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Mastering Elasticsearch Deployment on Azure: Your Comprehensive Guide

June 27, 2024 by OpenObserve Team
azure elastic search

Are you ready to revolutionize your search and analytics game? Deploying Elasticsearch on Azure is your key to unlocking scalable, real-time solutions that will elevate your business insights and user experiences. In this all-encompassing guide, we'll take you on a journey through setting up and managing Elasticsearch on Azure, empowering you to harness its full potential for your organization.

The Power of Elasticsearch on Azure

Before we embark on the deployment process, let's explore why the combination of Elasticsearch and Azure is a match made in data heaven:

Unrivaled Scalability

As your data and traffic grow, Azure's elastic infrastructure allows your Elasticsearch cluster to scale effortlessly. Say goodbye to performance bottlenecks and hello to seamless expansion.

Flexibility at Your Fingertips

With Azure's extensive range of virtual machine sizes and configurations, you can fine-tune your Elasticsearch deployment to perfectly suit your unique requirements. It's like having a bespoke suit for your data.

Seamless Integration

Azure's rich ecosystem enables you to integrate Elasticsearch with many Azure services, such as Azure Kubernetes Service (AKS) and Azure Machine Learning. It's an interconnected web of powerful tools at your disposal.

Ironclad Security

Rest easy knowing your Elasticsearch cluster is fortified with Azure's robust security features, including network security groups and Azure Active Directory integration. Your data is safe and sound.

Preparing for Take-off: Azure Prerequisites

To begin your Elasticsearch deployment odyssey on Azure, you must ensure your Azure account has the necessary permissions. Follow these steps to lay the groundwork:

  1. Double-check that your Azure account has owner access to the subscription where you plan to deploy Elasticsearch. You're the captain of this ship.
  2. Navigate to the Azure marketplace and search for "Elasticsearch." It's like finding a needle in a haystack but much more accessible.
  3. Choose the Elasticsearch offering that aligns with your needs and click "Create." And just like that, you're ready to embark on your deployment journey.

Building Your Elasticsearch Fortress

Now that you've located Elasticsearch in the Azure marketplace, it's time to construct your Elasticsearch resource. Here's your step-by-step blueprint:

  1. Select the subscription where you want to deploy Elasticsearch. Choose wisely, as this will be the foundation of your deployment.
  2. You can pick an existing resource group or create a new one to house your Elasticsearch resource. It's like assigning a dedicated team to manage your deployment.
  3. Give your Elasticsearch resource a name that will make it stand out. Make it memorable, make it unique.
  4. Choose the Azure region for your deployment, considering factors such as data locality and compliance requirements—location, location, location.
  5. Select the Elasticsearch version that aligns with your needs. Version control is critical to a smooth deployment.
  6. Configure the size and pricing plan for your Elasticsearch cluster based on your anticipated workload and budget. It's all about finding the perfect balance.

Streamlining Deployment with ARM Templates

Streamlining Deployment with ARM Template

To make your Elasticsearch deployment on Azure a breeze, you can harness the power of Azure Resource Manager (ARM) templates. These handy templates allow you to define and automate the deployment of your entire Elasticsearch topology, including data nodes, master nodes, coordinating nodes, ingest nodes, and machine learning nodes.

ARM templates come in two flavors:

  • Incremental deployment: Fine-tune existing Elasticsearch resources within a resource group while keeping unmodified resources untouched. It's like a surgical precision approach.
  • Full deployment: Replace all Elasticsearch resources within a resource group with the configurations specified in the template. This is a clean slate and a fresh start.

Imagine this: you've crafted an ARM template that defines your ideal Elasticsearch cluster configuration. With a single command, you can deploy this configuration consistently across multiple environments---it's like having a magic wand for your deployments.

