streamly/docs/Overview.md

Streaming

These are some notes from an old version of README that may be useful. For a quick introduction please read the README.md at the repo root first.

Streamly

Streamly is a general computing framework based on data flow programming also known as streaming. Moreover streamly supports concurrent dataflow programming.

Streaming in general enables writing modular, composable and scalable applications with ease, and concurrency allows you to make them scale and perform well. Streamly enables writing scalable concurrent applications without being aware of threads or synchronization. No explicit thread control is needed. Where applicable, concurrency rate is automatically controlled based on the demand by the consumer. However, combinators can be used to fine tune the concurrency control.

Streaming and concurrency together enable expressing reactive applications conveniently. See the @CirclingSquare@ example in <https://github.com/composewell/streamly-examples Streamly Examples> for a simple SDL based FRP example. To summarize, streamly provides a unified computing framework for streaming, non-determinism and functional reactive programming in an elegant and simple API that is a natural extension of pure lists to monadic streams.

What are streams?

In simple terms, streams are the functional equivalent of loops in imperative programming.

A stream is a representation of potentially infinite data sequence. You can compose a pipeline of functions or stream processors to process an input stream of data to produce an output stream. We call it a form of dataflow programming as data flows through the processing logic. In imperative programming there is no clear separation of data and logic. The logic can arbitrarily examine and mutate data which creates a problem due to complex interleaving of state and logic in the program.

In streamly there are two fundamental data structures, streams and arrays. Streams are for dataflow style processing while arrays are for storing data. Both taken together are powerful tools for general purpose programming in a functional or dataflow style.

Loops vs Streams

In imperative programming when we have to process a sequence of items or an array of data we run a loop over it, each iteration of the loop examines the data to do something with it and produce an output.

Loops are a low level, monolithic and general concept. Whereas streams are high level, structured and modular way of expressing what you usualy do with loops. Streams allow you to write different parts of the loop as separate modular combinators and then compose them to create bigger loops.

Streaming Concurrently

Haskell lists express pure computations using composable stream operations like :, unfold, map, filter, zip and fold. Streamly is exactly like lists except that it can express sequences of pure as well as monadic computations aka streams. More importantly, it can express monadic sequences with concurrent execution semantics without introducing any additional APIs.

Streamly expresses concurrency using standard, well known abstractions. Concurrency semantics are defined for list operations, semigroup, applicative and monadic compositions. Programmer does not need to know any low level notions of concurrency like threads, locking or synchronization. Concurrent and non-concurrent programs are fundamentally the same. A chosen segment of the program can be made concurrent by annotating it with an appropriate combinator. We can choose a combinator for lookahead style or asynchronous concurrency. Concurrency is automatically scaled up or down based on the demand from the consumer application, we can finally say goodbye to managing thread pools and associated sizing issues. The result is truly fearless and declarative monadic concurrency.

Where to use streamly?

Streamly is a general purpose programming framework. It can be used equally efficiently from a simple Hello World! program to a massively concurrent application. The answer to the question, "where to use streamly?" - would be similar to the answer to - "Where to use Haskell lists or the IO monad?".

Streamly simplifies streaming and makes it as intuitive as plain lists. Unlike other streaming libraries, no fancy types are required. Streamly is simply a generalization of Haskell lists to monadic streaming optionally with concurrent composition. The basic stream type in streamly SerialT m a can be considered as a list type [a] parameterized by the monad m. For example, SerialT IO a is a moral equivalent of [a] in the IO monad. SerialT Identity a, is equivalent to pure lists. Streams are constructed very much like lists, except that they use nil and cons instead of [] and :. Unlike lists, streams can be constructed from monadic effects, not just pure elements. Streams are processed just like lists, with list like combinators, except that they are monadic and work in a streaming fashion. In other words streamly just completes what lists lack, you do not need to learn anything new. Please see streamly vs lists for a detailed comparison.

Not surprisingly, the monad instance of streamly is a list transformer, with concurrency capability.

Why data flow programming?

If you need some convincing for using streaming or data flow programming paradigm itself then try to answer this question - why do we use lists in Haskell? It boils down to why we use functional programming in the first place. Haskell is successful in enforcing the functional data flow paradigm for pure computations using lists, but not for monadic computations. In the absence of a standard and easy to use data flow programming paradigm for monadic computations, and the IO monad providing an escape hatch to an imperative model, we just love to fall into the imperative trap, and start asking the same fundamental question again - why do we have to use the streaming data model?

Streaming Pipelines

The following snippet provides a simple stream composition example that reads numbers from stdin, prints the squares of even numbers and exits if an even number more than 9 is entered.

import qualified Streamly.Prelude as S
import Data.Function ((&))

main = S.drain $
       S.repeatM getLine
     & fmap read
     & S.filter even
     & S.takeWhile (<= 9)
     & fmap (\x -> x * x)
     & S.mapM print

Unlike pipes or conduit and like vector and streaming, streamly composes stream data instead of stream processors (functions). A stream is just like a list and is explicitly passed around to functions that process the stream. Therefore, no special operator is needed to join stages in a streaming pipeline, just the standard function application ($) or reverse function application (&) operator is enough.

