A streaming data library
Haskell C++

README.md

Conduit is a framework for dealing with streaming data, such as reading raw bytes from a file, parsing a CSV response body from an HTTP request, or performing an action on all files in a directory tree. It standardizes various interfaces for streams of data, and allows a consistent interface for transforming, manipulating, and consuming that data.

Some of the reasons you'd like to use conduit are:

  • Constant memory usage over large data
  • Deterministic resource usage (e.g., promptly close file handles)
  • Easily combine different data sources (HTTP, files) with data consumers (XML/CSV processors)

Want more motivation on why to use conduit? Check out this presentation on conduit.

Synopsis

Basic examples of conduit usage, much more to follow!

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

main = do
    -- Pure operations: summing numbers.
    print $ runConduitPure $ yieldMany [1..10] .| sumC

    -- Exception safe file access: copy a file.
    writeFile "input.txt" "This is a test." -- create the source file
    runConduitRes $ sourceFileBS "input.txt" .| sinkFile "output.txt" -- actual copying
    readFile "output.txt" >>= putStrLn -- prove that it worked

    -- Perform transformations.
    print $ runConduitPure $ yieldMany [1..10] .| mapC (+ 1) .| sinkList

Libraries

There are a large number of packages relevant to conduit, just search for conduit on the LTS Haskell package list page. In this tutorial, we're going to rely mainly on the conduit-combinators library, which provides a large number of common functions built-in. If you're looking for something lighter-weight, you can use the conduit library, which defines the core datatypes and primitive functions, and conduit-extra, which adds support for many common low-level operations

Generally, you should use conduit-combinators unless you're an open source library author looking to reduce your transitive dependency footprint.

This tutorial relies on conduit-combinators version 1.0.8 or later. The examples are Stack scripts.

Conduit as a bad list

Let's start off by comparing conduit to normal lists. We'll be able to compare and contrast with functions you're already used to working with.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = do
    putStrLn "List version:"
    print $ take 10 [1..]
    putStrLn ""
    putStrLn "Conduit version:"
    print $ runConduitPure $ yieldMany [1..] .| takeC 10 .| sinkList

Our list function is pretty straightforward: creating an infinite list from 1 and ascending, take the first 10 elements, and then print the list. The conduit version does the exact same thing, but:

  • In order to convert the [1..] list into a conduit, we use the yieldMany function. (And note that, like lists, conduit has no problem dealing with infinite streams.)
  • We're not just doing function composition, and therefore we need to use the .| composition operator. This combines multiple components of a conduit pipeline together.
  • Instead of take, we use takeC. The Conduit module provides many functions matching common list functions, but appends a C to disambguate the names. (If you'd prefer to use a qualified import, check out Data.Conduit.Combinators).
  • To consume all of our results back into a list, we use sinkList
  • We need to explicitly run our conduit pipeline to get a result from it. Since we're running a pure pipeline (no monadic effects), we can use runConduitPure.
  • And finally, the data flows from left to right in the conduit composition, as opposed to right to left in normal function composition. There's nothing deep to this; it's just intended to make conduit feel more like common streaming abstraction from other places. For example, notice how similar the code above looks to piping in a Unix shell: ps | grep ghc | wc -l.

Alright, so what we've established is that we can use conduit as a bad, inconvenient version of lists. Don't worry, we'll soon start to see cases where conduit far outshines lists, but we're not quite there yet. Let's build up a slightly more complex pipeline:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = do
    putStrLn "List version:"
    print $ takeWhile (< 18) $ map (* 2) $ take 10 [1..]
    putStrLn ""
    putStrLn "Conduit version:"
    print $ runConduitPure
          $ yieldMany [1..]
         .| takeC 10
         .| mapC (* 2)
         .| takeWhileC (< 18)
         .| sinkList

Nothing more magical going on, we're just looking at more functions. For our last bad-list example, let's move over from a pure pipeline to one which performs some side effects. Instead of printing the whole result list, let's use mapM_C to print each value individually.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = do
    putStrLn "List version:"
    mapM_ print $ takeWhile (< 18) $ map (* 2) $ take 10 [1..]
    putStrLn ""
    putStrLn "Conduit version:"
    runConduit
          $ yieldMany [1..]
         .| takeC 10
         .| mapC (* 2)
         .| takeWhileC (< 18)
         .| mapM_C print

For the list version, all we've done is added mapM_ at the beginning. In the conduit version, we replace print $ runConduitPure with runConduit (since we're no longer generating a result to print, and our pipeline now has effects), and replaced sinkList with mapM_C print. We're no longer reconstructing a list at the end, instead just streaming the values one at a time into the print function.

