# Plumbing and Graph: the Clojure utility belt

prismatic/plumbing logo

This first release includes our '[Graph](http://plumatic.github.io/prismatics-graph-at-strange-loop)' library, our `plumbing.core` library of very commonly used functions (the only namespace we `:use` across our codebase), and a few other supporting namespaces.

*New in 0.3.0: support for ClojureScript*

*New in 0.2.0: support for schema.core/defn-style schemas on fnks and Graphs.  See `(doc fnk)` for details.*

Leiningen dependency (Clojars): 

[![Clojars Project](http://clojars.org/prismatic/plumbing/latest-version.svg)](http://clojars.org/prismatic/plumbing) 

[Latest API docs](http://plumatic.github.io/plumbing).

**This is an alpha release.  We  are using it internally in production, but the API and organizational structure are subject to change.  Comments and suggestions are much appreciated.**

Check back often, because we'll keep adding more useful namespaces and functions as we work through cleaning up and open-sourcing our stack of Clojure libraries.

## Graph: the Functional Swiss-Army Knife

Graph is a simple and *declarative* way to specify a structured computation, which is easy to analyze, change, compose, and monitor. Here's a simple example of an ordinary function definition, and its Graph equivalent:

```clojure
(require '[plumbing.core :refer (sum)])
(defn stats
  "Take a map {:xs xs} and return a map of simple statistics on xs"
  [{:keys [xs] :as m}]
  (assert (contains? m :xs))
  (let [n  (count xs)
        m  (/ (sum identity xs) n)
        m2 (/ (sum #(* % %) xs) n)
        v  (- m2 (* m m))]
    {:n n   ; count
     :m m   ; mean
     :m2 m2 ; mean-square
     :v v   ; variance
     }))

(require '[plumbing.core :refer (fnk sum)])
(def stats-graph
  "A graph specifying the same computation as 'stats'"
  {:n  (fnk [xs]   (count xs))
   :m  (fnk [xs n] (/ (sum identity xs) n))
   :m2 (fnk [xs n] (/ (sum #(* % %) xs) n))
   :v  (fnk [m m2] (- m2 (* m m)))})
```

A Graph is just a map from keywords to keyword functions ([learn more](#fnk)).  In this case, `stats-graph` represents the steps in taking a sequence of numbers (`xs`) and producing univariate statistics on those numbers (i.e., the mean `m` and the variance `v`).  The names of arguments to each `fnk` can refer to other steps that must happen before the step executes. For instance, in the above, to execute `:v`, you must first execute the `:m` and `:m2` steps (mean and mean-square respectively).

We can "compile" this Graph to produce a single function (equivalent to `stats`), which also checks that the map represents a valid Graph:

```clojure
(require '[plumbing.graph :as graph] '[schema.core :as s])
(def stats-eager (graph/compile stats-graph))

(= {:n 4
    :m 3
    :m2 (/ 25 2)
    :v (/ 7 2)}
   (into {} (stats-eager {:xs [1 2 3 6]})))

;; Missing :xs key exception
(thrown? Throwable (stats-eager {:ys [1 2 3]}))
```

Moreover, as of the 0.1.0 release, `stats-eager` is *fast* -- only about 30% slower than the hand-coded `stats` if `xs` has a single element, and within 5% of `stats` if `xs` has ten elements.

Unlike the opaque `stats` fn, however, we can modify and extend `stats-graph` using ordinary operations on maps:

```clojure
(def extended-stats
  (graph/compile
    (assoc stats-graph
      :sd (fnk [^double v] (Math/sqrt v)))))

(= {:n 4
    :m 3
    :m2 (/ 25 2)
    :v (/ 7 2)
    :sd (Math/sqrt 3.5)}
   (into {} (extended-stats {:xs [1 2 3 6]})))
```

A Graph encodes the structure of a computation, but not how it happens, allowing for many execution strategies. For example, we can compile a Graph lazily so that step values are computed as needed.  Or, we can parallel-compile the Graph so that independent step functions are run in separate threads:

```clojure
(def lazy-stats (graph/lazy-compile stats-graph))

(def output (lazy-stats {:xs [1 2 3 6]}))
;; Nothing has actually been computed yet
(= (/ 25 2) (:m2 output))
;; Now :n and :m2 have been computed, but :v and :m are still behind a delay


(def par-stats (graph/par-compile stats-graph))

(def output (par-stats {:xs [1 2 3 6]}))
;; Nodes are being computed in futures, with :m and :m2 going in parallel after :n
(= (/ 7 2) (:v output))
```

We can also ask a Graph for information about its inputs and outputs (automatically computed from its definition):

```clojure
(require '[plumbing.fnk.pfnk :as pfnk])

;; stats-graph takes a map with one required key, :xs
(= {:xs s/Any}
   (pfnk/input-schema stats-graph))

;; stats-graph outputs a map with four keys, :n, :m, :m2, and :v
(= {:n s/Any :m s/Any :m2 s/Any :v s/Any}
   (pfnk/output-schema stats-graph))
```

If schemas are provided on the inputs and outputs of the node functions, these propagate through into the Graph schema as expected.

