Scala algorithm: Monitor success rate of a process that may fail

Published

Algorithm goal

In a distributed system, we wish to monitor processes which may succeed or fail; if a process fails too often, we should kill it. Implement an algorithm or data structure that, based on a set of observations of successes and failures, can tell us whether the process needs to be killed.

In software in practice, this is called flapping, where a service keeps on dying or not performing in some way; it could be due to a memory leak for instance, and such a service can recover by killing it and restarting it.

Assume the following inputs:

  1. A # of target successes (\(a\))
  2. A # of attempts minimum (\(b\)) (meaning, success ratio is \(a / b\) once \(b\) attempts are reached; as we'd otherwise have 100% failure rate if we only considered one attempt)

Test cases in Scala

assert(
  !SuccessMonitor(targetSuccesses = 4, outOfAttempts = 5).succeeded.shouldKill
)
assert(
  !SuccessMonitor(
    targetSuccesses = 4,
    outOfAttempts = 5
  ).succeeded.failed.shouldKill
)
assert(
  !SuccessMonitor(
    targetSuccesses = 4,
    outOfAttempts = 5
  ).succeeded.failed.succeeded.shouldKill
)
assert(
  !SuccessMonitor(
    targetSuccesses = 4,
    outOfAttempts = 5
  ).succeeded.failed.succeeded.failed.shouldKill
)
assert(
  SuccessMonitor(
    targetSuccesses = 4,
    outOfAttempts = 5
  ).succeeded.failed.succeeded.failed.succeeded.shouldKill
)
assert(
  !SuccessMonitor(
    targetSuccesses = 4,
    outOfAttempts = 5
  ).succeeded.succeeded.succeeded.failed.succeeded.shouldKill
)
assert(
  !SuccessMonitor(
    targetSuccesses = 4,
    outOfAttempts = 5
  ).failed.failed.shouldKill
)

Algorithm in Scala

17 lines of Scala (compatible versions 2.13 & 3.0), showing how concise Scala can be!

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Explanation

This is a State machine, which enables us to represent transitions in an immutable fashion. While the algorithm is fairly straightforward, the way to implement it in Scala in the neatest way is using an immutable Queue combined with pattern matching: basically, we dequeue from a list of results only when we had reached the # of attempts \(b\). (this is © from www.scala-algorithms.com)

Scala concepts & Hints

  1. Pattern Matching

    Pattern matching in Scala lets you quickly identify what you are looking for in a data, and also extract it.

    assert("Hello World".collect {
  case character if Character.isUpperCase(character) => character.toLower
} == "hw")

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Fully unit-tested, with explanations and relevant concepts; new algorithms published about once a week.

  1. Compute the length of longest valid parentheses
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  4. Remove duplicates from an unsorted List
  5. Make a queue using stacks (Lists in Scala)
  6. Find height of binary tree
  7. Single-elimination tournament tree
  8. Reverse Polish Notation calculator
  9. Quick Sort sorting algorithm in pure immutable Scala
  10. Check word in grid (depth-first search)
  11. Maximum wait at a fuel station
  12. Find minimum missing positive number in a sequence
  13. Least-recently used cache (LRU)
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  18. Matching parentheses algorithm with foldLeft and a state machine
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  21. Remove duplicates from a sorted list (state machine)
  22. Token Bucket Rate Limiter
  23. Check word in grid (stack-safe)
  24. Leaky Bucket Rate Limiter
  25. Merge Sort: stack-safe, tail-recursive, in pure immutable Scala, N-way
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  27. Longest increasing sub-sequence length
  28. Reverse first n elements of a queue
  29. Binary search a generic Array
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  31. Merge Sort: in pure immutable Scala
  32. Make a queue using Maps
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  35. Add numbers without using addition (plus sign)
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  39. Find kth largest element in a List
  40. Balanced parentheses algorithm with tail-call recursion optimisation
  41. Reverse a String's words efficiently
  42. Count number of changes (manipulations) needed to make an anagram with an efficient foldLeft
  43. Count passing cars
  44. Count dist intersections
  45. Establish execution order from dependencies
  46. Counting inversions of a sequence (array) using a Merge Sort
  47. Longest common prefix of strings
  48. Check if an array is a palindrome
  49. Compute missing ranges
  50. Check a directed graph has a routing between two nodes (depth-first search)
  51. Compute nth row of Pascal's triangle
  52. Run-length encoding (RLE) Decoder
  53. Check if a number is a palindrome
  54. In a range of numbers, count the numbers divisible by a specific integer
  55. Merge intervals
  56. Compute minimum number of Fibonacci numbers to reach sum
  57. Find the longest palindrome within a string
  58. Find the index of a substring ('indexOf')
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  74. Check if a String is a palindrome
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  76. Remove duplicates from a sorted list (Sliding)
  77. Monitor success rate of a process that may fail
  78. Least-recently used cache (MRU)
  79. Find sub-array with the maximum sum
  80. Find the minimum absolute difference of two partitions
  81. Find maximum potential profit from an array of stock price
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  85. Find combinations adding up to N (non-unique)
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  100. Rotate a matrix by 90 degrees clockwise

Explore the 22 most useful Scala concepts

To save you going through various tutorials, we cherry-picked the most useful Scala concepts in a consistent form.

  1. Class Inside Class
  2. Class Inside Def
  3. Collect
  4. Def Inside Def
  5. Drop, Take, dropRight, takeRight
  6. foldLeft and foldRight
  7. For-comprehension
  8. Lazy List
  9. Option Type
  10. Ordering
  11. Partial Function
  12. Pattern Matching
  13. Range
  14. scanLeft and scanRight
  15. Sliding / Sliding Window
  16. Stack Safety
  17. State machine
  18. Tail Recursion
  19. Type Class
  20. Variance
  21. View
  22. Zip

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