Link to slides

http://www.lvh.io/ClojureIntro/

https://www.github.com/lvh/ClojureIntro

Introduction

Who am I?

• Software engineer (distributed systems, infosec)

Rackspace

Python

Fellow of the Python Software Foundation Staff member of PyCon US

More importantly: Clojure!

BeClojure

Who are you?

Who are you?

• In this class? At KUL?
• Functional programming?
  • HOF? map, filter, reduce?
• Lisps? Clojure?

About this talk

A lot of content

• Play it by ear
• Questions after every section
• 15m break in the middle

About this talk

• High-and-wide overview
• Bit of philosophy
• Bunch of facts
• Lots of opinions, too

Hello, Clojure!

What’s Clojure?

(apply modern-lisp @jvm)

Official JS target, semi-mature CLR target, various experimental targets

Homoiconic syntax

(f a b c)

just a different spelling for:

f(a, b, c)

A lisp? Really?

Originally written for AI John McCarthy 1957 Interpreters, high-level metaprogramming, garbage collection, dynamic programming, functional programming, programming with recursive functions…

Rich Hickey

What’s so nice about it?

Lots of reasons:

• JVM, JS & CLR
• Immutability & functional programming
• General purpose, pragmatic
• Live and REPL-based programming
• “Just do it already” (my favorite)
• …

Live and REPL-based programming

How long does it take to try something?

• C++: ages
• Python, Go: pretty fast
• Clojure: C-c C-e

Live and REPL-based programming

Meta-eX

Just do it already

Just do it already

(Partial) static typing

• Python: talking about it forever
  • Annotation syntax is enough, right?
  • Finally going to make it in 3.whatever
• Clojure: core.typed

Just do it already

Software transactional memory

• Python: 8 working prototypes
  • ~2x slowdown from regular Python
  • Some of the smartest people on it
  • Difficult because Python is hard to optimize
• Clojure: STM since 1.0

Just do it already

Asynchronous programming

• Python: asyncio
  • (caveat: I am the original author of async-pep)
  • Yay! Yet another event loop framework!
• Clojure: core.async
  • Supports both threads & IOC
  • Goroutines? Library, not language feature

Just do it already

Logic programming

• Python: bunch of weekend hacks
• Clojure: popular library

Not about Python

Most other languages are the same or worse

Ball of mud

Lisp is a ball of mud

$LANG is a shiny diamond

Usually not considered a compliment But hopefully I’ve piqued your interest

Big ideas

Immutability by default

• Default types are immutable
• You can alway import ArrayList
• … unfortunately also Date :-(

Functional programming

“85% functional”

Java interoperability

Decomplection

Complex

• Does many things
• Related to, but ≠ hard!

Simple

• Does one thing
• Related to, but ≠ easy!

The value of values

Values are immutable!

Not just in Clojure; semantically, mutable values are lies

“Single” values

• Java: BigInt, BigDecimal…
• Python: int, float…
• Counterexample: java.util.Date

We’ve made a terrible mistake!

12 Jan 1991, 18 Mar 2002

How many dates?

• Two!
• In Java?
  • Take a Date, change day, month, year
  • Same Date, different date!
  • Why do we accept this?

Maybe I’m overreacting

• Most people agree Date was a mistake
  • Bloch has apologized for it profusely
• Most people agree immutable types are good
  • Numerics, strings…
• … but mutability is still the default!
  • someObject.setWhatever

“Compound” values

E.g. collections

• Java: ArrayList, Hash(Set|Map)
• Python: list, set, dict…
• Counterexample: tuple

For some reason we don’t accept this for compound values

We have made a terrible mistake!

{3, 5}, {3, 5, 7}

How many sets?

• Two!
• In Java? (and Python, and…)
  • Take a HashSet, add/remove some elements
  • Same Set, different set!
  • Why do we accept this?

Why are we here?

Imperative, single-threaded programming!

• You can work around mutability
• … iff you’re the only actor!

What’s wrong?

No concept of time or transitions

• Stop the world, do some stuff, continue
• Everything happens “right now”
• That’s great, but I have n cores…

Idea of values isn’t new

“No man can cross the same river twice.”

– Heraclitus, ~500 BC

Value, identity, state

Identity is just a name

The river doesn’t stop existing just because nobody is around to call it a river…

States are facts

• Fact ~ Lat. factum: “done”
• Perfect tense! Does not change!
• Example
  • Bill Clinton was president of the US.
  • We can have new presidents
  • Doesn’t change that fact!

We need facts for knowledge

• We combine, compare facts to make decisions
  • especially from different time points
• Imagine if you only knew current state!
  • Ever seen “Memento”?

Our own systems do this

E.g. logs, source control

What if they destroyed data?

