concurrently

A clojure library for making concurrent process-engine backed by core.async. Application can share the engine and put process-requests onto the engine. Engine will return a channel containing all processed results for the requests.

This library safely handle pipeline backed by core.async so that pipeline never stack by abuse of channels like stopping data-retrieving from channels before fully retrieving all data or forgetting to close channels.

This library backed by databox (https://github.com/tyano/databox) that wrap data in box and make handling data in channel-pipeline exception-safe. All exceptions occured in databox safely are wrapped and be passed-through channels until the end of channel-pipeline, so that users can handle exceptions at the end of pipeline.

get-results function handle all actions needed for protecting pipeline from accidental stacking. get-results try to retrieve all data from an output channel and unwrap databoxes. If an exception were occurred in a pipeline, unwrapping the failure-databox will throw the exception, but get-results handle all data remaining in channel-pipeline for protecting the pipeline from accidental stacking.

Usage

Create an process-engine by concurrent-process function

You can make an engine with a transducer, input and output channels and max parallel count by calling concurrent-process or concurrent-process-blocking functions. All data supplied into input-channel will be handled by a supplied transducer in parallel by go-blocks or threads of same number with parallel count. But you should not put data into the input-channel directly. You can supply data into an engine by another function.

Transducer must accept a map with :data and :options keys. :data is a value from input channel. :options is a option-map supplied to concurrently function.

Supply data to the created engine

concurrent-process and concurrent-process-blocking return a process-context. You should not use an input channel of pipeline directly. Instead of that, you should use concurrently function with the returned process-context, a channel which all input data can be read from, and an option map.

concurrently wraps all input data by databox and concurrent-process handles the databoxes so that if some exceptions occurred in a supplied transducer, the exception safely converted to a failure-box and passed-through into pipeline.

getting calculated results from Job

concurrently returns a Job. You can cancel the job by calling (cancel job) function. Job contains a field :channel. You can read all calculated results for all input data supplied to concurrently function from the channel, but should not read it directly. Use (get-results (:channel job)) function for safe-reading from channels.

get-results handles all databoxes from a channel and create a result vector. If a failure databox is found while handling databoxes, get-results will throw the exception and handle all remaining data in a channel in background for protecting the channel from stacking caused by never-read data in a channel.

Connecting channels

You should call get-results at last for safe processing of channels, but before calling it, you can connect the channel contained by a job to another channels. Although you can do it with pipe of core.async, but there is a safe utility function for doing it.

(chain channel transducer exception-handler)

You can connect a channel to a transducer by this chain function (with an exception handler if you want) and can get a next channel. This connection can be chained like:

(-> (:channel job)
    (chain xf1)
    (chain xf2)
    (chain xf3))

This will return a last channel and you can get the results by calling get-results on the last channel.

Difference of chain and pipe is that pipe stops reading from an input channel if the output channel is closed, but chain never stop reading input so that data never remain in an input channel. Remaining data in a channel might cause accidental channel-stacking. All data should be read fully.

Note that the data from a channel in a job always are databoxes. Transducer supplied to chain must handle databox and must return databox. Use databox.core/map, databox.core/mapcat or databox.core/filter transducers for handling databoxes for safe. Functions of databox safely handle exceptions occurred in databox-processing and always return a databox.

WORK THROUGH

(defn my-great-function
  [data options]
  ;; do something....
  )

;;; create an engine handle data by 8 threads in parallel.
(def shared-process (concurrent-process-blocking 8 
                                                 (chan 1) 
                                                 ;; transducer must accept a map with :data and :options keys
                                                 (map #(let [{:keys [data options]}] (my-great-function data options))
                                                 (chan 1)))
  
;;; pass data
(let [{:keys [channel] :as job} (concurrenty shared-process (to-chan [:a :b :c]) {:option-to-function true})
      next-ch (chain channel (databox.core/map #(upper-case %)))
      results (get-results next-ch 
                           {:catch (fn [ex] ...)
                            :finally (fn [] ...)
                            :timeout-ms 5000})]
  ;; you can handle results here
  )

License

Copyright © 2019-2020 Tsutomu YANO

This program and the accompanying materials are made available under the terms of the Eclipse Public License 2.0 which is available at http://www.eclipse.org/legal/epl-2.0.

This Source Code may also be made available under the following Secondary Licenses when the conditions for such availability set forth in the Eclipse Public License, v. 2.0 are satisfied: GNU General Public License as published by the Free Software Foundation, either version 2 of the License, or (at your option) any later version, with the GNU Classpath Exception which is available at https://www.gnu.org/software/classpath/license.html.