move cloudrun guide to docs/learn
Signed-off-by: Tihomir Jovicic <tihomir.jovicic.develop@gmail.com>
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Tihomir Jovicic
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---
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slug: /learn/106-cloudrun
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---
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# Dagger 106: deploy to CloudRun
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This tutorial illustrates how to use dagger to push and deploy Docker
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images to CloudRun.
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Deploy an application to GCP Cloud Run
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This example shows how to deploy an application to GCP Cloud Run. Read the deployment [plan](https://github.com/dagger/dagger/tree/main/examples/cloud-run-app)
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@ -40,3 +52,4 @@ How to run:
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```sh
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dagger up
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```
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@ -1,242 +0,0 @@
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---
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sidebar_position: 1
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slug: /programming
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---
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# Programming
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## Writing your first Dagger plan
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1\. Initialize a Dagger workspace anywhere in your git repository:
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`dagger init`
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It will create a `.dagger` directory in your current directory with an empty `env` directory inside it:
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```bash
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.dagger/
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└── env
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```
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2\. Create a new environment, for example `staging`:
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`dagger new staging`
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```bash
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.dagger/
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└── env
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└── staging
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├── plan
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└── values.yaml
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```
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3\. Create a new file [Cue](#programming-in-cue) config file in `.dagger/env/staging/plan`, and open it with any text editor or IDE:
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```bash
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.dagger/
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└── env
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└── staging
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├── plan
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│ └── staging.cue
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└── values.yaml
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```
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4\. Describe each [relay](#relays) in your plan as a field in the Cue configuration:
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For example:
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```cue
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package main
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import (
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"dagger.io/docker"
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"dagger.io/git"
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)
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// Relay for fetching a git repository
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repo: git.#Repository & {
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remote: "https://github.com/dagger/dagger"
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ref: "main"
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}
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// Relay for building a docker image
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ctr: docker.#Build & {
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source: repo
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}
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```
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For more inspiration, see these examples:
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- [Deploy a static page to S3](https://github.com/dagger/dagger/blob/main/examples/README.md#deploy-a-static-page-to-s3)
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- [Deploy a simple React application](https://github.com/dagger/dagger/blob/main/examples/README.md#deploy-a-simple-react-application)
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- [Deploy a complete JAMstack app](https://github.com/dagger/dagger/blob/main/examples/README.md#deploy-a-complete-jamstack-app)
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- [Provision a Kubernetes cluster on AWS](https://github.com/dagger/dagger/blob/main/examples/README.md#provision-a-kubernetes-cluster-on-aws)
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- [Add HTTP monitoring to your application](https://github.com/dagger/dagger/blob/main/examples/README.md#add-http-monitoring-to-your-application)
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- [Deploy an application to your Kubernetes cluster](https://github.com/dagger/dagger/blob/main/examples/README.md#deploy-an-application-to-your-kubernetes-cluster)
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- [Deploy an application to GCP Cloud Run](https://github.com/dagger/dagger/blob/main/examples/README.md#deploy-an-application-to-gcp-cloud-run)
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5\. Extend your plan with relay definitions from [Dagger
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Universe](https://github.com/dagger/dagger/tree/main/stdlib), an encyclopedia of
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Cue packages curated by the Dagger community.
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6\. If you can't find the relay you need in the Universe, you can simply create your own.
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For example:
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```cue
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import (
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"strings"
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)
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// Create a relay definition which generates a greeting message
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#Greeting: {
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salutation: string | *"hello"
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name: string | *"world"
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message: "\(strings.ToTitle(salutation)), \(name)!"
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}
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```
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You may then create any number of relays from the same definition:
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```cue
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french: #Greeting & {
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salutation: "bonjour"
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name: "monde"
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}
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american: #Greeting & {
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salutation: "hi"
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name: "y'all"
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}
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```
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## Programming in Cue
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[Cue](https://cuelang.org) is a next-generation data language by Marcel van Lohuizen and the spiritual successor
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of GCL, the language used to configure all of Google's infrastructure.
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Cue extends JSON with powerful features:
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- Composition: layering, templating, references
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- Correctness: types, schemas
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- Developer experience: comments, packages, first-class tooling, builtin functions
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- And much more.
