Learning Clojure has introduced me to some really fascinating ideas. I really believe in the importance of trying new things, so I’ve been playing with two of them — an old idea and a new one: the Clojure/core.async interpretation of C. Hoare’s Communicating Sequential Processes (CSP), and Rich Hickey’s transducers, coming soon to a Clojure near you.

Hopefully, this post will serve two purposes: to solidify these ideas in my mind by explaining them, and — by proxy — help someone else to understand. To weed out mistakes and weaknesses in my own thinking I’m pretty explicit each small conceptual step, particularly when it comes to transducers. It’s a long one, but hopefully useful!

tl;dr — There’s code on GitHub.

First, a quick introduction to the mass of prior work here…

CSP?

CSP is a formalised way to describe communication in concurrent systems. If that’s sounds a little dry, it’s because it is — but like many a snore-inducing concept, when hurled at problems in the real world things get a whole lot more interesting. A bit like yoghurt.

core.async?

Just over a year ago an implementation of CSP called core.async was released to the Clojure community, offering “facilities for async programming and communication.” It introduced channels, a simple way to coordinate entities in a system. The library is compile-target agnostic so it can also be used from ClojureScript.

Transducers?

The most recent development in this epic saga (spanning almost 40 years of computing history!) are transducers, a “powerful and composable way to build algorithmic transformations”. Again dry but very powerful in use — and very hard for me to understand!

Talks about how these concepts tie together fascinated me, and I’ve been toying with the ideas using ClojureScript and David Nolen’s excellent Om framework. In addition, these ideas tie closely with Twitter’s Flight framework on which I work.

However I’ve never felt truly comfortable with what’s going on under the hood, and since the best way to learn anything is to do it yourself, I’ve been experimenting!

Oh, and just quickly — I’m not going to spend very much time on why you might want this stuff. Many of the links above will help.

What’s the problem?

There’s a whole stack of ideas that combine to make channels and transducers valuable, but I’ll pick just one: events are a bad primitive for data flow. They require distribution of mutable state around your code, and it’s not idiomatic or pleasant to flow data through events:

pubsub . on ( ' users:response ' , function ( users ) { users . filter ( function ( user ) { return ! user . muted ; }) . forEach ( function ( user ) { pubsub . emit ( ' posts:request ' , { user : user . id }); }) }) }); pubsub . on ( ' posts:response ' , function ( data ) { ... }); pubsub . emit ( ' users:request ' );

Events are fine for one-shot notifications, but break down when you want to coordinate data from a number of sources. Event handlers tend to not be very reusable or composable.

core.async’s channels offer an alternative that is ideal for flow control, reuse and composability.

I’ll leave it to David Nolen to show you why.

Channels in JavaScript

The first step was to implement the core.async primitive — channels — and their fundamental operations: put and take .

Channels are pretty simple: they support producers and consumers that put values to, and take values from, the channel. The default behaviour is “one-in, one-out” — a take from the channel will give you only the least-recently put value, and you have to explicitly take again to get the next value. They’re like queues.

It’s immediately obvious that this decouples the producer and consumer – they each only have to know about the channel to communicate, and it’s many-to-many: multiple producers can put values for multiple consumers to take .

I’m not going to detail the exact implementation here, but making a new channel is as simple as asking for one: var c = chan() .

You can try channels out in this JS Bin:

JS Bin

If you get errors, make sure to click ‘Run’.

Stuck for ideas? Try:

> c = chan () ... > chan . put ( c , 10 ) ... > chan . take ( c , console . log . bind ( console , ' got: ' )) got : 10 ... > chan . take ( c , console . log . bind ( console , ' got: ' )) ... > chan . put ( c , 20 ) got : 20 ...

Nice. I’ve added a few upgrades, but fundamentally things stay the same.

By the way… these ideas are firmly rooted in functional programming, so I’m avoiding methods defined on objects where possible, instead preferring functions that operate on simple data structures.

We have working channels!

Transducers in JS

Above, transducers were described as a “powerful and composable way to build algorithmic transformations.” While enticing, this doesn’t really tell us much. Rich Hickey’s blog post, from which that quote is taken, expands somewhat but I still found them very hard to comprehend.

In fact, understanding them meant spending hours frustratedly scribbling on a mirror with a whiteboard pen.

To me, transducers are a generic and composable way to operate on a collection of values, producing a new value or new collection of new values. The word ‘transducer’ itself can be split into two parts that reflect this definition: ‘transform’ — to produce some value from another — and ‘reducer’ — to combine the values of a data structure to produce a new one.

To understand transducers I built up to them from first principles by taking a concrete example and incrementally making it more generic, and that’s what we’re going to do now.

We’re aiming for a “composable way to build algorithmic transformations.”

