What is log aggregation and what tools are commonly used for it?

Log aggregation is the process of collecting log data from many sources — application instances, containers, VMs, serverless functions — into a centralized system where it can be searched, analyzed, and retained. Without aggregation, debugging a failure across 50 pods means SSHing into each one individually, which is impractical and slow.

The classic stack is the ELK Stack: Elasticsearch stores and indexes logs, Logstash or Beats (Filebeat, Metricbeat) ship logs from hosts, and Kibana provides the visualization and search UI. Logstash parses and transforms logs before indexing; Filebeat is a lightweight shipper that tails log files and forwards them to Logstash or directly to Elasticsearch.

Grafana Loki takes a different approach: it indexes only metadata labels (pod name, namespace, app) rather than full-text indexing the log content. This makes it far cheaper to run at scale. Queries use LogQL, which filters by label first, then applies line filters on the matching log streams. The trade-off is that ad-hoc field searches are slower than Elasticsearch for unstructured text.

Splunk is the dominant enterprise option with powerful search (SPL), rich dashboards, and deep integrations, but at significantly higher cost.

Cloud-native options include AWS CloudWatch Logs, Google Cloud Logging, and Azure Monitor Logs, each tightly integrated with their respective compute platforms.