Interleaved effects

Let's make things a bit more difficult for lists. We've played to their strengths until now, having a pure series of functions composed, and then only performing effects at the end (either print or mapM_ print). Suppose we have some new function:

magic :: Int -> IO Int
magic x = do
    putStrLn $ "I'm doing magic with " ++ show x
    return $ x * 2

And we want to use this in place of the map (* 2) that we were doing before. Let's see how the list and conduit versions adapt:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

magic :: Int -> IO Int
magic x = do
    putStrLn $ "I'm doing magic with " ++ show x
    return $ x * 2

main :: IO ()
main = do
    putStrLn "List version:"
    mapM magic (take 10 [1..]) >>= mapM_ print . takeWhile (< 18)
    putStrLn ""
    putStrLn "Conduit version:"
    runConduit
          $ yieldMany [1..]
         .| takeC 10
         .| mapMC magic
         .| takeWhileC (< 18)
         .| mapM_C print

Notice how different the list version looks: we needed to break out >>= to allow us to have two different side-effecting actions (mapM magic and mapM_ print). Meanwhile, in conduit, all we did was replace mapC (* 2) with mapMC magic. This is where we begin to see the strength of conduit: it allows us to build up large pipelines of components, and each of those components can be side-effecting!

However, we're not done with the difference yet. Try to guess what the output will be, and then ideally run it on your machine and see if you're correct. For those who won't be running it, here's the output:

List version:
I'm doing magic with 1
I'm doing magic with 2
I'm doing magic with 3
I'm doing magic with 4
I'm doing magic with 5
I'm doing magic with 6
I'm doing magic with 7
I'm doing magic with 8
I'm doing magic with 9
I'm doing magic with 10
2
4
6
8
10
12
14
16

Conduit version:
I'm doing magic with 1
2
I'm doing magic with 2
4
I'm doing magic with 3
6
I'm doing magic with 4
8
I'm doing magic with 5
10
I'm doing magic with 6
12
I'm doing magic with 7
14
I'm doing magic with 8
16
I'm doing magic with 9

In the list version, we apply the magic function to all 10 elements in the initial list, printing all the output at once and generating a new list. We then use takeWhile on this new list and exclude the values 18 and 20. Finally, we print out each element in our new 8-value list. This has a number of downsides:

  • We had to force all 10 items of the list into memory at once. For 10 items, not a big deal. But if we were dealing with massive amounts of data, this could cripple our program.
  • We did "more magic" than was strictly necessary: we applied magic to 10 items in the list. However, our takeWhile knew when it looked at the 9th result that it was going to ignore the rest of the list. Nonetheless, because our two components (magic and takeWhile) are separate from each other, we couldn't know that.

Let's compare that to the conduit version:

  • From the output, we can see that the calls to magic are interleaved with the calls to print. This shows that our data flows through the whole pipeline one element at a time, and never needs to build up an intermediate list. In other words, we get constant memory usage in this pipeline, a huge selling point for conduit.
  • Notice that we only perform "magic" 9 times: once we run magic on 9, get a result of 18, and find out that it fails our takeWhileC (< 18), the conduit pipeline doesn't demand any more values, and therefore magic isn't run again. We'll describe in more detail later how conduit is consumer-driven, but this is your first taste of this.

To be clear, it's entirely possible to get this behavior with a list-based program. What you'll lose is easy composition. For example, here's one way to get the same behavior as was achieved with conduit:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

magic :: Int -> IO Int
magic x = do
    putStrLn $ "I'm doing magic with " ++ show x
    return $ x * 2

main :: IO ()
main = do
    let go [] = return ()
        go (x:xs) = do
            y <- magic x
            if y < 18
                then do
                    print y
                    go xs
                else return ()

    go $ take 10 [1..]

Notice how we've had to reimplement the behavior of takeWhile, mapM, and mapM_ ourselves, and the solution is less compositional. Conduit makes it easy to get the right behavior: interleaved effects, constant memory, and (as we'll see later) deterministic resource usage.

Terminology and concepts

Let's take a step back from the code and discuss some terminology and concepts in conduit. Conduit deals with streams of data. Each component of a pipeline can consume data from upstream, and produce data to send downstream. For example:

runConduit $ yieldMany [1..10] .| mapC show .| mapM_C print

In this snippet, yieldMany [1..10], mapC show, and mapM_C print are each components. We use the .| operator - a synonym for the fuse function - to compose these components into a pipeline. Then we run that pipeline with runConduit.

From the perspective of mapC show, yieldMany [1..10] is its upstream, and mapM_C is its downstream. When we look at yieldMany [1..10] .| mapC show, what we're actually doing is combining these two components into a larger component. Let's look at the streams involved:

  • yieldMany consumes nothing from upstream, and produces a stream of Ints
  • mapC show consumes a stream of Ints, and produces a stream of Strings
  • When we combine these two components together, we get something which consumes nothing from upstream, and produces a stream of Strings.

To add some type signatures into this:

yieldMany [1..10] :: ConduitM ()  Int    IO ()
mapC show         :: ConduitM Int String IO ()

There are four type parameters to ConduitM

  • The first indicates the upstream value, or input. For yieldMany, we're using (), though really it could be any type since we never read anything from upstream. For mapC, it's Int
  • The second indicates the downstream value, or output. For yieldMany, this is Int. Notice how this matches the input of mapC, which is what lets us combine these two. The output of mapC is String.
  • The third indicates the base monad, which tells us what kinds of effects we can perform. A ConduitM is a monad transformer, so you can use lift to perform effects. (We'll learn more about conduit's monadic nature later.) We're using IO in our example.
  • The final indicates the result type of the component. This is typically only used for the most downstream component in a pipeline. We'll get into this when we discuss folds below.

