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Install the microservice Helm chart

This Helm Chart deploys Grafana Loki on Kubernetes.

This Helm chart deploys Loki to run Loki in microservice mode within a Kubernetes cluster. The microservices deployment mode runs components of Loki as distinct processes.

The default Helm chart deploys the following components:

  • Compactor component (1 replica): Compacts and processes stored data.
  • Distributor component (3 replicas, maxUnavailable: 2): Distributes incoming requests. Up to 2 replicas can be unavailable during updates.
  • IndexGateway component (2 replicas, maxUnavailable: 1): Handles indexing. Up to 1 replica can be unavailable during updates.
  • Ingester component (3 replicas): Handles ingestion of data.
  • Querier component (3 replicas, maxUnavailable: 2): Processes queries. Up to 2 replicas can be unavailable during updates.
  • QueryFrontend component (2 replicas, maxUnavailable: 1): Manages frontend queries. Up to 1 replica can be unavailable during updates.
  • QueryScheduler component (2 replicas): Schedules queries.

Note

We do not recommend running in Microservice mode with filesystem storage. For the purpose of this guide, we will use MinIO as the object storage to provide a complete example.

Prerequisites

  • Helm 3 or above. See Installing Helm.
  • A running Kubernetes cluster (must have at least 3 nodes).

Deploying the Helm chart for development and testing

  1. Add Grafana’s chart repository to Helm:

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

  2. Update the chart repository:

    bash 
    helm repo update

  3. Create the configuration file values.yaml. The example below illustrates how to deploy Loki in test mode using MinIO as storage:

    yaml 
    loki:
   schemaConfig:
     configs:
       - from: "2024-04-01"
         store: tsdb
         object_store: s3
         schema: v13
         index:
           prefix: loki_index_
           period: 24h
   ingester:
     chunk_encoding: snappy
   querier:
     # Default is 4, if you have enough memory and CPU you can increase, reduce if OOMing
     max_concurrent: 4
   pattern_ingester:
     enabled: true
   limits_config:
     allow_structured_metadata: true
     volume_enabled: true
     retention_period: 672h
   compactor:
     retention_enabled: true 
     delete_request_store: s3

deploymentMode: Distributed

ingester:
  replicas: 3 # To ensure data durability with replication
querier:
  replicas: 3 # Improve query performance via parallelism
  maxUnavailable: 2
queryFrontend:
  replicas: 2
  maxUnavailable: 1
queryScheduler:
  replicas: 2
distributor:
  replicas: 3 
  maxUnavailable: 2
compactor:
  replicas: 1
indexGateway:
  replicas: 2
  maxUnavailable: 1

bloomCompactor:
  replicas: 0
bloomGateway:
  replicas: 0

backend:
   replicas: 0
read:
   replicas: 0
write:
   replicas: 0

singleBinary:
   replicas: 0

 # This exposes the Loki gateway so it can be written to and queried externaly
gateway:
   service:
     type: LoadBalancer


# Enable minio for storage
minio:
  enabled: true

  4. Install or upgrade the Loki deployment.

    • To install:
      bash 
      helm install --values values.yaml loki grafana/loki
    • To upgrade:
      bash 
      helm upgrade --values values.yaml loki grafana/loki

  5. Verify that Loki is running:

    bash 
    kubectl get pods -n loki

    The output should an output similar to the following:

    bash 
      loki-canary-8thrx                      1/1     Running   0          167m
  loki-canary-h965l                      1/1     Running   0          167m
  loki-canary-th8kb                      1/1     Running   0          167m
  loki-chunks-cache-0                    2/2     Running   0          167m
  loki-compactor-0                       1/1     Running   0          167m
  loki-compactor-1                       1/1     Running   0          167m
  loki-distributor-7c9bb8f4dd-bcwc5      1/1     Running   0          167m
  loki-distributor-7c9bb8f4dd-jh9h8      1/1     Running   0          167m
  loki-distributor-7c9bb8f4dd-np5dw      1/1     Running   0          167m
  loki-gateway-77bc447887-qgc56          1/1     Running   0          167m
  loki-index-gateway-0                   1/1     Running   0          167m
  loki-index-gateway-1                   1/1     Running   0          166m
  loki-ingester-zone-a-0                 1/1     Running   0          167m
  loki-ingester-zone-b-0                 1/1     Running   0          167m
  loki-ingester-zone-c-0                 1/1     Running   0          167m
  loki-minio-0                           1/1     Running   0          167m
  loki-querier-bb8695c6d-bv9x2           1/1     Running   0          167m
  loki-querier-bb8695c6d-bz2rw           1/1     Running   0          167m
  loki-querier-bb8695c6d-z9qf8           1/1     Running   0          167m
  loki-query-frontend-6659566b49-528j5   1/1     Running   0          167m
  loki-query-frontend-6659566b49-84jtx   1/1     Running   0          167m
  loki-query-frontend-6659566b49-9wfr7   1/1     Running   0          167m
  loki-query-scheduler-f6dc4b949-fknfk   1/1     Running   0          167m
  loki-query-scheduler-f6dc4b949-h4nwh   1/1     Running   0          167m
  loki-query-scheduler-f6dc4b949-scfwp   1/1     Running   0          167m
  loki-results-cache-0                   2/2     Running   0          167m

Object Storage Configuration

After testing Loki with MinIO, we recommend configuring Loki with an object storage provider. The following examples shows how to configure Loki with different object storage providers:

Caution

When deploying Loki using S3 Storage DO NOT use the default bucket names; chunk, ruler and admin. Choose a unique name for each bucket. For more information see the following security update. This caution does not apply when you are using MinIO. When using MinIO we recommend using the default bucket names.

To configure other storage providers, refer to the Helm Chart Reference.

Deploying the Loki Helm chart to a Production Environment

Note

We are actively working on providing more guides for deploying Loki in production.

We recommend running Loki at scale within a cloud environment like AWS, Azure, or GCP. The below guides will show you how to deploy a minimally viable production environment.

Next Steps