Optimizing Enterprise Search: A Resilient Multi-Node Elasticsearch Cluster with Terraform Explained

Introduction

Elasticsearch is a powerful search engine that’s commonly used for log and data analytics. Setting a multi-node cluster enhances the availability, fault tolerance, and performance of Elasticsearch, making it a preferred choice for production environments.

In this blog post, I’ll walk you through the steps to create a multi-node Elasticsearch cluster using Terraform ensuring that you have a scalable and resilient search solution.

Why Use Terraform?

Terraform is an Infrastructure as Code (IaC) tool that allows you to define and provision infrastructure using a high-level configuration language. Using Terraform for setting up an Elasticsearch cluster provides several benefits:

• Version Control: Track changes to your infrastructure with version control systems like Git.
• Consistency: Ensure that infrastructure is deployed in a consistent manner.
• Automation: Automate the provisioning of infrastructure, reducing manual errors and effort.

Need for multi node Elasticsearch

Elasticsearch is a powerful distributed search and analytics engine. While it can run on a single node, deploying it in a multi-node cluster offers several critical advantages that are essential for handling large-scale, high-availability, and high-performance applications. Below are the primary reasons why a multi-node Elasticsearch cluster is needed:

• • High Availability and Fault Tolerance:

1. 1. 1. In a single-node setup, if the node goes down, the entire Elasticsearch service becomes unavailable. A multi-node cluster ensures that if one or more nodes fail, the cluster can continue to operate, providing high availability.
      2. Data in Elasticsearch is stored in shards, which are replicated across nodes. If a node fails, the replicas on other nodes ensure that data is not lost, and the cluster remains operational.

• • Scalability:

1. 1. 1. As data grows, a single node may struggle to handle the load. A multi-node cluster allows you to horizontally scale by adding more nodes to distribute data and search/analytics operations across them.
      2. This scaling ensures that Elasticsearch can handle increasing amounts of data and queries without compromising performance.

• • Improved Performance:

1. 1. 1. A multi-node cluster can distribute query processing across multiple nodes, which speeds up search and indexing operations.
      2. Elasticsearch divides data into shards and assigns these shards across nodes, enabling parallel processing. This parallelism is key to the system’s ability to perform searches and analyse large datasets quickly.

• • Load Balancing:

1. 1. 1. In a multi-node cluster, requests can be load-balanced across different nodes, ensuring that no single node becomes a bottleneck.
      2. This load distribution leads to better resource utilisation and prevents scenarios where one node might be overwhelmed by requests.

• • Resource Optimisation:

1. 1. 1. Different nodes in a cluster can be assigned specific roles, such as master nodes and data nodes allowing for resource optimisation based on workload.
      2. For instance, data nodes can be optimized for storage and retrieval, while master nodes can handle cluster management tasks, ensuring that each node is specialized for its function.

Use Cases for a Multi-Node Elasticsearch Cluster

• Large-Scale E-Commerce Platform: An e-commerce platform with millions of products and users needs to quickly and reliably handle a vast number of search queries, product recommendations, and personalized content delivery. A multi-node Elasticsearch cluster can handle this scale, ensuring fast search responses and continuous uptime, even during high-traffic periods like sales events.

• Log and Event Data Analysis (ELK Stack): Organisations often deploy Elasticsearch as part of the ELK stack for log and event data analysis. In scenarios where terabytes of log data are ingested daily from various sources, a multi-node cluster can process, index, and analyze the data in real time, providing valuable insights and monitoring capabilities across the infrastructure.

• Content Management Systems (CMS) and Enterprise Search: In large enterprises or content-heavy websites, Elasticsearch is used to power internal or external search capabilities. With a multi-node setup, the search functionality remains fast and reliable even as the amount of indexed content grows and the number of concurrent users increases.

• Geospatial Data Analysis: Applications that involve geospatial data, such as mapping services, real-time traffic updates, or location-based services, can benefit from Elasticsearch’s geospatial querying capabilities. A multi-node cluster allows for storing and querying large datasets with geospatial attributes, providing quick responses, and handling complex queries across multiple data points.

Prerequisites

Before we get started, ensure you have the following prerequisites in place:

• • Basic knowledge of Elasticsearch and Terraform: Familiarity with these tools will help you understand the concepts better.
  • AWS Account: We’ll be deploying our Elasticsearch cluster on AWS.
  • Terraform installed: Make sure Terraform is installed on your local machine.
  • AWS CLI configured: Ensure that your AWS credentials are set up using the AWS CLI.

Elasticsearch Cluster Architecture Overview

Master Nodes:

 1.  Responsibilities:

a. Cluster Management: Handle cluster-wide operations like creating or deleting indices.
b. Cluster State Management: Keep track of the state of the cluster, including which nodes are part of the cluster and their roles.

 2.  Characteristics:

a. High Availability: Typically, multiple master-eligible nodes are configured to ensure cluster stability and high availability. A majority of master-eligible nodes (a quorum) must agree on cluster changes.
b. No Data Storage: Master nodes do not store data or handle data-related queries.

Data Nodes:

1. Responsibilities:

a. Data Storage: Store the actual data and perform operations like CRUD (Create, Read, Update, Delete) on documents.
b. Search and Aggregations: Handle search queries and aggregations, which involve scanning and processing data

2. Characteristics:

a. High Performance: These nodes are optimised for high I/O operations, including indexing and querying large volumes of data.
b. Scalability: Additional data nodes can be added to scale horizontally and handle larger datasets and higher query loads.

Terraform Configuration:

 1. Directory Structure

• Start by organising your Terraform files. Below is a recommended structure:
  

 2. Set Up the Terraform Configuration

• First, start by defining the AWS provider in your provider.tf . The configuration sets up the AWS provider, specifying the region where your Elasticsearch cluster will be deployed.
• On _data.tf we describe how to set up VPC and subnets
• In the iam.tf file, define the required IAM roles and policies for your Elasticsearch nodes. These roles will allow your EC2 instances to interact with other AWS services like S3.
• In the var.tf file, define all the variables needed for your setup, such as instance types, ami-id etc.
• On sg.tf we will create our security group
• In the main.tf file, define your EC2 instances. For a multi-node Elasticsearch cluster, we will need both master and data nodes
• Define your user data scripts in user_data_master.sh and user_data_data.sh. These scripts will install and configure Elasticsearch on each node.

3. Initialise and Apply Terraform

     Once your configuration is ready, you can initialise Terraform and apply the configuration to create your multi-node Elasticsearch cluster.

terraform init

terraform plan

terraform apply

Conclusion:

By following these steps, you have successfully created a multi-node Elasticsearch cluster using Terraform. This setup provides high availability and ensures your Elasticsearch deployment can scale horizontally. With Terraform managing your infrastructure, you gain consistency and automation, allowing you to focus more on optimizing your Elasticsearch deployment. Deploying Elasticsearch as a multi-node cluster is essential for organizations that require high availability, scalability, and performance in their search and analytics infrastructure. Whether it’s handling large volumes of data, ensuring continuous uptime, or optimizing resource usage, a multi-node Elasticsearch cluster is a robust solution for a variety of use cases across industries.

Feel free to adjust the configuration according to your specific requirements, such as adding more nodes or adjusting instance types.

To explore the complete code and setup for this multi-node Elasticsearch cluster, feel free to check out the GitHub repository.