Concurrent Stream Generation

consM or its operator form |: can be used to construct a stream from monadic actions. A stream constructed with consM can run the monadic actions in the stream concurrently when used with appropriate stream type combinator (e.g. fromAsync, fromAhead or fromParallel).

The following code finishes in 3 seconds (6 seconds when serial), note the order of elements in the resulting output, the outputs are consumed as soon as each action is finished (asyncly):

> let p n = threadDelay (n * 1000000) >> return n
> S.toList $ S.fromAsync $ p 3 |: p 2 |: p 1 |: S.nil
[1,2,3]

Use fromAhead if you want speculative concurrency i.e. execute the actions in the stream concurrently but consume the results in the specified order:

> S.toList $ S.fromAhead $ p 3 |: p 2 |: p 1 |: S.nil
[3,2,1]

Monadic stream generation functions e.g. unfoldrM, replicateM, repeatM, iterateM and fromFoldableM etc. can work concurrently.

The following finishes in 10 seconds (100 seconds when serial):

S.drain $ S.fromAsync $ S.replicateM 10 $ p 10

Concurrent Streaming Pipelines

Use |& or |$ to apply stream processing functions concurrently. The following example prints a "hello" every second; if you use & instead of |& you will see that the delay doubles to 2 seconds instead because of serial application.

main = S.drain $
      S.repeatM (threadDelay 1000000 >> return "hello")
   |& S.mapM (\x -> threadDelay 1000000 >> putStrLn x)

Mapping Concurrently

We can use mapM or sequence functions concurrently on a stream.

> let p n = threadDelay (n * 1000000) >> return n
> S.drain $ S.fromAhead $ S.mapM (\x -> p 1 >> print x) (S.fromSerial $ S.repeatM (p 1))

Serial and Concurrent Merging

Semigroup and Monoid instances can be used to fold streams serially or concurrently. In the following example we compose ten actions in the stream, each with a delay of 1 to 10 seconds, respectively. Since all the actions are concurrent we see one output printed every second:

import qualified Streamly.Prelude as S
import Control.Concurrent (threadDelay)

main = S.toList $ S.fromParallel $ foldMap delay [1..10]
 where delay n = S.fromEffect $ threadDelay (n * 1000000) >> print n

Streams can be combined together in many ways. We provide some examples below, see the tutorial for more ways. We use the following delay function in the examples to demonstrate the concurrency aspects:

import qualified Streamly.Prelude as S
import Control.Concurrent

delay n = S.fromEffect $ do
    threadDelay (n * 1000000)
    tid <- myThreadId
    putStrLn (show tid ++ ": Delay " ++ show n)

Serial

main = S.drain $ delay 3 <> delay 2 <> delay 1

ThreadId 36: Delay 3
ThreadId 36: Delay 2
ThreadId 36: Delay 1

Parallel

main = S.drain . S.fromParallel $ delay 3 <> delay 2 <> delay 1

ThreadId 42: Delay 1
ThreadId 41: Delay 2
ThreadId 40: Delay 3

Nested Loops (aka List Transformer)

The monad instance composes like a list monad.

import qualified Streamly.Prelude as S

loops = do
    x <- S.fromFoldable [1,2]
    y <- S.fromFoldable [3,4]
    S.fromEffect $ putStrLn $ show (x, y)

main = S.drain loops

(1,3)
(1,4)
(2,3)
(2,4)

Concurrent Nested Loops

To run the above code with speculative concurrency i.e. each iteration in the loop can run concurrently but the results are presented to the consumer of the output in the same order as serial execution:

main = S.drain $ S.fromAhead $ loops

Different stream types execute the loop iterations in different ways. For example, fromWSerial interleaves the loop iterations. There are several concurrent stream styles to execute the loop iterations concurrently in different ways, see the Streamly.Tutorial module for a detailed treatment.

Magical Concurrency

Streams can perform semigroup (<>) and monadic bind (>>=) operations concurrently using combinators like fromAsync, parallelly. For example, to concurrently generate squares of a stream of numbers and then concurrently sum the square roots of all combinations of two streams:

import qualified Streamly.Prelude as S

main = do
    s <- S.sum $ S.fromAsync $ do
        -- Each square is performed concurrently, (<>) is concurrent
        x2 <- foldMap (\x -> return $ x * x) [1..100]
        y2 <- foldMap (\y -> return $ y * y) [1..100]
        -- Each addition is performed concurrently, monadic bind is concurrent
        return $ sqrt (x2 + y2)
    print s

Rate Limiting

For bounded concurrent streams, stream yield rate can be specified. For example, to print hello once every second you can simply write this:

import Streamly.Prelude as S

main = S.drain $ S.fromAsync $ S.avgRate 1 $ S.repeatM $ putStrLn "hello"

For some practical uses of rate control, see AcidRain.hs and CirclingSquare.hs . Concurrency of the stream is automatically controlled to match the specified rate. Rate control works precisely even at throughputs as high as millions of yields per second. For more sophisticated rate control see the haddock documentation.

