Separate Chaining
Multiple keys can occupy the same bucket.
Collision entries are linked or otherwise grouped together.
Average lookup remains close to O(1) when the load factor is controlled.
Worst-case lookup can become O(n) if many keys fall into one bucket.
Separate chaining makes deletion simpler than many open-addressing designs.
Modern implementations may treeify heavily populated buckets.
If you need to implement a hash map for storing usernames, how would you handle collisions using separate chaining?
What happens when you insert a key that hashes to a bucket that already contains a linked list of entries? Walk me through the steps.
We have a service that uses a hash table with separate chaining, and after a traffic spike we notice lookups are slower. What could be causing the slowdown and how would you investigate?
Suppose we need to support delete operations in a hash table that uses separate chaining. How would you implement delete, and what edge cases must you watch out for?
Design a thread‑safe hash map for a high‑throughput caching layer that uses separate chaining. What synchronization strategy would you choose and why?
If the average chain length grows to 10 because the load factor is high, what redesign options do you consider to keep performance predictable at scale?
Our legacy system uses a custom hash table with separate chaining and is being migrated to a distributed key‑value store. How would you plan the migration to minimize downtime and ensure data consistency?
Across multiple services, some teams prefer open addressing while others use separate chaining. How would you evaluate the long‑term maintainability and performance trade‑offs to set a company‑wide standard?