Documentation Grafana Loki Set up Install Install using Helm Cloud Deployment Guides Deploy on Azure

Deploy the Loki Helm chart on Azure

This guide shows how to deploy a minimally viable Loki in microservice mode on Azure using the Helm chart. In order to successfully complete this guide, you must have the necessary tools and permissions to deploy resources on Azure, such as:

• Full access to AKS (Azure Kubernetes Service)
• Full access to Azure Blob Storage
• Sufficient permissions to create federated credentials and roles in Azure AD (Active Directory)

There are three primary ways to authenticate Loki with Azure:

• Hard coding a connection string - this is the simplest method but is not recommended for production environments.
• Manged identity
• Federated token

In this guide, we will use the federated token method to deploy Loki on Azure. This method is more secure than hard coding a connection string and is more suitable for production environments.

Considerations

• AD Role: In this tutorial we will create a role in Azure Active Directory (Azure AD) to allow Loki to read and write from Azure Blob Storage. This role will be assigned to the Loki service account. You may want to adjust the permissions based on your requirements.

• Authentication: Grafana Loki comes with a basic authentication layer. The Loki gateway (NGINX) is exposed to the internet using basic authentication in this example. NGINX can also be replaced with other open-source reverse proxies. Refer to Authentication for more information.

• Retention: The retention period is set to 28 days in the values.yaml file. You may wish to adjust this based on your requirements.

• Costs: Running Loki on Azure will incur costs. Make sure to monitor your usage and costs to avoid any unexpected bills. In this guide we have used a simple AKS cluster with 3 nodes and Standard_E2ds_v5 instances. You may wish to adjust the instance types and number of nodes based on your workload.

Prerequisites

• Helm 3 or above. Refer to Installing Helm. This should be installed on your local machine.
• Kubectl installed on your local machine. Refer to Install and Set Up kubectl.
• Azure CLI installed on your local machine. Refer to Installing the Azure CLI. This is a requirement for following this guide as all resources will be created using the Azure CLI.

AKS minimum requirements

Before creating an AKS cluster in Azure you need to create a resource group. You can create a resource group using the Azure CLI:

bash Copy

az group create --name <INSERT-NAME> -location <INSERT-AZURE-REGION>

The minimum requirements for deploying Loki on AKS are:

• Kubernetes version 1.30 or above.
• 3 nodes for the AKS cluster.
• Instance type depends on your workload. A good starting point for a production cluster in the free tier is Standard_E2ds_v5 instances and for large production workloads Standard_D4_v5 instances.

Here is how to create an AKS cluster with the minimum requirements:

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az aks create \
  --resource-group <MY_RESOURCE_GROUP_NAME> \
  --name <MY_AKS_CLUSTER_NAME> \
  --node-count 3 \
  --node-vm-size Standard_E2ds_v5 \
  --generate-ssh-keys \
  --enable-workload-identity \
  --enable-oidc-issuer

Note in the above command we have enabled workload identity and OIDC issuer. This is required for the Loki service account to authenticate with Azure AD. If you have already created an AKS cluster, you can enable these features using the following command:

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az aks update \
  --resource-group <MY_RESOURCE_GROUP_NAME> \
  --name <MY_AKS_CLUSTER_NAME> \
  --enable-workload-identity \
  --enable-oidc-issuer

The Azure CLI also lets you bind the AKS cluster to kubectl. You can do this by running the following command:

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az aks get-credentials --resource-group <MY_RESOURCE_GROUP_NAME> --name <MY_AKS_CLUSTER_NAME>

Configuring Azure Blob Storage

Before deploying Loki, you need to create two Azure storage containers; one to store logs (chunks), the second to store alert rules. You can create the containers using the Azure CLI. Containers must exist inside a storage account.

1. Create a storage account:

   bash Copy

   az storage account create \
   --name <NAME> \
   --location <REGION> \
   --sku Standard_ZRS \
   --encryption-services blob \
   --resource-group <MY_RESOURCE_GROUP_NAME>

   Replace the placeholders with your desired values.

