Choosing the Right Log Stack: ELK vs EFK vs PLG (Loki) Explained

1. ELK Stack Overview

The classic ELK architecture, now called the Elastic Stack, combines Elasticsearch, Logstash, Kibana, and Beats.

Beats handle log collection.

Logstash aggregates and processes logs.

Elasticsearch stores and searches logs.

Kibana provides visual front‑end display.

2. EFK Log System

In containerized scenarios, especially Kubernetes, users often adopt the EFK architecture. The "F" stands for Fluent Bit, an open‑source, multi‑platform log processor and forwarder.

Collects data/logs from various sources.

Unifies and forwards to multiple destinations.

Fully compatible with Docker and K8s environments.

3. PLG Log System

3.1 Prometheus + k8s Log System

3.2 PLG Architecture

Grafana Labs offers another logging solution, PLG, which is gaining popularity. PLG consists of Promtail, Loki, and Grafana.

Grafana is an open‑source visualization and analysis tool that allows users to query, visualize, alert, and explore monitoring metrics, supporting dozens of data sources.

Loki forms a full‑featured log stack; unlike other systems, it indexes only log labels, not the raw log messages, resulting in lower operational costs and higher efficiency.

3.3 Loki Design Philosophy

Inspired by Prometheus, Loki aims to provide a horizontally scalable, highly available, multi‑tenant log system. Its architecture consists of several cooperating components.

Promtail is a log‑collecting agent that ships local log content to a Loki instance. It runs on each machine/container that needs monitoring, discovers targets, attaches labels to log streams, and pushes logs to Loki. Currently, Promtail supports two log sources: local log files and systemd logs (AMD64 only).

4. PLG vs ELK Comparison

4.1 Elasticsearch vs Loki

ELK/EFK stacks are proven in many production environments. Logs stored in Elasticsearch are saved as unstructured JSON objects with a full index for each object, enabling powerful full‑text search via a specific query language.

Loki decouples storage: logs can be stored on disk or in cloud storage such as Amazon S3. Loki logs carry a set of label name/value pairs; only the labels are indexed, which reduces indexing cost but requires LogQL for content‑based queries.

4.2 Fluentd vs Promtail

Compared to Fluentd, Promtail is tailored for Loki. It performs service discovery for Kubernetes Pods on the same node and reads logs from specified folders. Because Loki uses a Prometheus‑style labeling approach, logs collected by Promtail often share the same labels as application metrics, simplifying unified label management.

4.3 Grafana vs Kibana

Kibana offers many visualization tools for data analysis, including advanced features like anomaly detection powered by machine learning. Grafana, built for time‑series data such as Prometheus and Loki, allows users to view logs and metrics on the same dashboard.