Here's a real-world example of deploying a 3-node Elasticsearch cluster using an ARM template:

{
  "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
  "contentVersion": "1.0.0.0",
  "parameters": {
    "clusterName": {
      "type": "string",
      "defaultValue": "my-elasticsearch-cluster",
      "metadata": {
        "description": "The name of the Elasticsearch cluster"
      }
    },
    "vmSize": {
      "type": "string",
      "defaultValue": "Standard_D2_v2",
      "metadata": {
        "description": "The size of the virtual machines"
      }
    }
  },
  "variables": {
    "namespace": "[concat(parameters('clusterName'), uniqueString(resourceGroup().id))]"
  },
  "resources": [
    {
      "type": "Microsoft.Compute/virtualMachineScaleSets",
      "apiVersion": "2019-03-01",
      "name": "[variables('namespace')]",
      "location": "[resourceGroup().location]",
      "sku": {
        "name": "[parameters('vmSize')]",
        "tier": "Standard",
        "capacity": 3
      },
      "properties": {
        "overprovision": true,
        "upgradePolicy": {
          "mode": "Manual"
        },
        "virtualMachineProfile": {
          "storageProfile": {
            "imageReference": {
              "publisher": "Canonical",
              "offer": "UbuntuServer",
              "sku": "16.04-LTS",
              "version": "latest"
            },
            "osDisk": {
              "createOption": "FromImage"
            }
          },
          "osProfile": {
            "computerNamePrefix": "[variables('namespace')]",
            "adminUsername": "azureuser",
            "adminPassword": "[uniqueString(resourceGroup().id)]"
          },
          "networkProfile": {
            "networkInterfaceConfigurations": [
              {
                "name": "[concat(variables('namespace'), '-nic')]",
                "properties": {
                  "primary": true,
                  "ipConfigurations": [
                    {
                      "name": "ipconfig",
                      "properties": {
                        "subnet": {
                          "id": "[resourceId('Microsoft.Network/virtualNetworks/subnets', concat(variables('namespace'), '-vnet'), concat(variables('namespace'), '-subnet'))]"
                        }
                      }
                    }
                  ]
                }
              }
            ]
          }
        }
      }
    },
    {
      "type": "Microsoft.Network/virtualNetworks",
      "apiVersion": "2019-04-01",
      "name": "[concat(variables('namespace'), '-vnet')]",
      "location": "[resourceGroup().location]",
      "properties": {
        "addressSpace": {
          "addressPrefixes": [
            "10.0.0.0/16"
          ]
        },
        "subnets": [
          {
            "name": "[concat(variables('namespace'), '-subnet')]",
            "properties": {
              "addressPrefix": "10.0.0.0/24"
            }
          }
        ]
      }
    }
  ]

In this example, the ARM template defines a Virtual Machine Scale Set (VMSS) with 3 instances running Ubuntu Server 16.04 LTS. It also creates a virtual network and subnet for the Elasticsearch nodes. By adjusting the parameters and adding more resources, you can tailor the deployment to your specific requirements.

To enhance the basic ARM template, you can add the following sections to fully set up the Elasticsearch cluster, including networking, load balancing, and custom script execution.

Additional Parameters and Variables

{
  "parameters": {
    "adminUsername": {
      "type": "string",
      "defaultValue": "azureuser",
      "metadata": {
        "description": "Admin username for the VMs"
      }
    },
    "adminPassword": {
      "type": "securestring",
      "metadata": {
        "description": "Admin password for the VMs"
      }
    }
  },
  "variables": {
    "subnetName": "[concat(variables('namespace'), '-subnet')]",
    "vnetName": "[concat(variables('namespace'), '-vnet')]",
    "publicIPAddressName": "[concat(variables('namespace'), '-ip')]",
    "loadBalancerName": "[concat(variables('namespace'), '-lb')]",
    "frontendIPConfigName": "[concat(variables('namespace'), '-feipconfig')]",
    "backendPoolName": "[concat(variables('namespace'), '-bepool')]",
    "probeName": "[concat(variables('namespace'), '-probe')]",
    "inboundNatRuleName": "[concat(variables('namespace'), '-inboundNAT')]",
    "nicName": "[concat(variables('namespace'), '-nic')]",
    "vmNamePrefix": "[variables('namespace')]",
    "vmScriptUri": "https://<your-storage-account>.blob.core.windows.net/scripts/install-elasticsearch.sh"
  }
}

