concurrencpp | Modern concurrency for C++ | Reactive Programming library

 by   David-Haim C++ Version: v.0.1.6 License: MIT

kandi X-RAY | concurrencpp Summary

kandi X-RAY | concurrencpp Summary

concurrencpp is a C++ library typically used in Programming Style, Reactive Programming applications. concurrencpp has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

concurrencpp is a task-centric library. A task is an asynchronous operation. Tasks offer a higher level of abstraction for concurrent code than traditional thread-centric approaches. Tasks can be chained together, meaning that tasks pass their asynchronous result from one to another, where the result of one task is used as if it were a parameter or an intermediate value of another ongoing task. Tasks allow applications to utilize available hardware resources better and scale much more than using raw threads, since tasks can be suspended, waiting for another task to produce a result, without blocking underlying OS-threads. Tasks bring much more productivity to developers by allowing them to focus more on business-logic and less on low-level concepts like thread management and inter-thread synchronization. While tasks specify what actions have to be executed, executors are worker-objects that specify where and how to execute tasks. Executors spare applications the managing of thread pools and task queues themselves. Executors also decouple those concepts away from application code, by providing a unified API for creating and scheduling tasks. Tasks communicate with each other using result objects. A result object is an asynchronous pipe that pass the asynchronous result of one task to another ongoing-task. Results can be awaited and resolved in a non-blocking manner. These three concepts - the task, the executor and the associated result are the building blocks of concurrencpp. Executors run tasks that communicate with each-other by sending results through result-objects. Tasks, executors and result objects work together symbiotically to produce concurrent code which is fast and clean. concurrencpp is built around the RAII concept. In order to use tasks and executors, applications create a runtime instance in the beginning of the main function. The runtime is then used to acquire existing executors and register new user-defined executors. Executors are used to create and schedule tasks to run, and they might return a result object that can be used to marshal the asynchronous result to another task that acts as its consumer. When the runtime is destroyed, it iterates over every stored executor and calls its shutdown method. Every executor then exits gracefully. Unscheduled tasks are destroyed, and attempts to create new tasks will throw an exception. In this basic example, we created a runtime object, then we acquired the thread executor from the runtime. We used submit to pass a lambda as our given callable. This lambda returns void, hence, the executor returns a result object that marshals the asynchronous result back to the caller. main calls get which blocks the main thread until the result becomes ready. If no exception was thrown, get returns void. If an exception was thrown, get re-throws it. Asynchronously, thread_executor launches a new thread of execution and runs the given lambda. It implicitly co_return void and the task is finished. main is then unblocked. In this example, we start the program by creating a runtime object. We create a vector filled with random numbers, then we acquire the thread_pool_executor from the runtime and call count_even. count_even is a coroutine that spawns more tasks and co_awaits for them to finish inside. max_concurrency_level returns the maximum amount of workers that the executor supports, In the threadpool executor case, the number of workers is calculated from the number of cores. We then partition the array to match the number of workers and send every chunk to be processed in its own task. Asynchronously, the workers count how many even numbers each chunk contains, and co_return the result. count_even sums every result by pulling the count using co_await, the final result is then co_returned. The main thread, which was blocked by calling get is unblocked and the total count is returned. main prints the number of even numbers and the program terminates gracefully.

            kandi-support Support

              concurrencpp has a medium active ecosystem.
              It has 1513 star(s) with 146 fork(s). There are 43 watchers for this library.
              There were 1 major release(s) in the last 12 months.
              There are 3 open issues and 41 have been closed. On average issues are closed in 32 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of concurrencpp is v.0.1.6

            kandi-Quality Quality

              concurrencpp has 0 bugs and 0 code smells.

            kandi-Security Security

              concurrencpp has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              concurrencpp code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              concurrencpp is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              concurrencpp releases are available to install and integrate.
              Installation instructions are not available. Examples and code snippets are available.

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            concurrencpp Key Features

            No Key Features are available at this moment for concurrencpp.

            concurrencpp Examples and Code Snippets

            No Code Snippets are available at this moment for concurrencpp.

            Community Discussions


            How to use RXJS to share a pool of resources between multiple consumers
            Asked 2022-Mar-31 at 12:55

            How can we divide work of consumers over a limited set of resources in RXJS?

            I have a Pool class here (simplified):



            Answered 2022-Mar-31 at 12:55

            So the main thing is you need to share the actual part that does the work, not only the resources.

            Here's a solution from me:




            How to implement the logic without state variables
            Asked 2022-Mar-23 at 18:10

            There are two observables: the first named activator emits booleans. The second named signaler emits void events. There's a function f() which must be called under the next conditions:

            If the last event from activator is true, and event from signaler comes, call f(). Otherwise (the last activator's event is false, or activator has not yet emitted anything), "remember" that signaler sent the event. As soon as activator emits true, call f() and clear "remembered" flag.




            Answered 2022-Mar-23 at 18:10

            You need a state machine, but you can contain the state so you aren't leaving the monad... Something like this:



            Caching parallel request in Spring Webflux Mono
            Asked 2022-Mar-10 at 21:09

            We are using spring webflux (project reactor), as part of the requirement we need to call one API from our server.

