IdrisPipes: a library for composable and effectful stream processing in Idris

We will continue our series on post on Idris, by implementing a pipe library for Idris, inspired by the great Haskell pipes and Haskell conduit libraries. The goal is to provide Idris with a library for composable and effectful production, transformation and consumption of streams of data.

In short, IdrisPipes is an Idris package that aims at providing the means to write:

  • Effectful programs, with side effects such as IO
  • Over a stream of data, potentially infinite
  • Efficiently, by streaming data and controlling memory consumption
  • In a composable way, allowing problem decomposition and reuse

In this first post, we will illustrate with examples the motivations behind this library, and give a quick overview of the features it offers. Future posts will be dedicated to explain how it works in more details, how to build your own pipes easily, as well as describing some of the difficulties I had implementing it in Idris.


Motivations & Starting examples

To explain the goal of this library, we will first illustrate how to use it on a small example of effectful code, and see how we can use IdrisPipes to improve on it.


A world without pipes – first example

We will start with a small effectful function, which we will later enrich with additional requirements. This small function is named “echo”:

  • It acquires some strings from the standard inputs
  • Echo them back in the standard output
  • And stops upon receiving the string “quit” as input

Here is one possible implementation of this need, without using pipe-like libraries:

Let us run it into the REPL. Upon each line entered after the “in” prompt, it echos back the same string in the “out” prompt, and the “quit” string correctly terminates the execution of the function:

Simple enough. Let us now enrich our “echo” function with an additional requirement.


A world without pipes – Adding concerns

Our “echo” function should not repeat the same output twice in a row anymore. In other words, if we write two times in a row the same string, we want it to ignore the second occurrence:

We can easily adapt our previous “echo” function to support this new requirement, with just a few additional lines of code:

While the number of lines did not increase that much, the overall complexity of the function did.

The loop has to maintain a state that allows us to identify whether the new value is different from the previous one. This state is visible from the whole loop, and comes with additional conditional branching. In short, the additional concern is coupled with the rest of the “echo” function.


Refactoring with Idris Pipes

We will now see how to get rid of this coupling by refactoring the “echo” function using IdrisPipes. We will start with our initial “echo” function and then add the additional requirement of deduplicating entries afterwards.


Thinking in terms of streams

IdrisPipes is based on the idea of building small pipes dedicated to one specific task to perform on a stream of data (like production, transformation or consumption). Each pipe is only concerned with dealing with awaiting for new inputs, transforming this piece of data, and yielding resulting outputs.

These pipes can be connected together to form more complex pipes: the outputs of one pipes become the inputs of the next pipe. Ultimately, these pipes are assembled as a complete pipeline, an Effect in the vocabulary of IdrisPipes, a recipe for stream processing.

The key to our refactoring of “echo” to use IdrisPipes is thus to observe in what way the “echo” function corresponds to the processing of a stream of data.


Refactoring “echo”

We can easily view our “echo” function as processing a stream of lines read from the standard input and flowing down to standard output. We can therefore use IdrisPipes to model the recipe matching it:

This recipe is made of several pipes, connected together with the .| pipe operator:

  • stdinLn feeds the pipe with strings read from the standard input
  • takingWhile forwards elements passing through it, interrupting the pipeline if it sees the string “quit”
  • mapping transforms elements passing through it (here to prepend a prompt to each string)
  • stdoutLn writes in standard output any string which goes through it

Assembled together, these Pipes build an Effect that corresponds to the recipe of echoing back strings as long as the string read is not “quit”.


Running the recipe

The Effect we just wrote is but the description for a recipe for processing a stream. To actually run the recipe, and start flowing some data (from left to right) inside it, we use the runEffect function:

We are done for the first phase. This code is equivalent to our initial “echo” function:

The big difference is that each concerns are kept separated: for instance, the acquisition of inputs (stdinLn) and the stop condition (takingWhile) are fully decoupled. As we will see, this will help us a lot when comes to some additional requirements in our program.


Using pipes to decouple concerns

In our initial implement of “echo”, adding the requirement of deduplicating the input strings led to coupled concerns. Using IdrisPipes, we can instead isolate this additional concern into a specific pipe, named deduplicating.

This pipe will await values, keep track of the last value read, and only yields those that are different from the previous one. The full code of deduplicating is shown below as reference (*). We will later explain how to build our own pipelines in more details:

This deduplicating logic is isolated, decoupled from the rest of the pipeline, and can now be used elsewhere in the code. In particular, we can add this new pipe in our pipeline to support the new requirement, and no other parts of the pipeline need to know about it:

And we are done. We managed to add the requirement of deduplicated entries as a separate concern, decoupled from the rest of the pipeline.

(*) This Pipe is also available in the library in Pipes.Prelude, along with plenty other standard pipes. You should consider taking a look at the rich set of already available pipes before implementing your own.


A Quick tour of IdrisPipes support for stream processing

Let us now do a quick tour of the design and model followed by IdrisPipes and the features it provides to build effectful streaming programs.


