Implementing the LHC on a Whiteboard

A survival guide for passing coding interviews.

Transcript

1. Art and photos by Summer Gray Implementing the LHC on a Whiteboard How to survive coding interviews of all shapes and sizes
2. Why Coding Interviews?
4. This Mismatch… ❖ Is keeping programmers from getting jobs ❖ Is likely due to a lack of interview training (especially compared to the other training we do in our industry) ❖ Is fixable
5. What Is a Coding Interview? ❖ Take homes ❖ With or without time limits ❖ Technical interviews ❖ “Pairing” ❖ The dreaded whiteboard ❖ Auditions
6. What Do I Know About This? ❖ I work for NoRedInk ❖ We get a lot of applicants ❖ Roughly 75% of the candidates fail our “take home” ❖ Around 30% of the rest fail their first technical interview ❖ I see a lot of what doesn’t work
7. We’re hiring It’s In My Interest To Improve How You Do The better candidates do, the faster we hire and the less I interview
8. What This Talk Is Not? ❖ Guaranteed ❖ A moral judgement ❖ All-knowing ❖ Respectful of your time
9. How Can You Prepare?
10. Study ❖ Interviews commonly focus on a certain subset of programming knowledge ❖ This probably isn’t knowledge that you use daily ❖ Ensuring it’s fresh in your brain can help
11. Concepts Data Structures Algorithms Big O Notation Hash Tables Binary Search Dynamic Programming/ Memoization Trees/Tries/Graphs Bread-First Search/ Depth-First Search Recursion Linked Lists Merge Sort/ Quicksort Bit Manipulation Heaps Stacks and Queues
12. Concepts Data Structures Algorithms Big O Notation Hash Tables Binary Search Dynamic Programming/ Memoization Trees/Tries/Graphs Bread-First Search/ Depth-First Search Recursion Linked Lists Merge Sort/ Quicksort Bit Manipulation Heaps Stacks and Queues
13. http://bigocheatsheet.com/
14. Big O Notation ❖ A metric used to describe an upper bound on algorithm efficiency ❖ Can be used for time or space ❖ Drops constants and non-dominant terms ❖ Adds independent work; multiplies dependent work ❖ Not just an academic curiosity! It can help you solve problems faster and easier!
15. def sum_and_product(nums) { sum: nums.inject(0, :+), product: nums.inject(1, :*) } end p sum_and_product(1..10)
16. def sum_and_product(nums) { sum: nums.inject(0, :+), product: nums.inject(1, :*) } end p sum_and_product(1..10) O(n)
17. def sum_and_product(nums) { sum: nums.inject(0, :+), product: nums.inject(1, :*) } end p sum_and_product(1..10) O(n)
18. module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end end p Fib.recursive(10)
19. module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end end p Fib.recursive(10) O(2n)
20. module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end end p Fib.recursive(10) O(branchesdepth) O(2n)
21. def shared_count(a, b) sorted = b.sort a.count { |i| sorted.bsearch { |j| j >= i } == i } end p shared_count( [35, 55, 13, 49, 40, 59, 27], [39, 40, 60, 17, 55, 58, 35] )
22. def shared_count(a, b) sorted = b.sort a.count { |i| sorted.bsearch { |j| j >= i } == i } end p shared_count( [35, 55, 13, 49, 40, 59, 27], [39, 40, 60, 17, 55, 58, 35] ) O(b log b + a log b)
23. def shared_count(a, b) sorted = b.sort a.count { |i| sorted.bsearch { |j| j >= i } == i } end p shared_count( [35, 55, 13, 49, 40, 59, 27], [39, 40, 60, 17, 55, 58, 35] ) O(b log b + a log b)
24. def shared_count(a, b) sorted = b.sort a.count { |i| sorted.bsearch { |j| j >= i } == i } end p shared_count( [35, 55, 13, 49, 40, 59, 27], [39, 40, 60, 17, 55, 58, 35] ) O(b log b + a log b)
25. A: 13, 27, 35, 40, 49, 55, 59 B: 17, 35, 39, 40, 55, 58, 60 Given two sorted arrays, find the elements in common. The arrays are the same length and each has all distinct elements.
26. A Brute Force Solution a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] shared = a.select { |n| b.include?(n) } p shared
27. A Brute Force Solution a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] shared = a.select { |n| b.include?(n) } p shared
28. Bungling Towards Ideal a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] i = 0 shared = b.each_with_object([ ]) do |n, array| while i < a.size && a[i] < n i += 1 end array << n if a[i] == n end p shared
29. Bungling Towards Ideal a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] i = 0 shared = b.each_with_object([ ]) do |n, array| while i < a.size && a[i] < n i += 1 end array << n if a[i] == n end p shared
30. Bungling Towards Ideal a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] i = 0 shared = b.each_with_object([ ]) do |n, array| while i < a.size && a[i] < n i += 1 end array << n if a[i] == n end p shared
31. Straightforward Linear a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] c = b.each_with_object({ }) do |n, hash| hash[n] = true end shared = a.select { |n| c.include?(n) } p shared
32. Straightforward Linear a = [13, 27, 35, 40, 49, 55, 59] b = [17, 35, 39, 40, 55, 58, 60] c = b.each_with_object({ }) do |n, hash| hash[n] = true end shared = a.select { |n| c.include?(n) } p shared
33. Ruby Can Sometimes Help ❖ Minitest::Benchmark supports: ❖ assert_performance_constant() ❖ assert_performance_exponential() ❖ assert_performance_linear() ❖ assert_performance_logarithmic() ❖ assert_performance_power()
