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4d47's solution

to ETL in the Clojure Track

Test suite

We are going to do the Transform step of an Extract-Transform-Load.


Extract-Transform-Load (ETL) is a fancy way of saying, "We have some crufty, legacy data over in this system, and now we need it in this shiny new system over here, so we're going to migrate this."

(Typically, this is followed by, "We're only going to need to run this once." That's then typically followed by much forehead slapping and moaning about how stupid we could possibly be.)

The goal

We're going to extract some scrabble scores from a legacy system.

The old system stored a list of letters per score:

  • 1 point: "A", "E", "I", "O", "U", "L", "N", "R", "S", "T",
  • 2 points: "D", "G",
  • 3 points: "B", "C", "M", "P",
  • 4 points: "F", "H", "V", "W", "Y",
  • 5 points: "K",
  • 8 points: "J", "X",
  • 10 points: "Q", "Z",

The shiny new scrabble system instead stores the score per letter, which makes it much faster and easier to calculate the score for a word. It also stores the letters in lower-case regardless of the case of the input letters:

  • "a" is worth 1 point.
  • "b" is worth 3 points.
  • "c" is worth 3 points.
  • "d" is worth 2 points.
  • Etc.

Your mission, should you choose to accept it, is to transform the legacy data format to the shiny new format.


A final note about scoring, Scrabble is played around the world in a variety of languages, each with its own unique scoring table. For example, an "E" is scored at 2 in the Māori-language version of the game while being scored at 4 in the Hawaiian-language version.


The Jumpstart Lab team http://jumpstartlab.com

Submitting Incomplete Solutions

It's possible to submit an incomplete solution so you can see how others have completed the exercise.


(ns etl-test
  (:require [clojure.test :refer [deftest is]]

(deftest transform-one-value
  (is (= {"world" 1}
         (etl/transform {1 ["WORLD"]}))))

(deftest transform-more-values
  (is (= {"world" 1 "gschoolers" 1}
         (etl/transform {1 ["WORLD" "GSCHOOLERS"]}))))

(deftest more-keys
  (is (= {"apple" 1 "artichoke" 1 "boat" 2 "ballerina" 2}
         (etl/transform {1 ["APPLE" "ARTICHOKE"], 2 ["BOAT" "BALLERINA"]}))))

(deftest full-dataset
  (is (= {"a"  1 "b"  3 "c" 3 "d" 2 "e" 1
          "f"  4 "g"  2 "h" 4 "i" 1 "j" 8
          "k"  5 "l"  1 "m" 3 "n" 1 "o" 1
          "p"  3 "q" 10 "r" 1 "s" 1 "t" 1
          "u"  1 "v"  4 "w" 4 "x" 8 "y" 4
          "z" 10}
         (etl/transform {1  (re-seq #"\w" "AEIOULNRST")
                         2  (re-seq #"\w" "DG")
                         3  (re-seq #"\w" "BCMP")
                         4  (re-seq #"\w" "FHVWY")
                         5  (re-seq #"\w" "K")
                         8  (re-seq #"\w" "JX")
                         10 (re-seq #"\w" "QZ")}))))
(ns etl
  (:require [clojure.string :refer [lower-case]]))

(defn transform [m]
  (into {}
        (for [[score words] m word words]
          [(lower-case word) score])))

What can you learn from this solution?

A huge amount can be learnt from reading other people’s code. This is why we wanted to give exercism users the option of making their solutions public.

Here are some questions to help you reflect on this solution and learn the most from it.

  • What compromises have been made?
  • Are there new concepts here that I could read more about to develop my understanding?