MultiInstance Characteristic example – Reward Process

One of a great features of BPMN (and not only) is the multi instance activity aka for each. To put it simple same activity is repeated for each item from input collection. That is usually good fit for distributing work across number of people to gather their input or opinion.
jBPM provides support for it since version 5 but it was enhanced with every version. Current support covers:

  • subprocess and individual task
  • input and output as collection
  • completion condition on entire multi instance activity (subprocess or task)
Equipped with that we can build powerful processes with simplified structure. Even more than that: it’s all dynamic meaning number of instances can (and actually should as best practice) reference process variables for both input and output.
To show it in practice we go over an example that is based on Eric Shabell’s Reward demo. It has been only slightly modified to focus mainly on the multi instance support jBPM comes with in version 6.2 community. 
So what we have in this process?
  1. When process start it will ask for some details about the person who shall receive award and its details
  2. Once instance is started it will go into ‘Associate Reviews’ user task where associate needs to provide following information:
    1. how many peer reviews should be performed
    2. how many of them are required to move on without waiting for all to be completed
  3. Once this information is available ‘Setup Reviews’ task will prepare all required structures and populate process variables that will feed multi instance subprocess
  4. ‘Evaluate Award’ task will be created multiple times based on (2.1) selection
  5. Each instance of that user task will ask for approval and result of it will be kept in multi instance output collection
  6. It has a completion condition that will evaluate result every time one instance of ‘Evaluate Award’ task is completed as soon as it becomes true it will move on and cancel remaining task instances of ‘Evaluate Award’
  7. ‘Calculate results’ will be performed to check what output is collected – if award is approved or rejected and based on that will go to one of the paths.
So let’s review process configuration in details, starting with process variables
This in general is nothing special, just worth noting two variables:
  • reviews_collection
  • reviews_results
both are of type java.util.ArrayList (they can be of any collection type) and they will be used for configuring multi instance subprocess. Important to note is that both must be non null before they can be used in multi instance activity.
Next let’s review how the multi instance activity is configured:
MI collection input – this is the collection which will be used to create individual instances of given activity. In other words, each element from that collection will be assigned to separate activity instance.
MI collection output – this is the collection which will collect results of execution of multi instance activity – will aggregate all results produced. This is optional as not every multi instance activity must produce result that shall be collected.
MI completion condition – expression (currently MVEL) that will be evaluated at every completion of activity instance and as soon as it becomes true it will leave multi instance activity even if not all instances are completed. Those not completed will be canceled.
MI data input – this is the variable name that will be given to individual activity instances produced by multi instance activity. 
MI data output – this is the variable name where the output of individual activity instance will be stored. It’s optional as not all activities must produce results
Last but not least let’s take a look in details at the completion condition as it provide quite powerful way of controlling multi instance activity completion.
In case it won’t be readable (or for copy paste reason) here is the expression:
($ in reviews_results if $ == true).size() == approvalsRequired;
so what does it say? In general it evaluates all items in the ‘reviews_results’ collection and counts all elements that has value set to ‘true’. Next it compares its size with the ‘approvalsRequired’ process variable to check if already collected approvals is enough.
For those interested, this is MVEL projections example which gives very powerful option to operate on collections.
That would be all to this article. For complete runnable example visit github. You can directly clone it from your kie-workbench installation (aka jbpm console) and run it there. It comes with all required parts:
  • process
  • forms
  • data model
All configured and ready to be executed (just make sure you run on jBPM 6.2 or higher). Enjoy
As usual comments and feedback most welcome.

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