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.
concurrent-process
functionYou 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.
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.
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.
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.
(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
)
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.
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