Book Review - Coders At Work


I just finished reading the book Coders at Work by Peter Seibel. This wonderful book is filled with interviews with prominent programmers: Joe Armstrong, Simon Peyton Jones, Donald Knuth, among others. My review isn't going to be the standard book review. Instead I'm going to talk about some of the specific ideas I picked up while reading the book and discuss those ideas, rather than the book itself. For those of you who are looking for more of a standard review, all I can really say is that Coders at Work is well worth the time for a programmer or a computer historian/hobbyist. Unfortunately for the casual non-computer-field reader, the interview topics (and the interviewees) assume a certain level of prior knowledge. What makes this book so good is that different people will be able to walk away from it with different ideas, depending on their areas of expertise and interest. As the industry grows and changes, we definitely do things based on what the people before us have already discovered - Seibel’s interview subjects are the fascinating programmers whose ideas and hard work make possible all the cool things we get to play with today.

Code Readings:

In the interview with Douglas Crockford there was a lot of talk about code readings. If you’re not sure what those are (I wasn’t), from the way Douglas describes it they are regular meetings where programmers read the code base together. In my mind it sounds like someone is giving a presentation but instead of it being PowerPoint slides, it’s code. I picture the presenter going line by line and describing what is going on and why he or she decided to do things a particular way. These sound like an excellent idea to me. In my experience so far, the code review process seems to have been relegated to using Reviewboard or other similar website-based tools. I'm not trying to insult these tools, but they remove the human factor of the code review and hinder the discussion that can happen over a particular piece of code. I understand that programmer time is expensive and managers should be careful how much of that time is spent in meetings. But I think these meetings could benefit everyone and will have a return on investment (that is the time spent) in ways that cannot be quickly seen. One benefit is that everyone who attends will come out with a basic idea of what the code looks like within a given project, even if they’re not working on that project directly. So if things shift around (as they inevitably do) and a new person is moved into that project, he or she won’t be starting from scratch. Even just a little bit of knowledge might help a programmer get over the initial shock of having to dive into a new project.

Programmers need Empathy:

In the interview with Joshua Bloch there was a quick chat about programmer personalities. There were a couple of choice quotes regarding programmer empathy for the users, like, “...intelligence is not a scalar quantity; it’s a vector quantity. And if you lack empathy or emotional intelligence, then you shouldn’t be designing APIs or GUIs or languages.” and “What we’re doing is an aesthetic pursuit. It involves craftsmanship as well as mathematics and it involves people skills and prose skills—all of these things that we don’t necessarily think of as engineering but without which I don’t think you’ll ever be a really good engineer.”

I bring these quotes up because I agree with them, and because they speak of issues that are largely ignored in the engineering community (again, in my experience). I've sure we’ve all run into programmers that only care about the technical aspects of the project or the correctness of their code and do not concern themselves for how the user might interact with the system. For example, the company I work for designed a web-app that included separate “Kill” and “Terminate” buttons. This was a complete UI fail that could have been avoided, had the programmer (we’ll call him/her “Terry”) had empathy for the user. As a programmer, I understand the difference between SIGKILL and SIGTERM, but those buttons would be confusing to even the more-advanced-than-average user (and go far beyond failing the grandmother test). When this was brought up as a ticket for “Terry” to fix, “Terry” argued that there was nothing technically incorrect about the problem and closed the ticket “Won't Fix.”

Hiring:

One thing I noticed in a lot of the interviews was that Peter asked the interviewee what he or she looked for in a potential new hire. What I found fascinating is that none of them said, “I like to ask really hard questions.” or “I ask really obscure questions about the programming language they use to see how well they know it.” They mostly talked about looking for someone who had passion for the craft.

As someone who is frequently on both sides of the hiring table, I know that the current hiring process in Silicon Valley doesn't normally ask questions about WHO is being hired. Questions about hobbies or interests are rare. It's mostly just technical questions, as if the person who is applying for the job is another computer whose sole purpose is to solve a company's problems. All the emphasis seems to be focused on the technical skills of the interviewee. While it's important to know if someone can “do the job,” tech skills alone do not make a person a great programmer - a good one, yes, but I don’t get the impression that companies are settling for “good” anymore. Passion is what gives someone the tenacity to work through the obstacles and become a great programmer, and if we as interviewers insist on focusing only on hard skills, we risk cheating ourselves out of the truly great engineers.