Network and Load Balancer Configuration

{
  "resources": [
    {
      "type": "Microsoft.Network/publicIPAddresses",
      "apiVersion": "2019-04-01",
      "name": "[variables('publicIPAddressName')]",
      "location": "[resourceGroup().location]",
      "properties": {
        "publicIPAllocationMethod": "Dynamic"
      }
    },
    {
      "type": "Microsoft.Network/loadBalancers",
      "apiVersion": "2019-04-01",
      "name": "[variables('loadBalancerName')]",
      "location": "[resourceGroup().location]",
      "properties": {
        "frontendIPConfigurations": [
          {
            "name": "[variables('frontendIPConfigName')]",
            "properties": {
              "publicIPAddress": {
                "id": "[resourceId('Microsoft.Network/publicIPAddresses', variables('publicIPAddressName'))]"
              }
            }
          }
        ],
        "backendAddressPools": [
          {
            "name": "[variables('backendPoolName')]"
          }
        ],
        "probes": [
          {
            "name": "[variables('probeName')]",
            "properties": {
              "protocol": "Tcp",
              "port": 9200,
              "intervalInSeconds": 15,
              "numberOfProbes": 4
            }
          }
        ],
        "loadBalancingRules": [
          {
            "name": "[variables('inboundNatRuleName')]",
            "properties": {
              "frontendIPConfiguration": {
                "id": "[resourceId('Microsoft.Network/loadBalancers/frontendIPConfigurations', variables('loadBalancerName'), variables('frontendIPConfigName'))]"
              },
              "backendAddressPool": {
                "id": "[resourceId('Microsoft.Network/loadBalancers/backendAddressPools', variables('loadBalancerName'), variables('backendPoolName'))]"
              },
              "probe": {
                "id": "[resourceId('Microsoft.Network/loadBalancers/probes', variables('loadBalancerName'), variables('probeName'))]"
              },
              "protocol": "Tcp",
              "frontendPort": 9200,
              "backendPort": 9200,
              "enableFloatingIP": false,
              "idleTimeoutInMinutes": 4,
              "loadDistribution": "Default"
            }
          }
        ]
      }
    }
  ]
}

Virtual Machine Scale Set with Custom Script

Use a Virtual Machine Scale Set (VMSS) to deploy three VMs. The custom script will automate the installation of Elasticsearch on each VM.

Output Configuration

{
  "outputs": {
    "loadBalancerPublicIP": {
      "type": "string",
      "value": "[reference(resourceId('Microsoft.Network/publicIPAddresses', variables('publicIPAddressName'))).dnsSettings.fqdn]"
    }
  }
}

Custom Script for Installing Elasticsearch

Additionally, you need a script to install Elasticsearch on the VMs. This script should be hosted in an accessible location, such as Azure Blob Storage.

Example install-elasticsearch.sh Script:

#!/bin/bash
# Install Java
sudo apt-get update
sudo apt-get install -y openjdk-11-jdk
# Download and install Elasticsearch
wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.10.0-amd64.deb
sudo dpkg -i elasticsearch-7.10.0-amd64.deb
# Enable and start Elasticsearch service
sudo systemctl enable elasticsearch
sudo systemctl start elasticsearch

By leveraging ARM templates, you can ensure consistent and repeatable deployments, making it easier to manage your Elasticsearch infrastructure at scale. It's like having a trusty sidekick that ensures your deployments are always on point.

With your Elasticsearch castle in the cloud expertly constructed, it's time to don your crown and learn how to reign over your kingdom with ease and finesse.

Managing Your Elasticsearch Kingdom

Managing Your Elasticsearch Kingdom

With your Elasticsearch cluster standing tall on Azure, it's crucial to understand how to manage and monitor its health and performance. The Elasticsearch resource page in the Azure portal serves as your command center, providing a centralized view of your cluster's vital signs and configuration options.

From this resource page, you can:

  • Monitor the cluster health and performance metrics closely. It's like having a crystal ball for your Elasticsearch kingdom.
  • Adjust the cluster size and configuration settings to improve performance. Dial it in, and make it purr.
  • Upgrade to the latest and most excellent Elasticsearch versions. Stay ahead of the curve.
  • Dive into Kibana for data visualization and exploration. It's like having a treasure map for your data insights.

