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Tutorials, Errors and Exceptions
Its a journey to understand things better. It will have tutorials, any error/exceptions encountered, its resolutions and lots of learning.
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![You think HashMap is always O(1).
It isn't. Here's what actually happens. 🧵
HashMap stores pairs using `index = hash(key) % capacity` — direct slot access, no scanning. Pure O(1). Until two keys land on the same slot. That's a collision — not a bug, a math inevitability.
Two ways to fix it 👇
🔗 Chaining — each bucket holds a linked list. Collisions append to the list. Simple, handles high load, easy deletion. Downside: pointer overhead, poor cache performance, chains degrade to O(n) at high load. Java's fix? At 8 nodes, the list auto-converts to a Red-Black Tree → O(log n) worst case.
📦 Open Addressing — no linked lists. Collision at slot X? Probe X+1, X+2 until empty. Cache-friendly, zero memory overhead. Downside: deletion needs tombstone markers, and keys cluster together making future collisions worse. Used by C++, Go, Redis.
⚖️ Load Factor = entries ÷ capacity
🟢 Below 0.5 → rare collisions, wasted memory
🟠 0.75 → Java's sweet spot, triggers resize + rehash
🔴 Above 0.9 → collision cascade, O(n) territory
Double hashing kills clustering by varying the probe step per key:
`probe(i) = (h1 + i × h2) % m`
Elements scatter evenly. No bunching. O(1) preserved.
The truth: HashMap is O(1) until a bad hash function, wrong load factor, or wrong strategy turns it into O(n).
Three things protect you:
→ Well-distributed hash function
→ Load factor under 0.75
→ Right collision strategy for your use case
💬 Java interview question: what happens when a chain hits 8 nodes?
Drop your answer below 👇
🔖 Save this before your next interview.
#java #hashmap #datastructures #dsa #algorithms
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