Donald Knuth:

Peter also asked every single interviewee about Donald Knuth's books and his “literate programming”
style of writing code. I will admit that I have not read his books, but I want to. I'm not sure what this literate programming style is, but I'm curious enough about it to learn, and see if there are some aspects of it I can incorporate into my own coding and documentation style. Next step, pick up an electronic version of the book, “Literate Programming” and give it a good read.

I fully enjoyed Coders at Work. I took away much more from the interviews than I talked about here, and I’m sure there’s even more to learn. I'm planning on rereading it again in a couple of years; as I grow as a programmer I will have a different perspective on the craft, and might find a whole new or different set of lessons from what I got this time. If not, at the very least I’ll have the pleasure of reading it again.

Project Euler: Problem 14

“The following iterative sequence is defined for the set of positive integers: n → n/2 (n is even) n → 3n + 1 (n is odd).  Using the rule above and starting with 13, we generate the following sequence: 13 → 40 → 20 → 10 → 5 → 16 → 8 → 4 → 2 → 1.  It can be seen that this sequence (starting at 13 and finishing at 1) contains 10 terms.  Although it has not been proved yet (Collatz Problem), it is thought that all starting numbers finish at 1.  Which starting number, under one million, produces the longest chain?  NOTE: Once the chain starts the terms are allowed to go above one million.”

Not many of you may be aware of this, but about a year ago I wrote up a blog post that discussed Collatz chains in Haskell.  You can find that post here: . Having some of the code already written made coming up with the solution easier.  However, just because I had one function doesn't mean I had the whole problem licked.  I still had a fair amount of work in front of me.  Below is my code from the first attempt at a solution:

  1. module Main where
  2.  
  3. import Data.List
  4.  
  5. chain' :: Integer -> [Integer]
  6. chain' 1 = [1]
  7. chain' n
  8.    | n <= 0 = []
  9.    | even n = n : chain' (n `div` 2)
  10.    | odd n = n : chain' (n * 3 + 1)
  11.  
  12. main :: IO ()
  13. main = do
  14.     let seqx = map chain' [3..1000000]
  15.     let lengthx = map length seqx
  16.     print . maximum $ zip lengthx seqx

This code appears to be logically correct but was incredibly slow - so slow that after over 2 minutes it still hadn’t completed.  I admit I can be a little impatient with these things from time to time, but in this case something was obviously wrong.

I devised two optimizations:

  • Reverse the order of the list. I will be more likely to find the number with the longest chain near 1,000,000 than 3.
  • Use odd numbers only. This is based on the fact that in the chain' function an odd number gets multiplied right off the bat, whereas an even number is instantly divided by 2, and also on the assumption that a higher number will be more likely to have a longer chain.  (I admit this was a complete experiment - I had no proof that it would work ahead of time, and knew it gave me the right answer only after the fact.)

The code then morphed into:

  1. module Main where
  2.  
  3. import Data.List
  4.  
  5. chain' :: Integer -> [Integer]
  6. chain' 1 = [1]
  7. chain' n
  8.    | n <= 0 = []
  9.    | even n = n : chain' (n `div` 2)
  10.    | odd n = n : chain' (n * 3 + 1)
  11.  
  12. main :: IO ()
  13. main = do
  14.     let seqx = map chain' [999999,999997..3]
  15.     let lengthx = map length seqx
  16.     print . maximum $ zip lengthx seqx

The problem I ran into with this code was that I received stack overflow errors; my list of tuples holding another long list of int’s was taking up to much memory.  I fixed this problem by computing the length of the list immediately after generating it.  The new code looked like this:

  1. import Data.List
  2.  
  3. chain' :: Integer -> [Integer]
  4. chain' 1 = [1]
  5. chain' n
  6.    | n <= 0 = []
  7.    | even n = n : chain' (n `div` 2)
  8.    | odd n = n : chain' (n * 3 + 1)
  9.  
  10. main :: IO ()
  11. main = do
  12.     let seqx = map (\x → (x, length $ chain' x) [999999,999997..3]
  13.     print . maximum $ seqx

This got me a result within the one minute time frame, but it still wasn't the right answer.  Can you figure out why?  Using the great code Jedai posted in the comments of my Apache log post, I was able to get my answer and finally complete the problem:

  1. module Main where                                                                             
  2.  
  3.  import Data.Tuple
  4.  import Data.List (sortBy)
  5.  import Data.Function (on)
  6.  
  7.  chain' :: Integer -> [Integer]
  8.  chain' 1 = [1]
  9.  chain' n
  10.    | n <= 0 = []
  11.    | even n = n : chain' (n `div` 2)
  12.    | odd n = n : chain' (n * 3 + 1)
  13.  
  14.  main :: IO ()
  15.  main = do
  16.      let seqx = map (\x -> (x, length $ chain' x)) [999999,999997..3]
  17.      print . fst . head $ sortBy (flip compare `on` snd) seqx

After figuring that out, getting the python answer was a breeze:

  1. #!/usr/bin/python
  2. """Python solution for Project Euler problem #14."""
  3.  
  4. from itertools import imap
  5.  
  6. def sequence(number):
  7. t_num = number
  8. count = 1
  9.  
  10. while(t_num > 1):
  11. if t_num % 2 == 0:
  12. t_num /= 2
  13. else:
  14. t_num = (t_num * 3) + 1
  15.  
  16. count += 1
  17.  
  18. return (count, number)
  19.  
  20. if __name__ == "__main__":
  21. print max(imap(sequence, xrange(999999,3,-2)))

Here are the speed numbers:
Haskell (complied) : 14.758s
Python : 18.537s
Haskell (runghc): 15.217s

I think the use of recursion in my Haskell code is affecting its speed of computation.  As I learned from problem 12, I can use the State Monad again to speed things up.  But I also learned from the comments of problem 12 that some people were able to substitute a scan or fold in the State Monad’s place.  So I decided to shoot for one more solution.  After studying up on scan and fold, and finding that neither was really what I wanted, I found iterate. Using iterate I was able to change the program to this:

  1. module Main where
  2.  
  3. import Data.Tuple
  4. import Data.List (sortBy, iterate)
  5. import Data.Function (on)
  6.  
  7. chain' :: Integer -> Int
  8. chain' n  
  9.     | n < 1 = 0
  10.     | otherwise = 1 + (length $ (takeWhile ( > 1) $ iterate (\x -> if even x then x `div` 2 else x * 3 + 1) n))
  11.  
  12. main :: IO ()
  13. main = do
  14.     let seqx = map (\x -> (x, chain' x)) [999999,999997..3]
  15.     print . fst . head $ sortBy (flip compare `on` snd) seqx

The new chain' function doesn't read as cleanly as the old one, but it does remove the recursion I was talking about earlier.  The computer gods rewarded my efforts by reducing the run times to these:

Haskell (complied) : 10.933s
Haskell (runghc): 11.744s

From 14.758 to 10.933 - almost 4 seconds taken off the clock!  I think a speed up like that calls for some celebrating.  Which is exactly what I'm going to do before I start on problem 15.

More than 1095 days of existence!! Or...Happy third birthday to Scrollingtext!!!


Another 365 have come and gone since the last time I've written one of these birthday posts, and like last time I'm still surprised and still proud that this blog and my creative juices have survived this long.

I will be the first to admit that I'm totally amazed over 17 THOUSAND people from all over the world have dropped by my site and read my writing. Who know what I had to say was that interesting? Knowing that interest is growing really makes me want to continue improving my writing and finding more interesting projects to explore. Last year Scrollingtext saw a little more than 10 thousand people - there’s been an amazing amount of growth over the last year, and I think I'm going to make it a personal goal to continue this trend.

The five most popular posts for the year are:

  • PXE Network Boot VirtualBox, PT 2 – This one has been up here for a long time. Seeing this in the top 5 makes me think I should think about doing follow-up posts on VirtualBox. Apparently people find this helpful.
  • Using curl and a user agent string for web scraping – I had no idea that web scraping would be so popular. Maybe I should write a follow-up article for this one too.
  • Project Euler: Problem 5 – The biggest thing for me about this post was learning about lcm's. As has been pointed out to me in the comments for Problem 12, and as a statement about programming in general, understanding the problem and knowing the right algorithm (or two) that solve it is more than half the battle.
  • Project Euler: Problem 4 – I think this one takes the cake for most comments on a single post. Oh and OJ, I was just reading over those comments and I think I picked up on your subtle optimization. Nice little trick!
  • Project Euler: Problem 12 – I was really proud of this post. I spent a lot of time and energy learning how to use the State Monad to get the problem to run and the result was totally worth the effort.

