How to evaluate the efficiency of a data structure?

The efficiency of a data structure is assessed by how fast and with what resources it allows key operations to be performed. Analysis is usually built around three groups of criteria:

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#1. Operation execution time (Time Complexity)

They look at how much time, on average and in the worst case, is taken by:

• accessing an element
• search
• insertion
• deletion
• iteration

The evaluation is done in Big-O notation. For example:

• array: access O(1), search O(n)
• hash table: search O(1) on average, but O(n) in the worst case
• tree: search O(log n), if balanced

The main question: how much slower will the algorithm become if the data grows by a factor of 100 or 1,000,000?

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#2. Memory (Space Complexity)

They look at how much additional memory the structure requires.

• An array is minimal (only the data)
• A list spends memory on references
• A hash table stores "empty buckets"
• Trees store references to children and parents

The trade-off is usually: less memory → slower operations, more memory → faster operations.

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#3. Suitability for a specific type of task

A structure is considered efficient only when it fits the scenario. For example:

• if search is critical → tree, hash table
• if insertions/deletions in the middle are critical → linked list
• if strict ordering is needed → tree or heap
• if FIFO/LIFO is needed → queue or stack

In other words, logical efficiency is also evaluated, i.e. how well the structure "fits" the task.

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#Summary

Efficiency is determined by the formula:

Efficiency = speed (O in time) + memory (O in space) + applicability to the task

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If you want, I can put together a comparison table (array vs list vs hash vs tree) or break down efficiency using task examples, so you can choose a structure "on autopilot". Which should we do?