thinking functionally - john stevenson - codemotion rome 2017
TRANSCRIPT
Complex Systems
… are simply not easy to understand
@jr0cketJosh Smith - Tweet Original Gif
The Complexity Iceberg
- @krisajenkins
● complexity is very dangerous when hidden
● You can't know what a function does for certain if it has side effects
Pure FunctionsThe results of the function are purely determined by its initial output and its own code
- no external influence, a function only uses local values- referential transparency (the function can be replaced by its value)
Impure Functions - side causesThe results of the function are purely determined by its initial output and its own code
- behaviour externally influenced and non-deterministic
Eliminating Side Effects
Functional programming is about eliminating side effects where you can, controlling them where you can't - @krisajenkins
The features in Functional Programming come from a desire to reduce side effects
Clojure / ClojureScript
A hosted language with simple interoperability with the host language
- (java.Util.Date.)- (js/alert “Client side apps are easier in Clojure”)
Clojure - basic syntax for this talk( ) ;; an empty list. The first element of a list is evaluated as a function call
(function-name data) ;; call a function with the data as its argument
(def name “data-or-value”) ;; assign (bind) a name to a data or legal value
:keyword-name ;; a keyword is a name that points to itself
;; Thread-first macro - chain function calls, passing the result of each call as the first argument to the next function. The ,,, indicates where the resulting argument goes.
(-> (function-a “data”) (function-b ,,,) ;; In Clojure commas , are whitespace (function-c ,,, “data”))
List, Vector, Map & Set
Clojure’s built-in data structures are all immutable- returning a new data structure when a function is applied
(list 1 2 3 4 5) ‘(“fish” “chips” 42)
(vec ‘(1 2 3 4)) [1 2 3 4]
{:key “value”} {:name “John” :skill “conferencing”}
(set ‘(1 2 3 4 4)) #{1 2 3 4}
Persistent Data Structures - shared memory
Each function creates a new vector
Memory space for values is shared between each vector
Persistent Data Structures - shared memory
By sharing memory you can apply functions over and over again effectively
Values persist until they are no longer referenced
Concurrency is Easier
Concurrency is much easier to write and reason about because of
- Pure functions- Immutability is encouraged by default- Persistent Data Structures- All values are immutable
- unless explicitly wrapped in an atom or ref
- state changes managed atomically (software transactional memory)- core.async library allows you to write asynchronous code as easily as sequential
code
Using persistent data structuresIterate over collectionsGenerate new collections by applying functions
Sequence / List Comprehension
Iterating through a range of generated values to create a list of 2 value vectors
Immutability - local binding
Assignments made locally are immutable
- words is a local binding to the result of running the function upper-case on “Hello World”
- letter->clack is a function that converts a character to a code
Lazy EvaluationClojure's lazy sequences- returns a reference to a file and step through it one line at a time- line-seq returns a lazy sequence of lines, so we can read files larger than available memory, one line at a time
Recursion & PolymorphismProcess a collection of values by feeding the remaining elements back to the function
- the sum function is polymorphic, it has different behaviours that could be evaluated depending on if passed 1 or 2 arguments
Recursion - tail call optimisationProtect the heap space from blowing by using the recur function
Using recur in the last line is the same as calling sum, however the memory required from the previous sum function call is over-written in memory. So only 1 memory slot is used instead of 10 billion
Higher Order FunctionsA function that takes one or more functions as arguments, ora function that returns a function
Higher Order FunctionsFunctions always return a value & can be used as an argument to another function
Composing functions togetherExample: current value of the Clojure project from the configuration file- `slurp` in the project file, convert into a string and return the value at index 2
Design idiom:Composing functions together
You can think of functional design as evaluating one or more functions in series of functions
Functional Composition - map
Map the function inc over each number of the collection, returns a new collection of numbers incremented by 1
Functional Composition - reduce
Reduce the numbers inside a collection to a single value by applying the + function,returns a single value
Higher Order functions - partial, compHigher order functions can return a function
- The comp function composes other functions together - The partial function allows you to lazily add another argument to a function
Safe State changes
Changing state safely by not changing it
● Persistent data structures● Local bindings
Changing state safely by changing it atomically
● Software Transactional Memory (STM)○ Gives an mechanism like an in-memory atomic database that manages mutable state changes
under the covers
● Atoms● core.async
Concurrency syntax - atoms
An online card game has players that can join and have their winnings tracked
Concurrency syntax - atoms
The join-game function adds players to the atom by their name, but only up to 2 players
Concurrency syntax - refs for sync updatesThe join-game-safely adds players to the ref and alters their account & game account
Parallelism - reduceSignificantly lower completion time running in parallel using Immutable data structures
https://github.com/aphyr/tesser
Over 20 Books on Clojure...
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Clojurian Community in Person
Probably the most active language-specific developer communities in London
Learning by teaching others
I really started thinking in Clojure when I started talking to & teaching others
- Coding dojos- talks on Clojure (starting with the basics, showing the art of the possible)- moving on to running conferences- workshops at hack days
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