2. Create the containers for chunks and ruler:

   bash Copy

   az storage container create --account-name <STORAGE-ACCOUNT-NAME> --name <CHUNK-BUCKET-NAME> --auth-mode login && \
   az storage container create --account-name <STORAGE-ACCOUNT-NAME> --name <RULER-BUCKET-NAME> --auth-mode login

   Make sure --account-name matches the account you just created

With the storage account and containers created, you can now proceed to creating the Azure AD role and federated credentials.

Creating the Azure AD role and federated credentials

The recommended way to authenticate Loki with Azure Blob Storage is to use federated credentials. This method is more secure than hard coding a connection string directly into the Loki configuration. In this next section, we will create an Azure AD role and federated credentials for Loki to allow it to read and write from Azure Blob Storage:

1. Locate the OpenID Connect (OIDC) issuer URL:

   bash Copy

   az aks show \
   --resource-group <MY_RESOURCE_GROUP_NAME> \
   --name <MY_AKS_CLUSTER_NAME> \
   --query "oidcIssuerProfile.issuerUrl" \
   -o tsv

   This command will return the OIDC issuer URL. You will need this URL to create the federated credentials.

2. Generate a credentials.json file with the following content:

   json Copy

   {
       "name": "LokiFederatedIdentity",
       "issuer": "<OIDC-ISSUER-URL>",
       "subject": "system:serviceaccount:loki:loki",
       "description": "Federated identity for Loki accessing Azure resources",
       "audiences": [
         "api://AzureADTokenExchange"
       ]
   }

   Replace <OIDC-ISSUER-URL> with the OIDC issuer URL you found in the previous step.

3. Make sure you to save the credentials.json file before continuing.

4. Next generate an Azure directory app. We will use this to assign our federated credentials to:

   bash Copy

    az ad app create \
    --display-name loki \
    --query appId \
    -o tsv

   This will return the app ID. Save this for later use. If you need to find the app ID later you can run the following command:

   bash Copy

   az ad app list --display-name loki --query "[].appId" -o tsv

5. The app requires a service principal to authenticate with Azure AD. Create a service principal for the app:

   bash Copy

   az ad sp create --id <APP-ID>

   Replace <APP-ID> with the app ID you generated in the previous step.

6. Next assign the federated credentials to the app:

   bash Copy

   az ad app federated-credential create \
     --id <APP-ID> \
     --parameters credentials.json 

   Replace <APP-ID> with the app ID you generated in the previous step.

7. Lastly add a role assignment to the app:

   bash Copy

   az role assignment create \
     --role "Storage Blob Data Contributor" \
     --assignee <APP-ID> \
     --scope /subscriptions/<SUBSCRIPTION-ID>/resourceGroups/<RESOURCE-GROUP>/providers/Microsoft.Storage/storageAccounts/<STORAGE-ACCOUNT-NAME>

   Replace the placeholders with your actual values.

Now that you have created the Azure AD role and federated credentials, you can proceed to deploying Loki using the Helm chart.

Deploying the Helm chart

The following steps require the use of helm and kubectl. Make sure you have run the az command to bind your AKS cluster to kubectl:

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az aks get-credentials --resource-group <MY_RESOURCE_GROUP_NAME> --name <MY_AKS_CLUSTER_NAME>

Before we can deploy the Loki Helm chart, we need to add the Grafana chart repository to Helm. This repository contains the Loki Helm chart.

1. Add the Grafana chart repository to Helm:

   bash Copy

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

2. Update the chart repository:

   bash Copy

   helm repo update

3. Create a new namespace for Loki:

   bash Copy

   kubectl create namespace loki

Loki basic authentication

Loki by default does not come with any authentication. Since we will be deploying Loki to Azure and exposing the gateway to the internet, we recommend adding at least basic authentication. In this guide we will give Loki a username and password:

1. To start we will need create a .htpasswd file with the username and password. You can use the htpasswd command to create the file:

   Tip

   If you don’t have the htpasswd command installed, you can install it using brew or apt-get or yum depending on your OS.

   bash Copy

   htpasswd -c .htpasswd <username>

   This will create a file called auth with the username loki. You will be prompted to enter a password.