For those who want to take their Elasticsearch management to the next level, the Elastic Cloud console is your portal to advanced configuration and management tasks. It's like having a secret lair for your Elasticsearch operations.

In the Elastic Cloud console, you can:

  • Adjust your cluster settings for optimal performance. Tweak, adjust, optimize.
  • Set up security options to make your data fortress secure. Lock it down.
  • Integrate with other Elastic Stack components, like Logstash and Beats, to assemble your own Justice League of data tools.

You're ruling the roost like a pro, but what's a kingdom without its treasure? Let's unlock the vault of insights by ingesting and analyzing Azure logs and metrics.

Ingesting and Analyzing Azure Logs and Metrics

One of the most potent aspects of running Elasticsearch on Azure is the ability to ingest and analyze Azure logs and metrics. By streaming your Azure diagnostic data into Elasticsearch, you gain deep, real-time visibility into the performance and health of your Azure resources.

Setting up Azure log and metric ingestion is a straightforward process:

  1. Turn on diagnostic settings for the Azure resources you choose. Flip the switch and let the data flow.
  2. Set up the diagnostic settings to route data to your Elasticsearch cluster. It's like building a data pipeline straight to your Elasticsearch kingdom.
  3. Use tag rules to fine-tune log and metric collection, ensuring you capture the most relevant data for your analysis.
  4. For even more granular control, leverage the Elastic VM Extension to collect logs and metrics directly from your Azure virtual machines. It's like having a magnifying glass for your VM data.
  5. Harness the power of Kibana to create mesmerizing visualizations and dashboards that provide real-time insights into your Azure environment. It's like having a window into the soul of your Azure infrastructure.

Now that you've got a river of data flowing into your Empire, it's time to sharpen our axes and optimize everything for efficiency, cost, and performance. Strong empires are built on intelligent strategies, after all.

Optimizing Your Elasticsearch Empire

Optimizing Your Elasticsearch Empire

As your Elasticsearch cluster expands and evolves, it's essential to optimize its performance and cost-efficiency continuously. Here are some strategies to keep in your toolbelt:

Right-size Your Cluster

Closely monitor your cluster's resource use and change the number and size of nodes to meet your workload requirements. It's like tailoring your Elasticsearch outfit to fit just right.

Leverage Azure Reserved Instances

Use Azure Reserved Instances to significantly reduce the cost of your Elasticsearch cluster. It's like getting a loyalty discount for your deployment.

Implement Index Lifecycle Management

Apply index lifecycle policies to manage the lifecycle of your Elasticsearch indices automatically, optimizing storage and performance. It's like having a personal assistant for your indices.

Utilize Azure Availability Zones

Distribute your Elasticsearch nodes across multiple availability zones to ensure high availability and resilience. It's like having a backup generator for your deployment.

Optimize Storage with NetApp Cloud Volumes ONTAP

Integrate NetApp Cloud Volumes ONTAP with your Elasticsearch deployment to enhance storage efficiency and cost-effectiveness. It's like having a trusty sidekick for your data storage needs.

Scaling Strategies for Elasticsearch on Azure:


As your data and traffic grow, it's essential to have effective scaling strategies in place to ensure optimal performance and resource utilization of your Elasticsearch cluster. There are two main approaches to scaling: vertical scaling and horizontal scaling.

    1. Vertical Scaling (Scaling Up): Vertical scaling involves increasing the resources (CPU, memory, storage) of individual Elasticsearch nodes to handle higher workloads. Here's how you can implement vertical scaling:
    • Monitor performance metrics: Keep a close eye on CPU usage, memory utilization, and disk I/O of your Elasticsearch nodes using Azure Monitor or Elasticsearch's built-in monitoring tools.
    • Identify resource bottlenecks: Analyze the performance metrics to determine which resources are being strained the most (e.g., high CPU usage, memory pressure).
    • Resize nodes: Use the Azure portal or ARM templates to resize your Elasticsearch nodes to a larger VM size that offers more resources. For example, you can upgrade from a D2s_v3 to a D4s_v3 VM size.
    • Adjust shard allocation: If you have increased the memory of your nodes, consider adjusting the cluster.routing.allocation.total_shards_per_node setting to allow more shards to be allocated per node, taking advantage of the increased memory capacity.
    • Monitor and repeat: After resizing your nodes, continue monitoring the performance metrics to ensure that the vertical scaling has resolved the resource bottlenecks. If necessary, repeat the process and scale up further.

