A clojure library for making concurrent process-engine backed by core.async. Application can share the engine and can put requests onto the engine. Engine will return a channel containing all processed results for each request.
All verbose processes for splitting or collecting results for each request and
putting the results onto a correct channel are handled by this library.
All things that programmer must do is 1) creating a shared pipeline,
2) put all data you want to process onto the input-channel of the shared pipeline,
3) handle the results by calling get-results!! on the output-channel returned by
the shared pipeline.
This library safely handles pipelines backed by core.async so that pipelines never stack by abuse of channels like stopping data-retrieving from channels before fully retrieving all data or forgetting to close channels.
This library is backed by databox (https://github.com/tyano/databox) that wraps data into a box and make data-handling 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 handles 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 the pipeline,
unwrapping the failure-databox will throw the exception,
but get-results!! handles all data remaining in then pipeline for protecting it
from accidental stacking.
Leiningen:
[concurrently "0.3.0"]
Clojure CLI:
concurrently/concurrently {:mvn/version "0.3.0"}
version 0.3.0 is not compatible with version 0.2.x.
Before 0.3.0, transducer for concurrent-process fn is restricted to only a map-transducer which
accepts a map with 'data' and 'option' keys like following:
(concurrent-process
8
(chan 1)
;; transducer must accept a map with :data and :options keys
(map #(fn [{:keys [data options]}] (my-great-function data options))
(chan 1))From 0.3.0, the transducer must accept a 'databox' containing a map of 'data' and 'option' keys. Fortunately 'databox' library contains some transducer functions like databox/map, databox/filter or databox/distinct. What programmers must care of is using databox's transducers instead of transducers from clojure.core.
Example:
(require '[databox.core :as databox])
(concurrent-process
8
(chan 1)
;; transducer must accept a databox containing a map of :data and :options keys
(databox/map #(fn [{:keys [data options]}] (my-great-function data options))
(chan 1))With this incompatible change, programmers now can use any kind of transducers like filter, mapcat or distinct,
and transducers composed by comp fn.
like following:
(require '[databox.core :as databox])
(concurrent-process
8
(chan 1)
;; you can use transducers like 'filter' other than map-transducer and
;; compose them with 'comp' function.
(comp (box/filter (fn [{:keys [data]}] (even? data)))
(box/map :data))
(chan 1))You can make an engine by calling concurrent-process or concurrent-process-blocking functions
with a transducer, input channel, output channels and max parallel count.
All data supplied into input-channel will be handled by a supplied transducer in parallel by
go-blocks or threads of same number with the parallel count.
But you should not put data into the input-channel directly. You must use the concurrently function
(Explanation of the concurrently fn is following).
Transducer must accept a databox(*) containing a map with :data and :options keys.
:data is a value from input channel.
:options is a option-map supplied to concurrently function.
(*) databox : https://github.com/tyano/databox
'databox' library contains some transducer functions like databox/map, databox/filter, databox/mapcat or databox/distinct, which automatically unwrap an incomming databox , re-wrap the result of function and wrap exceptions as databox/failure automatically. Just use them instead of transducers of clojure.core for easy use.
example:
(require '[databox.core :as databox])
(def shared-process (concurrent-process
8
(chan 1)
;; transducer must accept a databox containing a map of :data and :options keys
(databox/map #(fn [{:keys [data options]}] (my-great-function data options))
(chan 1)))All of pipeline functions of core.async only support ordered pipeline, on which all output data are produced in same order of input. But in real usecase you would want to get results as soon as possible just after the input data is processed.
This library supports 'unordered-pipeline'.
With onordered pipeline, output data are not in same order with input values. all results will be pushed to output channel as soon as possible after a parallel task finished.
For making unordered pipeline, just supply {:ordered false} as option map to
make-concurrent-process function.
example:
(require '[databox.core :as databox])
(def shared-process (concurrent-process
8
(chan 1)
(databox/map #(fn [{:keys [data options]}] (my-great-function data options))
(chan 1)
{:ordered? false})) ;; <<- This lineconcurrent-process and concurrent-process-blocking functions return a process-context which contains
an input channel.
But you should not use the input channel directly.
Instead, you should use the concurrently function with arguments of 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 concurrent-process, the exception will safely
be converted to failure-boxes and be passed-through the pipeline.
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 vector which contains all values of databoxes.
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 accidental stacking caused by
remaining never-read data in a channel.
get-results!! accepts arguments:
ch - a channel for reading. option-map - optional. a map containing the following keys.
keys: :catch - is a funciton called when an exception occurs. This fn is for closing related channels certainly. In many cases, if an exception occurred, no following channel-processsings are acceptable, So all read-channels must be closed at this time. It is recommended to supply this function always, but should not be used for handling application exceptions. Only for channel handling. Application exceptions should be handled in application code by a try-catch block which wraps this 'get-result' call.
:finally - is a function that will be called always, but be called after the ch is CLOSED. If the ch is not read fully, it will be read fully automatically. When the ch is read fully or be closed manually, this :finally fn will be called. So SHOULD NOT DO APPLICATION's FINALLY-PROCESS here. This function is for actions which must be occurred after the ch is closed. Application's finally-process must be handled in application code by a try-catch block which wraps this 'get-result' call.
:context-name - optional. a information which is used at logging for distincting processes.
:timeout-ms - optional. default is 120000 (ms). The time to give up reading the ch. An exception will be thrown after this timeout and the exception will be wrapped by a failure box and be returned.
You should call get-results!! at last for safe-processing of channels, but before calling it, you can connect
the job to other transducers. Although you can do it with pipe of core.async,
but there is a safe utility function for doing it.
(chain job-or-channel transducer exception-handler)
You can apply a transducer to a job-or-channel by chain function (with an exception handler if you want)
and can get a next job-or-channel. You can chain the calling of chain fns like:
(-> job
(chain xf1)
(chain xf2)
(chain xf3))This will return a last job-or-channel and you can get the results by calling get-results!! on the last job-or-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.
You can use chain on all instances that satisfies the Chainable protocol. Currently channels of core.async and
ConcurrentJob returned from the concurrently function satisfy the protocol. So you can:
;; You can apply a transducer to a ConcurrentJob.
;; The transducer will be applied to the :channel of the ConcurrentJob.
(let [job (-> (concurrently ...)
(chain xf)]
..do something..)chain always returns a same type of object that is passed to the chain function. So if you call chain on a channel, a channel will be returned, if you call chain on a ConcurrentJob, a ConcurrentJob will be returned.
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.
Because functions of databox safely handle exceptions that occurred while processing data and always return a databox,
and most of the functions in databox.core can be used as transducers. You should use them for handling databoxes.
For example:
(let [job (-> (concurrently ...)
(chain (databox.core/map #(your-great-mapper %))
(chain (databox.core/filter #(your-great-filter %)))]
..do something..)
(require '[databox.core :as databox] '[concurrently.core :refer :all])
(defn my-great-function
[data options]
;; do something and return a string
)
;;; create an engine handle data by 8 threads in parallel.
(def shared-process (concurrent-process-blocking 8
(chan 1)
;; transducer must accept a databox containing a map with :data and :options keys
(databox/map #(fn [{: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})
(chain (databox.core/map #(upper-case %))))
results (get-results!! channel
{:catch (fn [ex] (cancel job))
:finally (fn [] ...)
:timeout-ms 5000})]
;; you can handle results here
)Copyright © 2022 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.