Let's also look at the type of our .| operator:

(.|) :: Monad m
     => ConduitM a b m ()
     -> ConduitM b c m r
     -> ConduitM a c m r

This shows us that:

  • The output from the first component must match the input from the second
  • We ignore the result type from the first component, and keep the result of the second
  • The combined component consumes the same type as the first component and produces the same type as the second component
  • Everything has to run in the same base monad

Exercise Work through what happens when we add .| mapM_C print to the mix above.

Finally, let's look at the type of the runConduit function:

runConduit :: Monad m => ConduitM () Void m r -> m r

This gives us a better idea of what a pipeline is: just a self contained component, which consumes nothing from upstream (denoted by ()) and producing nothing to downstream (denoted by Void)*. When we have such a stand-alone component, we can run it to extract a monadic action that will return a result (the m r).

* The choice of () and Void instead of, say, both () or both Void, is complicated. For now, I recommend just accepting that this makes sense. The short explanation is that the input is in negative position whereas the output is in positive position, and therefore we can give the stronger Void guarantee in the output case.

Finally, we talked about pure pipelines before. Those are just pipelines with Identity as the base monad:

runConduitPure :: ConduitM () Void Identity r -> r

Folds

A common activity with lists is folding down to a single result. This concept translates directly into conduit, and works nicely at ensuring constant memory usage. If you're familiar with folding over lists, the concepts here should be pretty straightforward, so this will mostly just be a collection of examples.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..100 :: Int] .| sumC

Summing is straightforward, and can be done if desired with the foldlC function:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..100 :: Int] .| foldlC (+) 0

You can use foldMapC to fold monoids together:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import Data.Monoid (Sum (..))

main :: IO ()
main = print $ getSum $ runConduitPure $ yieldMany [1..100 :: Int] .| foldMapC Sum

Or you can use foldC as a shortened form of foldMapC id:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = putStrLn $ runConduitPure
     $ yieldMany [1..10 :: Int]
    .| mapC (\i -> show i ++ "\n")
    .| foldC

Though if you want to make that easier you can use unlinesC:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = putStrLn $ runConduitPure
     $ yieldMany [1..10 :: Int]
    .| mapC show
    .| unlinesC
    .| foldC

You can also do monadic folds:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import Data.Monoid (Product (..))

magic :: Int -> IO (Product Int)
magic i = do
    putStrLn $ "Doing magic on " ++ show i
    return $ Product i

main :: IO ()
main = do
    Product res <- runConduit $ yieldMany [1..10] .| foldMapMC magic
    print res

Or with foldMC:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import Data.Monoid (Product (..))

magic :: Int -> Int -> IO Int
magic total i = do
    putStrLn $ "Doing magic on " ++ show i
    return $! total * i

main :: IO ()
main = do
    res <- runConduit $ yieldMany [1..10] .| foldMC magic 1
    print res

There are plenty of other functions available in the conduit-combinator library. We won't be covering all of them in this tutorial, but hopefully this crash-course will give you an idea of what kinds of things you can do and help you understand the API docs.

Transformations

When learning lists, one of the first functions you'll see is map, which transforms each element of the list. We've already seen mapC, above, which does the same thing for conduit. This is just one of many functions available for performing transformations. Like folds, these functions are named and behave like their list counterparts in many examples, so we'll just blast through some examples.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yieldMany [1..10] .| mapC (* 2) .| mapM_C print

We can also filter out values:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yieldMany [1..10] .| filterC even .| mapM_C print

Or if desired we can add some values between each value in the list:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yieldMany [1..10] .| intersperseC 0 .| mapM_C print

It's also possible to "flatten out" a conduit, but converting a stream of chunks (like a list of vector) of data into the individual values.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit
     $ yieldMany (map (replicate 5) [1..10])
    .| concatC
    .| mapM_C print

NOTE This is our first exposure to "chunked data" in conduit. This is actually a very important and common use case, especially around ByteStrings and Texts. We'll cover it in much more detail in its own section later.

There are even some built-in functions for doing some more advanced operations, like base-64 encoding data.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
{-# LANGUAGE OverloadedStrings #-}
import Conduit
import Data.Text (Text)

message :: Text
message = "This is my message. Try to decode it with the base64 command.\n"

main :: IO ()
main = runConduit
     $ yield message
    .| encodeUtf8C
    .| encodeBase64C
    .| stdoutC

EXERCISE Pipe the output of this snippet to base64 -D on the command line.

You can also perform monadic actions while transforming. We've seen mapMC being used already, but other such functions exist:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
{-# LANGUAGE OverloadedStrings #-}
import Conduit

evenM :: Int -> IO Bool
evenM i = do
    let res = even i
    print (i, res)
    return res

main :: IO ()
main = runConduit
     $ yieldMany [1..10]
    .| filterMC evenM
    .| mapM_C print

Or you can use the iterM function, which performs a monadic action on the upstream values without modifying them:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = do
    res <- runConduit $ yieldMany [1..10] .| iterMC print .| sumC
    print res

EXERCISE Implement iterMC in terms of mapMC.

