What is Load Factor in Hashing
💡 Concept Name
Load Factor – The ratio that indicates how full a hash table is, calculated by dividing the number of stored entries by the total number of buckets (slots).
📘 Quick Intro
Load factor plays a crucial role in hash tables by signaling when the structure needs to grow. A higher load factor means more entries per bucket, which can lead to increased collisions and slower performance. Conversely, a very low load factor wastes memory.
🧠 Analogy / Short Story
Imagine a parking lot: if too many cars arrive (high load factor), finding a free spot becomes difficult, causing delays. If the lot is mostly empty (low load factor), space is wasted. The best scenario balances space use and ease of parking.
🔧 Technical Explanation
- 🔢 Definition: Load Factor = Number of Entries / Number of Buckets.
- ⚠️ Performance Impact: As load factor rises, collisions become more frequent, increasing lookup time.
- ⚙️ Resize Threshold: When the load factor crosses a set threshold (commonly around 0.75), the hash table resizes to maintain efficiency.
- 🧩 .NET Behavior: The Dictionary
- 🎯 Goal: Maintain a balance between memory usage and access speed by managing the load factor.
🎯 Purpose & Use Case
- ✅ Minimize collisions to keep hash table operations fast.
- ✅ Ensure quick data retrieval in dictionaries, hash sets, and caches.
- ✅ Automatically scale hash tables to adapt to data growth.
💻 Real Code Example
// .NET Dictionary manages load factor internally
Dictionary<string, int> inventory = new Dictionary<string, int>();
inventory["apple"] = 10;
inventory["banana"] = 20;
// Dictionary automatically resizes when needed
Console.WriteLine(inventory["banana"]); // Output: 20
❓ Interview Q&A
Q1: What is load factor in hashing?
A: The ratio of the number of stored entries to the total number of buckets in a hash table.
Q2: Why is load factor important?
A: It affects the performance of the hash table, influencing collision rates and lookup times.
Q3: What happens when the load factor is too high?
A: More collisions occur, leading to slower operations.
Q4: What is a typical threshold for load factor before resizing?
A: Around 0.7 to 0.75.
Q5: How does resizing affect the load factor?
A: It reduces the load factor by increasing the number of buckets.
Q6: Does a low load factor guarantee better performance?
A: Generally yes, but too low load factor wastes memory.
Q7: How is load factor calculated?
A: Load Factor = Number of Entries / Number of Buckets.
Q8: What effect does load factor have on collision resolution?
A: Higher load factor increases collisions, affecting chaining and probing efficiency.
Q9: Can load factor be greater than 1?
A: Yes, in separate chaining when multiple entries are in the same bucket.
Q10: Why do some hash tables resize dynamically?
A: To maintain an optimal load factor for performance balance.
📝 MCQs
Q1. What does load factor represent?
- Number of buckets
- Ratio of entries to buckets
- Number of collisions
- Table size
Q2. Why is load factor important?
- Reduces memory
- Affects collision and performance
- Increases speed
- Defines hash function
Q3. What happens when load factor is high?
- Less collisions
- More collisions
- No effect
- Hash table shrinks
Q4. Typical load factor threshold before resizing?
- 0.1
- 0.5
- 0.7 to 0.75
- 1.0
Q5. How does resizing affect load factor?
- Increases it
- Reduces it by adding buckets
- No effect
- Doubles entries
Q6. Does low load factor waste memory?
- No
- Yes
- Depends on data
- Never
Q7. Load factor formula?
- Buckets divided by entries
- Entries divided by buckets
- Entries plus buckets
- Buckets minus entries
Q8. How does load factor affect collision resolution?
- No effect
- Higher load factor causes more collisions
- Less collisions
- Faster lookup
Q9. Can load factor be greater than 1?
- No
- Yes, in separate chaining
- Only in open addressing
- Never
Q10. Why resize a hash table dynamically?
- Save memory
- Maintain optimal load factor
- Speed up CPU
- Avoid rehashing
💡 Bonus Insight
Choosing the right load factor threshold is a critical design decision when building custom hash tables, as it balances lookup speed and memory consumption effectively.
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