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https://github.com/chaps-io/gush

Fast and distributed workflow runner using ActiveJob and Redis
https://github.com/chaps-io/gush

activejob graph parallel parallelization queues redis ruby sidekiq workers workflow workflows

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Fast and distributed workflow runner using ActiveJob and Redis

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README 

        
# Gush



![Gem Version](https://img.shields.io/gem/v/gush)

![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/chaps-io/gush/ruby.yml)



Gush is a parallel workflow runner using only Redis as storage and [ActiveJob](http://guides.rubyonrails.org/v4.2/active_job_basics.html#introduction) for scheduling and executing jobs.



## Theory



Gush relies on directed acyclic graphs to store dependencies, see [Parallelizing Operations With Dependencies](https://msdn.microsoft.com/en-us/magazine/dd569760.aspx) by Stephen Toub to learn more about this method.



## **WARNING - version notice**



This README is about the latest `master` code, which might differ from what is released on RubyGems. See tags to browse previous READMEs.



## Installation



### 1. Add `gush` to Gemfile



```ruby

gem 'gush', '~> 3.0'

```



### 2. Create `Gushfile`



When using Gush and its CLI commands you need a `Gushfile` in the root directory.

`Gushfile` should require all your workflows and jobs.



#### Ruby on Rails



For RoR it is enough to require the full environment:



```ruby

require_relative './config/environment.rb'

```



and make sure your jobs and workflows are correctly loaded by adding their directories to autoload_paths, inside `config/application.rb`:



```ruby

config.autoload_paths += ["#{Rails.root}/app/jobs", "#{Rails.root}/app/workflows"]

```



#### Ruby



Simply require any jobs and workflows manually in `Gushfile`:



```ruby

require_relative 'lib/workflows/example_workflow.rb'

require_relative 'lib/jobs/some_job.rb'

require_relative 'lib/jobs/some_other_job.rb'

```



## Example



The DSL for defining jobs consists of a single `run` method.

Here is a complete example of a workflow you can create:



```ruby

# app/workflows/sample_workflow.rb

class SampleWorkflow < Gush::Workflow

  def configure(url_to_fetch_from)

    run FetchJob1, params: { url: url_to_fetch_from }

    run FetchJob2, params: { some_flag: true, url: 'http://url.com' }



    run PersistJob1, after: FetchJob1

    run PersistJob2, after: FetchJob2



    run Normalize,

        after: [PersistJob1, PersistJob2],

        before: Index



    run Index

  end

end

```



and this is how the graph will look like:



```mermaid

graph TD

    A{Start} --> B[FetchJob1]

    A --> C[FetchJob2]

    B --> D[PersistJob1]

    C --> E[PersistJob2]

    D --> F[NormalizeJob]

    E --> F

    F --> G[IndexJob]

    G --> H{Finish}

```    



## Defining workflows



Let's start with the simplest workflow possible, consisting of a single job:



```ruby

class SimpleWorkflow < Gush::Workflow

  def configure

    run DownloadJob

  end

end

```



Of course having a workflow with only a single job does not make sense, so it's time to define dependencies:



```ruby

class SimpleWorkflow < Gush::Workflow

  def configure

    run DownloadJob

    run SaveJob, after: DownloadJob

  end

end

```



We just told Gush to execute `SaveJob` right after `DownloadJob` finishes **successfully**.



But what if your job must have multiple dependencies? That's easy, just provide an array to the `after` attribute:



```ruby

class SimpleWorkflow < Gush::Workflow

  def configure

    run FirstDownloadJob

    run SecondDownloadJob



    run SaveJob, after: [FirstDownloadJob, SecondDownloadJob]

  end

end

```



Now `SaveJob` will only execute after both its parents finish without errors.



With this simple syntax you can build any complex workflows you can imagine!



#### Alternative way



`run` method also accepts `before:` attribute to define the opposite association. So we can write the same workflow as above, but like this:



```ruby

class SimpleWorkflow < Gush::Workflow

  def configure

    run FirstDownloadJob, before: SaveJob

    run SecondDownloadJob, before: SaveJob



    run SaveJob

  end

end

```



You can use whatever way you find more readable or even both at once :)



### Passing arguments to workflows



Workflows can accept any primitive arguments in their constructor, which then will be available in your

`configure` method.



Let's assume we are writing a book publishing workflow which needs to know where the PDF of the book is and under what ISBN it will be released:



```ruby

class PublishBookWorkflow < Gush::Workflow

  def configure(url, isbn, publish: false)

    run FetchBook, params: { url: url }

    if publish

      run PublishBook, params: { book_isbn: isbn }, after: FetchBook

    end

  end

end

```



and then create your workflow with those arguments:



```ruby

PublishBookWorkflow.create("http://url.com/book.pdf", "978-0470081204", publish: true)

```



and that's basically it for defining workflows, see below on how to define jobs:



## Defining jobs



The simplest job is a class inheriting from `Gush::Job` and responding to `perform` method. Much like any other ActiveJob class.



```ruby

class FetchBook < Gush::Job

  def perform

    # do some fetching from remote APIs

  end

end

```



But what about those params we passed in the previous step?