Monadic composition

We've so far only really explored half of the power of conduit: being able to combine multiple components together by connecting the output of the upstream to the input of the downstream (via the .| operator or the fuse function). However, there's another way to combine simple conduits into more complex ones, using the standard monadic interface (or do-notation). Let's start with some examples, beginning with a data producer:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

source :: Monad m => ConduitM i Int m ()
source = do
    yieldMany [1..10]
    yieldMany [11..20]

main :: IO ()
main = runConduit $ source .| mapM_C print

We've created a new conduit, source, which combines together two calls to yieldMany. Try to guess at intuitively what this will do before reading the explanation.

As you may have guessed, this program will print the numbers 1 through 20. What we've seen here is that, when you use monadic composition, the output from the first component is sent downstream, and then the output from the second component is sent downstream. Now let's look at the consuming side. Again, try to guess what this program will do before you read the explanation following it.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

sink :: Monad m => ConduitM Int o m (String, Int)
sink = do
    x <- takeC 5 .| mapC show .| foldC
    y <- sumC
    return (x, y)

main :: IO ()
main = do
    let res = runConduitPure $ yieldMany [1..10] .| sink
    print res

Let's first analyze takeC 5 .| mapC show .| foldC. This bit will take 5 elements from the stream, convert them to Strings, and then combine those Strings into one String. So if we actually have 10 elements on the stream, what happens to the other 5? Well, up until now, the answer would have been "disappears into the aether." However, we've now introduced monadic composition. In this world, those values are still sitting on the stream, ready to be consumed by whatever comes next. In our case, that's sumC.

EXERCISE Rewrite sink to not use do-notation. Hint: it'll be easier to go Applicative.

So we've seen how monadic composition works with both upstream and downstream, but in isolation. We can just as easily combine these two concepts together, and create a transformer using monadic composition.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

trans :: Monad m => ConduitM Int Int m ()
trans = do
    takeC 5 .| mapC (+ 1)
    mapC (* 2)

main :: IO ()
main = runConduit $ yieldMany [1..10] .| trans .| mapM_C print

Here, we've set up a conduit that takes the first 5 values it's given, adds 1 to each, and sends the result downstream. Then, it takes everything else, multiplies it by 2, and sends it downstream.

EXERCISE Modify trans so that it does something different for the first 3, second 3, and final 3 values from upstream, and drops all other values.

The only restriction we have in monadic composition is exactly what you'd expect from the types: the first three type parameters (input, output, and monad) must be the same for all components.

Primitives

We've worked with high-level functions in conduit so far. However, at its core conduit is built on top of a number of simple primitives. Combined with monadic composition, we can build up all of the more advanced functions from these primitives. Let's start with likely the more expected one: yield. It's just like the yieldMany function we've been using until now, except it works in a single value instead of a collection of them.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yield 1 .| mapM_C print

Of course, we're not limited to using just a single call to yield:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ (yield 1 >> yield 2) .| mapM_C print

EXERCISE Reimplement yieldMany for lists using the yield primitive and monadic composition.

Given that yield sends an output value downstream, we also need a function to get an input value from upstream. For that, we'll use await. Let's start really simple:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = do
    -- prints: Just 1
    print $ runConduitPure $ yield 1 .| await
    -- prints: Nothing
    print $ runConduitPure $ yieldMany [] .| await

    -- Note, that the above is equivalent to the following. Work out
    -- why this works:
    print $ runConduitPure $ return () .| await
    print $ runConduitPure await

await will ask for a value from upstream, and return a Just if there is a value available. If not, it will return a Nothing.

NOTE I was specific in my phrasing of "await will ask." This has to do with the evaluation of a conduit pipeline, and how it is driven by downstream. We'll cover this in more detail in the next section.

Of course, things get much more interesting when we combine both yield and await together. For example, we can implement our own mapC function:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

myMapC :: Monad m => (i -> o) -> ConduitM i o m ()
myMapC f =
    loop
  where
    loop = do
        mx <- await
        case mx of
            Nothing -> return ()
            Just x -> do
                yield (f x)
                loop

main :: IO ()
main = runConduit $ yieldMany [1..10] .| myMapC (+ 1) .| mapM_C print

EXERCISE Try implementing filterC and mapMC. For the latter, you'll need to use the lift function.

The next primitive requires a little motivation. Let's look at a simple example of using the takeWhileC function:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..10] .| do
    x <- takeWhileC (<= 5) .| sinkList
    y <- sinkList
    return (x, y)

As you may guess, this will result in the output ([1,2,3,4,5],[6,7,8,9,10]). Awesome. Let's go ahead and try to implement our own takeWhileC with just await and yield.

