How To Deploy Grafana Loki on Kubernetes with Helm: A Complete Guide

Efficient logging and monitoring are crucial for managing modern, cloud-native applications. While the EFK or ELK stack has been a popular choice, integrating Prometheus and Grafana with Grafana Loki can offer a more streamlined, cost-effective, and scalable solution.

In this guide, you will learn how to deploy Grafana Loki on Kubernetes using Helm. We’ll provide step-by-step instructions, tips to avoid common pitfalls, and answers to frequently asked questions (FAQs).

Why Grafana Loki?

Grafana Loki is a log aggregation tool purpose-built for Kubernetes. Unlike traditional log management tools, Loki only indexes metadata about logs, similar to how Prometheus works with metrics. This makes Loki lightweight and highly efficient. Logs are compressed and stored in object storage like Amazon S3, Google Cloud Storage (GCS), or even locally on your filesystem.

Key Features of Loki:

• LogQL Support: Query logs using LogQL, inspired by PromQL.
• Kubernetes-native: Automatically enrich logs with Kubernetes metadata.
• Seamless Grafana Integration: Out-of-the-box support for visualization in Grafana.

Prerequisites

Before we dive into the deployment process, ensure the following:

1. A running Kubernetes cluster.
2. kubectl CLI installed and configured.
3. Helm CLI installed.
4. Sufficient permissions to create namespaces and deploy resources.

Step-by-Step Guide to Deploy Grafana Loki on Kubernetes

Step 1: Create a Namespace

It’s a good practice to isolate monitoring tools in their namespace.

kubectl create namespace monitoring

Step 2: Add the Grafana Helm Repository

To install Loki, you need the official Grafana Helm repository:

helm repo add grafana https://grafana.github.io/helm-charts
helm repo update

Step 3: Prepare a Custom values.yaml File

Using the default Helm values can sometimes cause configuration issues. Below is an optimized values.yaml file for Loki and its components:

test_pod:
  enabled: true
  image: bats/bats:1.8.2
  pullPolicy: IfNotPresent
loki:
  enabled: true
  image:
    repository: grafana/loki
    tag: 2.9.3
  readinessProbe:
    httpGet:
      path: /ready
      port: http-metrics
    initialDelaySeconds: 45
  livenessProbe:
    httpGet:
      path: /ready
      port: http-metrics
    initialDelaySeconds: 45
promtail:
  enabled: true
  config:
    logLevel: info
    clients:
      - url: http://loki:3100/loki/api/v1/push
    serverPort: 3101
grafana:
  enabled: false
  sidecar:
    datasources:
      enabled: true
proxy:
  http_proxy: ""
  https_proxy: ""
  no_proxy: ""

Save this file as values.yaml.

Step 4: Install Grafana Loki with Helm

Deploy the Loki stack using the custom values.yaml:

helm install --values=values.yaml loki grafana/loki-stack -n monitoring

This command deploys Loki, Promtail, and other necessary components into the monitoring namespace.

Step 5: Verify the Deployment

Check the services in the monitoring namespace:

kubectl get services -n monitoring

Step 6: Port Forward Grafana for Access

To access Grafana locally, forward its service port:

kubectl port-forward service/prometheus-grafana -n monitoring 8080:80

Open your browser and go to:
http://localhost:8080

Step 7: Login to Grafana

The default Grafana credentials are:

• Username: admin
• Password: Retrieve using:

kubectl get secrets -n monitoring prometheus-grafana -o jsonpath="{.data.admin-password}" | base64 --decode

After logging in, go to Configuration > Data Sources and verify that Loki is connected.

Step 8: Explore Logs in Grafana

Navigate to Explore in Grafana, select Loki as the data source, and query logs using LogQL. Use the available labels (e.g., namespace, pod, container) to filter logs for specific components.

FAQs

1. Why use Grafana Loki over ELK or EFK stacks?

• Loki is lightweight and indexes only metadata instead of the full log content.
• Seamless integration with Prometheus and Grafana.
• Reduced infrastructure costs due to minimal resource requirements.

2. What are the common issues during Loki deployment?

• Connectivity Issues: Ensure Promtail’s url matches Loki’s service address.
• Namespace Errors: Use consistent namespaces for your monitoring stack.
• Authentication Problems: Verify the credentials for accessing Grafana.

3. How can I store logs persistently?

• Configure Loki to use an object storage backend like Amazon S3 or Google Cloud Storage in the values.yaml file.

4. How can I scale Loki for large workloads?

• Use Loki’s distributed mode to scale out components like ingesters, queriers, and distributors.

5. Can I add alerts for logs?

• Yes, you can create alerts in Grafana based on log queries.

6. How to solve “Unable to connect with Loki. Please check the server logs for more details.”

• simply use the values.yaml file above 

Conclusion

By deploying Grafana Loki on Kubernetes, you’ve set up a robust, scalable logging system integrated with Grafana for monitoring and visualization. This approach simplifies log management and ensures developers have easy access to logs, enabling faster debugging and improved observability.

If you found this guide helpful, share it with others and explore more about Grafana Loki’s advanced features for optimizing your logging setup!