prunner

Pipeline Runner

An embeddable task / pipeline runner with a HTTP API.

• Good for orchestrating long-running jobs with multiple steps; so if you think "I need a CI pipeline" but within your project, this is for you.
• The pipeline definition is done in a pipelines.yml file and is static.
• To start a new job, you use an authenticated HTTP API (see our API docs).
• Every task inside the pipeline is a script run on the command line.
• Tasks can have dependencies; so together, they form a graph (DAG)
• supports pipeline arguments
• supports configurable parallelism, also with a "wait-list" if the parallelism is exceeded.
• Persistent storage of jobs and their outputs

This is NOT a full CI pipeline solution.

For a full introduction, see the README of the prunner repo.

Components

prunner (this repository)

A single process, written in go, that provides the REST API, pipeline runner and persistence. It needs to be started in the background for integration into other applications.

prunner-ui

A minimalistic React UI to start and view pipelines, jobs and task details.

Flowpack.Prunner

A Neos/Flow PHP package providing a backend module for the current pipeline state, and a PHP API.

User Guide

Main Concepts

prunner controls a set of pipelines, which are defined in YAML files (typically pipelines.yml). The pipelines consist of tasks, which are executed as part of the pipeline. Each task has a script which are the CLI commands executed when the task is run.

A simple pipeline

Tasks are run in-parallel by default. In the example below, if the pipeline do_something is started, the two tasks do_foo and do_bar run in parallel to each other:

pipelines:
  do_something:
    tasks:
      do_foo:
        script:
          - pwd
      do_bar:
        script:
          - ls

Task Dependencies

In case you need to ensure certain steps are executed in-order, you can use task dependencies to order tasks using the depends_on key:

pipelines:
  do_something:
    tasks:
      do_foo:
        script:
          - pwd
      do_bar:
        script:
          - ls
        # here, we ensure that do_bar runs AFTER do_foo.
        depends_on:
          - do_foo

It is not possible to pass information from one task to the next one within prunner. This is an intended limitation to keep complexity low; so we do not plan to support "artifacts" or anything like this.

In case you need to store information from one task to the next, it is recommended that you do this outside prunner, and pass in a Job Argument with an identifier to every task (explained in the next section).

Job Variables

When starting a job, (i.e. do_something in the example below), you can send additional Variables as JSON. The script is passed through the text/template templating language, where you can access the variables. This way, you can pass the variable contents to your scripts.

pipelines:
  do_something:
    tasks:
      do_foo:
        script:
          - pwd {{ .myVariable }}
      do_bar:
        script:
          - echo {{ .myVariable }}

Limiting Concurrency

Certain pipelines, like deployment pipelines, usually should only run only once, and never be started concurrently. Prunner supports this via a configurable concurrency:

pipelines:
  do_something:
    concurrency: 1
    tasks: # as usual

Concurrency specifies how often the pipeline can run concurrently; NOT whether individual tasks in the pipeline run concurrently.

Now, when the concurrency limit is reached and you schedule the pipeline again while it is running, the job is queued to be worked on later - it is added to the wait list by default.

The Wait List

By default, if you limit concurrency, and the limit is exceeded, further jobs are added to the waitlist of the pipeline.

However, you have some options to configure this as well:

The waitlist can have a maximum size, denoted by queue_limit:

pipelines:
  do_something:
    queue_limit: 1 
    concurrency: 1
    tasks: # as usual

To deactivate the queuing altogether, set queue_limit: 0.

Now, if the queue is limited, an error occurs when it is full and you try to add a new job.

Alternatively, you can also set queue_strategy: replace to replace the last job in the queue by the newly added one:

pipelines:
  do_something:
    queue_limit: 1
    queue_strategy: replace
    concurrency: 1
    tasks: # as usual

So the example above means:

• at most one pipeline of do_something runs at any given time (concurrency: 1)
• in case a pipeline is running and a new job is added, this is added to the queue.
• when another job is added, it replaces the previously added job on the waitlist.

This is especially helpful for stuff like incremental content rendering, when you need to ensure that the system converges to the last known state.

Persistent Job State

Development

Requirements

• Go (>= 1.16)

Running locally

go run cmd/prunner/main.go

Note: for development a live reload wrapper like https://github.com/markbates/refresh is recommended.

The API should now be accessible at http://localhost:9009/. The log will contain an example JWT auth token that can be used for authentication in local development.

For interacting with the API, you need a JWT token which you can generate for developing using:

go run cmd/prunner/main.go debug

Building for different operating systems.

Using the standard GOOS environment variable, you can build for different operating systems. This is helpful when you want to use prunner inside a Docker container, but are developing on OSX. For this, a compile step like the following is useful:

# after building, copy the executable inside the docker container; so it can be directly used.
GOOS=linux go build cmd/prunner/main.go && docker cp main cms_neos_1:/app/main

Running Tests

To run all tests, use:

go test ./...

# to show test output, run with verbose flag:
go test ./... -v

# to run a single test, use -run:
go test ./... -v -run TestServer_HugeOutput

As linter, we use golangci-lint. See this page for platform-specific installation instructions. Then, to run the linter, use:

golangci-lint run

Generate OpenAPI (Swagger) spec

An OpenAPI 2.0 spec is generated from the Go types and annoations in source code using the go-swagger tool (it is not bundled in this module). See https://goswagger.io/install.html for installation instructions.

go generate ./server

Releasing

Releases are done using goreleaser and GitHub Actions. Simply tag a new version using the vX.Y.Z naming convention, and then all platforms are built automatically.

Security concept

• The HTTP server only listens on localhost by default
• prunner always enables authentication via JWT
• An application that wants to embed prunner should read the shared secret and generate a JWT auth token for accessing the API by doing internal HTTP requests. This way custom policies can be implemented for ensuring access to prunner.

UI

• Show auth errors in UI

• Idea: content ranges for polling streams

License

GPL, because we are building on taskctl - see LICENSE.