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Docker Swarm vs Terraform: What are the differences?
Introduction
Docker Swarm and Terraform are both widely used tools in the field of containerization and infrastructure management. However, there are key differences between these two platforms. In this markdown code, we will highlight and explain six major differences between Docker Swarm and Terraform.
Scaling: Docker Swarm is primarily designed for container orchestration and deployment, allowing for the scaling of containers within a cluster. It focuses on optimizing the scheduling and management of containers across multiple nodes. On the other hand, Terraform is an infrastructure provisioning tool that enables the creation, modification, and removal of various resources like servers, networks, and storage. It is not inherently focused on container scaling like Docker Swarm.
Multi-Cloud Support: Docker Swarm is tightly integrated with Docker Engine and is limited to managing the container environment. It does not provide native support for managing infrastructure resources across multiple cloud providers. In contrast, Terraform is cloud-agnostic and supports multiple cloud providers such as AWS, GCP, Azure, and more. It allows for the provisioning and management of infrastructure resources across different cloud platforms.
Infrastructure as Code: Terraform follows an Infrastructure as Code (IaC) approach, where infrastructure can be defined and managed through code. It uses declarative configuration files to define the desired state of the infrastructure and creates or updates resources to match that state. Docker Swarm, on the other hand, relies on a command-line interface and configuration files specific to container orchestration. Although it can be automated using scripts, it does not follow the same Infrastructure as Code principles as Terraform.
Resource Granularity: Docker Swarm operates at the container level, offering control and management of individual containers within a cluster. It focuses on managing the scheduling and scaling of containers. On the contrary, Terraform operates at a higher level of abstraction, allowing for the provisioning and management of entire infrastructure resources like servers, networks, and storage. It provides more granular control over infrastructure resources compared to Docker Swarm.
Dependency Management: Docker Swarm manages dependencies between containers using the concept of services, where services are defined as a group of related containers. It allows for the execution of containers with inter-container communication and load balancing. Terraform, on the other hand, is not specifically designed for managing container dependencies. It focuses on managing infrastructure resources but does not offer built-in mechanisms for container coordination and communication.
Ecosystem and Plugins: Docker Swarm has a growing ecosystem of Docker-related tools and plugins that integrate with its container orchestration capabilities. It benefits from the extensive Docker community and its rich set of tools. On the other hand, Terraform has its own extensive ecosystem of providers and plugins, enabling integration with various cloud providers, services, and resources. It offers a broader scope of infrastructure management beyond containerization.
In summary, Docker Swarm is primarily focused on container orchestration and scaling, while Terraform is an infrastructure provisioning tool that supports multi-cloud environments and follows an Infrastructure as Code approach. Docker Swarm operates at the container level, while Terraform operates at a higher level of infrastructure resources.
Because Pulumi uses real programming languages, you can actually write abstractions for your infrastructure code, which is incredibly empowering. You still 'describe' your desired state, but by having a programming language at your fingers, you can factor out patterns, and package it up for easier consumption.
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
We use Terraform to manage AWS cloud environment for the project. It is pretty complex, largely static, security-focused, and constantly evolving.
Terraform provides descriptive (declarative) way of defining the target configuration, where it can work out the dependencies between configuration elements and apply differences without re-provisioning the entire cloud stack.
AdvantagesTerraform is vendor-neutral in a way that it is using a common configuration language (HCL) with plugins (providers) for multiple cloud and service providers.
Terraform keeps track of the previous state of the deployment and applies incremental changes, resulting in faster deployment times.
Terraform allows us to share reusable modules between projects. We have built an impressive library of modules internally, which makes it very easy to assemble a new project from pre-fabricated building blocks.
DisadvantagesSoftware is imperfect, and Terraform is no exception. Occasionally we hit annoying bugs that we have to work around. The interaction with any underlying APIs is encapsulated inside 3rd party Terraform providers, and any bug fixes or new features require a provider release. Some providers have very poor coverage of the underlying APIs.
Terraform is not great for managing highly dynamic parts of cloud environments. That part is better delegated to other tools or scripts.
Terraform state may go out of sync with the target environment or with the source configuration, which often results in painful reconciliation.
I personally am not a huge fan of vendor lock in for multiple reasons:
- I've seen cost saving moves to the cloud end up costing a fortune and trapping companies due to over utilization of cloud specific features.
- I've seen S3 failures nearly take down half the internet.
- I've seen companies get stuck in the cloud because they aren't built cloud agnostic.
I choose to use terraform for my cloud provisioning for these reasons:
- It's cloud agnostic so I can use it no matter where I am.
- It isn't difficult to use and uses a relatively easy to read language.
- It tests infrastructure before running it, and enables me to see and keep changes up to date.
- It runs from the same CLI I do most of my CM work from.
Context: I wanted to create an end to end IoT data pipeline simulation in Google Cloud IoT Core and other GCP services. I never touched Terraform meaningfully until working on this project, and it's one of the best explorations in my development career. The documentation and syntax is incredibly human-readable and friendly. I'm used to building infrastructure through the google apis via Python , but I'm so glad past Sung did not make that decision. I was tempted to use Google Cloud Deployment Manager, but the templates were a bit convoluted by first impression. I'm glad past Sung did not make this decision either.
Solution: Leveraging Google Cloud Build Google Cloud Run Google Cloud Bigtable Google BigQuery Google Cloud Storage Google Compute Engine along with some other fun tools, I can deploy over 40 GCP resources using Terraform!
Check Out My Architecture: CLICK ME
Check out the GitHub repo attached
Pros of Docker Swarm
- Docker friendly55
- Easy to setup46
- Standard Docker API40
- Easy to use38
- Native23
- Free22
- Clustering made easy13
- Simple usage12
- Integral part of docker11
- Cross Platform6
- Labels and annotations5
- Performance5
- Easy Networking3
- Shallow learning curve3
Pros of Terraform
- Infrastructure as code121
- Declarative syntax73
- Planning45
- Simple28
- Parallelism24
- Well-documented8
- Cloud agnostic8
- It's like coding your infrastructure in simple English6
- Immutable infrastructure6
- Platform agnostic5
- Extendable4
- Automation4
- Automates infrastructure deployments4
- Portability4
- Lightweight2
- Scales to hundreds of hosts2
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Cons of Docker Swarm
- Low adoption9
Cons of Terraform
- Doesn't have full support to GKE1
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