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

myTakeWhileC :: Monad m => (i -> Bool) -> ConduitM i i m ()
myTakeWhileC f =
    loop
  where
    loop = do
        mx <- await
        case mx of
            Nothing -> return ()
            Just x
                | f x -> do
                    yield x
                    loop
                | otherwise -> return ()

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..10] .| do
    x <- myTakeWhileC (<= 5) .| sinkList
    y <- sinkList
    return (x, y)

I'd recommend looking over myTakeWhileC and making sure you're comfortable with what it's doing. When you've done that, run the program and compare the output. To make it easier, I'll put the output of the original (with the real takeWhileC) vs this program:

takeWhileC:
([1,2,3,4,5],[6,7,8,9,10])
myTakeWhileC:
([1,2,3,4,5],[7,8,9,10])

What happened to 6? Well, in the otherwise branch of the case statement, we've determined that the value that we received from upstream does not match our predicate function f. So what do we do with it? Well, we just throw it away! In our program, the first value to fail the predicate is 6, so it's discarded, and then our second sinkList usage grabs the next value, which is 7.

What we need is a primitive that let's us put a value back on the stream. And we have one that does just that: leftover. Let's fix up our myTakeWhileC:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

myGoodTakeWhileC :: Monad m => (i -> Bool) -> ConduitM i i m ()
myGoodTakeWhileC f =
    loop
  where
    loop = do
        mx <- await
        case mx of
            Nothing -> return ()
            Just x
                | f x -> do
                    yield x
                    loop
                | otherwise -> leftover x

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..10] .| do
    x <- myGoodTakeWhileC (<= 5) .| sinkList
    y <- sinkList
    return (x, y)

As expected, this has the same output as using the real takeWhileC function.

EXERCISE Implement a peek function that gets the next value from upstream, if available, and then puts it back on the stream.

We can also call leftover as many times as we want, and even use values that didn't come from upstream, though this is a fairly unusual use case. Just to prove it's possible though:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = print $ runConduitPure $ return () .| do
    mapM_ leftover [1..10]
    sinkList

There are two semi-advanced concepts to get across in this example:

  1. If you run this, the result is a descending list from 10 to 1. This is because using leftover works in a LIFO (last in first out) fashion.
  2. If you take off the return () .| bit, this example will fail to compile. That's because, by using leftover, we've stated that our conduit actually takes some input from upstream. If you remember, when you use runConduitPure, the complete pipeline cannot be expected any input (it must have an input of type ()). Adding return () .| says "we're connecting you to an empty upstream component" so satisfy the type system.

The other primitives have to do with resource finalization. These primitives are not commonly used, instead relying on the more reliable bracketP function, which we'll cover below. If you want to learn more about these lower level functions, please see the API docs for addCleanup and yieldOr.

Evaluation strategy

Let's talk about the evaluation strategy of a conduit pipeline. The most important thing to remember is everything is driven by downstream. To see what I mean, consider this example:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yieldMany [1..10] .| iterMC print .| return ()

This program will generate no output. The reason is that the most downstream component is return (), which never awaits any values from upstream and immediately exits. Once it exits, the entire pipeline exits. As a result, the two upstream components are never run at all. If you wanted to instead force all of the values and just discard them, you could use sinkNull:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yieldMany [1..10] .| iterMC print .| sinkNull

Now try and guess what the following program outputs:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit $ yieldMany [1..10] .| iterMC print .| return () .| sinkNull

Answer: nothing! The sinkNull will await for all values from its immediate upstream. But its immediate upstream is return (), which never yields any value, causing the sinkNull to exit immediately.

Alright, let's tweak this slightly: what will this one output:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduit
     $ yieldMany [1..10]
    .| iterMC print
    .| liftIO (putStrLn "I was called")
    .| sinkNull

In this case, sinkNull calls await, which forces execution to defer to the next upstream component (the liftIO ... bit). In order to see if it yields, that component must be evaluated until it either (1) exits, (2) yields, or (3) awaits. We see that it exits after calling liftIO, causing the pipeline to terminate, but not before it prints its "I was called" message.

There's really not too much to understanding conduit evaluation. It mostly works the way you'd expect, as long as you remember that downstream drives.

Type synonyms

If you've been reading the API docs at all, you may have been confused by type signatures stating Source, Sink, Producer, Consumer, or Conduit. These are all type synonyms provided by the conduit library to make working with it easier (though, to be honest, it's not clear that they actually help at all). Here are the simple definitions of these types:

  • A Source produces values but does not consume any
  • A Sink consumes values and and provides a return value, but produces none as an output stream
  • A Conduit consumes and produces values
  • A Producer is something that produces values, and may or may not consume
  • A Consumer is something that consumes values and provides a return value, but may or may not produce

But it might be easier to understand all of this if you just look at the actual definitions:

type Source     m o   =           ConduitM () o    m ()
type Sink     i m   r =           ConduitM i  Void m r
type Conduit  i m o   =           ConduitM i  o    m ()
type Producer   m o   = forall i. ConduitM i  o    m ()
type Consumer i m   r = forall o. ConduitM i  o    m r

We can see that a Source produces a stream of o values in the m base monad. We've set its upstream value to () to indicate it consumes nothing, and set its return value to () to indicate that it produces no (meaningful) return value. By constrast, a Sink consumes i values and returns an r result in the m base monad, but produces no output (Void).

