Expanding reducers

By: on July 31, 2013

When playing with a new bit of language, it can be helpful to restrict the problem space to an old, well understood algorithm. For me at least, learning one thing at a time is easier! For this post, It’ll be prime sieves, and I’ll be exploring clojure reducers.

A quick recap, the sieve of eratosthenes is a not-maximally-non-optimal way of finding primes. It’s usually expressed as follows:
To find primes below n: generate a list of n integers greater than 1 while the list is not empty: take the head of the list and: add it to the output remove all numbers evenly divisible by it from the list
In clojure, something like:
(defn sieve ([n] (sieve [] (range 2 n))) ([primes xs] (if-let [prime (first xs)] (recur (conj primes prime) (remove #(zero? (mod % prime)) xs)) primes))) (sieve 10) ;= [2 3 5 7]
Which is fine, but I’d like it lazy so I only pay for what I use, and I can use as much as I’m willing to pay for. Let’s look at lazy sequences. Luckily for us, there is an example of exactly this on the lazy-seq documentation, which we slightly modify like so:
(defn lazy-sieve [s] (cons (first s) (lazy-seq (lazy-sieve (remove #(zero? (mod % (first s))) (rest s)))))) (defn primes [] (lazy-seq (lazy-sieve (iterate inc 2)))) (take 5 (primes)) ;= (2 3 5 7)
So now we have a nice generic source of primes that grows only as we take more. But is there another way?

A few months ago Rich Hickey introduced reducers. By turning the concept of ‘reducing’ inside out the new framework allows a parallel reduce (fold) in some circumstances. Which doesn’t apply here. But let’s see if we can build a different form of sieve using the new framework. First a quick overview (cribbing from the original blog post):

Collections are now ‘reducible’, in that they implement a reduce protocol. Filter, map, etc are implemented as functions that can be applied by a reducible to itself to return another reducible, but lazily, and possibly in parallel. So in the example below we have a reducible (a vector), that maps inc to itself to return a reducible that is then wrapped with a filter on even? which returns a further reducible, that reduce then collects with +.
(require '[clojure.core.reducers :as r])
We’ll be referring to r here and there – just remember it’s the clojure.core.reducers namespace
(reduce + (r/filter even? (r/map inc [1 1 1 2]))) ;= 6
These are composable, so we can build ‘recipes’.
;;red is a reducer awaiting a collection (def red (comp (r/filter even?) (r/map inc))) (reduce + (red [1 1 1 2])) ;= 6
into uses reduce internally, so we can use it to build collections instead of reducing:
(into [] (r/filter even? (r/map inc [1 1 1 2]))) ;= [2 2 2]
So here’s the core of ‘reducer’, which “Given a reducible collection, and a transformation function xf, returns a reducible collection, where any supplied reducing fn will be transformed by xf. xf is a function of reducing fn to reducing fn.”
(defn reducer ([coll xf] (reify clojure.core.protocols/CollReduce (coll-reduce [_ f1 init] (clojure.core.protocols/coll-reduce coll (xf f1) init)))))
And we can then use that to implement mapping as so:
(defn mapping [f] (fn [f1] (fn [result input] (f1 result (f input))))) (defn rmap [f coll] (reducer coll (mapping f))) (reduce + 0 (rmap inc [1 2 3 4])) ;= 14
Fine. So what about sieves? One thought is we could build up a list of composed filters, built as new primes are found (see the lazy-seq example above). But there’s no obvious place to do the building, as applying the reducing functions is left to the reducible implementation. Another possibility is to introduce a new type of reducing function, the ‘progressive-filter’, which keeps track of past finds and can filter against them.
(defn prog-filter [f] (let [flt (atom [])] (fn [f1] (fn [result input] (if (not-any? #(f input %) @flt) (do (swap! flt conj input) (f1 result input)) result))))) (defn progressive-filter [f coll] (reducer coll (prog-filter f)))
And we then reduce with a filtering function that is a function of the current candidate and one of the list of found primes (see the #(f input %) bit above)
(into [] (progressive-filter #(zero? (mod %1 %2)) (range 2 10)) ;= [2 3 5 7]
It’s nicely lazy, so we can use iterate to generate integers, and take only a few (r/take, as it’s operating on a reducer):
(into [] (r/take 5 (progressive-filter #(zero? (mod %1 %2)) (iterate inc 2)))) ;= [2 3 5 7 11]
Or even
(def primes (progressive-filter #(zero? (mod %1 %2)) (iterate inc 2))) (into [] (r/take 5 primes)) ;= [2 3 5 7 11]
You get the idea.

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