## Passing parameters into jobs



To do that, simply provide a `params:` attribute with a hash of parameters you'd like to have available inside the `perform` method of the job.



So, inside workflow:



```ruby

(...)

run FetchBook, params: {url: "http://url.com/book.pdf"}

(...)

```



and within the job we can access them like this:



```ruby

class FetchBook < Gush::Job

  def perform

    # you can access `params` method here, for example:



    params #=> {url: "http://url.com/book.pdf"}

  end

end

```



## Executing workflows



Now that we have defined our workflow and its jobs, we can use it:



### 1. Start background worker process



**Important**: The command to start background workers depends on the backend you chose for ActiveJob.

For example, in case of Sidekiq this would be:



```

bundle exec sidekiq -q gush

```



**[Click here to see backends section in official ActiveJob documentation about configuring backends](http://guides.rubyonrails.org/active_job_basics.html#backends)**



**Hint**: gush uses `gush` queue name by default. Keep that in mind, because some backends (like Sidekiq) will only run jobs from explicitly stated queues.



### 2. Create the workflow instance



```ruby

flow = PublishBookWorkflow.create("http://url.com/book.pdf", "978-0470081204")

```



### 3. Start the workflow



```ruby

flow.start!

```



Now Gush will start processing jobs in the background using ActiveJob and your chosen backend.



### 4. Monitor its progress:



```ruby

flow.reload

flow.status

#=> :running|:finished|:failed

```



`reload` is needed to see the latest status, since workflows are updated asynchronously.



## Loading workflows



### Finding a workflow by id



```

flow = Workflow.find(id)

```



### Paging through workflows



To get workflows with pagination, use start and stop (inclusive) index values:



```

flows = Workflow.page(0, 99)

```



Or in reverse order:



```

flows = Workflow.page(0, 99, order: :desc)

```



## Advanced features



### Global parameters for jobs



Workflows can accept a hash of `globals` that are automatically forwarded as parameters to all jobs.



This is useful to have common functionality across workflow and job classes, such as tracking the creator id for all instances:



```ruby

class SimpleWorkflow < Gush::Workflow

  def configure(url_to_fetch_from)

    run DownloadJob, params: { url: url_to_fetch_from }

  end

end



flow = SimpleWorkflow.create('http://foo.com', globals: { creator_id: 123 })

flow.globals

=> {:creator_id=>123}

flow.jobs.first.params

=> {:creator_id=>123, :url=>"http://foo.com"}

```



**Note:** job params with the same key as globals will take precedence over the globals.



### Pipelining



Gush offers a useful tool to pass results of a job to its dependencies, so they can act differently.



**Example:**



Let's assume you have two jobs, `DownloadVideo`, `EncodeVideo`.

The latter needs to know where the first one saved the file to be able to open it.



```ruby

class DownloadVideo < Gush::Job

  def perform

    downloader = VideoDownloader.fetch("http://youtube.com/?v=someytvideo")



    output(downloader.file_path)

  end

end

```



`output` method is used to ouput data from the job to all dependant jobs.



Now, since `DownloadVideo` finished and its dependant job `EncodeVideo` started, we can access that payload inside it:



```ruby

class EncodeVideo < Gush::Job

  def perform

    video_path = payloads.first[:output]

  end

end

```



`payloads` is an array containing outputs from all ancestor jobs. So for our `EncodeVideo` job from above, the array will look like:



```ruby

[

  {

    id: "DownloadVideo-41bfb730-b49f-42ac-a808-156327989294" # unique id of the ancestor job

    class: "DownloadVideo",

    output: "https://s3.amazonaws.com/somebucket/downloaded-file.mp4" #the payload returned by DownloadVideo job using `output()` method

  }

]

```



**Note:** Keep in mind that payloads can only contain data which **can be serialized as JSON**, because that's how Gush stores them internally.



### Dynamic workflows



There might be a case when you have to construct the workflow dynamically depending on the input.



As an example, let's write a workflow which accepts an array of users and has to send an email to each one. Additionally after it sends the e-mail to every user, it also has to notify the admin about finishing.



```ruby



class NotifyWorkflow < Gush::Workflow

  def configure(user_ids)

    notification_jobs = user_ids.map do |user_id|

      run NotificationJob, params: {user_id: user_id}

    end



    run AdminNotificationJob, after: notification_jobs

  end

end

```



We can achieve that because `run` method returns the id of the created job, which we can use for chaining dependencies.



