Noppanit

08 Oct 2015

# Why algorithm matters?

If you have been to one of those technical interviews, you will like this.

I’m not going to rant about how broken the technical interviews are. There’s enough people who are more qualified to rant about this more than me.

So, why this matters? I just want to give a quick example of why algorithm matters. Please no haters! I know some of you might read this and will say “I do that all the time, what’s the big deal”. I’m still a bad developer and I’m still learning.

## Scenario

You are a general, your home country is at war and you have to fight for your country. You are given a group of soldiers. You want to come up with a strategy to win the battle. Here’s the example of the soldiers.


[
{
"category": "machine-gun",
"id": 0,
"name": "D408CF"
},
{
"category": "machine-gun",
"id": 1,
},
{
"category": "rocket-man",
"id": 2,
"name": "04B5E4"
},
{
"category": "rocket-man",
"id": 3,
"name": "22F3CF"
},
{
"category": "machine-gun",
"id": 4,
"name": "811E8A"
}
,
...
]



You came out of a high-profile meeting and all the generals agree that this formation will be best to fight the enemy; infantry, machine-gun and rocket-man. How can I rearrange this quick enough because we’re going to attack tomorrow? Simple I go ahead and write the code.

First, let’s generate a bunch of soldiers


class Soldier(object):
def __init__(self, id, name, category):
self.id = id
self.name = name
self.category = category

number_of_soldier = 10000

soldiers = []
for i in range(0, number_of_soldier):
name = str(uuid.uuid4().get_hex().upper()[0:6])
soldiers.append(Soldier(i, name, random.choice(categories)))



Then let’s rearrange them.


import json
import uuid
import random
import time

categories = ['infantry', 'machine-gun', 'rocket-man']

start = time.time()
result = []
for c in categories:
s_list = []
for s in soldiers:
if s.category == c:
s_list.append(s)

result.extend(s_list)
s_list = []

end = time.time()
print(end - start)



It works, but you’re too late you can’t form the soldiers in time. If you take a closer look, this algorithm takes O(nm)* for the time complexity given the number of soldiers is n and the category is m. If you have a million soldiers and a million categories you would get O(n^2). How can we make this one faster?

Here’s my second version. Hmm, rearrange into category… category is bucket. How about using map?


from collections import defaultdict

start = time.time()
map_of_soldiers = defaultdict(list)

for s in soldiers:
map_of_soldiers[s.category].append(s)

result = []
for c in categories:
result.extend(map_of_soldiers.get(c))

end = time.time()
print(end - start)



This is the time difference of those two algorithms.


0.00743103027344
0.00331783294678



By just changing the data structure, you can see that the map version is almost twice as fast. I hope I can demostrate how choosing the right algorithm matters in your program.

Til next time,
noppanit at 15:55