34. Analyzing Algorithms module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end def memoized(i) cache = Hash.new { |hash, n| hash[n] = n < 2 ? n : hash[n - 2] + hash[n - 1] } cache[i] end def dynamic(i) return i if i < 2 a, b = 0, 1 (i - 2).times do a, b = b, a + b end a + b end end
35. Analyzing Algorithms module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end def memoized(i) cache = Hash.new { |hash, n| hash[n] = n < 2 ? n : hash[n - 2] + hash[n - 1] } cache[i] end def dynamic(i) return i if i < 2 a, b = 0, 1 (i - 2).times do a, b = b, a + b end a + b end end
36. Analyzing Algorithms module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end def memoized(i) cache = Hash.new { |hash, n| hash[n] = n < 2 ? n : hash[n - 2] + hash[n - 1] } cache[i] end def dynamic(i) return i if i < 2 a, b = 0, 1 (i - 2).times do a, b = b, a + b end a + b end end
37. Analyzing Algorithms module Fib module_function def recursive(i) return i if i < 2 recursive(i - 2) + recursive(i - 1) end def memoized(i) cache = Hash.new { |hash, n| hash[n] = n < 2 ? n : hash[n - 2] + hash[n - 1] } cache[i] end def dynamic(i) return i if i < 2 a, b = 0, 1 (i - 2).times do a, b = b, a + b end a + b end end
38. Almost Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibRecursiveBench < Minitest::Benchmark def self.bench_range [1, 3, 30] end def bench_recursive_fib assert_performance_exponential do |n| Fib.recursive(n) end end end
39. Almost Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibRecursiveBench < Minitest::Benchmark def self.bench_range [1, 3, 30] end def bench_recursive_fib assert_performance_exponential do |n| Fib.recursive(n) end end end
40. Almost Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibRecursiveBench < Minitest::Benchmark def self.bench_range [1, 3, 30] end def bench_recursive_fib assert_performance_exponential do |n| Fib.recursive(n) end end end
41. More Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibMemoizedBench < Minitest::Benchmark def self.bench_range bench_exp(1, 1_000) end def bench_memoized_fib assert_performance_linear do |n| Fib.memoized(n) end end end
42. More Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibMemoizedBench < Minitest::Benchmark def self.bench_range bench_exp(1, 1_000) end def bench_memoized_fib assert_performance_linear do |n| Fib.memoized(n) end end end
43. More Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibMemoizedBench < Minitest::Benchmark def self.bench_range bench_exp(1, 1_000) end def bench_memoized_fib assert_performance_linear do |n| Fib.memoized(n) end end end
44. The Most Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibDynamicBench < Minitest::Benchmark def bench_dynamic_fib assert_performance_linear do |n| Fib.dynamic(n) end end end
45. The Most Helpful require "minitest/autorun" require "minitest/benchmark" require_relative "fib" class FibDynamicBench < Minitest::Benchmark def bench_dynamic_fib assert_performance_linear do |n| Fib.dynamic(n) end end end
46. The classic “Nine Balls” interview brainteaser You have nine balls. Eight are the same weight and one is heavier. You are given a balance which tells you only wether the left side or right side is heavier. Find the heavy ball in just two uses of the scale.
49. Practice ❖ Work code challenges ❖ http://exercism.io/ ❖ http://rubyquiz.com/ ❖ You need to be able to consistently solve Exercism size problems in one hour
50. More Study and Practice
51. Have Some Open Source ❖ Be able to point to a published gem ❖ It barely matters what it does ❖ The most important details are a strong README, clean code, tests, and documentation ❖ A small gem is fine
52. What To Do While Coding?
53. Goals ❖ Not the same as normal on the job programming ❖ You need to avoid being seen as a bad hire ❖ If you can avoid major mistakes, you’re ahead
54. Tip #1 Read Carefully And/or ask as many clarifying questions as needed
55. Tip #2 Follow Directions Many candidates do not do this
56. Tip #3 Stop and Think You don’t have time not to do this
58. Tip #4 Think Out Loud Magic words: • “My first thought is…” • “I’m considering the tradeoffs…” • “I might circle back to this…” • “I don’t know.”
59. Tip #5 Don’t Make Offensive Comments Show that you have personality and a little bit of humor, but keep it appropriate
60. Tip #6 Start at the Hard Part • Don’t begin by designing objects • Jump straight to the interesting bit • Fake input as needed • Ignore UI completely
61. Tip #7 Test the Core Pretty much no one tests anything, so just a little, at the key place, is a win
62. Tip #8 Avoid Dependencies And don’t recreate them manually
63. Tip #9 Write Less Code Don’t “gold plate”
64. Tip #10 Ignore “Bonus” Challenges Seriously, it’s just not worth it
65. Tip #11 Consider Time Limits Soft Submit on time, but resubmit later if needed
66. Tip #12 Show Off Your Text Editing Skills • Know five editing shortcuts • Make one multiple cursors or macros
67. Maximizing for Success ❖ You probably won’t nail all of my tips every time ❖ That’s fine ❖ Interviews are hard for everyone and everyone makes mistakes ❖ Hit as many of the points we’ve discussed as you can ❖ Those you get will help
68. Thanks and I hope you ace your interviews!