They would be totally useless

Recap

• Things don’t change in place
• The future is a function of the past
  • The future does not change the past
• Concurrency makes everything worse

Persistent data structures

What are they?

• Class of immutable data structures
• (f x) gives you new data structure
• Not about persisting to a database

Performance?

• Typically not an issue
  • Modern implementations are very efficient
  • JVM is an impressive piece of engineering
• There are always options:
  • transient, persistent!
  • import classic data structures

Performance is often better

• E.g. pointer equality checks
  • Example: Om beating React.js
• Get data sharing for free
  • No defensive copying, cloning, locks…
• Conclusion
  • Maybe some operations may be slower…
  • Entire program can still be faster!

New possibilities

Keeping old versions around is cheap!

• Easy “undo”, “time travel”
• Speculative evaluation

How does it actually work?

Bit-partitioned hash tries

Bit partitioning

Hash trie

Path copying

How deep does it go?

Garbage-efficient

Reference types & concurrency

Conventional OOP vs Clojure

Conventional OOP model

Encapsulation doesn’t fix this!

Clojure model

Indirect reference to immutable value

Updating

Doesn’t affect readers; not affected by readers

Clojure reference types

Consistent interface

• (transition-fn ref func [& args])
• new-state: (func current-state &args)
• Get current value: @ref
• No user locking; no deadlocks

Ref types provide time semantics

Consistency models

Deep similarity!

• Describe how concurrent ops can interact
• E.g. linearizability, serailizability, RYW, MR, MW…
• Gives you “time” (not wallclock time)

Example: atoms

• (atom init-val)
• (swap! some-atom f & args) to modify
  • compare-and-set! too (bit more low level)
• reset! to rudely modify

Example: atoms

(def n (atom 1))

(swap! n inc)
;; => 2

(swap! n * 10)
;; => 20

Transitions could be swap!

STM

What is STM?

• Software transactional memory
• Concurrency model
• Transactions (ACI, not D)
• Backed by MVCC

Transactions: atomicity, consistency, isolation (not durability) Since Clojure 1.0

What makes STM special?

• In Clojure (vs. other ref types):
  • Coordination between refs
• In general (vs. other concurrency models):
  • Alternative to manual locking

Clojure API

• ref reference type
• dosync to make transactions
• alter and commute to modify
  • (ref-set to rudely modify)
• ensure to check the current value

Get the current value:

(def n (ref "xyzzy"))

@n
;; => "xyzzy"

(dosync
 (prn @n))
;; xyzzy

Modify inside transactions

(def n (ref 0))

(alter n inc)
;; IllegalStateException
;; No transaction running ...
@n
;; => 0

(dosync (alter n inc))
@n
;; => 1

STM example

(defn transfer
  [amount from to]
  (dosync
   (alter from - amount)
   (alter to + amount)))

alter vs commute

• Virtually the same thing
• commute allows more orderings
  • More orderings: better concurrency
• Are those orderings OK?
  • commutative func
  • last-write-wins

Example: alter vs commute

(def counter (ref 0))

(defn slow-inc!
  [alter-fn counter]
  (dosync
   (Thread/sleep 100)
   (alter-fn counter inc)))

(defn bombard-counter!
  [n f counter]
  (apply pcalls (repeat n #(f counter))))

alter performance

(dosync (ref-set counter 0))
(time (doall (bombard-counter! 20
                               (partial slow-inc! alter)
                               counter)))
;; => (1 2 4 3 5 6 13 12 9 8 7 11 10 15 17 14 20 18 19 16)
;; "Elapsed time: 2025.646 msecs"
;; 20 incs * 100 ms = 2000 ms...

commute performance

(dosync (ref-set counter 0))
(time (doall (bombard-counter! 20
                               (partial slow-inc! commute)
                               counter)))
;; => (3 6 1 1 7 1 1 8 8 8 8 8 10 14 15 15 19 15 15 19)
;; "Elapsed time: 305.23 msecs"
;; Without delay: virtually instant, so 3 txn attempts

Implementation

Pretty intricate

• MVCC + adaptive history queues
• This gives you snapshot isolation
• One global counter: the ref timestamp

Create a transaction

• Gets a unique identifier
• Gets the current timestamp: “read point”

Do stuff inside transaction

• Per-transaction “cache”
• Keep track of writes, ensures, commutes

(Try to) commit the transaction

• Compare our timestamps with global timestamps
• Conflict? Retry!

Retry? That sounds inefficient

Implementation has a number of clever tricks…

Locks + wait/notify

• Internal dependency tracking
• (vs. optimistic lock-free spinning)
• Avoids churn (constant retrying without progress)

Barging

• Writes are publicly marked as being-written-to
• Eager detection of write conflicts, before commit
• Oldest transaction continues, new one restarts
• This clearly violates isolation
  • Remember: isolation is just a model
  • No promises about how it actually works

It all comes together

• Relies on speculative execution
• Needs persistent data structures!

