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state_machines-audit_trail

Log transitions on a state_machines gem to support auditing and business process analytics.

Description

This plugin for the state_machines gem adds support for keeping an audit trail for any state machine. Having an audit trail gives you a complete history of the state changes in your model. This history allows you to investigate incidents or perform analytics, like: "How long does it take on average to go from state a to state b?", or "What percentage of cases goes from state a to b via state c?"

For more information read Why developers should be force-fed state machines.

ORM support

Note: while the state_machines gem integrates with multiple ORMs, this plugin is currently limited to the following ORM backends:

  • ActiveRecord
  • Mongoid

It should be easy to add new backends by looking at the implementation of the current backends. Pull requests are welcome!

Installation

First, make the gem available by adding it to your Gemfile, and run bundle install:

# this gem
gem 'state_machines-audit_trail'

# required runtime dependency for your ORM; either
gem 'state_machines-activerecord'

# OR
gem 'state_machines-mongoid'

Usage

For the examples below, we will assume you have a pre-existing model called Subscription that has a state_machine configured to utilize the state attribute.

Create/generate a model and migration that holds the audit trail specific to your target model

A Rails generator is provided to create a model and a migration, call it with:

rails generate state_machines:audit_trail Subscription state

will generate the SubscriptionStateTransition model and an accompanying migration.

Configure audit_trail in your state_machine:

class Subscription < ActiveRecord::Base
  state_machine :state, initial: :start do
    audit_trail
    ...

That's it!

audit_trail will register an after_transition callback that is used to log all transitions including the initial state if there is one.

Upgrading from state_machine-audit_trail

See the wiki, https://github.com/state-machines/state_machines-audit_trail/wiki/Converting-from-former-state_machine-audit_trail-to-state_machines-audit_trail

Configuration options

:initial - turn off initial state logging

By default, upon instantiation, a StateTransition is saved for null => initial state. This is useful to understand the full history of any model, but there are cases where this can pollute the audit_trail. For example, when a model has multiple state_machines that use a single StateTransition model for persistence (in conjunction with context below), there would be multiple initial state transitions. By configuring initial: false, it will skip the initial state transition logging for this specific state_machine, while leaving the others in the model unaffected.

audit_trail initial: false

:class - custom state transition class

If your Transition model does not use the default naming scheme, provide it using the :class option:

audit_trail class: FooStateTransition

An example use of a custom :class and :context (below) would be for a model that has multiple state_machine definitions. The combination of these options would allow the use of one transition class that logged state information from both state machines.

:context - storing additional attribute or method values

Using the :context option, you can store method results (or attributes exposed as methods) in the state transition class.

In order to utilize this feature, you need to:

  1. add a field/column to your state transition class (i.e. SubscriptionStateTransitions) and perhaps underlying database through a migration
  2. expose the attribute as a method, or create a method to compute a dynamic value
  3. configure :context

Example 1 - Store a single attribute value

Store Subscription field1 in Transition field field1:

audit_trail context: :field1

Example 2 - Store multiple attribute values

Store Subscription field1 and field2 in Transition fields field1 and field2:

audit_trail context: [:field1, :field2]

Example 3 - Store multiple values from a single context object

Store Subscription user in Transition fields user_id and user_name:

class Subscription < ActiveRecord::Base
  state_machine :state, initial: :start do
    audit_trail context: :user
    ...
  end
end

class SubscriptionStateTransition < ActiveRecord::Base
  def user=(u)
    self.user_id = u.id
    self.user_name = u.name
  end
end

Example 4 - Store simple method results

Store simple method results.

Sometimes it can be useful to store dynamically computed information, such as those from a Subscription method #plan_time_remaining

class Subscription < ActiveRecord::Base
  state_machine :state, initial: :start do
    audit_trail :context: :plan_time_remaining
    ...

  def plan_time_remaining
    # Dynamically computed field e.g., based on other models
    ...

Example 5 - Store advanced method results

Store method results that interrogate the transition for information such as event arguments:

class Subscription < ActiveRecord::Base
  state_machine :state, initial: :start do
    audit_trail :context: :user_name
    ...

  # method receives a state_machines transition object
  def user_name(transition)
    if transition.args.present?
      user_id = transition.args.last.delete(:user_id)
      User.find(user_id).name
    else
      'Undefined User'
    end
    ...

model = Subscription.first
model.ignite!('arg1, 'arg2', 'arg3', user_id: 1)

About

Maintainers

Conversion from the original code to state_machines by AlienFast.

Original Authors

The original plugin was written by Jesse Storimer and Willem van Bergen for Shopify. Mongoid support was contributed by Siddharth.

License

Released under the MIT license (see LICENSE).

Contributing

  1. Fork it ( https://github.com/state-machines/state_machines-audit_trail/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 a new Pull Request

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Log transitions on a state_machines gem to support auditing and business process analytics.

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