Hey there! So you’re learning about data structures and stumbled upon this thing called rehashing? Sounds a bit technical, right? Don’t worry we’ll break it down in plain English, no fancy jargon, and with examples that actually make sense. Let’s get into it!
Table of Contents
What Is Rehashing in Data Structure?
Alright, first things first.
Rehashing is a process used in hash tables when the existing table becomes too full and starts causing problems like collisions. Basically, we create a bigger hash table and reinsert all the existing elements into it using a new hash function or the same one.
It’s like moving from a small apartment to a bigger one because all your stuff just doesn’t fit anymore.
Why Do We Need Rehashing?
Good question! Hash tables work great until they start getting full. When there’s not enough space, multiple keys might start landing at the same spot. This is called a collision, and too many of them slow everything down.
Rehashing helps by:
- Reducing collisions
- Maintaining performance
- Making space for more elements
Basically, it keeps your data structure fast and efficient.
When Does Rehashing Happen?
Usually, rehashing is triggered when a certain load factor is reached.
Load Factor = (Number of elements) / (Size of the hash table)
If the load factor goes beyond, let’s say, 0.7 or 70%, rehashing kicks in and resizes the table (often doubling its size).
Example:
- Old table size: 10
- Items inserted: 7
Load factor = 0.7 → time to rehash!
How Does Rehashing Work?
Here’s how rehashing usually goes:
- Create a new hash table (larger in size)
- Recalculate the index for each existing key using the hash function
- Insert all elements into the new table based on new indices
It sounds like a lot, but it usually happens behind the scenes.
Simple Example of Rehashing
Let’s say we have a hash table of size 5:
Index | Value
-----------------
0 | 20
1 | 15
2 | 25
3 | 30
4 | 10Now we try to insert another value—boom! No space left. So we rehash:
- New size = 10
- Recalculate index for each value with new hash function
- Insert again
Everything gets a fresh spot in a bigger table.
Rehashing vs. Resizing – Same Thing?
Not exactly, but they go hand in hand.
- Resizing means increasing the size of the table.
- Rehashing means recalculating the positions and reinserting the items.
So yeah, resizing usually leads to rehashing.
Downsides of Rehashing
It’s not all sunshine and rainbows though. There are a few things to keep in mind:
- It takes time – rehashing is an expensive operation, especially if your table is huge.
- It uses extra memory – while creating the new table.
- It may cause delays – if done frequently.
That’s why some smart techniques like dynamic resizing or incremental rehashing are used in real-world systems to manage it better.
Where Is Rehashing Used?
You’ll find rehashing in action in:
- HashMaps in Java
- Dictionaries in Python
- Unordered maps in C++
- Caching systems
- Database indexing
Anywhere a hash table is used, rehashing probably exists under the hood to keep things running smooth.
Final Thoughts
So to wrap things up:
Rehashing in data structures is just a smart way to keep your hash table working well when it starts to get full. It helps reduce collisions, improves speed, and makes your programs more efficient.
It’s one of those behind-the-scenes heroes in programming that makes sure things don’t fall apart when your data grows.
Still confused or want to see a basic code example? Let me know—I’d love to help you out more!
