📌 a tougher one, but again, these resources will help you big-time (visual explanations and diagrams are 🔥 there so have a look as this is smth you’ll need in your interviews):

• algoMonster’s briefdown: https://algo.monster/liteproblems/642
• our guy Jordan 👉 https://www.youtube.com/watch?v=MCKX3n4-UR4

we’re typing something into Google, and BAM! A list of suggestions pops up, finishing your thought. That's autocomplete in action! It's a feature we all take for granted, but it's actually a pretty neat engineering challenge. Let's break it down.

Why Bother with Autocomplete?

Why is this feature so popular?

• Speeds things up: Folks find what they want way faster.
• Guides the user: It shows possibilities people might not have even thought of.
• Reduces typos: Suggesting correct spellings before someone messes up is a huge win.
• Improves user happiness: A snappy, helpful search is a good search!

Core Components: What Makes it Tick?

At its heart, an autocomplete system is pretty simple. It's like this:

1. User types: Someone starts typing a query (like "best pizz...")
2. System searches: The system looks for popular or relevant suggestions that start with that input.
3. System returns: The top suggestions get sent back to the user's screen.

But how does it actually do that search, quickly and accurately, with millions of users? That's where the fun starts.

Design Choices: Let's Get Specific

Now, let's zoom in on the different ways we can design this thing.

• Data Storage:

  • Trie (Prefix Tree): This is a fantastic choice for autocomplete! It's a tree-like data structure where each node represents a letter, and paths down the tree represent prefixes. It's super efficient for finding words that start with a given string.
  • Example: Let's say we have the words "cat", "car", "can", and "dog". A trie would look something like this (roughly):

        root
       /   \\
      c     d
     / \\     \\
    a   o     o
   / \\
  t   n       
  |
  r