Collection Performance - Choosing the Right Collection for Speed and Efficiency

Vaibhav • September 10, 2025

As your programs grow, the choice of collection type and how you use it can have a big impact on performance. In this article, we’ll compare arrays, List<int>, Dictionary<string,int>, HashSet<int>, and other common collections, focusing on how they perform for adding, searching, removing, and iterating. We’ll also cover practical tips for measuring and improving performance, and how to avoid common pitfalls. Concepts from previous chapters (like initialization and iteration) will be referenced but not re-explained in detail.

This article assumes you’re comfortable with basic collection usage and iteration. We’ll focus on performance characteristics and practical decision-making, not on syntax or foundational concepts.

Why collection performance matters

The right collection can make your code faster, more memory-efficient, and easier to maintain. The wrong choice can slow down your app, waste resources, or make code harder to debug. Performance matters most when:

  • You’re working with large datasets (thousands or millions of items).
  • Your code runs in tight loops or real-time scenarios.
  • You need fast lookups, inserts, or removals.

Arrays - Fastest for fixed-size, indexed data

Arrays are the simplest and fastest collection for fixed-size data. Access by index is O(1) (constant time), and memory layout is compact. 

int[] numbers = new int[1000];
numbers[500] = 42; // direct access, very fast
  • Best for: Known-size, numeric or simple data, fast indexed access.
  • Limitations: Size is fixed; adding/removing requires copying or resizing.

List<int> - Flexible, fast for most dynamic lists

List<int> is a resizable array. Index access is O(1), adding at the end is usually O(1), but inserting/removing in the middle is O(n) (linear time). 

var items = new List<string>();
items.Add("A");              // fast
items.Insert(0, "B");        // slower for large lists
  • Best for: Dynamic lists, frequent additions/removals at the end, indexed access.
  • Limitations: Inserting/removing in the middle shifts elements; can be slow for large lists.

If you know the expected size, set the initial capacity to avoid resizing overhead: var bigList = new List<int>(10000);

Dictionary<string,int> - Fast key-based lookup

Dictionaries provide O(1) average-time lookup, add, and remove by key. They’re ideal for mapping unique keys to values. 

var ages = new Dictionary<string, int>();
ages["Ada"] = 35; // fast lookup and assignment
  • Best for: Fast lookups, unique keys, reference data, caching.
  • Limitations: No guaranteed order; keys must be unique; memory overhead is higher than arrays/lists.

HashSet<int> - Fast membership checks, uniqueness

HashSet<int> is optimized for checking if an item exists (Contains) and for storing unique values. Add, remove, and check are all O(1) on average. 

var seen = new HashSet<int>();
seen.Add(42);
if (seen.Contains(42)) { /* ... */ }
  • Best for: Fast membership tests, enforcing uniqueness, set operations.
  • Limitations: No order; not suitable for indexed access.

Queue and Stack - Specialized for FIFO/LIFO

Queue<string> (first-in, first-out) and Stack<int> (last-in, first-out) are optimized for adding/removing at one end. 

var queue = new Queue<string>();
queue.Enqueue("A");
queue.Dequeue(); // fast

var stack = new Stack<int>();
stack.Push(1);
stack.Pop(); // fast
  • Best for: Processing items in order (queues), undo/redo (stacks).
  • Limitations: No random access; not for searching or indexed data.

Practical performance snapshots (no table)

Use this quick-reference when choosing a collection for a hotspot in your code:

  • Array: Index access O(1). No growth. Searching O(n). Ordered.
  • List<int>: Index access O(1). Add-end ~O(1). Insert/remove middle O(n). Ordered.
  • Dictionary<string,int>: Lookup/add/remove by key ~O(1). Keys unique. Unordered.
  • HashSet<int>: Add/remove/contains ~O(1). Unique elements. Unordered.
  • Queue/Stack: Enqueue/dequeue or push/pop O(1). No random access. Process order-defined.

The actual performance depends on data size and distribution. For small collections, differences are minimal; for large or hot paths, the right choice can be dramatic.

Common performance pitfalls

  • Using lists for frequent key lookups - Use Dictionary<string,int> instead.
  • Resizing arrays manually - Prefer List<int> for dynamic size.
  • Removing items from the middle of a list - This shifts later elements; avoid in hot paths.
  • Assuming order in dictionaries or sets - If you need order, sort after collecting.
  • Not setting initial capacity - For large lists/dictionaries, set capacity to reduce resizing.

Measuring and improving performance

The best way to improve performance is to measure it. Use Stopwatch from System.Diagnostics to time operations: 

var sw = new System.Diagnostics.Stopwatch();
sw.Start();
// perform operation
sw.Stop();
Console.WriteLine("Elapsed: " + sw.ElapsedMilliseconds + " ms");
  • Test with realistic data sizes.
  • Compare different collection types for your use case.
  • Profile hot paths (loops, searches, bulk operations).

Optimize only when you have measured a real bottleneck. Premature optimization can make code harder to read and maintain.

Checklist: picking the right collection for performance

  • Fixed-size, indexed data: Array
  • Dynamic, ordered list: List<int>
  • Fast key lookup: Dictionary<string,int>
  • Unique items, fast membership: HashSet<int>
  • FIFO/LIFO processing: Queue<string>/Stack<int>

Summary

Collection performance is about choosing the right tool for the job. Arrays are fastest for fixed-size, indexed data; lists are flexible for dynamic sequences; dictionaries and sets excel at fast lookups and uniqueness; queues and stacks are best for ordered processing. Measure your code, set initial capacities for large collections, and avoid common pitfalls like slow removals or unnecessary resizing. With these principles, your programs will be faster, more efficient, and easier to maintain as they scale.