Mirror of Dave Thomas' work_queue. In terms of Kanban this could be helpful in determining the Markov chain. Applications in manufacturing and software engineering.
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README.md

WorkQueue

A simple implementation of the hungry consumer work scheduling model.

Given a queue of work to be processed, we create a a pool of workers. Each worker requests the next item to process from the queue. When it finishes processing, it reports the result back and then requests the next item of work.

The intent is that we do not need a central control point which preassigns work—that is done dynamically by the workers.

This has a couple of advantages. First, it locally maximizes CPU utilization, as workers are always busy as long as there is work in the queue.

Second, it offers a degree of resilience, both against CPU hogging work items and against worker failure.

Simple Example

results = WorkQueue.process(
  fn (val,_) -> { :ok, val*2 } end,   # worker function
  [ 1, 2, 3 ]                         # work items to process
)

assert length(results) == 3
for {input, output} <- results, do: assert(output == input * 2)

This code will allocate a number of workers (the default is 2/3rds of the available processing units in the node). Each worker then runs the given function for as long as there are items to process. The results are collected (in the order the workers return them) and returned.

The API

results = WorkQueue.process(work_processor, item_source, options \\ [])
  • work_processor is a function that transforms an item from the work queue. It receives a value, and returns either {:ok, result} or {:error, reason}

  • item_source is the source of the items to be processed. In the simplest case, this is just an enumerable. Successive items are taken from it and fed to the workers.

In other situations, you may need to perform additional processing in order to generate the items. In particular, the item source may be unbounded. In this case, you need to provide a get_next_item function (using the options—see below). This function receives the item_source as its initial state.

  • options is an optional keyword list:

    • worker_count: count

    If count is an integer, start that number of workers. If it is a float, it becomes a factor by which we multiply the number of processing units on the node (so specifying 0.5 will start workers for one-half the number of available processing units). You can also pass the symbols :cpu_bound and :io_bound. The former creates a worker for each processing unit, the latter creates 10 workers per processing unit.

    • get_next_item: func

    The function that fetches the next item to be given to a worker. It initially receives item_source as its parameter. It returns a three element tuple. The first element is :ok if an item has been returned, or :done otherwise. The second element is the item to be returned, and the third is the updated item source state.

    The default value of get_next_item for list values of the item source is

     defp traverse_list([]),    do: {:done, nil, []}
     defp traverse_list([h|t]), do: {:ok,   h,   t}
  • report_each_result_to: func

    Invoke func as each result becomes available. The function receives a tuple containing the original work item and the result of running the calculation on that item. Its return value is ignored.

     test "notifications of results" do
       WorkQueue.process(
         &double/2,
         [ 1, 2, 3 ],
         report_each_result_to:
           fn {input, output} -> assert(output == input*2) end
       )
     end
  • report_progress_to: func, report_progress_interval: ms

    Invoke func to report progress. It is passed

    • {:starting} when work is about to start
    • {:progress, count} every ms milliseconds, indicating the total number of items processed so far
    • {:finished, results} reported when processing finishes

    Progress reporting is disabled when report_progress_interval is false (the default).