For you as an aspiring programmer, few techniques unlock efficient data manipulation like hashing. By mapping inputs to array slots via hash functions, we can insert and retrieve items in constant time regardless of overall size. Whether building a fast cache or lookup table, grasp how hash tables, sets, maps and more apply hashing across real-world systems.
We‘ll explore:
- Hashing data structures like tables, maps and bloom filters
- Common hash functions powering encryption
- Collision handling techniques
- Optimization methods like rehashing
- Historical context around adoption
You‘ll gain plain English insight into hashing internals that textbooks gloss over. Let‘s get started!
Hashing 101: Rapid Data Access via Hash Functions
In simplest terms, hashing uses hash functions to enable speedy data access.
A hash function takes inputs like strings or numbers and computes a seemingly-random hash code:
Input -> Hash Function -> Hash Code
"John" > 2ff3g23This hash code gets used as an index to store and lookup associated records. By hashing, we map similar inputs to completely different codes to distribute data evenly across a key-value store.
For example, let‘s say we have user IDs mapped to corresponding user profiles. Without hashing, locating records means slow searches across giant arrays. With hashing, we derive hash codes from IDs for direct access instead.
Hash tables enable this by storing data in array slots matching hash code indices. The catch is unlike codes lead to collisions where different records share slots. Luckily tricks like chaining handle clashes gracefully.
Now let‘s unpack common structures and algorithms making efficient hashing possible!
Hashing Data Structures
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