Things I've learned:
For the last year I've had an actual programming job, and within that job I've learned a lot of python programming tricks and idioms - some of which I know have carried over into my personal style. My playing around with Haskell as also affected my style of programming. My boss can identify my code on sight because I use fewer variables than my co-workers. So in a way I've learned that everything I do, both at work and on my own, are connected. The time spent thinking about Project Euler problems and writing that code may not directly translate to what I do at work, but it definitely has an effect on the code I end up writing at work (and the quality of that code).

No retrospective post would not be complete without the “Thank yous.” First, thank you to my girlfriend , Kelsey. If it wasn't for her I wouldn't be able to properly articulate my thoughts to the world. And secondly thank you to the 17 thousand people who visited the site last year, all the people who visited in the previous two, and everyone who keeps coming back. Having a place where I can post what I'm currently working on in my spare time is a nifty thing. But having people actually read it makes it so much cooler! So thank you to everyone who's ever read something on my site, where ever you are now.

On that note, Happy 3rd birthday Scrollingtext. Here’s to another year and hopefully many more.

** Photo copied from here: http://www.flickr.com/photos/ebelbeb/5244753318/

Project Euler: Problem 13

Problem thirteen from Project Euler is one of those problems that's so simple, I don't understand why it's in the double digits section. The problem reads: “Work out the first ten digits of the sum of the following one-hundred 50-digit numbers.”
It then proceeds to list 100 long numbers. I'm not going to paste them here because they are in the code solutions below and I don't want to clog up the “tubez” with more redundant information than I'm about to.

Enough of my jibber-jabber. Here is my Haskell solution first (trying to change things up here):

  1. module Main where
  2.  
  3. main :: IO()
  4. main = do
  5. print . take 10 . show $ sum big_number
  6. where big_number = [ 37107287533902102798797998220837590246510135740250
  7. , 46376937677490009712648124896970078050417018260538
  8. , 74324986199524741059474233309513058123726617309629
  9. , 91942213363574161572522430563301811072406154908250
  10. , 23067588207539346171171980310421047513778063246676
  11. , 89261670696623633820136378418383684178734361726757
  12. , 28112879812849979408065481931592621691275889832738
  13. , 44274228917432520321923589422876796487670272189318
  14. , 47451445736001306439091167216856844588711603153276
  15. , 70386486105843025439939619828917593665686757934951
  16. , 62176457141856560629502157223196586755079324193331
  17. , 64906352462741904929101432445813822663347944758178
  18. , 92575867718337217661963751590579239728245598838407
  19. , 58203565325359399008402633568948830189458628227828
  20. , 80181199384826282014278194139940567587151170094390
  21. , 35398664372827112653829987240784473053190104293586
  22. , 86515506006295864861532075273371959191420517255829
  23. , 71693888707715466499115593487603532921714970056938
  24. , 54370070576826684624621495650076471787294438377604
  25. , 53282654108756828443191190634694037855217779295145
  26. , 36123272525000296071075082563815656710885258350721
  27. , 45876576172410976447339110607218265236877223636045
  28. , 17423706905851860660448207621209813287860733969412
  29. , 81142660418086830619328460811191061556940512689692
  30. , 51934325451728388641918047049293215058642563049483
  31. , 62467221648435076201727918039944693004732956340691
  32. , 15732444386908125794514089057706229429197107928209
  33. , 55037687525678773091862540744969844508330393682126
  34. , 18336384825330154686196124348767681297534375946515
  35. , 80386287592878490201521685554828717201219257766954
  36. , 78182833757993103614740356856449095527097864797581
  37. , 16726320100436897842553539920931837441497806860984