I hope you’re excited.

From the bottom to the top…

First, we have to realise that many array (or other collection) operations like map , filter and reverse can be defined in terms of a reduce .

To start with, here’s an example that maps over an array to increment all its values:

[ 1 , 2 , 3 , 4 ]. map ( function ( input ) { return input + 1 ; }) // => [2,3,4,5]

Pretty simple. Note that two things are implicit here:

The return value is built up from a new, empty array.

Each value returned is added to the end of the new array as you would do manually using JavaScript’s concat .

With this in mind, we can convert the example to use .reduce :

[ 1 , 2 , 3 , 4 ]. reduce ( function ( result , input ) { return concat ( result , input + 1 ); }, []) // => [2,3,4,5]

To get around JavaScript’s unfortunate Array concat behaviour, I’ve redefined it to a function called concat that adds a single value to an array:

function concat ( a , b ) { return a . concat ([ b ]); }

By the way, we’re about to get into higher-order function territory. If that makes you queasy, it might be time to do some reading and come back later!

Our increment-map-using-reduce example isn’t very generic, but we can make it more so by wrapping it up in a function that takes an array to be incremented:

function mapWithIncr ( collection ) { return collection . reduce ( function ( result , input ) { return concat ( result , input + 1 ); }, []); } mapWithIncrement ([ 1 , 2 , 3 , 4 ]) // => [2,3,4,5]

This can be taken a step further by passing the transformation as a function. We’ll make one called inc :

function inc ( x ) { return x + 1 ; }

Using this with any collection requires another higher-order function, map , that combines the transform and the collection.

This is where things start to get interesting: this function contains the essence of what it means to map — we reduce one collection to another by transforming the values and concatenating the results together.

function map ( transform , collection ) { return collection . reduce ( function ( result , input ) { return concat ( result , transform ( input )); }, []); }

In use, it looks like this:

map ( inc , [ 1 , 2 , 3 , 4 ]) // => [2,3,4,5]

Very nice.

Algorithmic transformations

So what’s the next abstraction in our chain? It’s perhaps worth restating the goal: a “composable way to build algorithmic transformations.” There’s two key phrases there: “algorithmic transformations” and “composable”. We’ll deal with them in that order.

map , defined above, is a kind of algorithmic transformation. Another I mentioned earlier is filter , so let’s define that in same way we did for map .

Filter better fits the word “reduce” because it can actually produce fewer values than it was given.

We’re going to quickly jump from a concrete example, through the reduce version, to a generic filter function that defines the essence of what it means to filter .

// Basic filter [ 1 , 2 , 3 , 4 ]. filter ( function ( input ) { return ( input > 2 ); }) // => [3,4] // Filter with reduce [ 1 , 2 , 3 , 4 ]. reduce ( function ( result , input ) { return ( input > 2 ? concat ( result , input ) : result ); }, []) // => [3,4] // Transform (called the predicate) function greaterThanTwo ( x ) { return ( x > 2 ); } // And finally, filter as function function filter ( predicate , collection ) { return collection . reduce ( function ( result , input ) { return ( predicate ( input ) ? concat ( result , input ) : result ); }, []) }

In use, it looks like this:

filter ( greaterThanTwo , [ 1 , 2 , 3 , 4 ]) // => [3,4]

Composable

Now we can construct a couple of different algorithmic transformations, we’re missing “composable” bit from that original definition. We should fix that.

How does composability apply to the algorithmic transformations we’ve already defined — map and filter ? There are two ways to combine these transformations:

Perform the first transformation on the whole collection before moving on to the second.

Perform all transformations on the first element of the collection before moving on to the second.

We can already do the former:

filter ( greaterThanTwo , map ( inc , [ 1 , 2 , 3 , 4 ])) // => [3,4,5]

We can even use compose :

var incrementAndFilter = compose ( filter . bind ( null , greaterThanTwo ), map . bind ( null , inc ) ); incrementAndFilter ([ 1 , 2 , 3 , 4 ]) // => [3,4,5]

compose is a function that combines functions:

compose ( f , g )( 10 ) === f ( g ( 10 ));

However, this has a number of issues:

It cannot be parallelised.

It cannot be done lazily.

The operations are tied very closely to input and output data structure.

The converse is true for the latter way of combining the transformations, and so is the much more desirable end result.

For a discussion of why this is the case, look into the fork-join model.

Frankly, I found this extremely difficult; I just couldn’t understand how they could be composed generically.

Time to dig deeper, and talk about reducing functions.

Reducing functions

A reducing function is any function that can be passed to reduce . They have the form: (something, input) -> something . They’re the inner-most function in the map and filter examples.