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To get started with Cue, we recommend the following resources:
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- [Cuetorials](https://cuetorials.com)
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- [Cue playground](https://cuelang.org/play)
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## Concepts
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### Overview
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1. A developer writes a _plan_ specifying how to deliver their application. Plans are written in the [Cue](https://cuelang.org) data language.
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2. Dagger executes plans in isolated _environments_. Each environment has its own configuration and state.
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### Plans
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A _plan_ specifies, in code, how to deliver a particular application in a particular way.
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It lays out the application's supply chain as a graph of interconnected nodes:
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- Development tools: source control, CI, build systems, testing systems
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- Hosting infrastructure: compute, storage, networking, databases, CDN..
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- Software dependencies: operating systems, languages, libraries, frameworks, etc.
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The graph models the flow of code and data through the supply chain:
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- source code flows from a git repository to a build system;
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- system dependencies are combined in a docker image, then uploaded to a registry;
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- configuration files are generated then sent to a compute cluster or load balancer;
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- etc.
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Dagger plans are written in [Cue](https://cuelang.org), a powerful declarative language by the creator of GQL, the language used to deploy all applications at Google.
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### Environments
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An _environment_ is a live implementation of a _plan_, with its own user inputs and state.
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The same plan can be executed in multiple environments, for example to differentiate production from staging.
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An environment can be updated with `dagger up`. When updating an environment, Dagger determines which inputs have
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changed since the last update, and runs them through the corresponding pipelines to produce new outputs.
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For example, if an application has a new version of its frontend source code available, but no changes to
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the frontend, it will build, test and deploy the new frontend, without changing the backend.
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### Relays
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_Relays_ are the basic components of a _plan_. Each relay is a node in the graph defined by the plan,
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performing the task assigned to that node. For example one relay fetches source code; another runs a build;
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another uploads a container image; etc.
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Relays are standalone software components: they are defined in [Cue](https://cuelang.org/), but can
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execute code in any language using the [Dagger pipeline
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API](https://github.com/dagger/dagger/blob/main/stdlib/dagger/op/op.cue).
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A relay is made of 3 parts:
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- Inputs: data received from the user, or upstream relays
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- A processing pipeline: code executed against each new input, using the
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[pipeline
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API](https://github.com/dagger/dagger/blob/main/stdlib/dagger/op/op.cue)
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- Outputs: data produced by the processing pipeline
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Relays run in parallel, with their inputs and outputs interconnected into a special kind of graph,
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called a _DAG_. When a relay receives a new input, it runs it through the processing pipeline,
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and produces new outputs, which are propagated to downstream relays as inputs, and so on.
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### Using third-party relays
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Cue includes a complete package system. This makes it easy to create a complex plan in very few
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lines of codes, simply by importing relays from third-party packages.
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For example, to create a plan involving Github, Heroku and Amazon RDS, one might import the three
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corresponding packages:
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```cue
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import (
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"dagger.io/github"
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"dagger.io/heroku"
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"dagger.io/amazon/rds"
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)
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repo: github.#Repository & {
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// Github configuration values
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}
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backend: heroku.#App & {
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// Heroku configuration values
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}
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db: rds.#Database & {
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// RDS configuration values
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}
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```
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### Creating a new relay
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Sometimes there is no third-party relay available for a particular task in your workflow; or it may exist but need to be customized.
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A relay is typically contained in a [cue definition](https://cuetorials.com/overview/foundations/#definitions), with the definition name describing its function.
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For example a relay for a git repository might be defined as `#Repository`.
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The processing pipeline is a crucial feature of Dagger. It uses the [LLB](https://github.com/moby/buildkit)
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executable format pioneered by the BuildKit project. It allows Dagger components to run
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sophisticated pipelines to ingest produce artifacts such as source code, binaries, database exports, etc.
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Best of all, LLB pipelines can securely build and run any docker container, effectively making Dagger
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scriptable in any language.
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### Docker compatibility
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Thanks to its native support of LLB, Dagger offers native compatibility with Docker.
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This makes it very easy to extend an existing Docker-based workflow, including:
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- Reusing Dockerfiles and docker-compose files without modification
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- Wrapping other deployment tools in a Dagger relay by running them inside a container
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- Robust multi-arch and multi-OS support, including Arm and Windows.
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- Integration with existing Docker engines and registries
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- Integration with Docker for Mac and Docker for Windows on developer machines
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