Difference between Terraform and Kubernetes
The industry is still moving at a breakneck speed and is constantly changing. Service-oriented architectures have evolved from ubiquitous systems and structures, and Microservices are becoming more prevalent in these solutions today. Scalability is the primary driver of change, but there are other elements at play. In this article, we will learn about Terraform and Kubernetes and their differences.
Terraform is a solution for managing infrastructure setup securely and efficiently. It provides users with a framework for designing infrastructure and enables resource providers to provide access to resources. The providers cover various notable public cloud platforms and Git hosting platforms, and general HTTP and FTP solutions.
Working of Terraform
Infrastructure as Code (IaC) is an essential notion to grasp to comprehend Terraform. HCL (HashiCorp Configuration Language) is a declarative configuration language for defining infrastructure resources in the tool.
Terraform users can use HCL to define their whole infrastructure in code, regardless of employing one or numerous infrastructure suppliers.
Terraform Kubernetes providers make it easier to work with Kubernetes-compatible resources. While tools like kubectl can be used to manage Kubernetes resources, Terraform unifies the workflow and provides complete lifecycle control.
Terraform includes commands like:
Terraform init — Creates a directory in which Terraform configuration files can be found.
Terraform plan — Generates an execution plan that includes reading the current state of objects to ensure they are up-to-date, comparing the system’s current state to the initial state, and proposing object changes to fit the defined configuration.
Terraform apply — Puts the proposed strategy into action.
Terraform destroy — Destroys items controlled by a configuration.
Kubernetes is a container orchestration technology for automating containerized software deployment, scaling, and management. Kubernetes allows distributed container systems to function without downtime by controlling groups of hosts structured in clusters.
Working of Kubernetes
Kubernetes works with clusters, groupings of machines called nodes integrated to make containerized applications easier to operate.
A Kubernetes cluster is composed of the following components:
- Container groupings that work together are referred to as pods
- Services are groups of pods that do the same task
- Replication Controllers are frameworks for managing pod replicas
The Kubernetes system is made up of two major components:
- Along with the tools required to run the worker node as part of the K8s cluster, a containerized application is stored on the worker node
- The control plane consists of head nodes that run cluster management tools
Terraform focuses on codifying resources needed by your applications, whereas Kubernetes is designed to automate container tasks.
- Configuration Language
Terraform uses the HashiCorp Configuration Language, or HCL, to define declarative objects. You can construct resources that run on different cloud platforms using an HCL file. On the other hand, declarative objects are defined by Kubernetes as YAML or JSON files that show how to define and manage Kubernetes objects. YAML is recommended over JSON in authoring configuration files because it is more user-friendly, but they can be used interchangeably.
- Resource Creation
Terraform’s CLI is a command-line interface that includes subcommands and switches like terraform plan and terraform apply. Terraform uses the CLI to execute and maintain declarative configurations to create any resource.
For handling container clusters and Kubernetes resources, Kubernetes offers its command-line tool, kubectl. kubectl can be used to create resources such as assigning memory and CPU to containers, building nodes, and deploying applications.
Pros and Cons of Terraform
- It assists in avoiding downtime
- Allows you to record, track, and manage changes easily
- Includes the declarative syntax
- Documentation that is both readable and comprehensive
- GKE (Google Kubernetes Engine) is not completely supported
- Error handling is not included
- There is no rollback; if the need arises, the user must destroy and re-apply the managed object
Common Use Cases
Terraform can be used for various jobs, but these are the most popular.
- Infrastructure for Tracking
Terraform uses a state file to keep track of your deployed resources, which may be used as a point of reference for modifying the resources. Terraform can use a state file to figure out what changes to your infrastructure are required to match any desired end state.
- Deployment of Multiple Clouds
Terraform is a platform that works with any cloud. To manage infrastructure resources distributed across various cloud providers, you can utilize a configuration file written in HCL, which takes care of cross-cloud dependencies.
When resources are distributed across cloud environments, they gain resilience and fault tolerance.
- Managing Multi-Tier Applications
A workflow isolates features in multi-tier applications by specifying a specific logic to execute for each layer. Each tier is described as a collection of resources, and Terraform automatically manages the relationships between them. Dependencies and plugins must provide consistency between the tiers, which might be challenging to install manually.
Terraform provisioning ensures that these dependencies are always correctly deployed and applied. Terraform, for example, checks the database tier’s availability before creating the web servers and load balancer.
- Software-Defined Networking (SDN)
Terraform may work with Software-Defined Networks (SDNs) to autonomously configure a network based on the requirements of its applications. This allows you to go from a ticket-based to an automated approach, lowering deployment times.
Kubernetes: Pros and Cons
- Resource-friendly — infrastructure can be scaled horizontally
- It aids in the avoidance of infrastructure lock-ins
- The declarative syntax is included
- Monitors replicas and ensures the system is constantly healthy to automate the healing process
- Container management tool with extensive documentation, endorsed by Google
- Difficult to master
- Only infrastructure orchestration is possible
Common Use Cases
Kubernetes can be used for various activities, and this section goes over some of the most common usage cases.
- Container Orchestration Across Multiple Hosts
Kubernetes can host your container apps across numerous physical machines or cloud instances because it is platform agnostic. To achieve high availability, it additionally maintains a failover watch layer.
- Taking Care of Computer Resources
Running a single dedicated Kubernetes cluster is typically less expensive than running numerous separate servers. It is also easier to maintain a group like this than to manage many hosts running multiple servers.
- Orchestration of Storage
Kubernetes support dynamic storage volume provisioning. Any platform, including local storage, cloud storage, and network storage, can be used to connect storage systems.
Choosing Terraform vs. Kubernetes
Terraform and Kubernetes are potent tools that can assist users in improving project lifecycle efficiency. So, which of these software platforms is the better fit for DevOps? To answer this question and choose the best product for your needs, keep in mind that these tools are meant to perform distinct duties throughout the application development lifecycle. As a result, their utility is determined by your requirements.
Terraform, for example, includes infrastructure creation and automation features that can be useful for customers that want to provision infrastructures periodically. Kubernetes, on the other hand, enables users to orchestrate their containerized applications, making it a superior choice for teams managing container workloads.
If each of these solutions appears to be a good fit for your DevOps operations, you can combine them to increase their capabilities.
Kubernetes is a container tool, while Terraform is an infrastructure tool. The difference in application and implementation allows developers to leverage both tools, resulting in a highly resilient system.