@Raw

Not complaining about Steegmans or his class He’s a great guy, hates giving that class in Java as much as we hated getting it in Java But @Raw makes me sad

Need a synchronization primitive?

Why not locks?

Locks are incredibly hard to use

• Very tricky to reason about
  • Deadlock free?
  • Livelock free?
  • Are you sure?
• Some patterns are easy, but inefficient
  • Example: GIL, BKL
• Requires extensive error handling
  • Yay, orphaned locks!
• Worst part: often looks like it’s working
  • … even when the program is incorrect

Failure modes

• Segmentation fault
• (Silent?) data corruption

Comparison

Manual memory management

versus

GC and lifetime analysis

Concurrency in practice

New users

• Used to imperative patterns
• They want variables
  • How else would you write software?
• Result: atoms, atoms everywhere
  • Eventually: sadness

Usage pattern: 1 big atom

One atom, usually with a map, to hold state:

(def app-state
  (atom {:user-name "lvh"
         :todo-items ["Take out trash"
                      "Present Clojure intro"]
         :done-items #{"Make slides"}}))

Why not STM?

I don’t know for sure, but I have some hypotheses:

• Real app state usually less than you might think
• High concurrency is not always necessary
• Single atom still allows sane state changes
• Easy to dump current state: reproducibility!

Real answer is probably all of the above & more :-)

Transducers

Fairly new feature

(1.7, beta)

Only bad part is the name

• What’s a transducer?
  • It’s a reducing function transformer!
• What’s a reducing function?
  • It’s a function you’d pass to reduce!
• Gee, thanks!

Monads

Just monoids in the category of endofunctors!

My spell checker is not convinced these are words

map

(map f coll)

((f x) for all x in coll)

(map inc [1 2 3]) ;; => (2 3 4)

filter

(filter f coll)

(all of the x in coll, if (f x))

(filter even? [1 2 3]) ;; => (2)

reduce

(reduce f coll)

(accumulate over coll with f)

(reduce + [1 2 3 4]) ;; => 10

Problem!

We kept implementing map, reduce, etc.

• Collections (the ones we just saw)
• Streams
• Observables
• Channels (core.async)

Big idea

Extract the essence of map, reduce…

• Not just for colls, channels…
• Implement as process transformations

Processes

• Succession of steps
• Each step takes an input
• Example: building a collection
• Generally: seeded left reduce

Transducer vs regular map

(map f)

vs.

(map f coll)

Transducer vs partial map

(map f)

vs.

(partial map f)

So what can I do with transducers?

(Examples adapted from Rich Hickey’s Strange Loop talk)

;; Build concrete collections
(into airplane process-bags pallets)

;; Build a lazy sequence
(sequence process-bags pallets)

;; "Reduce" a collection
(transduce (comp process-bags (map weigh))
           + pallets)

;; core.async channels
(chan 1 process-bags)

Example use case

http://blog.eikeland.se/

Probe + heater + egg + Arduino

Results: delicious!

(def xform (comp (partial map inc) (partial filter odd?) (partial map #(* 3 %))))

(xform [1 2 3])

Big example

• Stole adapted from Rich Hickey at Strange Loop
• Airport: airplanes get loaded with luggage
• What happens to luggage is a process

As a transducer

(comp
 (mapcat unbundle-pallet)
 (take-while (complement ticking?))
 (filter (complement smells-like-food?))
 (map label-heavy-items)
 (take max-plane-capacity))

Why is this awesome?

• Fast!
• Efficient (no intermediate collections)
• Concrete re-use!
  • Across sources
  • Direct re-use of transducers

Macrology

Code ≡ data

Many basic “language features” are macros:

defn, and, cond…

(Just like Racket)

Domain specific languages

• Default Lisper behavior

Polymorphism

Protocols

Multimethods

x.m(a, b, c)

Which m?

m depends on type of x

• Single dispatch
• Java, C++, C#…

C# 4.0 apparently supports multimethods

Python: bit more complicated

Not just type of x, but the value of x.m

• Override x.m on the instance
• __getattr(ibute)__ hacks

x still picks the m!

“Sending a message”

(Smalltalk parlance)

x ← m(a, b, c)

No interesting differences

• Logic is fixed
• Always up to x

We can do better!

Multimethods

Routing logic: f(x)

Example

Icecap example?

core.logic

The Reasoned Schemer

Conclusion

A modern, pragmatic Lisp

Don’t learn Clojure!

Going back is painful ;-)

Thank you!

Questions?

Suggested rants

Bad type systems

• Scala: def map[B, That](f: A => B)(implicit bf: CanBuildFrom[Repr, B, That]): That
• Go: interface{}