  38. , 48403098129077791799088218795327364475675590848030
  39. , 87086987551392711854517078544161852424320693150332
  40. , 59959406895756536782107074926966537676326235447210
  41. , 69793950679652694742597709739166693763042633987085
  42. , 41052684708299085211399427365734116182760315001271
  43. , 65378607361501080857009149939512557028198746004375
  44. , 35829035317434717326932123578154982629742552737307
  45. , 94953759765105305946966067683156574377167401875275
  46. , 88902802571733229619176668713819931811048770190271
  47. , 25267680276078003013678680992525463401061632866526
  48. , 36270218540497705585629946580636237993140746255962
  49. , 24074486908231174977792365466257246923322810917141
  50. , 91430288197103288597806669760892938638285025333403
  51. , 34413065578016127815921815005561868836468420090470
  52. , 23053081172816430487623791969842487255036638784583
  53. , 11487696932154902810424020138335124462181441773470
  54. , 63783299490636259666498587618221225225512486764533
  55. , 67720186971698544312419572409913959008952310058822
  56. , 95548255300263520781532296796249481641953868218774
  57. , 76085327132285723110424803456124867697064507995236
  58. , 37774242535411291684276865538926205024910326572967
  59. , 23701913275725675285653248258265463092207058596522
  60. , 29798860272258331913126375147341994889534765745501
  61. , 18495701454879288984856827726077713721403798879715
  62. , 38298203783031473527721580348144513491373226651381
  63. , 34829543829199918180278916522431027392251122869539
  64. , 40957953066405232632538044100059654939159879593635
  65. , 29746152185502371307642255121183693803580388584903
  66. , 41698116222072977186158236678424689157993532961922
  67. , 62467957194401269043877107275048102390895523597457
  68. , 23189706772547915061505504953922979530901129967519
  69. , 86188088225875314529584099251203829009407770775672
  70. , 11306739708304724483816533873502340845647058077308
  71. , 82959174767140363198008187129011875491310547126581
  72. , 97623331044818386269515456334926366572897563400500
  73. , 42846280183517070527831839425882145521227251250327
  74. , 55121603546981200581762165212827652751691296897789
  75. , 32238195734329339946437501907836945765883352399886
  76. , 75506164965184775180738168837861091527357929701337
  77. , 62177842752192623401942399639168044983993173312731
  78. , 32924185707147349566916674687634660915035914677504
  79. , 99518671430235219628894890102423325116913619626622
  80. , 73267460800591547471830798392868535206946944540724
  81. , 76841822524674417161514036427982273348055556214818
  82. , 97142617910342598647204516893989422179826088076852
  83. , 87783646182799346313767754307809363333018982642090
  84. , 10848802521674670883215120185883543223812876952786
  85. , 71329612474782464538636993009049310363619763878039
  86. , 62184073572399794223406235393808339651327408011116
  87. , 66627891981488087797941876876144230030984490851411
  88. , 60661826293682836764744779239180335110989069790714
  89. , 85786944089552990653640447425576083659976645795096
  90. , 66024396409905389607120198219976047599490197230297
  91. , 64913982680032973156037120041377903785566085089252
  92. , 16730939319872750275468906903707539413042652315011
  93. , 94809377245048795150954100921645863754710598436791
  94. , 78639167021187492431995700641917969777599028300699
  95. , 15368713711936614952811305876380278410754449733078
  96. , 40789923115535562561142322423255033685442488917353
  97. , 44889911501440648020369068063960672322193204149535
  98. , 41503128880339536053299340368006977710650566631954
  99. , 81234880673210146739058568557934581403627822703280
  100. , 82616570773948327592232845941706525094512325230608
  101. , 22918802058777319719839450180888072429661980811197
  102. , 77158542502016545090413245809786882778948721859617
  103. , 72107838435069186155435662884062257473692284509516
  104. , 20849603980134001723930671666823555245252804609722
  105. , 53503534226472524250874054075591789781264330331690]