Vertical scaling is suitable when you have a relatively small Elasticsearch cluster and need to handle increased workloads without adding more nodes. However, there are limits to vertical scaling, as you can only scale up to the largest available VM size.

    2. Horizontal Scaling (Scaling Out): Horizontal scaling involves adding more Elasticsearch nodes to your cluster to distribute the workload and improve performance. Here's how you can implement horizontal scaling:
    • Monitor performance metrics: Similar to vertical scaling, monitor CPU usage, memory utilization, and disk I/O of your Elasticsearch nodes.
    • Identify scaling thresholds: Define thresholds for when to trigger horizontal scaling based on performance metrics. For example, you might decide to add nodes when CPU usage consistently exceeds 80% or when the cluster's disk usage reaches a certain level.
    • Use auto-scaling policies: Implement auto-scaling policies in Azure to automatically add or remove Elasticsearch nodes based on predefined metrics and thresholds. You can use Azure's built-in auto-scaling feature or leverage third-party tools like Elastic Cloud on Kubernetes (ECK) for more advanced auto-scaling capabilities.
    • Manually add nodes: If you prefer manual control over scaling, you can add Elasticsearch nodes to your cluster using the Azure portal, ARM templates, or Elasticsearch's API. Make sure to configure the new nodes with the appropriate settings and allow sufficient time for data rebalancing.
    • Adjust shard allocation: As you add more nodes, Elasticsearch automatically redistributes shards across the cluster. However, you can fine-tune shard allocation using settings like cluster.routing.allocation.total_shards_per_node or cluster.routing.allocation.awareness.* to ensure optimal shard distribution.
    • Monitor and optimize: After adding nodes, monitor the cluster's performance and ensure that the workload is evenly distributed. Optimize your indexing and query patterns, and consider using techniques like index partitioning or routing to further improve performance.

Horizontal scaling is suitable when you have a large and growing Elasticsearch cluster that needs to handle increasing data volumes and query traffic. It allows you to scale your cluster dynamically based on workload demands.

Practical tips for scaling Elasticsearch:

  • Regularly monitor and analyze performance metrics to identify scaling needs proactively.
  • Use auto-scaling policies whenever possible to automatically adjust cluster size based on workload demands.
  • When manually scaling, add nodes incrementally and monitor the impact on performance before adding more.
  • Consider the costs associated with scaling and optimize your cluster configuration to strike a balance between performance and cost-efficiency.
  • Keep your Elasticsearch version up to date to benefit from the latest performance improvements and scaling capabilities.
  • Implement proper indexing strategies, such as sharding and index lifecycle management, to optimize performance and storage utilization.
  • Use caching techniques, like query result caching or shard request caching, to reduce the load on your Elasticsearch cluster.
  • Monitor and optimize your application's query patterns to minimize unnecessary or inefficient queries.

By implementing effective scaling strategies and following best practices, you can ensure that your Elasticsearch cluster on Azure can handle growing data and traffic demands while maintaining optimal performance and resource utilization.

Set Up Disaster Recovery and Backup

Protecting your Elasticsearch data is crucial for maintaining business continuity and compliance. By implementing a robust disaster recovery and backup strategy, you can ensure that your data is always available and recoverable in the event of a disaster.