We can also have higher-order functions on Graphs to wrap the behavior on each step. For instance, we can automatically profile each sub-function in 'stats' to see how long it takes to execute:

```clojure
(def profiled-stats (graph/compile (graph/profiled ::profile-data stats-graph)))

;;; times in milliseconds for each step:
(= {:n 1.001, :m 0.728, :m2 0.996, :v 0.069}
   @(::profile-data (profiled-stats {:xs (range 10000)})))
```

… and so on.  For more examples and details about Graph, check out the [graph examples test](https://github.com/plumatic/plumbing/blob/master/test/plumbing/graph_examples_test.cljx).

## Bring on (de)fnk

Many of the functions we write (in Graph and elsewhere) take a single (nested) map argument with keyword keys and have expectations about which keys must be present and which are optional. We developed a new style of binding ([read more here](https://github.com/plumatic/plumbing/tree/master/src/plumbing/fnk)) to make this a lot easier and to check that input data has the right 'shape'. We call these 'keyword functions' (defined by `defnk`) and here's what one looks like:

```clojure
(use 'plumbing.core)
(defnk simple-fnk [a b c]
  (+ a b c))

(= 6 (simple-fnk {:a 1 :b 2 :c 3}))
;; Below throws: Key :c not found in (:a :b)
(thrown? Throwable (simple-fnk {:a 1 :b 2}))
```

You can declare a key as optional and provide a default:
```clojure
(defnk simple-opt-fnk [a b {c 1}]
  (+ a b c))

(= 4 (simple-opt-fnk {:a 1 :b 2}))
```

You can do nested map bindings:
```clojure
(defnk simple-nested-fnk [a [:b b1] c]
  (+ a b1 c))

(= 6 (simple-nested-fnk {:a 1 :b {:b1 2} :c 3}))
;; Below throws: Expected a map at key-path [:b], got type class java.lang.Long
(thrown? Throwable (simple-nested-fnk {:a 1 :b 1 :c 3}))
```

Of course, you can bind multiple variables from an inner map and do multiple levels of nesting:
```clojure
(defnk simple-nested-fnk2 [a [:b b1 [:c {d 3}]]]
  (+ a b1 d))

(= 4 (simple-nested-fnk2 {:a 1 :b {:b1 2 :c {:d 1}}}))
(= 5 (simple-nested-fnk2 {:a 1 :b {:b1 1 :c {}}}))
```

You can also use this binding style in a `let` statement using `letk`
or within an anonymous function by using `fnk`.


## More good stuff

There are a bunch of functions in `plumbing.core` that we can't live without. Here are a few of our favorites.

When we build maps, we often use `for-map`, which works like `for` but for maps:

```clojure
(use 'plumbing.core)
(= (for-map [i (range 3)
             j (range 3)
	         :let [s (+ i j)]
			 :when (< s 3)]
	  [i j]
	  s)
   {[0 0] 0, [0 1] 1, [0 2] 2, [1 0] 1, [1 1] 2, [2 0] 2})
```

`safe-get` is like `get` but throws when the key doesn't exist:

```clojure
;; IllegalArgumentException Key :c not found in {:a 1, :b 2}
(thrown? Exception (safe-get {:a 1 :b 2} :c))
```

Another frequently used map function is `map-vals`:

```clojure
;; return k -> (f v) for [k, v] in map
(= (map-vals inc {:a 0 :b 0})
   {:a 1 :b 1})
```

Ever wanted to conditionally do steps in a `->>` or `->`? Now you can with our
'penguin' operators. Here's a few examples:

```clojure
(use 'plumbing.core)
(= (let [add-b? false]
     (-> {:a 1}
         (merge {:c 2})
         (?> add-b? (assoc :b 2))))
   {:a 1 :c 2})

(= (let [inc-all? true]
     (->> (range 10)
          (filter even?)
          (?>> inc-all? (map inc))))
	[1 3 5 7 9])
```

Check out [`plumbing.core`](https://github.com/plumatic/plumbing/blob/master/src/plumbing/core.cljx) for many other useful functions.

## ClojureScript

As of 0.3.0, plumbing is available in ClojureScript! The vast majority of the
library supports ClojureScript, with the only exceptions that are JVM-specific
optimizations.

Here's an example usage of `for-map`:

```clojure
(ns plumbing.readme
  (:require [plumbing.core :refer-macros [for-map]]))

(defn js-obj->map
  "Recursively converts a JavaScript object into a map with keyword keys"
  [obj]
  (for-map [k (js-keys obj)
            :let [v (aget obj k)]]
    (keyword k) (if (object? v) (js-obj->map v) v)))

(is (= {:a 1 :b {:x "x" :y "y"}}
       (js-obj->map
        (js-obj "a" 1
                "b" (js-obj "x" "x"
                            "y" "y")))))

;; Note: this is a contrived example; you would normally use `cljs.core/clj->js`
```

## Community

Plumbing now has a [mailing list](https://groups.google.com/forum/#!forum/prismatic-plumbing).  Please feel free to join and ask questions or discuss how you're using Plumbing and Graph.

## Supported Clojure versions

Plumbing is currently supported on Clojure 1.5.x and 1.6.x.

## License

Distributed under the Eclipse Public License, the same as Clojure.
