Why Manually Pulling Server Logs Is Inefficient: Comparing ELK, EFK, and PLG Stacks

ELK Log System

Classic Elastic Stack consists of Elasticsearch, Logstash, Kibana and Beats. Beats collect logs, Logstash aggregates and processes them, Elasticsearch stores and indexes logs, and Kibana provides a visual front‑end.

EFK Log System

In containerized environments, especially Kubernetes, users often adopt the EFK stack where Fluent Bit replaces Beats. Fluent Bit collects data from various sources, forwards it to Logstash or directly to Elasticsearch, and fully supports Docker and k8s.

PLG Log System

Grafana Labs promotes the PLG stack: Promtail, Loki and Grafana. Promtail runs on each node to discover logs and attach labels, Loki stores only label indexes to reduce indexing cost, and Grafana visualizes logs and metrics.

Loki’s design, inspired by Prometheus, enables horizontal scaling and multi‑tenant log storage. It indexes only label pairs, requiring LogQL queries for content‑based search.

Comparisons

ELK vs PLG

ELK/EFK stores logs as unstructured JSON objects with full indexing, enabling powerful full‑text search. Loki decouples storage, allowing disk or cloud (e.g., Amazon S3) and indexes only labels, lowering operational cost but needing LogQL for content queries.

Fluentd vs Promtail

Promtail is purpose‑built for Loki, offering Kubernetes pod service discovery and label alignment with Prometheus, whereas Fluentd is a general‑purpose log collector.

Grafana vs Kibana

Kibana offers extensive visualizations and machine‑learning features for anomaly detection. Grafana focuses on time‑series data and can display both logs from Loki and metrics from Prometheus on a unified dashboard.