Logs, metrics & alerts
Structured application logging, queue-backed log forwarding, metric collection, and alert routing as code.
The system at a glance.
A conceptual view of the flow and its boundaries.
The challenge.
Serverless, container, and data workloads need consistent operational signals so engineers can connect failures to the service and execution context that produced them.
My contribution.
Worked across shared logging, containerized forwarding, monitoring configuration, and notification automation.
Implementation.
- Use shared Python log handlers to attach Lambda/ECS metadata and ship structured records through Kinesis or SQS.
- Forward workflow logs through Lambda and Vector running on ECS Fargate.
- Configure container autoscaling based on queue depth and CPU utilization.
- Maintain metrics scrape configuration, alert rules, notification routes, Elasticsearch mappings, and GitHub Actions deployment workflows.
Design decisions.
Queue-backed transport separates producers from indexing, but buffering and overflow behavior affect log completeness under load.
Alert routing and log transport complement metric collection; each signal answers a different operational question.
Validation.
Logging and forwarder unit tests are complemented by metrics-rule checks, configuration validation and Helm dry runs.
The outcome.
Connected service execution context with central log transport, metric collection, and actionable notification paths.
Explore the pattern.
The companion repository is an independent educational demonstration built with synthetic examples. It illustrates related engineering patterns and does not contain employer source code or production data. Repositories are currently private; please contact me for access.
Open private GitHub demo