Here's how you can implement disaster recovery and backup for Elasticsearch on Azure:

  1. Use Azure Backup for Elasticsearch:
    • Enable Azure Backup for your Elasticsearch cluster through the Azure portal or ARM templates.
    • Create a backup policy that defines the backup schedule, retention period, and storage settings.
    • Configure the backup policy to take regular snapshots of your Elasticsearch indices.
    • Monitor the backup jobs and ensure that they complete successfully.
    • In case of data loss, use Azure Backup to restore your Elasticsearch indices to a specific point in time.
  2. Implement Cross-Region Replication:
    • Set up a secondary Elasticsearch cluster in another Azure region.
    • Configure cross-cluster replication (CCR) between your primary and secondary clusters using Elasticsearch's built-in CCR feature.
    • Define replication policies to specify which indices should be replicated and the replication frequency.
    • Monitor the replication process and ensure that data is being replicated accurately and efficiently.
    • In the event of a regional outage, switch your application to use the secondary cluster until the primary region is available again.
  3. Leverage Snapshot and Restore:
    • Create a snapshot repository in Azure Blob Storage or another supported storage service.
    • Configure Elasticsearch to take periodic snapshots of your indices and store them in the snapshot repository.
    • Define snapshot policies to specify the snapshot schedule, retention period, and storage settings.
    • Regularly test the snapshot and restore process to ensure that you can successfully recover your data.
    • In case of data corruption or loss, use the snapshot and restore feature to recover your indices to a specific point in time.
  4. Use Azure Site Recovery:
    • Enable Azure Site Recovery for your Elasticsearch cluster through the Azure portal.
    • Configure replication settings, such as the target Azure region, replication frequency, and failover policies.
    • Replicate your Elasticsearch cluster, including data and configuration, to the secondary region.
    • Regularly perform failover drills to test the disaster recovery process and ensure that your cluster can be successfully failed over to the secondary region.
    • In the event of a regional outage, initiate a failover to the secondary region and redirect your application traffic to the replicated Elasticsearch cluster.
  5. Implement Data Archiving:
    • Configure Elasticsearch's snapshot lifecycle management feature to automatically take snapshots of older indices based on predefined policies.
    • Define archiving policies that specify the criteria for archiving indices, such as age or size.
    • Set up a separate snapshot repository in a cost-effective storage option like Azure Blob Storage or Azure Data Lake Storage for archiving purposes.
    • Monitor the archiving process and ensure that older indices are being properly archived and removed from the primary cluster.
    • In case of compliance or data retrieval needs, use the snapshot and restore feature to recover archived indices from the archival storage.

By following these implementation steps, you can set up a comprehensive disaster recovery and backup strategy for your Elasticsearch cluster on Azure. This ensures that your data is protected, recoverable, and compliant with your business requirements.

Having honed your Elasticsearch Empire to the pinnacle of performance and cost-efficiency, your journey from novice to master is nearly complete. There's a world of possibilities ahead---ready to take the next step?

Advanced Deployment Options

Advanced Deployment Options

For those ready to take their Elasticsearch deployment to the next level, Azure offers advanced options to supercharge your setup:

  • Deploy Kibana and Logstash: Enhance your data analysis and log processing capabilities by deploying Kibana and Logstash alongside your Elasticsearch cluster. It's like adding turbochargers to your data engine.
  • Automate log and metric ingestion: Streamline your data ingestion process by automating the flow of logs and metrics from your Azure services into Elasticsearch. It's like having a well-oiled data machine.
  • Leverage Azure Kubernetes Service: Deploy Elasticsearch on Azure Kubernetes Service (AKS) for ultimate scalability and flexibility. It's like giving your deployment superpowers.

Conclusion: Your Elasticsearch Adventure Awaits

Deploying Elasticsearch on Azure is your gateway to a world of real-time search, analytics, and insights. Following the steps outlined in this comprehensive guide, you can confidently set up and manage your Elasticsearch cluster on Azure, unlocking its full potential to propel your business forward.

Remember, your Elasticsearch deployment is a living, breathing entity. Continuously monitor and optimize your cluster, leveraging Azure's scalability, flexibility, and integration capabilities. With the proper configuration and management approach, Elasticsearch on Azure becomes an unstoppable force in your quest for data-driven success.

The world of Elasticsearch on Azure awaits you. Embrace the power, unleash the insights, and embark on your deployment journey today. The future of your search and analytics starts now.

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