📌Data Structures the Fun Way - 16. Conclusion

Book wrap-up!

🔖 16.1 The Impact of Data Structure

Adding just a little structure to data can dramatically change algorithm performance.
Data structures help us access values quickly, efficiently combine multiple values, or reduce unnecessary searches.
For example, binary search trees, tries, quadtrees, and k-d trees allow us to prune unnecessary paths during search.
Tree-based data structures can exclude large portions of complex search spaces with simple condition checks.

Dynamic data structures can change size flexibly, allowing for adaptable approaches.
Since we don't need to pre-allocate memory with a fixed size, we can reduce memory waste.
However, we need to connect scattered memory blocks with pointers, which can make the structure complex.
Access speed and operation performance can vary depending on the pointer connection method.

When deciding whether to use a data structure, we must consider both the construction cost and the benefits we can gain.
For example, sorting an array or creating a binary search tree has high initial cost, but subsequent repeated searches are much more efficient.
On the other hand, if we only need to find a value once, a simple sequential search might be better than creating a complex data structure.
If we need to search data repeatedly, it's worth paying the initial cost.

Basic data structures provide a foundation that can be applied to various problems.
Like spatial data structures, modifying or combining data structures can provide more specialized performance for specific situations.
Ultimately, data structures are tools that can be continuously evolved to fit problem-solving needs.

We can approach problems by pre-storing calculation results or using more memory to increase operation speed.
For example, using a heap allows us to quickly find the largest or smallest values.
Also, in spatial partitioning trees, pre-calculating and storing the range of each node can reduce unnecessary searches.
Ultimately, this can be seen as a strategy of using more memory to save time.

Some data structures have parameters that affect performance.
How these parameters are adjusted can significantly change performance.
For example, in nearest neighbor search, the cell size of a grid affects precision and performance.
In B-trees, we can improve cache efficiency by appropriately adjusting node size.
It's important how well the internal settings of the data structure match the characteristics of the problem.

Some problems can have extremely different performance depending on input data.
In such cases, introducing randomness into data structures or algorithms can avoid worst-case scenarios.
For example, using random heights like in skip lists can prevent performance from being biased toward one side.
However, randomization isn't always good and should be used appropriately depending on the nature of the problem.

Data structures have as much impact on program performance and complexity as programming languages or algorithms.
Therefore, it's important to understand the internal working principles of each data structure and grasp in which situations they are most effective.
Ultimately, good programs start with choosing and applying the right data structure for the problem.

Last updated 6 days ago