Now, when we create the workflow like this:



```ruby

flow = NotifyWorkflow.create([54, 21, 24, 154, 65]) # 5 user ids as an argument

```



it will generate a workflow with 5 `NotificationJob`s and one `AdminNotificationJob` which will depend on all of them:



```mermaid

graph TD

    A{Start} --> B[NotificationJob]

    A --> C[NotificationJob]

    A --> D[NotificationJob]

    A --> E[NotificationJob]

    A --> F[NotificationJob]

    B --> G[AdminNotificationJob]

    C --> G

    D --> G

    E --> G

    F --> G

    G --> H{Finish}

```



### Dynamic queue for jobs



There might be a case you want to configure different jobs in the workflow using different queues. Based on the above the example, we want to config `AdminNotificationJob` to use queue `admin` and `NotificationJob` use queue `user`.



```ruby



class NotifyWorkflow < Gush::Workflow

  def configure(user_ids)

    notification_jobs = user_ids.map do |user_id|

      run NotificationJob, params: {user_id: user_id}, queue: 'user'

    end



    run AdminNotificationJob, after: notification_jobs, queue: 'admin'

  end

end

```



### Dynamic waitable time for jobs



There might be a case you want to configure a job to be executed after a time. Based on above example, we want to configure `AdminNotificationJob` to be executed after 5 seconds.



```ruby



class NotifyWorkflow < Gush::Workflow

  def configure(user_ids)

    notification_jobs = user_ids.map do |user_id|

      run NotificationJob, params: {user_id: user_id}, queue: 'user'

    end



    run AdminNotificationJob, after: notification_jobs, queue: 'admin', wait: 5.seconds

  end

end

```



### Customization of ActiveJob enqueueing



There might be a case when you want to customize enqueing a job with more than just the above two options (`queue` and `wait`).



To pass additional options to `ActiveJob.set`, override `Job#worker_options`, e.g.:



```ruby



class ScheduledJob < Gush::Job



  def worker_options

    super.merge(wait_until: Time.at(params[:start_at]))

  end



end

```



Or to entirely customize the ActiveJob integration, override `Job#enqueue_worker!`, e.g.:



```ruby



class SynchronousJob < Gush::Job



  def enqueue_worker!(options = {})

    Gush::Worker.perform_now(workflow_id, name)

  end



end

```



## Command line interface (CLI)



### Checking status



- of a specific workflow:



  ```

  bundle exec gush show 

  ```



- of a page of workflows:



  ```

  bundle exec gush list

  ```



- of the most recent 100 workflows



  ```

  bundle exec gush list -99 -1

  ```



### Vizualizing workflows as image



This requires that you have imagemagick installed on your computer:



```

bundle exec gush viz 

```



### Customizing locking options



In order to prevent getting the RedisMutex::LockError error when having a large number of jobs, you can customize these 2 fields `locking_duration` and `polling_interval` as below



```ruby

# config/initializers/gush.rb

Gush.configure do |config|

  config.redis_url = "redis://localhost:6379"

  config.concurrency = 5

  config.locking_duration = 2 # how long you want to wait for the lock to be released, in seconds

  config.polling_interval = 0.3 # how long the polling interval should be, in seconds

end

```



### Cleaning up afterwards



Running `NotifyWorkflow.create` inserts multiple keys into Redis every time it is run.  This data might be useful for analysis but at a certain point it can be purged.  By default gush and Redis will keep keys forever.  To configure expiration you need to do two things.



1. Create an initializer that specifies `config.ttl` in seconds. Best NOT to set TTL to be too short (like minutes) but about a week in length.



```ruby

# config/initializers/gush.rb

Gush.configure do |config|

  config.redis_url = "redis://localhost:6379"

  config.concurrency = 5

  config.ttl = 3600*24*7

end

```



2. Call `Client#expire_workflows` periodically, which will clear all expired stored workflow and job data and indexes. This method can be called at any rate, but ideally should be called at least once for every 1000 workflows created.



If you need more control over individual workflow expiration, you can call `flow.expire!(ttl)` with a TTL different from the Gush configuration, or with -1 to never expire the workflow.



### Avoid overlapping workflows



Since we do not know how long our workflow execution will take we might want to avoid starting the next scheduled workflow iteration while the current one with same class is still running.  Long term this could be moved into core library, perhaps `Workflow.find_by_class(klass)`



```ruby

# config/initializers/gush.rb

GUSH_CLIENT = Gush::Client.new

# call this method before NotifyWorkflow.create

def find_by_class klass

  GUSH_CLIENT.all_workflows.each do |flow|

    return true if flow.to_hash[:name] == klass && flow.running?

  end

  return false

end

```



## Gush 3.0 Migration



Gush 3.0 adds indexing for fast workflow pagination and changes the mechanism for expiring workflow data from Redis.



### Migration



Run `bundle exec gush migrate` after upgrading. This will update internal data structures.



### Expiration API



Periodically run `Gush::Client.new.expire_workflows` to expire data. Workflows will be automatically enrolled in this expiration, so there is no longer a need to call `workflow.expire!`.



## Contributors



- [Mateusz Lenik](https://github.com/mlen)

- [Michał Krzyżanowski](https://github.com/krzyzak)

- [Maciej Nowak](https://github.com/keqi)

- [Maciej Kołek](https://github.com/ferusinfo)



## Contributing



1. Fork it ( http://github.com/chaps-io/gush/fork )

2. Create your feature branch (`git checkout -b my-new-feature`)

3. Commit your changes (`git commit -am 'Add some feature'`)

4. Push to the branch (`git push origin my-new-feature`)

5. Create new Pull Request