followed by my Python solution:

  1. #!/usr/bin/python
  2. """
  3. code solution for project euler's problem #13 in python.
  4. """
  5. from __future__ import print_function
  6.  
  7. def print_10(number):
  8. print(str(number)[0:10])
  9.  
  10. if __name__ == "__main__":
  11.  
  12. big_number = [ 37107287533902102798797998220837590246510135740250,
  13. 46376937677490009712648124896970078050417018260538,
  14. 74324986199524741059474233309513058123726617309629,
  15. 91942213363574161572522430563301811072406154908250,
  16. 23067588207539346171171980310421047513778063246676,
  17. 89261670696623633820136378418383684178734361726757,
  18. 28112879812849979408065481931592621691275889832738,
  19. 44274228917432520321923589422876796487670272189318,
  20. 47451445736001306439091167216856844588711603153276,
  21. 70386486105843025439939619828917593665686757934951,
  22. 62176457141856560629502157223196586755079324193331,
  23. 64906352462741904929101432445813822663347944758178,
  24. 92575867718337217661963751590579239728245598838407,
  25. 58203565325359399008402633568948830189458628227828,
  26. 80181199384826282014278194139940567587151170094390,
  27. 35398664372827112653829987240784473053190104293586,
  28. 86515506006295864861532075273371959191420517255829,
  29. 71693888707715466499115593487603532921714970056938,
  30. 54370070576826684624621495650076471787294438377604,
  31. 53282654108756828443191190634694037855217779295145,
  32. 36123272525000296071075082563815656710885258350721,
  33. 45876576172410976447339110607218265236877223636045,
  34. 17423706905851860660448207621209813287860733969412,
  35. 81142660418086830619328460811191061556940512689692,
  36. 51934325451728388641918047049293215058642563049483,
  37. 62467221648435076201727918039944693004732956340691,
  38. 15732444386908125794514089057706229429197107928209,
  39. 55037687525678773091862540744969844508330393682126,
  40. 18336384825330154686196124348767681297534375946515,
  41. 80386287592878490201521685554828717201219257766954,
  42. 78182833757993103614740356856449095527097864797581,
  43. 16726320100436897842553539920931837441497806860984,
  44. 48403098129077791799088218795327364475675590848030,
  45. 87086987551392711854517078544161852424320693150332,
  46. 59959406895756536782107074926966537676326235447210,
  47. 69793950679652694742597709739166693763042633987085,
  48. 41052684708299085211399427365734116182760315001271,
  49. 65378607361501080857009149939512557028198746004375,
  50. 35829035317434717326932123578154982629742552737307,
  51. 94953759765105305946966067683156574377167401875275,
  52. 88902802571733229619176668713819931811048770190271,
  53. 25267680276078003013678680992525463401061632866526,
  54. 36270218540497705585629946580636237993140746255962,
  55. 24074486908231174977792365466257246923322810917141,
  56. 91430288197103288597806669760892938638285025333403,
  57. 34413065578016127815921815005561868836468420090470,
  58. 23053081172816430487623791969842487255036638784583,
  59. 11487696932154902810424020138335124462181441773470,
  60. 63783299490636259666498587618221225225512486764533,
  61. 67720186971698544312419572409913959008952310058822,
  62. 95548255300263520781532296796249481641953868218774,
  63. 76085327132285723110424803456124867697064507995236,
  64. 37774242535411291684276865538926205024910326572967,
  65. 23701913275725675285653248258265463092207058596522,
  66. 29798860272258331913126375147341994889534765745501,
  67. 18495701454879288984856827726077713721403798879715,
  68. 38298203783031473527721580348144513491373226651381,
  69. 34829543829199918180278916522431027392251122869539,
  70. 40957953066405232632538044100059654939159879593635,
  71. 29746152185502371307642255121183693803580388584903,
  72. 41698116222072977186158236678424689157993532961922,
  73. 62467957194401269043877107275048102390895523597457,
  74. 23189706772547915061505504953922979530901129967519,
  75. 86188088225875314529584099251203829009407770775672,
  76. 11306739708304724483816533873502340845647058077308,
  77. 82959174767140363198008187129011875491310547126581,
  78. 97623331044818386269515456334926366572897563400500,
  79. 42846280183517070527831839425882145521227251250327,
  80. 55121603546981200581762165212827652751691296897789,
  81. 32238195734329339946437501907836945765883352399886,
  82. 75506164965184775180738168837861091527357929701337,
  83. 62177842752192623401942399639168044983993173312731,
  84. 32924185707147349566916674687634660915035914677504,
  85. 99518671430235219628894890102423325116913619626622,
  86. 73267460800591547471830798392868535206946944540724,
  87. 76841822524674417161514036427982273348055556214818,
  88. 97142617910342598647204516893989422179826088076852,
  89. 87783646182799346313767754307809363333018982642090,
  90. 10848802521674670883215120185883543223812876952786,
  91. 71329612474782464538636993009049310363619763878039,
  92. 62184073572399794223406235393808339651327408011116,
  93. 66627891981488087797941876876144230030984490851411,
  94. 60661826293682836764744779239180335110989069790714,
  95. 85786944089552990653640447425576083659976645795096,
  96. 66024396409905389607120198219976047599490197230297,
  97. 64913982680032973156037120041377903785566085089252,
  98. 16730939319872750275468906903707539413042652315011,
  99. 94809377245048795150954100921645863754710598436791,
  100. 78639167021187492431995700641917969777599028300699,
  101. 15368713711936614952811305876380278410754449733078,
  102. 40789923115535562561142322423255033685442488917353,
  103. 44889911501440648020369068063960672322193204149535,
  104. 41503128880339536053299340368006977710650566631954,
  105. 81234880673210146739058568557934581403627822703280,
  106. 82616570773948327592232845941706525094512325230608,
  107. 22918802058777319719839450180888072429661980811197,
  108. 77158542502016545090413245809786882778948721859617,
  109. 72107838435069186155435662884062257473692284509516,
  110. 20849603980134001723930671666823555245252804609722,
  111. 53503534226472524250874054075591789781264330331690]
  112.  
  113. print_10(sum(big_number))

and to continue adding in the spice, I have included a solution in Scala:

