flowbase
Flow
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| flowbase | Flow | |
|---|---|---|
| 3 | 0 | |
| 161 | 23 | |
| 0.0% | - | |
| 0.0 | 0.0 | |
| almost 2 years ago | over 3 years ago | |
| Go | Go | |
| MIT License | MIT License |
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flowbase
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The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software (2005)
I see a lot of potential in pipeline concurrency, as seen in dataflow (DF) and flow-based programming (FBP). That is, modeling computation as pipelines where one component sends data to the next component via message passing. As long as there is enough data it will be possible for multiple components in the chain to work concurrently.
The benefits are that no other synchronization is needed than the data sent between processes, and race conditions are ruled out as long as only one process is allowed to process a data item at a time (this is the rule in FBP).
The main blockers I think is that it requires quite a rethink of the architecture of software. I see this rethink happening in larger, especially distributed systems, which are modeled a lot around these principles already, using systems such as Kafka and message queues to communicate, which more or less forces people to model computations around the data flow.
I think the same could happen inside monolithic applications too, with the right tooling. The concurrency primitives in Go are superbly suited to this in my experience, given that you work with the right paradigm, which I've been writing about before [1, 2], and started making a micro-unframework for [3] (though the latter one will be possible to make so much nicer after we get generics in Go).
But then, I also think there are some lessons to be learned about the right granularity for processes and data in the pipeline. Due to the overhead of message passing, it will not make sense performance-wise to use dataflow for the very finest-grain data.
Perhaps this in a sense parallels what we see with distributed computing, where there is a certain breaking point before which it isn't really worth it to go with distributed computing, because of all the overhead, both performance-wise and complexity-wise.
[1] https://blog.gopheracademy.com/composable-pipelines-pattern/
[2] https://blog.gopheracademy.com/advent-2015/composable-pipeli...
Flow
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Tracking mentions began in Dec 2020.
What are some alternatives?
go-workers - 👷 Library for safely running groups of workers concurrently or consecutively that require input and output through channels
routine - go routine control, abstraction of the Main and some useful Executors.如果你不会管理Goroutine的话，用它
Bifrost - Golang query-able job queue
Goflow - Simply way to control goroutines execution order based on dependencies
sworker - Easy worker setup for your code
semaphore go - Fast resizable golang semaphore primitive
neilotoole/errgroup - errgroup with goroutine worker limits
pond - 🔘 Minimalistic and High-performance goroutine worker pool written in Go
gowp - golang worker pool , Concurrency limiting goroutine pool
concurrencpp - Modern concurrency for C++. Tasks, executors, timers and C++20 coroutines to rule them all
GoSlaves
go-floc - Floc: Orchestrate goroutines with ease.