Producer and Consumer are both generalized versions of Source and Sink, which instead of specifying the input or output types to be () or Void, simply says they can be anything. These are the types that end up getting used more often in the conduit libraries, since it generalizes to working in more contexts. However, due to weaknesses in the ImpredicativeTypes language extension, these types can't always be used.

You lose nothing by just sticking with the ConduitM data type directly and bypassing the type synonyms completely. In 20/20 hindsight, that's probably the better direction for the conduit library to have been designed with in the first place. It's entirely possible that in future releases of the library, type signatures will bypass those synonyms completely. (If you want to weigh in on this, please see the relevant Github issue.)

Resource allocation

Let's copy a file with conduit:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import qualified System.IO as IO
import qualified Data.Conduit.Binary as CB

main :: IO ()
main = IO.withBinaryFile "input.txt" IO.ReadMode $ \inH ->
       IO.withBinaryFile "output.txt" IO.WriteMode $ \outH ->
       runConduit $ CB.sourceHandle inH .| CB.sinkHandle outH

This works nicely, and follows the typical bracket pattern we typically expect in Haskell. However, it's got some downsides:

  • You have to allocate all of your resources outside of the conduit pipeline. (This is because conduit is coroutine based, and coroutines/continuations cannot guarantee a cleanup action is called.)
  • You will sometimes end up needing to allocate too many resources, or holding onto them for too long, if you allocate them in advance instead of on demand.
  • Some control flows are impossible. For example, if you wanted to write a function to traverse a directory tree, you can't open up all of the directory handles before you enter your conduit pipeline.

To solve these problems (and some others), conduit provides built in support for a related package (resourcet), which allows you to allocate resources and be guaranteed that they will be cleaned up. The basic idea is that you'll have a block like:

runResourceT $ do
    foo
    bar
    baz

Any resources that foo, bar, or baz allocate have a cleanup function registered in a mutable map. When the runResourceT call exits, all of those cleanup functions are called, regardless of whether the exiting occured normally or via an exception.

In order to do this in a conduit, we have the built-in function bracketP, which takes an allocation function and a cleanup function, and provides you a resource. Putting this all together, we can rewrite our example as:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import qualified System.IO as IO
import Data.ByteString (ByteString)

sourceFile' :: MonadResource m => FilePath -> ConduitM i ByteString m ()
sourceFile' fp =
    bracketP (IO.openBinaryFile fp IO.ReadMode) IO.hClose sourceHandle

sinkFile' :: MonadResource m => FilePath -> ConduitM ByteString o m ()
sinkFile' fp =
    bracketP (IO.openBinaryFile fp IO.WriteMode) IO.hClose sinkHandle

main :: IO ()
main = runResourceT
     $ runConduit
     $ sourceFile' "input.txt"
    .| sinkFile' "output.txt"

But that's certainly too tedious. Fortunately, conduit provides the sourceFile and sinkFile functions built in (as well as sourceFileBS and sinkFileBS, which are specialized to ByteString to avoid type inference issues), and defines a helper runConduitRes which is just runResourceT . runConduit. Putting all of that together, copying a file becomes absolutely trivial:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduitRes $ sourceFileBS "input.txt" .| sinkFileBS "output.txt"

Let's get a bit more inventive though. Let's traverse an entire directory tree and write the contents of all files with a .hs file extension into the file "all-haskell-files".

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import System.FilePath (takeExtension)

main :: IO ()
main = runConduitRes
     $ sourceDirectoryDeep True "."
    .| filterC (\fp -> takeExtension fp == ".hs")
    .| awaitForever sourceFileBS
    .| sinkFileBS "all-haskell-files"

What's great about this example is:

  • It guarantees that only two file handles are open at a time: the all-haskell-files destination file and whichever file is being read from.
  • It will only open as many directory handles as needed to traverse the depth of the file structure.
  • If any exceptions occur, all resources will be cleaned up.

Chunked data

I'd like to read a file, convert all of its characters to upper case, and then write it to standard output. That looks pretty straightforward:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import qualified Data.Text as T
import Data.Char (toUpper)

main :: IO ()
main = runConduitRes
     $ sourceFile "input.txt"
    .| decodeUtf8C
    .| mapC (T.map toUpper)
    .| stdoutC

This works just fine, but is inconvenient: isn't that mapC (T.map ...) repetition just completely jarring? The issue is that instead of having a stream of Char values, we have a stream of Text values, and our mapC function will work on the Texts. But our toUpper function works on the Chars inside of the Text. We want to use Text (or ByteString, or sometimes Vector) because it's a more efficient representation of data, but don't want to have to deal with this overhead.

This is where the chunked functions in conduit come into play. In addition to functions that work directly on the values in a stream, we have functions that work on the elements inside those values. These functions get a CE suffix instead of C, and are very straightforward to use. To see it in action:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import qualified Data.Text as T
import Data.Char (toUpper)

main :: IO ()
main = runConduitRes
     $ sourceFile "input.txt"
    .| decodeUtf8C
    .| omapCE toUpper
    .| stdoutC

NOTE We also had to prepend o to get the monomorphic mapping function, since Text is a monomorphic container.