  1. import BigInt._
  2.  
  3. object problem_13 {
  4. def main (args : Array[String]){
  5. val big_number = List("37107287533902102798797998220837590246510135740250",
  6. "46376937677490009712648124896970078050417018260538",
  7. "74324986199524741059474233309513058123726617309629",
  8. "91942213363574161572522430563301811072406154908250",
  9. "23067588207539346171171980310421047513778063246676",
  10. "89261670696623633820136378418383684178734361726757",
  11. "28112879812849979408065481931592621691275889832738",
  12. "44274228917432520321923589422876796487670272189318",
  13. "47451445736001306439091167216856844588711603153276",
  14. "70386486105843025439939619828917593665686757934951",
  15. "62176457141856560629502157223196586755079324193331",
  16. "64906352462741904929101432445813822663347944758178",
  17. "92575867718337217661963751590579239728245598838407",
  18. "58203565325359399008402633568948830189458628227828",
  19. "80181199384826282014278194139940567587151170094390",
  20. "35398664372827112653829987240784473053190104293586",
  21. "86515506006295864861532075273371959191420517255829",
  22. "71693888707715466499115593487603532921714970056938",
  23. "54370070576826684624621495650076471787294438377604",
  24. "53282654108756828443191190634694037855217779295145",
  25. "36123272525000296071075082563815656710885258350721",
  26. "45876576172410976447339110607218265236877223636045",
  27. "17423706905851860660448207621209813287860733969412",
  28. "81142660418086830619328460811191061556940512689692",
  29. "51934325451728388641918047049293215058642563049483",
  30. "62467221648435076201727918039944693004732956340691",
  31. "15732444386908125794514089057706229429197107928209",
  32. "55037687525678773091862540744969844508330393682126",
  33. "18336384825330154686196124348767681297534375946515",
  34. "80386287592878490201521685554828717201219257766954",
  35. "78182833757993103614740356856449095527097864797581",
  36. "16726320100436897842553539920931837441497806860984",
  37. "48403098129077791799088218795327364475675590848030",
  38. "87086987551392711854517078544161852424320693150332",
  39. "59959406895756536782107074926966537676326235447210",
  40. "69793950679652694742597709739166693763042633987085",
  41. "41052684708299085211399427365734116182760315001271",
  42. "65378607361501080857009149939512557028198746004375",
  43. "35829035317434717326932123578154982629742552737307",
  44. "94953759765105305946966067683156574377167401875275",
  45. "88902802571733229619176668713819931811048770190271",
  46. "25267680276078003013678680992525463401061632866526",
  47. "36270218540497705585629946580636237993140746255962",
  48. "24074486908231174977792365466257246923322810917141",
  49. "91430288197103288597806669760892938638285025333403",
  50. "34413065578016127815921815005561868836468420090470",
  51. "23053081172816430487623791969842487255036638784583",
  52. "11487696932154902810424020138335124462181441773470",
  53. "63783299490636259666498587618221225225512486764533",
  54. "67720186971698544312419572409913959008952310058822",
  55. "95548255300263520781532296796249481641953868218774",
  56. "76085327132285723110424803456124867697064507995236",
  57. "37774242535411291684276865538926205024910326572967",
  58. "23701913275725675285653248258265463092207058596522",
  59. "29798860272258331913126375147341994889534765745501",
  60. "18495701454879288984856827726077713721403798879715",
  61. "38298203783031473527721580348144513491373226651381",
  62. "34829543829199918180278916522431027392251122869539",
  63. "40957953066405232632538044100059654939159879593635",
  64. "29746152185502371307642255121183693803580388584903",
  65. "41698116222072977186158236678424689157993532961922",
  66. "62467957194401269043877107275048102390895523597457",
  67. "23189706772547915061505504953922979530901129967519",
  68. "86188088225875314529584099251203829009407770775672",
  69. "11306739708304724483816533873502340845647058077308",
  70. "82959174767140363198008187129011875491310547126581",
  71. "97623331044818386269515456334926366572897563400500",
  72. "42846280183517070527831839425882145521227251250327",
  73. "55121603546981200581762165212827652751691296897789",
  74. "32238195734329339946437501907836945765883352399886",
  75. "75506164965184775180738168837861091527357929701337",
  76. "62177842752192623401942399639168044983993173312731",
  77. "32924185707147349566916674687634660915035914677504",
  78. "99518671430235219628894890102423325116913619626622",
  79. "73267460800591547471830798392868535206946944540724",
  80. "76841822524674417161514036427982273348055556214818",
  81. "97142617910342598647204516893989422179826088076852",
  82. "87783646182799346313767754307809363333018982642090",
  83. "10848802521674670883215120185883543223812876952786",
  84. "71329612474782464538636993009049310363619763878039",
  85. "62184073572399794223406235393808339651327408011116",
  86. "66627891981488087797941876876144230030984490851411",
  87. "60661826293682836764744779239180335110989069790714",
  88. "85786944089552990653640447425576083659976645795096",
  89. "66024396409905389607120198219976047599490197230297",
  90. "64913982680032973156037120041377903785566085089252",
  91. "16730939319872750275468906903707539413042652315011",
  92. "94809377245048795150954100921645863754710598436791",
  93. "78639167021187492431995700641917969777599028300699",
  94. "15368713711936614952811305876380278410754449733078",
  95. "40789923115535562561142322423255033685442488917353",
  96. "44889911501440648020369068063960672322193204149535",
  97. "41503128880339536053299340368006977710650566631954",
  98. "81234880673210146739058568557934581403627822703280",
  99. "82616570773948327592232845941706525094512325230608",
  100. "22918802058777319719839450180888072429661980811197",
  101. "77158542502016545090413245809786882778948721859617",
  102. "72107838435069186155435662884062257473692284509516",
  103. "20849603980134001723930671666823555245252804609722",
  104. "53503534226472524250874054075591789781264330331690") map {BigInt(_)}
  105. val sums = big_number sum
  106. val su = sums toString
  107. val su10 = su take 10
  108. println(su10)
  109. }
  110. }

