Hash Tables: Fast Lookup

In the previous chapter we studied stacks and queues.

Now we shift to a structure built for fast retrieval by key.

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The Core Idea of Hashing

A hash table stores key-value pairs.

It uses a hash function to convert a key into an index location.

graph LR
  A[Key] --> B[Hash Function]
  B --> C[Index]
  C --> D[Stored Value]

Instead of scanning many items, you jump to a computed location.

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Why Hash Tables Are Powerful

Typical operations are very fast on average:

• insert by key
• lookup by key
• delete by key

This is why dictionaries/maps in many languages are hash-table-based.

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Collisions

Different keys can map to the same index. This is called a collision.

Collision handling strategies include:

• chaining
• open addressing

Collisions do not break correctness, but they can affect performance.

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Real-World Uses

Hash tables appear in many systems:

• language dictionaries/maps
• caches
• symbol tables in compilers
• indexing components in data systems

When you need fast key-to-value access, hash tables are often the first candidate.

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Tradeoffs

Hash tables are usually excellent for exact-key lookup.

But they are less ideal when you need ordered traversal or range queries.

That is where tree-based structures often help.

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Key Ideas to Remember

• Hash tables map keys to storage positions via hashing.
• They enable fast average-case key lookup.
• Collisions are expected and must be handled.
• They power dictionaries, caches, and many infrastructure components.
• Structure choice still depends on workload needs.

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What Comes Next

Hash tables handle fast key lookup.

Next we move to hierarchical organization:

trees and parent-child relationships.