We can use this for other things too. For example, let's get just the first line of content:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import qualified Data.Text as T
import Data.Char (toUpper)

main :: IO ()
main = runConduitRes
     $ sourceFile "input.txt"
    .| decodeUtf8C
    .| takeWhileCE (/= '\n')
    .| stdoutC

Or just the first 5 bytes:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit

main :: IO ()
main = runConduitRes
     $ sourceFile "input.txt"
    .| takeCE 5
    .| decodeUtf8C
    .| stdoutC

There are many other functions available for working on chunked data. In fact, most non-chunked functions have a chunked equivalent. This means that most of the intuition you've built up for working with streams of values will automatically translate to dealing with chunked streams, a big win for binary and textual processing.

EXERCISE Try to implement the takeCE function on ByteStrings. Hint: you'll need to use leftover to make it work correctly!

ZipSink

So far we've had very linear pipelines: a component feeds into exactly one downstream component, and so on. However, sometimes we may wish to allow for multiple consumers of a single stream. As a motivating example, let's consider taking the average of a stream of Doubles. In the list world, this may look like:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc
doubles :: [Double]
doubles = [1, 2, 3, 4, 5, 6]

average :: [Double] -> Double
average xs = sum xs / fromIntegral (length xs)

main :: IO ()
main = print $ average doubles

However, performance afficionados will quickly point out that this has a space leak: the list will be traversed once for the sum, kept in memory, and then traversed a second time for the length. We could work around that by using lower-level functions, but we lose composability. (Though see the foldl package for composable folding.)

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
{-# LANGUAGE ExtendedDefaultRules #-}
import Conduit
import Data.Void (Void)

doubles :: [Double]
doubles = [1, 2, 3, 4, 5, 6]

average :: Monad m => ConduitM Double Void m Double
average =
    getZipSink (go <$> ZipSink sumC <*> ZipSink lengthC)
  where
    go total len = total / fromIntegral len

main :: IO ()
main = print $ runConduitPure $ yieldMany doubles .| average

ZipSink is a newtype wrapper which provides an different Applicative instance than the standard one for ConduitM. Instead of sequencing the consumption of a stream, it allows two components to consume in parallel. Now, our sumC and lengthC are getting values at the same time, and then those values can be immediately thrown away. This leads to easy composition and constant memory usage.

NOTE Both the list and conduit versions of this are subject to a divide-by-zero error. You'd probably in practice want to make average return a Maybe Double.

Another real world example of ZipSink is when you want to both consume a file and calculate its cryptographic hash. Working with the cryponite package:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators --package cryptonite-conduit
{-# LANGUAGE ExtendedDefaultRules #-}
{-# LANGUAGE PackageImports #-}
import Conduit
import "cryptonite-conduit" Crypto.Hash.Conduit (sinkHash)
import "cryptonite" Crypto.Hash (Digest, SHA256)
import Data.Void (Void)

main :: IO ()
main = do
    digest <- runConduitRes
            $ sourceFile "input.txt"
           .| getZipSink (ZipSink (sinkFile "output.txt") *> ZipSink sinkHash)
    print (digest :: Digest SHA256)

Or we can get slightly more inventive, and read from an HTTP connection instead of a local file:

#!/usr/bin/env stack
{- stack --resolver lts-6.19 runghc
    --package conduit-combinators
    --package cryptonite-conduit
    --package http-conduit
-}
{-# LANGUAGE PackageImports #-}
{-# LANGUAGE OverloadedStrings #-}
import Conduit
import "cryptonite-conduit" Crypto.Hash.Conduit (sinkHash)
import "cryptonite" Crypto.Hash (Digest, SHA256)
import Data.Void (Void)
import Network.HTTP.Simple (httpSink)

main :: IO ()
main = do
    digest <- runResourceT $ httpSink "http://httpbin.org"
              (\_res -> getZipSink (ZipSink (sinkFile "output.txt") *> ZipSink sinkHash))
    print (digest :: Digest SHA256)

This provides a convenient and efficient method to consume data over a network connection.

ZipSource

Let's keep a good thing going. In addition to consuming in parallel, we may wish to produce in parallel. For this, we'll use the ZipSource newtype wrapper, which is very similar in concept to the ZipList wrapper for those familiar. As a simple example, let's create a stream of the Fibonacci numbers, together with each one's index in the sequence:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

fibs :: [Int]
fibs = 0 : 1 : zipWith (+) fibs (drop 1 fibs)

indexedFibs :: Source IO (Int, Int)
indexedFibs = getZipSource
    $ (,)
  <$> ZipSource (yieldMany [1..])
  <*> ZipSource (yieldMany fibs)

main :: IO ()
main = runConduit $ indexedFibs .| takeC 10 .| mapM_C print

ZipConduit

To round out the collection of newtype wrappers, we've got ZipConduit, which is certainly the most complicated of the bunch. It allows you to combine a bunch of transformers in such a way that:

  • Drain all of the ZipConduits of all yielded values, until they are all awaiting
  • Grab the next value from upstream, and feed it to all of the ZipConduits
  • Repeat

