f595e28533
Signed-off-by: satotake <doublequotation@gmail.com>
212 lines
6.3 KiB
Markdown
212 lines
6.3 KiB
Markdown
---
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slug: /1012/ci
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---
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# Make your CI workflow portable
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## Problems with existing CIs
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- The CI code is sticky to the underlying CI infrastructure.
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- It's hard to migrate the CI from a runner to another.
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- The CI syntax is different with each CI runner.
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- Most CI workflows are represented using YAML.
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Implement the CI workflow using dagger solves all of those problems.
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## Benefits of defining your CI workflow in Dagger
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- **Your CI only runs dagger.** It moves all the CI logic from the non-portable
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CI syntax, to a dagger plan, that runs the same way everywhere.
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- **Develop and run your CI workflow locally.** No need to create a Pull Request
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to trigger CI, you can run the workflow locally. Since dagger workflows
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are containerized, you can expect the same results no matter where the workflow is executed.
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- **Write once, Run anywhere.** The same workflow can run in any CI, goodbye
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vendor lock in.
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- **Blazing Fast**: Dagger will automatically build an optimized execution
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graph, completing the job as fast as possible. Spend less time staring at CI to
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complete and reduce costs.
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- **Effortless Cache Optimizations**. Dagger will automatically
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re-use cached execution results if no changes are detected. Made a change to the
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frontend? The backend won't be built again.
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- **Composable & Reusable**. Define re-usable steps and [share](../learn/1010-dev-cue-package.md) them across all
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your projects. Or with the world. Dagger
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ships with [dozens of re-usable components](../reference/README.md)
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## Example
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This example illustrates how to use Dagger to test and lint a Go project. The
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use of Go is irrelevant and the example can be easily adapted to other languages.
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From the project repository, create a file named `ci/main.cue` and add the
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following configuration to it.
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```cue title="ci/main.cue"
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package main
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import (
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"alpha.dagger.io/dagger"
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"alpha.dagger.io/os"
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"alpha.dagger.io/docker"
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)
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// Source directory of the repository. We'll connect this from the CLI using `dagger input`
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source: dagger.#Artifact
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// Here we define a test phase.
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// We're using `os.#Container`, a built-in package that will spawn a container
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// We're also using `docker.#Pull` to execute the container from a Docker image
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// coming from a registry.
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test: os.#Container & {
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image: docker.#Pull & {
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from: "golang:1.16-alpine"
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}
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mount: "/app": from: source
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command: "go test -v ./..."
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dir: "/app"
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}
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// Likewise, here we define a lint phase.
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lint: os.#Container & {
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image: docker.#Pull & {
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from: "golangci/golangci-lint:v1.39.0"
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}
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mount: "/app": from: source
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command: "golangci-lint run -v"
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dir: "/app"
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}
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```
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The configuration above defines:
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- **source** code of the project. More on this later.
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- **test** _task_ which executes `go test` inside the source artifact
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using the `golang` Docker image
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- **lint** _task_ which executes `golangci-lint` inside the source artifact
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using the `golangci-lint` Docker image.
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Before we can execute the configuration, we need to set up the Dagger project and environment.
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```shell
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# Initialize a dagger project at the root of your project
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dagger init
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# Create a CI environment using the CUE files in the `./ci` directory
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dagger new ci -p ./ci
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# Link the `source` artifact defined in the configuration with the project
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# source code.
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dagger input dir source .
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```
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Next, bring up the CI environment (e.g. execute the CI configuration):
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```shell
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$ dagger up
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# ...
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7:15PM INF test | computing environment=ci
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7:15PM INF lint | computing environment=ci
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# ...
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```
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Since `test` and `lint` do not depend on each other, they are executed in
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parallel.
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Running `dagger up` a second time will return almost immediately: since no
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changes were made to `source`, Dagger will re-use the cached results for both `test` nor `lint`.
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## Integrating with CI
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All it takes to execute a Dagger workflow in CI is to run `dagger up`.
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We provide a [GitHub Actions](../learn/1009-github-actions.md) to make
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integration with GitHub easier.
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## Monorepos
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Dagger workflows scale really well with CI complexity.
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The following example illustrates how to test two different projects in the same
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repository: a _Node_ frontend (located in _./frontend_) along with a _Go_ backend (located in _./backend_).
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From the project repository, update the file named `ci/main.cue` with the
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following configuration.
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```cue title="ci/main.cue"
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package main
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import (
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"alpha.dagger.io/dagger"
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"alpha.dagger.io/os"
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"alpha.dagger.io/docker"
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)
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// Source directory of the repository. We'll connect this from the CLI using `dagger input`
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source: dagger.#Artifact
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backend: {
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// We use `os.#Dir` to grab a sub-directory of `source`
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code: os.#Dir & {
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from: source
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path: "./backend"
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}
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test: os.#Container & {
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image: docker.#Pull & {
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from: "golang:1.16-alpine"
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}
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mount: "/app": from: code
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command: "go test -v ./..."
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dir: "/app"
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}
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}
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frontend: {
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// We use `os.#Dir` to grab a sub-directory of `source`
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code: os.#Dir & {
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from: source
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path: "./frontend"
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}
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test: os.#Container & {
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image: docker.#Pull & {
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from: "node:16-alpine"
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}
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mount: "/app": from: code
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command: """
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# Install Dependencies
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yarn
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# Run the test script
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yarn test
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"""
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dir: "/app"
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}
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}
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```
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:::tip
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Larger configurations can be split into multiple files, for example `backend.cue` and `frontend.cue`
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:::
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The configuration above defines a _frontend_ and _backend_ structure, each
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containing:
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- A **code** directory, defined as a subdirectory of **source**
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- A language specific **test** task using either `yarn` or `go`
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```shell
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$ dagger up
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7:15PM INF frontend.test | computing environment=ci
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7:15PM INF backend.test | computing environment=ci
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7:15PM INF frontend.test | #8 0.370 yarn install v1.22.5 environment=ci
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7:15PM INF frontend.test | #8 0.689 [1/4] Resolving packages... environment=ci
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7:15PM INF frontend.test | #8 1.626 [2/4] Fetching packages... environment=ci
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...
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```
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`frontend.test` and `backend.test` are running in parallel since there are no
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dependencies between each other.
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If you were to make changes to the _./frontend_ directory, only
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`frontend.test` will be executed.
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