8.2 KiB
| sidebar_position | slug |
|---|---|
| 1 | /programming |
Programming
Writing your first Dagger plan
1. Initialize a Dagger workspace anywhere in your git repository:
dagger init
It will create a .dagger directory in your current directory with an empty env directory inside it:
.dagger/
└── env
2. Create a new environment, for example staging:
dagger new staging
.dagger/
└── env
└── staging
├── plan
└── values.yaml
3. Create a new file Cue config file in .dagger/env/staging/plan, and open it with any text editor or IDE:
.dagger/
└── env
└── staging
├── plan
│ └── staging.cue
└── values.yaml
4. Describe each relay in your plan as a field in the Cue configuration:
For example:
package main
import (
"dagger.io/docker"
"dagger.io/git"
)
// Relay for fetching a git repository
repo: git.#Repository & {
remote: "https://github.com/dagger/dagger"
ref: "main"
}
// Relay for building a docker image
ctr: docker.#Build & {
source: repo
}
For more inspiration, see these examples:
- Deploy a static page to S3
- Deploy a simple React application
- Deploy a complete JAMstack app
- Provision a Kubernetes cluster on AWS
- Add HTTP monitoring to your application
- Deploy an application to your Kubernetes cluster
- Deploy an application to GCP Cloud Run
5. Extend your plan with relay definitions from Dagger Universe, an encyclopedia of Cue packages curated by the Dagger community.
6. If you can't find the relay you need in the Universe, you can simply create your own.
For example:
import (
"strings"
)
// Create a relay definition which generates a greeting message
#Greeting: {
salutation: string | *"hello"
name: string | *"world"
message: "\(strings.ToTitle(salutation)), \(name)!"
}
You may then create any number of relays from the same definition:
french: #Greeting & {
salutation: "bonjour"
name: "monde"
}
american: #Greeting & {
salutation: "hi"
name: "y'all"
}
Programming in Cue
Cue is a next-generation data language by Marcel van Lohuizen and the spiritual successor of GCL, the language used to configure all of Google's infrastructure.
Cue extends JSON with powerful features:
- Composition: layering, templating, references
- Correctness: types, schemas
- Developer experience: comments, packages, first-class tooling, builtin functions
- And much more.
To get started with Cue, we recommend the following resources:
Concepts
Overview
- A developer writes a plan specifying how to deliver their application. Plans are written in the Cue data language.
- Dagger executes plans in isolated environments. Each environment has its own configuration and state.
Plans
A plan specifies, in code, how to deliver a particular application in a particular way.
It lays out the application's supply chain as a graph of interconnected nodes:
- Development tools: source control, CI, build systems, testing systems
- Hosting infrastructure: compute, storage, networking, databases, CDN..
- Software dependencies: operating systems, languages, libraries, frameworks, etc.
The graph models the flow of code and data through the supply chain:
- source code flows from a git repository to a build system;
- system dependencies are combined in a docker image, then uploaded to a registry;
- configuration files are generated then sent to a compute cluster or load balancer;
- etc.
Dagger plans are written in Cue, a powerful declarative language by the creator of GQL, the language used to deploy all applications at Google.
Environments
An environment is a live implementation of a plan, with its own user inputs and state. The same plan can be executed in multiple environments, for example to differentiate production from staging.
An environment can be updated with dagger up. When updating an environment, Dagger determines which inputs have
changed since the last update, and runs them through the corresponding pipelines to produce new outputs.
For example, if an application has a new version of its frontend source code available, but no changes to the frontend, it will build, test and deploy the new frontend, without changing the backend.
Relays
Relays are the basic components of a plan. Each relay is a node in the graph defined by the plan, performing the task assigned to that node. For example one relay fetches source code; another runs a build; another uploads a container image; etc.
Relays are standalone software components: they are defined in Cue, but can execute code in any language using the Dagger pipeline API.
A relay is made of 3 parts:
- Inputs: data received from the user, or upstream relays
- A processing pipeline: code executed against each new input, using the pipeline API
- Outputs: data produced by the processing pipeline
Relays run in parallel, with their inputs and outputs interconnected into a special kind of graph, called a DAG. When a relay receives a new input, it runs it through the processing pipeline, and produces new outputs, which are propagated to downstream relays as inputs, and so on.
Using third-party relays
Cue includes a complete package system. This makes it easy to create a complex plan in very few lines of codes, simply by importing relays from third-party packages.
For example, to create a plan involving Github, Heroku and Amazon RDS, one might import the three corresponding packages:
import (
"dagger.io/github"
"dagger.io/heroku"
"dagger.io/amazon/rds"
)
repo: github.#Repository & {
// Github configuration values
}
backend: heroku.#App & {
// Heroku configuration values
}
db: rds.#Database & {
// RDS configuration values
}
Creating a new relay
Sometimes there is no third-party relay available for a particular task in your workflow; or it may exist but need to be customized.
A relay is typically contained in a cue definition, with the definition name describing its function.
For example a relay for a git repository might be defined as #Repository.
The processing pipeline is a crucial feature of Dagger. It uses the LLB executable format pioneered by the BuildKit project. It allows Dagger components to run sophisticated pipelines to ingest produce artifacts such as source code, binaries, database exports, etc. Best of all, LLB pipelines can securely build and run any docker container, effectively making Dagger scriptable in any language.
Docker compatibility
Thanks to its native support of LLB, Dagger offers native compatibility with Docker.
This makes it very easy to extend an existing Docker-based workflow, including:
- Reusing Dockerfiles and docker-compose files without modification
- Wrapping other deployment tools in a Dagger relay by running them inside a container
- Robust multi-arch and multi-OS support, including Arm and Windows.
- Integration with existing Docker engines and registries
- Integration with Docker for Mac and Docker for Windows on developer machines