These are the things we need to be composing, but right now they are hidden away in map and filter .

function map ( transform , collection ) { return collection . reduce ( // Reducing function! function ( result , input ) { return concat ( result , transform ( input )); }, [] ); } function filter ( predicate , collection ) { return collection . reduce ( // Reducing function! function ( result , input ) { return ( predicate ( input ) ? concat ( result , input ) : result ); }, [] ) }

To get at the reducing functions, we need to make map and filter more generic by extracting the pieces they have in common:

Use of collection.reduce

The ‘seed’ value is an empty array

The concat operation performed on result and the input ( transform -ed or not)

First, let’s pull out the use of collection.reduce and the seed value. Instead we can produce reducing functions and pass them to .reduce :

function mapper ( transform ) { return function ( result , input ) { return concat ( result , transform ( input )); }; } function filterer ( predicate ) { return function ( result , input ) { return ( predicate ( input ) ? concat ( result , input ) : result ); }; } [ 1 , 2 , 3 , 4 ]. reduce ( mapper ( inc ), []) // => [2,3,4,5] [ 1 , 2 , 3 , 4 ]. reduce ( filterer ( greaterThanTwo ), []) // => [3,4]

Nice! We’re getting closer but we still cannot compose two or more reducing functions. The last piece of shared functionality is the key: the concat operation performed on result and the input .

Remember we said that reducing functions have the form (something, input) -> something ? Well, concat is just one such function:

function concat ( a , b ) { return a . concat ([ b ]); }

That means there’s actually two reducing functions:

One that defines the job (mapping, filtering, reversing…)

Another that, within the job, combines the existing result with the input

So far we have only used concat for the latter, but who says we have to? Could we use another, completely different reducing function – like, say, one produced from mapper ?

Yes, we could.

To build up to composing our reducing functions we’ll start with a very explicit example, rewriting filterer to use mapper to combine the result with the input , and explore how the data flows around.

Before we do that, we need a new function: identity . It simply returns whatever it is given:

function identity ( x ) { return x ; } [ 1 , 2 , 3 , 4 ]. reduce ( mapper ( identity ), []) // => [1,2,3,4]

We can rewrite filter to use mapper quite easily:

function lessThanThree ( x ) { return ( x < 3 ); } function mapper ( transform ) { return function ( result , input ) { return concat ( result , transform ( input )); }; } function filterer ( predicate ) { return function ( result , input ) { return ( predicate ( input ) ? mapper ( identity )( result , input ) : result ); }; } [ 1 , 2 , 3 , 4 ]. reduce ( filterer ( lessThanThree ), []) // => [1,2]

To see how this works, let’s step through it:

filterer(lessThanThree) produces a reducing function which is passed to .reduce . The reducing function is passed result — currently [] — and the first input — 1 . The predicate is called and returns true , so the first expression in the ternary is evaluated. mapper(identity) returns a reducing function, then called with [] and 1 . The reducing function’s transform function — identity — is called, returning the same input it was given. The input is concat -ed onto the result and returned. The new result — now [1] — is returned, and so the reduce cycle continues.

I’d recommend running this code and looking for yourself!

What has this gained us? Well, now we can see that a reducing function can make use of another reducing function – it doesn’t have to be concat !

In fact, if we altered filterer to use mapper(inc) , we’d get:

[1,2,3,4].reduce(filterer(lessThanThree), []) // => [2,3]

This is starting to feel a lot like composable algorithmic transformation, but we don’t want to be manually writing composed functions – we want to use compose !

If we pull out the inner reducing function (the combiner), we make reducing functions that express the essence of their job without being tied to any particular way of combining their arguments.

We’ll change the names again to express the nature of what’s going on here:

function mapping ( transform ) { return function ( reduce ) { return function ( result , input ) { return reduce ( result , transform ( input )); }; }; } function filtering ( predicate ) { return function ( reduce ) { return function ( result , input ) { return ( predicate ( input ) ? reduce ( result , input ) : result ); }; }; }

Those new inner functions – the ones that take a reduce function — are transducers. They encapsulate some reducing behaviour without caring about the nature of the result data structure.

In fact, we’ve offloaded the responsibility of combining the transformed input with the result to the user of the transducer, rather than expressing it within the reducing function. This means we can reduce generically into any data structure!

Let’s see this in use by creating that filtering-and-incrementing transducer again:

var filterLessThanThreeAndIncrement = compose ( filtering ( lessThanThree ), mapping ( inc ) ); [ 1 , 2 , 3 , 4 ]. reduce ( filterLessThanThreeAndIncrement ( concat ), []) // => [2,3]

Wow. Notice:

We only specify the seed data structure once, when we use the transducer.

We only tell the transducers how to combine their input with the result once (in this case, with concat ), by passing it to the filterLessThanThreeAndIncrement transducer.