2. Create a Kubernetes secret with the .htpasswd file:

   bash Copy

   kubectl create secret generic loki-basic-auth --from-file=.htpasswd -n loki

   This will create a secret called loki-basic-auth in the loki namespace. We will reference this secret in the Loki Helm chart configuration.

3. Create a canary-basic-auth secret for the canary:

   bash Copy

   kubectl create secret generic canary-basic-auth \
     --from-literal=username=<USERNAME> \
     --from-literal=password=<PASSWORD> \
     -n loki

   We create a literal secret with the username and password for Loki canary to authenticate with the Loki gateway. Make sure to replace the placeholders with your desired username and password.

Loki Helm chart configuration

Create a values.yaml file choosing the configuration options that best suit your requirements. Below there is an example of values.yaml files for the Loki Helm chart in microservices mode.

yaml Copy

loki:
   podLabels:
    "azure.workload.identity/use": "true" # Add this label to the Loki pods to enable workload identity
   schemaConfig:
     configs:
       - from: "2024-04-01"
         store: tsdb
         object_store: azure
         schema: v13
         index:
           prefix: loki_index_
           period: 24h
   storage_config:
     azure:
      account_name: "<INSERT-STORAGE-ACCOUNT-NAME>" 
      container_name: "<CHUNK-CONTAINER-NAME>" # Your actual Azure Blob Storage container name (loki-azure-dev-chunks)
      use_federated_token: true # Use federated token for authentication
   ingester:
       chunk_encoding: snappy
   pattern_ingester:
       enabled: true
   limits_config:
     allow_structured_metadata: true
     volume_enabled: true
     retention_period: 672h # 28 days retention
   compactor:
     retention_enabled: true 
     delete_request_store: azure
   ruler:
    enable_api: true
    storage:
      type: azure
      azure:
        account_name: <INSERT-STORAGE-ACCOUNT-NAME>
        container_name: <RULER-CONTAINER-NAME> # Your actual Azure Blob Storage container name (loki-azure-dev-ruler)
        use_federated_token: true # Use federated token for authentication
      alertmanager_url: http://prom:9093 # The URL of the Alertmanager to send alerts (Prometheus, Mimir, etc.)

   querier:
      max_concurrent: 4

   storage:
      type: azure
      bucketNames:
        chunks: "<CHUNK-CONTAINER-NAME>" # Your actual Azure Blob Storage container name (loki-azure-dev-chunks)
        ruler: "<RULER-CONTAINER-NAME>" # Your actual Azure Blob Storage container name (loki-azure-dev-ruler)
        # admin: "admin-loki-devrel" # Your actual Azure Blob Storage container name (loki-azure-dev-admin)
      azure:
        accountName: <INSERT-STORAGE-ACCOUNT-NAME>
        useFederatedToken: true # Use federated token for authentication

# Define the Azure workload identity
serviceAccount:
  name: loki
  annotations:
    "azure.workload.identity/client-id": "<APP-ID>" # The app ID of the Azure AD app
  labels:
    "azure.workload.identity/use": "true"

deploymentMode: Distributed

ingester:
 replicas: 3
 zoneAwareReplication:
  enabled: false

querier:
 replicas: 3
 maxUnavailable: 2

queryFrontend:
 replicas: 2
 maxUnavailable: 1

queryScheduler:
 replicas: 2

distributor:
 replicas: 3
 maxUnavailable: 2
compactor:
 replicas: 1

indexGateway:
 replicas: 2
 maxUnavailable: 1

ruler:
 replicas: 1
 maxUnavailable: 1


# This exposes the Loki gateway so it can be written to and queried externaly
gateway:
 service:
   type: LoadBalancer
 basicAuth: 
     enabled: true
     existingSecret: loki-basic-auth

