Choosing the Right Data Structure – Real-World Tradeoffs
Choosing the Right Data Structure – Real-World Tradeoffs
We’ve explored arrays, linked lists, stacks, queues, hash tables, trees, heaps, tries, graphs, and even advanced tools like bloom filters and Fenwick trees. But here’s the real challenge: How do you choose the right one?
In this final CQH102 post, we’ll wrap up the series by looking at how to make smart decisions about data structures in real applications—and how they tie directly into algorithm design. If you haven’t yet, check out CQH103’s real-world algorithm wrap-up for the other half of this equation.
Start With the Problem
Every problem has its own shape:
- Is the data mostly read or written?
- Does order matter?
- Do you need to search or look up by key?
- Are operations local (e.g. one item at a time) or global (e.g. aggregates)?
Start by understanding what you need to do—the right structure is the one that makes those operations fast and simple.
Case Studies
🧮 Arrays vs Linked Lists
- Use arrays when you need fast indexing (e.g., leaderboard scores).
- Use linked lists when insertions/deletions are frequent (e.g., music playlist editing).
📚 Stacks and Queues
- Use stacks for undo functionality, backtracking, or syntax parsing.
- Use queues for scheduling tasks or simulating real-world lines.
🔍 Hash Tables vs Trees
- Use hash tables when fast lookups are key and order doesn’t matter.
- Use trees (especially BSTs) when you need sorted data or efficient range queries.
🌐 Graphs vs Trees
- Use graphs when data is interconnected in a non-hierarchical way (e.g., road maps, networks).
- Use trees for hierarchical relationships (e.g., file systems, org charts).
🧠 Tries vs Hash Tables
- Use tries when dealing with prefixes (e.g., autocomplete, dictionaries).
- Use hash tables when you don’t care about order or structure within keys.
Performance Considerations
| Operation | Array | Linked List | Hash Table | BST | Heap | Trie |
|---|---|---|---|---|---|---|
| Access by index | O(1) | O(n) | – | O(log n) | – | – |
| Insert/Delete | O(n) | O(1) | O(1)* | O(log n) | O(log n) | O(m) |
| Lookup by key | – | O(n) | O(1)* | O(log n) | – | O(m) |
| Memory overhead | Low | Medium | Medium | High | Medium | High |
*Assumes good hash distribution and low collision rate.
Simplicity vs Speed
Sometimes the “technically faster” structure isn’t worth it for small problems. For example:
- Binary search on a sorted array might beat a hash table for <100 items.
- A simple list might outperform a tree for shallow hierarchies.
Choose the simplest structure that gets the job done until performance says otherwise.
From Data Structure to Algorithm
Data structures and algorithms go hand in hand:
- A graph enables Dijkstra’s and A*
- A stack enables Depth-First Search
- A heap enables Greedy algorithms and priority queues
- A Trie powers fast search in strings
See how we tie it all together in CQH103: Real-World Applications of Algorithms.
Challenge Time! 🛠️
- Revisit a past project and ask: Did I use the best structure for the job?
- Create a cheat sheet or flash cards summarizing key structure traits.
- Build a “structure selector” quiz or flowchart for common problem types!
Thanks for joining us on the CQH102 journey! Now go build something brilliant—with the right tools for the job.