To prove that this works, let’s turn it into an object with the resulting values as keys without altering the reducing functions.

[ 1 , 2 , 3 , 4 ]. reduce ( filterLessThanThreeAndIncrement ( function ( result , input ) { result [ input ] = true ; return result ; }), {}) // => { 2: true, 3: true }

Let’s try it with some more complex data. Say we have some posts :

var posts = [ { author : ' Agatha ' , text : ' just setting up my pstr ' }, { author : ' Bert ' , text : ' Ed Balls ' }, { author : ' Agatha ' , text : ' @Bert fancy a thumb war? ' }, { author : ' Charles ' , text : ' #subtweet ' }, { author : ' Bert ' , text : ' Ed Balls ' }, { author : ' Agatha ' , text : ' @Bert m( ' } ];

Let’s pull out who’s talked to who and build a graph-like data structure.

function graph ( result , input ) { result [ input . from ] = result [ input . from ] || []; result [ input . from ]. push ( input . to ); return result ; } var extractMentions = compose ( // Find mentions filtering ( function ( post ) { return post . text . match ( /^@/ ); }), // Build object with {from, to} keys mapping ( function ( post ) { return { from : post . author , to : post . text . split ( ' ' ). slice ( 0 , 1 ). join ( '' ). replace ( /^@/ , '' ) }; }) ); posts . reduce ( extractMentions ( graph ), {}) /* => { Agatha: ['Bert', 'Charles'], Bert: ['Agatha'], Charles: ['Bert'] } */

Applying transducers to channels

Now we have all the parts of a “composable way to build algorithmic transformations” we can start applying them to any data pipeline – so let’s try channels. Again, I’m not going to show you the channel-level implementation, just some usage examples.

We’re going to listen for DOM events and put them into a channel that filters only those that occur on even x & y positions and maps them into a triple of [type, x, y] .

First, two additions to our function library:

// Put DOM events into the supplied a channel function listen ( elem , type , c ) { elem . addEventListener ( type , function ( e ) { chan . put ( c , e ); }); } function even ( x ) { return ( x % 2 === 0 ); }

Now let’s create a channel, and pass it a transducer. The transducer will be used to reduce the data that comes down the channel.

var c = chan ( 1 , // Fixed buffer size (only one event allowed) compose ( // Only events with even x & y filtering ( function ( e ) { return ( even ( e . pageX ) && even ( e . pageY ) ); }), // e -> [type, x, y] mapping ( function ( e ) { return [ e . type , e . pageX , e . pageY ]; }) ) );

Next we’ll hook-up the events and the channel:

listen ( document , ' mousemove ' , c );

And, finally, take in a recursive loop:

( function recur () { chan . take ( c , function ( v ) { console . log ( ' got ' , v ); recur () }); }());

Running this code, you should see lots of events in your console – but only those with even x & y positions:

> got ["mousemove", 230, 156] > got ["mousemove", 232, 158] > got ["mousemove", 232, 160] > got ["mousemove", 234, 162]

Stateful transducers

Finally, let’s take a look at a stateful transducer, building a gateFilter to detect “dragging” using mousedown and mouseup event, and a keyFilter that matches against a property of the channel data.

function gateFilter ( opener , closer ) { var open = false ; return function ( e ) { if ( e . type === opener ) { open = true ; } if ( e . type === closer ) { open = false ; } return open ; }; } function keyFilter ( key , value ) { return function ( e ) { return ( e [ key ] === value ); }; } var c = chan ( 1 , compose ( // Only allow through when mouse has been down filtering ( gateFilter ( ' mousedown ' , ' mouseup ' )), // Filter by e.type === 'mousemove' filtering ( keyFilter ( ' type ' , ' mousemove ' )), // e -> [type, x, y] mapping ( function ( e ) { return [ e . pageX , e . pageY ]; }) ) ); // Listen for relevant events listen ( document , ' mousemove ' , c ); listen ( document , ' mouseup ' , c ); listen ( document , ' mousedown ' , c ); // Take in a loop ( function recur () { chan . take ( c , function ( v ) { console . log ( ' got ' , v ); recur () }); }());

Whew. Pretty cool, eh?

And finally…

I think there’s a great deal of expressive power here, particularly in making it easy to reason about data flow in large application.

My real goal is to explore the Actor model as it relates to front-end engineering, particularly in preventing an explosion of complexity with increasing scale. It’s the model Flight uses, but I’m not wholly convinced events — while perfect for one-shot notifications — are the right primitive for coordinating behaviour and flow-control.

The result of this work is on Github so please do check that out, and email or Tweet me with feedback.

Finally finally, a massive thank-you to Stuart & Passy who gave me top-notch feedback on this article!