Some of you may be wondering, “Why a Scala solution?” To which I respond, “Why not?” Because that's a little short, I'll add that it has something to do with Scala starting to gain traction in the industry and me seeing if I would like to get paid to program in it.

The solution, in all three languages, is pretty simple. The recipe essentially says, “Put all numbers into a list. Get the sum of that list, turn that number into a string, and get the first 10 characters of that string.”

Times:
Haskell (compiled) : real 0m0.004s
Haskell (runghc) : real 0m0.314s
Python : real 0m0.059s
Scala (compiled) : real 0m0.757s

For the most part it's pretty standard in these tests to see performance times such that Haskell (compiled) < Python < Haskell (runghc). Java and Perl usually fall somewhere between the Haskell (compiled) and Python, in that order. To see Scala be 2x slower than Haskell (runghc) was a shocker. The only thing that makes sense to me for the slowdown is having to use the BigInt library. That is probably the biggest thing I took away from these time tests - if I want to do REALLY large number crunching and performance DOES matter, JVM-based languages might not be the best option.

A few thoughts on Scala:
If I haven't stated it already in this blog, I should now give the disclaimer that I'm not a Java fan. I know it still has its loyal followers, but I'm not one of them. Moving on. This was my first time working with Scala, and I'd like to finally welcome Java to the 21st century. While doing some research on the Scala language itself I read that “the industry” was moving to replace Java with Scala. I welcome that change. Does that mean I “like” Scala? The honest answer is, to butcher the quote the appliances from the Flintstones, “Eh, it's a language.” Scala is definitely an improvement over Java – not really that hard to do in my opinion – but, the language still feels unpolished. One quick way to kill the interpreter in Scala is to type “Int” then hit the enter key. Instead of error-ing out, the interpreter does a great job of interpreting a crash test car hitting a cement wall (I had to restart the whole thing.) When I tried the same “technique” in the Python interpreter, I got as a response and for Haskell's interpreter I received “Not in scope: data constructor `Int'”. I also found Scala's function composition to be a little lacking when compared to Haskell. I wasn't able to cleanly change the BigInt data type to String, and then only print out ten characters without requiring three separate val's. Yes, I could have used one var instead, but that's beside the point. I will admit it could be my inexperience with the language showing, so if anyone knows a smoother way to do this in Scala please share it in the comments.

All that being said, I do like the way Scala is trying to handle the reducing of Java's dot notation, and I think it's starting to make strides in the right direction in other areas. I'm open to working with Scala more, and look forward to seeing how it evolves over the next few years.

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