Functional Leap of Faith

Keynote talk given at JDay Lviv 2014 in Ukraine (http://www.jday.com.ua/)

Some say that there's nothing new under the sun. However, looking back on five to six decades of computing, it's easy to see that things progress at their own leisurely pace. Structured programming, originating in the '60s, did not gain mainstream adoption until the '80s; object-oriented programming was hotly debated in the '70s and '80s but only gained widespread acceptance in the '90s. Every couple of decades sees an engineering leap that radically improves the software engineering discipline across the board.

I believe we are now at such an inflection point, with functional programming concepts slowly sifting into the mainstream. After this talk, I hope you will too.

Transcript

1. Functional Leap of Faith Tomer Gabel, Wix JDay Lviv 2014
2. Through the Looking Glass 20 years ago… •  C/C++ rule the market [1] •  First release of Java [1]  TIOBE  Index  (historical  data)  
3. … since then?* •  C and Java are neck-and-neck •  Virtual machines are everywhere •  Garbage collection is everywhere •  OOP is everywhere *  Not  exhaus<ve  
4. Virtual Machines 1967 BCPL 1978 UCSD p-System 1994 JVM 2014 Everywhere!
5. Garbage Collection 1960 Formalized[1] 1970s Research 1994 JVM 2014 Everywhere! [1]  Recursive  Func<ons  of  Symbolic  Expressions  and  Their  Computa<on  by  Machine,  Part  I  
6. Object-Oriented Programming 1967 Simula 67 1980 Smalltalk 80 1983 C++ 2014 Everywhere!
7. Advances take decades to become mainstream
8. Market Growth[1] 0 500 1000 1500 2000 2500 3000 1975 1980 1985 1990 1995 2000 2005 2010 2015* 2020* Units (Millions) [1]  eTForecasts  Worldwide  PC  Market  Research  Report  
9. 0 500 1000 1500 2000 2500 3000 1975 1980 1985 1990 1995 2000 2005 2010 2015* 2020* Units (Millions) Market Growth •  Structured Programming •  Module Systems •  Dynamic Memory Allocation
10. 0 500 1000 1500 2000 2500 3000 1975 1980 1985 1990 1995 2000 2005 2010 2015* 2020* Units (Millions) Market Growth •  Garbage Collection •  OOP •  VMs and JIT
11. 0 500 1000 1500 2000 2500 3000 1975 1980 1985 1990 1995 2000 2005 2010 2015* 2020* Units (Millions) Market Growth You are here!
12. What’s holding us back?
13. The Market has Shifted •  SaaS is the norm •  Shorter time to market •  Latency requirements are stricter •  Availability is business-critical
14. The Free Lunch is Over[1] [1]  The  Free  Lunch  is  Over,  Herb  StuOer  (Dr.  Dobb's  Journal,  2005)   •  CPUs aren’t getting (much) faster •  Rather, they’re getting parallel •  Concurrency is the new kid on the block
15. Datasets are Getting Bigger •  Terabyte-scale data is common •  Computation demand –  NoSQL –  Big Data •  Specialized hardware (SAN) is a hindrance
16. We need new abstractions.
17. Three desirable attributes:
18. Today’s systems are highly available & reliable. Tomorrow’s abstraction must… encourage correct code.
19. Complexity is the Mind Killer •  Complex system è bigger codebase •  More code è more bugs •  We want less code •  We want correct code
20. Control flow è Data flow We need to invert our thinking.
21. How è What We need to invert our thinking.
22. Control flow describes the how List<Person>  adults(List<Person>  in)  {        ArrayList<Person>  out  =  new  ArrayList<>();        for  (Person  p  :  in)          if  (p.getAge()  >=  18)                out.add(p);        return  out;   }  
23. Data flow describes the what List<Person>  adults(List<Person>  in)  {      return  in.stream()                        .filter(p  -­‐>  p.age  >=  18)                        .collect(Collectors.toList());   }  
24. “What” is Better •  Focuses on the problem •  Reduces accidental complexity •  Decouples intent from execution
25. Today’s systems are highly concurrent. Tomorrow’s abstraction must… encourage immutability.
26. The Case for Immutability •  Inherently thread- safe •  Referentially transparent •  Easier to reason about!
27. Today’s systems are horizontally scalable. Tomorrow’s abstraction must… simplify workload distribution.
28. Distribution 101 1.  Take a core computation 2.  Split it out 3.  Run on many nodes 4.  Profit
29. Why is this hard? •  Behavior-as-data •  Side effects •  High-performance frameworks è Lambdas è Immutability è Akka, Hadoop, Storm…
30. The future is functional. 1.  Data flow-oriented 2.  Immutable 3.  Easy to distribute
31. THANK YOU FOR YOUR TIME! tomer@tomergabel.com @tomerg http://il.linkedin.com/in/tomergabel