure and user interactions to analyze performance, availability and errors.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

OpenTelemetry CollectorPrometheus for metricsElasticsearch / Grafana for storage and dashboards

Principles & goals

Collect relevant telemetry rather than everythingEnable correlation of logs, metrics and tracesMeasurements should be productivity-friendly and SLA-oriented
Run
Team, Domain, Enterprise

Use cases & scenarios

Compromises

  • Alert fatigue from too many or imprecise alerts
  • Privacy issues for sensitive log contents
  • Wrong conclusions from incomplete data
  • Preserve context via trace IDs in logs
  • Mask sensitive data before telemetry transmission
  • Link alerts to clear runbooks

I/O & resources

  • Instrumented applications (metrics, logs, traces)
  • Telemetry pipeline (collector, ingest)
  • Dashboards and alerting rules
  • Dashboards with service health views
  • Alerts and incident tickets
  • Trend and capacity reports

Description

App monitoring collects runtime data from applications, infrastructure and user interactions to analyze performance, availability and errors. It combines metrics, logs and traces to quickly identify root causes and monitor SLAs. Useful for operations, SRE teams and architects to improve system reliability.

✔

  • Faster fault diagnosis and reduced MTTR
  • Better basis for capacity planning decisions
  • Transparency into user and system behavior

✖

  • Requires correct instrumentation of applications
  • High data volumes can increase costs and storage
  • Not every root cause can be inferred from telemetry alone

  • Error rate

    Percentage of failed requests per time window.

  • Latency (P95/P99)

    Response time distribution to capture outliers.

  • Throughput (requests/s)

    Number of processed requests per second.

Checkout optimization in webshop

Measuring and analyzing latency spikes led to caching and DB query optimizations.

SaaS multi-tenant SLA reporting

Tenant-specific dashboards identified deployments that caused regressions.

Mobile API error analysis

Tracing of partially instrumented endpoints revealed missing timeouts on upstream calls.

1

Instrument basic metrics and logs

2

Deploy OpenTelemetry Collector

3

Set up central storage and dashboard solution

4

Define SLA/SLO and configure alerting

5

Iteratively adjust sampling and retention strategies

⚠️ Technical debt & bottlenecks

  • Incomplete instrumentation in critical paths
  • Monolithic telemetry pipelines without modularity
  • No automated retention or cost control
Telemetry volumeStorage costsAlert management
  • Using only infrastructure metrics, not application-specific ones
  • Alerting on every error without threshold validation
  • Storing telemetry indefinitely without a retention plan
  • Wrong sampling rate hides rare errors
  • Too broad dashboards cause signal-to-noise issues
  • Ignored alerts lead to blindness to real incidents
SRE/operations skillsObservability instrumentationData analysis and query syntax
Detectability of performance degradationScalability of the telemetry pipelineMinimal runtime overheads
  • • Network bandwidth for telemetry
  • • Privacy and compliance requirements
  • • Legacy systems without instrumentation