# Since we are using basic auth, we need to pass the username and password to the canary
lokiCanary:
  extraArgs:
    - -pass=$(LOKI_PASS)
    - -user=$(LOKI_USER)
  extraEnv:
    - name: LOKI_PASS
      valueFrom:
        secretKeyRef:
          name: canary-basic-auth
          key: password
    - name: LOKI_USER
      valueFrom:
        secretKeyRef:
          name: canary-basic-auth
          key: username

# Enable minio for storage
minio:
 enabled: false

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

singleBinary:
 replicas: 0

It is critical to define a valid values.yaml file for the Loki deployment. To remove the risk of misconfiguration, let’s break down the configuration options to keep in mind when deploying to Azure:

• Loki Config vs. Values Config:

  • The values.yaml file contains a section called loki, which contains a direct representation of the Loki configuration file.
  • This section defines the Loki configuration, including the schema, storage, and querier configuration.
  • The key configuration to focus on for chunks is the storage_config section, where you define the Azure container name and storage account. This tells Loki where to store the chunks.
  • The ruler section defines the configuration for the ruler, including the Azure container name and storage account. This tells Loki where to store the alert and recording rules.
  • For the full Loki configuration, refer to the Loki Configuration documentation.

• Storage:

  • Defines where the Helm chart stores data.
  • Set the type to azure since we are using Azure Blob Storage.
  • Configure the container names for the chunks and ruler to match the containers created earlier.
  • The azure section specifies the storage account name and also sets useFederatedToken to true. This tells Loki to use federated credentials for authentication.

• Service Account:

  • The serviceAccount section is used to define the federated workload identity Loki will use to authenticate with Azure AD.
  • We set the azure.workload.identity/client-id annotation to the app ID of the Azure AD app.

• Gateway:

  • Defines how the Loki gateway will be exposed.
  • We are using a LoadBalancer service type in this configuration.

Deploy Loki

Now that you have created the values.yaml file, you can deploy Loki using the Helm chart.

1. Deploy using the newly created values.yaml file:

   bash Copy

   helm install --values values.yaml loki grafana/loki -n loki --create-namespace

   It is important to create a namespace called loki as our federated credentials were generated with the value system:serviceaccount:loki:loki. This translates to the loki service account in the loki namespace. This is configurable but make sure to update the federated credentials file first.

2. Verify the deployment:

   bash Copy

   kubectl get pods -n loki

   You should see the Loki pods running.

   console Copy

   NAME                                    READY   STATUS    RESTARTS   AGE
   loki-canary-crqpg                       1/1     Running   0          10m
   loki-canary-hm26p                       1/1     Running   0          10m
   loki-canary-v9wv9                       1/1     Running   0          10m
   loki-chunks-cache-0                     2/2     Running   0          10m
   loki-compactor-0                        1/1     Running   0          10m
   loki-distributor-78ccdcc9b4-9wlhl       1/1     Running   0          10m
   loki-distributor-78ccdcc9b4-km6j2       1/1     Running   0          10m
   loki-distributor-78ccdcc9b4-ptwrb       1/1     Running   0          10m
   loki-gateway-5f97f78755-hm6mx           1/1     Running   0          10m
   loki-index-gateway-0                    1/1     Running   0          10m
   loki-index-gateway-1                    1/1     Running   0          10m
   loki-ingester-zone-a-0                  1/1     Running   0          10m
   loki-ingester-zone-b-0                  1/1     Running   0          10m
   loki-ingester-zone-c-0                  1/1     Running   0          10m
   loki-querier-89d4ff448-4vr9b            1/1     Running   0          10m
   loki-querier-89d4ff448-7nvrf            1/1     Running   0          10m
   loki-querier-89d4ff448-q89kh            1/1     Running   0          10m
   loki-query-frontend-678899db5-n5wc4     1/1     Running   0          10m
   loki-query-frontend-678899db5-tf69b     1/1     Running   0          10m
   loki-query-scheduler-7d666bf759-9xqb5   1/1     Running   0          10m
   loki-query-scheduler-7d666bf759-kpb5q   1/1     Running   0          10m
   loki-results-cache-0                    2/2     Running   0          10m
   loki-ruler-0                            1/1     Running   0          10m

