Gush is a parallel workflow runner using only Redis as storage and ActiveJob for scheduling and executing jobs.
Gush relies on directed acyclic graphs to store dependencies, see Parallelizing Operations With Dependencies by Stephen Toub to learn more about this method.
This README is about the latest master
code, which might differ from what is released on RubyGems. See tags to browse previous READMEs.
gem 'gush', '~> 3.0'
When using Gush and its CLI commands you need a Gushfile
in the root directory.
Gushfile
should require all your workflows and jobs.
For RoR it is enough to require the full environment:
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
:
config.autoload_paths += ["#{Rails.root}/app/jobs", "#{Rails.root}/app/workflows"]
Simply require any jobs and workflows manually in Gushfile
:
require_relative 'lib/workflows/example_workflow.rb'
require_relative 'lib/jobs/some_job.rb'
require_relative 'lib/jobs/some_other_job.rb'
The DSL for defining jobs consists of a single run
method.
Here is a complete example of a workflow you can create:
# 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:
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}
Let's start with the simplest workflow possible, consisting of a single job:
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:
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:
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!
run
method also accepts before:
attribute to define the opposite association. So we can write the same workflow as above, but like this:
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 :)
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:
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:
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:
The simplest job is a class inheriting from Gush::Job
and responding to perform
method. Much like any other ActiveJob class.
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?
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:
(...)
run FetchBook, params: {url: "http://url.com/book.pdf"}
(...)
and within the job we can access them like this:
class FetchBook < Gush::Job
def perform
# you can access `params` method here, for example:
params #=> {url: "http://url.com/book.pdf"}
end
end
Now that we have defined our workflow and its jobs, we can use it:
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
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.
flow = PublishBookWorkflow.create("http://url.com/book.pdf", "978-0470081204")
flow.start!
Now Gush will start processing jobs in the background using ActiveJob and your chosen backend.
flow.reload
flow.status
#=> :running|:finished|:failed
reload
is needed to see the latest status, since workflows are updated asynchronously.
flow = Workflow.find(id)
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)
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:
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.
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.
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:
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:
[
{
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.
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.
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:
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:
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}
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
.
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
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.
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
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.:
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.:
class SynchronousJob < Gush::Job
def enqueue_worker!(options = {})
Gush::Worker.perform_now(workflow_id, name)
end
end
-
of a specific workflow:
bundle exec gush show <workflow_id>
-
of a page of workflows:
bundle exec gush list
-
of the most recent 100 workflows
bundle exec gush list -99 -1
This requires that you have imagemagick installed on your computer:
bundle exec gush viz <NameOfTheWorkflow>
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
# 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
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.
- 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.
# config/initializers/gush.rb
Gush.configure do |config|
config.redis_url = "redis://localhost:6379"
config.concurrency = 5
config.ttl = 3600*24*7
end
- 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.
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)
# 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 adds indexing for fast workflow pagination and changes the mechanism for expiring workflow data from Redis.
Run bundle exec gush migrate
after upgrading. This will update internal data structures.
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!
.
- Fork it ( http://github.com/chaps-io/gush/fork )
- Create your feature branch (
git checkout -b my-new-feature
) - Commit your changes (
git commit -am 'Add some feature'
) - Push to the branch (
git push origin my-new-feature
) - Create new Pull Request