Dictionary or List: Which is More Efficient for a 10 Million Value Lookup Table?

Python: List vs. Dict for Lookup Table Efficiency

When constructing a lookup table with a vast number of values (10 million in this case), choosing the appropriate data structure is crucial for both efficiency and memory optimization. The two primary options are lists and dictionaries.

Lookup Speed

• List: Lookup in a list is a linear search operation, meaning it iterates through each item in order to find the desired value. This is O(n) complexity, where n is the number of items in the list.
• Dictionary: Dictionary lookups utilize hashing, providing an amortized O(1) complexity. This means that the lookup time remains relatively constant regardless of the number of items in the dictionary.

Memory Usage

Both dictionaries and sets employ hashing for efficient lookups. However, this hash table implementation often maintains a 2/3 fullness level, which can result in wasted memory.

In cases where only lookup efficiency is required, sets can be considered. Sets support faster lookups but do not provide the ability to associate values.

Conclusion

Based on the provided context, where lookup efficiency takes precedence and values are not associated with keys, the optimal choice is a dictionary. Its O(1) amortized lookup complexity guarantees a rapid search regardless of the table size. However, if memory constraints are a major concern, using a sorted list with binary search could be an alternative solution, offering O(log n) performance at the cost of potentially slower lookup times, especially for strings or objects without natural ordering.

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