            For the API call, we need to cache the response. So we are using Mono.cache operator.

            It caches the response Mono and the next time the same API call happens, it will get it from the cache. Following is example implementation



            Answered 2022-Mar-03 at 14:54

            You can initialize the Mono in the constructor (assuming it doesn't depend on any request time parameter). Using cache operator will prevent multiple subscriptions to the source.



            Mirror at most 1 value from source, then temporarily drop values until another observable emits
            Asked 2022-Jan-20 at 13:05

            I would like to combine two observables in such a way that

            • I mirror at most 1 value from the source observable (same moment it arrives),
            • Then ignore its subsequent values until the notifier observable emits;
            • Then, I allow to mirror at most 1 more value from the source;
            • After which I again ignore elements until the notifier observable emits
            • etc.




            Answered 2022-Jan-20 at 13:05

            I believe this is a simple use case of the throttle() operator.



            Project Reactor: buffer with parallel execution
            Asked 2021-Dec-10 at 14:44

            I need to copy date from one source (in parallel) to another with batches.

            I did this:



            Answered 2021-Dec-04 at 19:50

            You need to do your heavy work in individual Publisher-s which will be materialized in flatMap() in parallel. Like this



            UseCases or Interactors with Kt Flow and Retrofit
            Asked 2021-Dec-06 at 15:34


            I started working on a new project and I've decided to move from RxJava to Kotlin Coroutines. I'm using an MVVM clean architecture, meaning that my ViewModels communicate to UseCases classes, and these UseCases classes use one or many Repositories to fetch data from network.

            Let me give you an example. Let's say we have a screen that is supposed to show the user profile information. So we have the UserProfileViewModel:



            Answered 2021-Dec-06 at 14:53

            The most obvious problem I see here is that you're using Flow for single values instead of suspend functions.

            Coroutines makes the single-value use case much simpler by using suspend functions that return plain values or throw exceptions. You can of course also make them return Result-like classes to encapsulate errors instead of actually using exceptions, but the important part is that with suspend functions you are exposing a seemingly synchronous (thus convenient) API while still benefitting from asynchronous runtime.

            In the provided examples you're not subscribing for updates anywhere, all flows actually just give a single element and complete, so there is no real reason to use flows and it complicates the code. It also makes it harder to read for people used to coroutines because it looks like multiple values are coming, and potentially collect being infinite, but it's not the case.

            Each time you write flow { emit(x) } it should just be x.

            Following the above, you're sometimes using flatMapMerge and in the lambda you create flows with a single element. Unless you're looking for parallelization of the computation, you should simply go for .map { ... } instead. So replace this:



            DT: Dynamically change column values based on selectinput from another column in R shiny app
            Asked 2021-Nov-15 at 09:56

            I am trying to create a table (with DT, pls don't use rhandsontable) which has few existing columns, one selectinput column (where each row will have options to choose) and finally another column which will be populated based on what user select from selectinput dropdown for each row.

            in my example here, 'Feedback' column is the user dropdown selection column. I am not able to update the 'Score' column which will be based on the selection from 'Feedback' column dropdown.



            Answered 2021-Sep-30 at 14:31

            I'd suggest using dataTableProxy along with replaceData to realize the desired behaviour. This is faster than re-rendering the datatable.

            Furthermore, re-rendering the table seems to be messing around with the bindings of the selectInputs.

            Also please note: for this to work I needed to switch to server = TRUE



            Send a list of objects one at time in reactive way
            Asked 2021-Oct-18 at 21:46

            I'm receiving a request through a rest controller method with an object that I'm then passing to a method in the service layer.

            The object in this request contains a list as follows:



            Answered 2021-Oct-18 at 16:21

            The expected way to do that is to actually use the fromIterable method and provide your List:



            Changing the target of a `whenever` block from the inside
            Asked 2021-Oct-07 at 10:20

            The following code attempts to react to one Supply and then, based on the content of some message, change its mind and react to messages from a different Supply. It's an attempt to provide similar behavior to Supply.migrate but with a bit more control.



            Answered 2021-Oct-07 at 10:20

            I tend to consider whenever as the reactive equivalent of for. (It even supports the LAST loop phaser for doing something when the tapped Supply is done, as well as supporting next, last, and redo like an ordinary for loop!) Consider this:



            How to apply back pressure with Combine buffer operator to avoid flatMap to ask an infinite demand upstream?
            Asked 2021-Oct-05 at 15:18

            I'm trying to use Combine to do several millions concurrent request through the network. Here is a mock up of the naive approach I'n using:



            Answered 2021-Oct-05 at 15:18

            The issue appears to be a Combine bug, as pointed out here. Using Publishers.Sequence causes the following operator to accumulate every value sent downstream before proceeding.

            A workaround is to type-erase the sequence publisher:


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install concurrencpp

            You can download it from GitHub.


            Operating systems: Linux, macOS, Windows (Windows 10 and above)Compilers: MSVC (Visual Studio 2019 version 16.8.2 and above), Clang (Clang-11 and above)Tools: CMake (3.16 and above)
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