Find the Loki gateway service

The Loki gateway service is a load balancer service that exposes the Loki gateway to the internet. This is where you will write logs to and query logs from. By default NGINX is used as the gateway.

To find the Loki gateway service, run the following command:

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kubectl get svc -n loki

You should see the Loki gateway service with an external IP address. This is the address you will use to write to and query Loki.

console Copy

  NAME                             TYPE           CLUSTER-IP       EXTERNAL-IP    PORT(S)              AGE
loki-gateway                     LoadBalancer   10.100.201.74     134.236.21.145  80:30707/TCP         46m

Congratulations! You have successfully deployed Loki on Azure using the Helm chart. Before we finish, let’s test the deployment.

Testing Your Loki Deployment

k6 is one of the fastest ways to test your Loki deployment. This will allow you to both write and query logs to Loki. To get started with k6, follow the steps below:

1. Install k6 with the Loki extension on your local machine. Refer to Installing k6 and the xk6-loki extension.

2. Create a azure-test.js file with the following content:

   JavaScript Copy

    import {sleep, check} from 'k6';
    import loki from 'k6/x/loki';
   
    /**
    * URL used for push and query requests
    * Path is automatically appended by the client
    * @constant {string}
    */
   
    const username = '<USERNAME>';
    const password = '<PASSWORD>';
    const external_ip = '<EXTERNAL-IP>';
   
    const credentials = `${username}:${password}`;
   
    const BASE_URL = `http://${credentials}@${external_ip}`;
   
    /**
    * Helper constant for byte values
    * @constant {number}
    */
    const KB = 1024;
   
    /**
    * Helper constant for byte values
    * @constant {number}
    */
    const MB = KB * KB;
   
    /**
    * Instantiate config and Loki client
    */
   
    const conf = new loki.Config(BASE_URL);
    const client = new loki.Client(conf);
   
    /**
    * Define test scenario
    */
    export const options = {
      vus: 10,
      iterations: 10,
    };
   
    export default () => {
      // Push request with 10 streams and uncompressed logs between 800KB and 2MB
      var res = client.pushParameterized(10, 800 * KB, 2 * MB);
      // Check for successful write
      check(res, { 'successful write': (res) => res.status == 204 });
   
      // Pick a random log format from label pool
      let format = randomChoice(conf.labels["format"]);
   
      // Execute instant query with limit 1
      res = client.instantQuery(`count_over_time({format="${format}"}[1m])`, 1)
      // Check for successful read
      check(res, { 'successful instant query': (res) => res.status == 200 });
   
      // Execute range query over last 5m and limit 1000
      res = client.rangeQuery(`{format="${format}"}`, "5m", 1000)
      // Check for successful read
      check(res, { 'successful range query': (res) => res.status == 200 });
   
      // Wait before next iteration
      sleep(1);
    }
   
    /**
    * Helper function to get random item from array
    */
    function randomChoice(items) {
      return items[Math.floor(Math.random() * items.length)];
    }

   Replace <EXTERNAL-IP> with the external IP address of the Loki Gateway service.

   This script will write logs to Loki and query logs from Loki. It will write logs in a random format between 800KB and 2MB and query logs in a random format over the last 5 minutes.

3. Run the test:

   bash Copy

   ./k6 run azure-test.js

   This will run the test and output the results. You should see the test writing logs to Loki and querying logs from Loki.

Now that you have successfully deployed Loki in microservices mode on Microsoft Azure, you may wish to explore the following:

• Sending data to Loki
• Querying Loki
• Manage Loki