Lesson 5 of 5 · 15–25 minutes

Efficiency

🏠The everyday picture

Checking every guest's name against the list at a wedding of 50 people is fine. At a rally of 50,000, checking each person against every other person would take days. How work grows matters more than how fast one step is.

📘What it really is

Efficiency is about how the work grows as the data grows, often written in Big O notation:
O(1): the same work however big the data (a Map lookup);
O(log n): grows very slowly (binary search);
O(n): grows in step with the data (one loop over the list);
O(n²): grows with the square: a loop inside a loop over the same list. 1,000 items means a million steps; 100,000 items means ten billion.
The common accident is a hidden loop inside a loop, such as calling find or includes inside a map over another large list. Fix it by building a Map or Set first.
For a few hundred items, almost anything is fast enough. Write clear code first; speed it up when the data is big or something is measurably slow.
console.time("label") and console.timeEnd("label") measure how long code takes.

🖱️Try it

Make an array of 20,000 fake orders, each with a random productId from your products. Time looking up the product name for every order, first with products.find(...) inside a map, then with a Map built beforehand. Compare the times. (Ask an AI to help write the fake-data generator, and read what it gives you.)

✅Quick check

For each of 10,000 orders, code calls customers.find(...) over 10,000 customers. Roughly how many steps?

Show the answer

Up to 100 million (10,000 × 10,000). A Map of customers by id makes it about 10,000.

Should you always write the fastest possible code?

Show the answer

No. Write clear code first; optimise when the data is large or something is measurably slow.