Here's a silly example of using it, which demonstrates its most common use case: focusing in on a subset of a stream. We split a stream of numbers into evens (Left) and odds (Right). Then we have two transformers that each look at only half the stream, and combine those two transformers together into a single transformer that looks at the whole stream:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

tagger :: Monad m => ConduitM Int (Either Int Int) m ()
tagger = mapC $ \i -> if even i then Left i else Right i

evens, odds :: Monad m => ConduitM Int String m ()
evens  = mapC $ \i -> "Even number: " ++ show i
odds   = mapC $ \i -> "Odd  number: " ++ show i

left :: Either l r -> Maybe l
left = either Just (const Nothing)

right :: Either l r -> Maybe r
right = either (const Nothing) Just

inside :: Monad m => ConduitM (Either Int Int) String m ()
inside = getZipConduit
    $ ZipConduit (concatMapC left  .| evens)
   *> ZipConduit (concatMapC right .| odds)

main :: IO ()
main = runConduit $ enumFromToC 1 10 .| tagger .| inside .| mapM_C putStrLn

In my experience, the most useful of the three newtype wrappers is ZipSink, but your mileage may vary.

Forced consumption

Remember that, in our evaluation method for conduit, we stop processing as soon as downstream stops. There are some cases where this is problematic, specifically when we want to ensure a specific amount of data is consumed. Consider:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

withFiveSum :: Monad m
            => ConduitM Int o m r
            -> ConduitM Int o m (r, Int)
withFiveSum inner = do
    r <- takeC 5 .| inner
    s <- sumC
    return (r, s)

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..10] .| withFiveSum sinkList

Our withFiveSum function will let the provided inner conduit work on the first five values in the stream, then take the sum of the rest. All seems well, but now consider if we replace sinkList with return (). Our takeC 5 .| return () will no longer consume any of the first five values, and sumC will end up consuming them. Depending on your use case, this could be problematic, and very surprising.

We can work around this by forcing all other values to be dropped, e.g.:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

withFiveSum :: Monad m
            => ConduitM Int o m r
            -> ConduitM Int o m (r, Int)
withFiveSum inner = do
    r <- takeC 5 .| do
        r <- inner
        sinkNull
        return r
    s <- sumC
    return (r, s)

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..10] .| withFiveSum (return ())

However, there's also a convenience function which captures this pattern: takeExactlyC:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

withFiveSum :: Monad m
            => ConduitM Int o m r
            -> ConduitM Int o m (r, Int)
withFiveSum inner = do
    r <- takeExactlyC 5 inner
    s <- sumC
    return (r, s)

main :: IO ()
main = print $ runConduitPure $ yieldMany [1..10] .| withFiveSum (return ())

Notice that there's no .| operator between takeExactlyC 5 and inner. That's not a typo! takeExactlyC isn't actually a conduit, it's a combinator which, when given a conduit, will generate a conduit.

EXERCISE Try to write takeExactlyC as a conduit itself, and/or convince yourself why that's impossible.

This same kind of pattern is used to deal with the stream-of-streams problem. As a motivating example, consider processing a file, and wanting to work on it one line at a time. One possibility is to simply break the stream into one Text per line, but this can be dangerous if your input is untrusted and may contain an unbounded line length. Instead, we can just do:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

main :: IO ()
main = runConduitRes $ sourceFile "input.txt" .| decodeUtf8C .| do
    len <- lineC lengthCE
    liftIO $ print len

This program will print out the length of the first line of the input file. However, by combining with the peekForeverE combinator - which will continuously run a conduit as long as there is some input available in a chunked stream - we can print out the length of each line:

#!/usr/bin/env stack
-- stack --resolver lts-6.19 runghc --package conduit-combinators
import Conduit

main :: IO ()
main = runConduitRes $ sourceFile "input.txt" .| decodeUtf8C .| peekForeverE (do
    len <- lineC lengthCE
    liftIO $ print len)

FAQs

  • How do you deal with an upstream conduit that has a return value? The special fusion functions for it, see the haddocks.
  • How do you capture unconsumed leftover values? Again, the special fusion functions for it, see the haddocks.
  • How do I generalize from a Source to Producer or Sink to Consumer? See the haddocks.
  • How do I run a Source, take some of its output, and then run the rest of it later? Connect and resume

More exercises

Write a conduit that consumes a stream of Ints. It takes the first Int from the stream, and then multiplies all subsequent Ints by that number and sends them back downstream. You should use the mapC function for this.

Further exercises wanted, please feel free to send PRs!

Legacy syntax

As of version 1.2.8 of conduit, released September 2016, the above used operators and function names are recommended. However, prior to that, an alternate set of functions and operators was used instead. You may still find code and documentation out there which follows the legacy syntax, so it's worth being aware of it. Basically:

  • Instead of .|, we had three operators: $=, =$, and =$=. These were all synonyms, and existed for historical reasons.
  • The $$ operator is a combination of runConduit and .|.

To put it simply in code:

x $=  y = x .| y
x =$  y = x .| y
x =$= y = x .| y
x $$  y = runConduit (x .| y)

If the old operators seem needlessly confusing/redundant... well, that's why we have new operators :).

Further reading

Some blogs posts making heavy usage of conduit:

If you have other articles to include, please send a PR!