Arrays

The Streamly.Data.Array.Foreign module provides immutable arrays. Arrays are the computing duals of streams. Streams are good at sequential access and immutable transformations of in-transit data whereas arrays are good at random access and in-place transformations of buffered data. Unlike streams which are potentially infinite, arrays are necessarily finite. Arrays can be used as an efficient interface between streams and external storage systems like memory, files and network. Streams and arrays complete each other to provide a general purpose computing system. The design of streamly as a general purpose computing framework is centered around these two fundamental aspects of computing and storage.

Streamly.Data.Array.Foreign uses pinned memory outside GC and therefore avoid any GC overhead for the storage in arrays. Streamly allows efficient transformations over arrays using streams. It uses arrays to transfer data to and from the operating system and to store data in memory.

Folds

Folds are consumers of streams. Streamly.Data.Fold module provides a Fold type that represents a foldl'. Such folds can be efficiently composed allowing the compiler to perform stream fusion and therefore implement high performance combinators for consuming streams. A stream can be distributed to multiple folds, or it can be partitioned across multiple folds, or demultiplexed over multiple folds, or unzipped to two folds. We can also use folds to fold segments of stream generating a stream of the folded results.

If you are familiar with the foldl library, these are the same composable left folds but simpler and better integrated with streamly, and with many more powerful ways of composing and applying them.

Unfolds

Unfolds are duals of folds. Folds help us compose consumers of streams efficiently and unfolds help us compose producers of streams efficiently. Streamly.Data.Unfold provides an Unfold type that represents an unfoldr or a stream generator. Such generators can be combined together efficiently allowing the compiler to perform stream fusion and implement high performance stream merging combinators.

File IO

The following code snippets implement some common Unix command line utilities using streamly. You can compile these with ghc -O2 -fspec-constr-recursive=16 -fmax-worker-args=16 and compare the performance with regular GNU coreutils available on your system. Though many of these are not most optimal solutions to keep them short and elegant. Source file HandleIO.hs in the examples directory includes these examples.

module Main where

import qualified Streamly.Prelude as S
import qualified Streamly.Data.Fold as FL
import qualified Streamly.Data.Array.Foreign as A
import qualified Streamly.FileSystem.Handle as FH
import qualified System.IO as FH

import Data.Char (ord)
import System.Environment (getArgs)
import System.IO (openFile, IOMode(..), stdout)

withArg f = do
    (name : _) <- getArgs
    src <- openFile name ReadMode
    f src

withArg2 f = do
    (sname : dname : _) <- getArgs
    src <- openFile sname ReadMode
    dst <- openFile dname WriteMode
    f src dst

cat

cat = S.fold (FH.writeChunks stdout) . S.unfold FH.readChunks
main = withArg cat

cp

cp src dst = S.fold (FH.writeChunks dst) $ S.unfold FH.readChunks src
main = withArg2 cp

wc -l

wcl = S.length . S.splitOn (== 10) FL.drain . S.unfold FH.read
main = withArg wcl >>= print

Average Line Length

avgll =
      S.fold avg
    . S.splitOn (== 10) FL.length
    . S.unfold FH.read

    where avg      = (/) <$> toDouble FL.sum <*> toDouble FL.length
          toDouble = fmap (fromIntegral :: Int -> Double)

main = withArg avgll >>= print

Line Length Histogram

classify is not released yet, and is available in Streamly.Internal.Data.Fold

llhisto =
      S.fold (FL.classify FL.length)
    . S.map bucket
    . S.splitOn (== 10) FL.length
    . S.unfold FH.read

    where
    bucket n = let i = n `mod` 10 in if i > 9 then (9,n) else (i,n)

main = withArg llhisto >>= print

Exceptions

Exceptions can be thrown at any point using the MonadThrow instance. Standard exception handling combinators like bracket, finally, handle, onException are provided in Streamly.Prelude module.

In presence of concurrency, synchronous exceptions work just the way they are supposed to work in non-concurrent code. When concurrent streams are combined together, exceptions from the constituent streams are propagated to the consumer stream. When an exception occurs in any of the constituent streams other concurrent streams are promptly terminated.

There is no notion of explicit threads in streamly, therefore, no asynchronous exceptions to deal with. You can just ignore the zillions of blogs, talks, caveats about async exceptions. Async exceptions just don't exist. Please don't use things like myThreadId and throwTo just for fun!