Question

What tools do you use to manage application logs, and how do you handle log management at scale?

Centralized logging at scale: As the volume increases, managing logs at scale is done by collecting, storing, and analyzing massive datasets. Explain your use of centralised logging tools such as ELK Stack, Splunk, or DataDog, and strategies for dealing with data retention, indexing, and searching to make logs accessible and manageable as the volume grows. Also include practices like log rotation and archiving for efficient storage.

Answer 1

Centralization of Application Logs: Centralize application logs by aggregating them across environments on platforms like ELK Stack (Elasticsearch, Logstash, Kibana), Splunk, or Fluentd. All these tools support full-text search, visualization, and real-time monitoring to simplify the analysis and troubleshooting at scale.
Scaling Log Management: On very large environments, I force rotation policies and retention limits for controlling the cost of storage. Logs are archived with low cost in S3 Glacier (or other cold storage) while recent ones stay on fast-access storage. In Kubernetes, Grafana Loki is used for logging. It pairs really well with Prometheus to scale efficiently log aggregation since they index logs by labels instead of content, which cuts the cost of storage and retrieval time.