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Why Choose This Project?

In cloud-native environments, logs are the first line of defense for debugging and performance monitoring. Kubernetes generates massive volumes of logs across pods, services, and nodes. Manually checking logs via kubectl logs is inefficient. This project uses Fluentd as a log collector and forwarder and Elasticsearch as a searchable storage engine, making log management centralized, scalable, and searchable. It’s an essential project for mastering observability in DevOps.

What You Get

Centralized log management for all Kubernetes workloads
Searchable logs with Elasticsearch
Structured logging via Fluentd filters & parsers
Ability to trace issues across microservices
Foundation for enterprise-grade observability (can be extended with Kibana)

Key Features

Feature	Description
Fluentd Log Collection	Collects logs from all pods, containers, and nodes.
Log Forwarding	Sends structured logs to Elasticsearch.
Log Enrichment	Adds metadata like pod name, namespace, labels.
Elasticsearch Storage	Stores logs in a scalable, distributed database.
Kubernetes Integration	Uses DaemonSet to run Fluentd on every node.
Structured & Unstructured Logs	Handles JSON, plain text, or custom formats.
Scalability	Handles millions of log entries across clusters.

Technology Stack

Infrastructure Layer:

Kubernetes (EKS, AKS, GKE, Minikube, or bare-metal)
Docker

Logging & Storage Layer:

Fluentd (log collector, parser, forwarder)
Elasticsearch (log storage & indexing)

Optional Visualization Layer (Extension):

Kibana (for dashboards & log search)

Cloud Services Used

AWS Elasticsearch Service (OpenSearch) or Elastic Cloud
Cloud Storage (S3/Blob/GCS) for log backup
Kubernetes Cloud Provider (EKS, GKE, AKS, DigitalOcean, etc.)

Working Flow

Applications running inside pods generate logs.
Kubernetes nodes write container logs to /var/log/containers/.
Fluentd DaemonSet runs on every node, collecting logs.
Fluentd parses and enriches logs with metadata (namespace, pod, container).
Logs are forwarded to Elasticsearch, where they are indexed and stored.
(Optional) Kibana can be connected to Elasticsearch for log visualization and searching.

Main Modules

Fluentd DaemonSet – Collects and forwards logs from all nodes
Elasticsearch Cluster – Stores and indexes logs for search
Log Parsers & Filters – Structure unstructured logs (JSON, regex)
Kubernetes Metadata Plugin – Adds context to logs (pod, namespace, container)
(Optional) Kibana Dashboard – User interface for searching and visualizing logs