A few set of concepts – One composition operator

A pipeline of data transformation typically consists of three kind of elements: a source that streams pieces of data, intermediary pipes to transform that data, and a sink that consumes the data to produce a single result. Each of these elements can produce some side effects as well.

IdrisPipes defines a type for each of these kinds of pipes. These types are listed below, with m standing for the Monad matching the desired side effects:

  • A Pipe i m o awaits values of type i and yields values of type o
  • A Source m o yields values of type o, and cannot await any values
  • A Sink i m r awaits values of type i to build a result of type m r
  • A Effect m r cannot yield or await, and produces a m r when executed

In short, an Effect represents the complete pipeline, starting with a Source, ending with a Sink and possibly made of intermediary Pipes, and which can be run using runEffect:

All these types compose together nicely using a single .| operator, to produce new pipes that automatically match their associated semantic model:

  • A Source m a followed by a Pipe a m b is a Source m b
  • A Pipe a m b followed by a Sink b m r is a Sink a m r
  • A Pipe a m b followed by a Pipe b m c is a Pipe a m c
  • A Source m a followed by a Sink a m r is an Effect m r

All these types are in fact type synonyms for the core data type PipeM i o r1 m r2, with some of the type variables replaced by Void. We will explore in future posts what this type represents in more details.

Note: If you look at the API, you will also notice additional types such as SourceM, SinkM, PipeM. These types are more general than Source, Sink and Pipe and give full access to the return type of the pipe, something useful in particular cases. We will explore this in future posts.


Two main primitives, yield and await

Defining a new pipe is rather easy and relies upon just a few ingredients: yield (to send a value downstream) and await (to receive a value from upstream). To complete the picture, we can use tail recursion to build stateful pipes, and the Monad m to produce side-effects.

For instance, we can define an infinite Source that yield the repeated application of a given function (known as the function iterate in Idris) rather easily:

Similarly, we can easily define a Sink that folds over its inputs to build a result. It just awaits for inputs and combine them with an initial accumulator. Upon await returning Nothing, the stream does not have any more values to stream, and we can return the result:

We can plug these two pipes together to sum the integers from 0 to 4 as follows:

Note: these pipes are already available in IdrisPipes, although the actual implementation is more concise and general. The library also offer a function “awaitOr” that allows to capture the return type of the previous pipe.


Side note: return values and early termination

As demonstrated by the great Haskell pipes and Haskell conduit libraries, there is a huge design space for a pipe library such as IdrisPipes. In particular, there are many design choice we can make regarding the support of early termination inside the core of the library, or the support of return values at different stages of the pipeline.

Following the reading of this great post on the flows of Haskell Pipes and Conduit by Michael Snoyman, I decided to follow the path of Conduit and integrate early termination inside the core of IdrisPipes.

This way, you can define a Sink such as fold, or a pipe such as groupBy, which would not have been possible without the support for early termination, while still allowing for the pipes to return some values.


Pull-based streaming model

IdrisPipes follows a pull-based streaming model:

  • The Sink starts processing first
  • Each pipe gives control to the pipe before it when it awaits a value
  • Each pipe immediately releases control to the pipe after it when it yields a value

This allows keeping the memory consumption of the pipeline under control. You can tweak it, and exchange memory for speed in some cases, by implementing pipes that group individual pieces of data into chunks. In fact, an implementation of this pipe is available in Pipes.Prelude, named chunking:

One direct consequence of this pull-based streaming model is that the source might not be totally consumed. The pipeline will only consume as much as the source as needed.

It also means that you can use IdrisPipes to perform pure lazy computation (using runPure), which you can find useful as Idris is strict by default (there are other solutions available for you to do this though).


Large collection of built-in algorithms

In order to avoid you having to re-invent the wheel, IdrisPipes also comes with a large collection of already existing pipes in Pipes.Prelude, which will only grow as time goes by.

Here is a non-exhaustive list of existing pipes you can use:

  • mapping f maps each element of the pipe with f
  • filtering p only forward elements satisfying p
  • groupingBy p build chunks of elements comparable by p
  • splittingBy p splits in chunk at elements satisfying p
  • tracing f runs an effectful function f on each element

IdrisPipes also comes with some helper functions which help you building your own pipes. For instance, some helpers will take care of early termination and automatic forwarding of the return value of the previous pipe. In particular, the following function might prove useful to you:

  • awaitForever helps building stateless pipes
  • awaitOne helps building stateful pipes
  • each helps yielding several values
  • idP gives you an identity pipe

These helpers are available and documented in Pipes.Core.


Missing features – for now

IdrisPipes does not yet support leftovers, or the prompt release of resources, as Haskell pipes-safe and Haskell conduit allows you to do. These features will be integrated into the library in the coming months.


Conclusion and what’s next

In this post, we went over the motivations and goals behind IdrisPipes as well as a quick overview of the package and the features it offers.

In future posts, we will zoom into the implementation details of the package to explain how it works, and discuss some of the design choices and some possible alternatives.

You can have a look at the full package in this GitHub repo.

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