Construct a hash table using separate chaining

[bob]

You build a hash table with separate chaining by first making an array of lists. I see you starting with a basic array setup that holds pointers to chain heads. You pick a hash function that turns keys into array spots. Then collisions get handled when multiple keys land in one spot. You link them together in a list at that index. I always tell you to think about the array size first because it affects how often chains grow. You resize the whole thing when load gets high. But resizing means rehashing every item into the new array.

I find it helps you to picture each array slot as the start of its own chain. You add a new node to the front or end of the list when inserting. Search walks down that chain until it finds the match or hits null. Deletion removes the node from the chain without breaking links. You keep track of the count of items overall to decide on growth. Perhaps you test with strings or numbers to see chains in action. Now the average case stays fast if chains stay short.

You avoid long chains by choosing a good hash that spreads things out. I notice bad hashes clump everything into few lists. Then performance drops like a linear scan. You can tweak the hash with simple math on the key bits. Also you monitor the longest chain length during use. Perhaps you switch to a different function if one chain dominates. I see you handling deletes carefully so you do not leave dangling pointers.

Load factor tells you when to grow the table. You calculate it as items divided by slots. When it passes a threshold like three quarters you double the array. Rehashing moves every entry to fresh spots. You do this in batches if the table gets huge. But you keep the old table around until the move finishes. I think you should track memory use because lists add overhead per node.

Separate chaining beats open addressing when you expect many collisions. You never worry about primary clustering here. Each chain works alone. You free nodes when removing to avoid leaks. Perhaps you reuse deleted nodes in some pools. Now you test search times by measuring chain walks. I always suggest random key sets to check real spread.

You implement the list nodes with key value and next pointer. Insert checks for duplicates first if your table forbids them. Search returns the value or signals not found. You can sort each chain if keys allow it but that adds cost. Perhaps you keep chains unsorted for speed on inserts. I find it useful to count collisions separately for tuning.

Growth happens smoothly if you pick prime sizes for the array. You hash and mod by the current size to pick the slot. Then you attach to that chain head. Deletion scans the chain and splices the node out. You update the total count after each change. But you skip resize on every delete to save work. Perhaps you shrink when load drops low.

I watch you debug by printing chain lengths after loads of inserts. You spot if one chain grabs most items. Then you change the hash multiplier or add bits. You measure time for a thousand searches to compare. Also you consider cache effects from scattered list nodes. Now the table stays responsive even with uneven keys.

You handle variable length keys by hashing their bytes step by step. I see you combining characters with shifts and adds. That spreads similar strings better. You test edge cases like all identical keys. Then every insert lands in one chain. You learn to pick better functions from those failures. Perhaps you add salt to the hash for security too.

Separate chaining lets you store many duplicates if needed. You just append to the list. Search still works by checking each one. You free the whole chain when clearing the table. I think you should benchmark against simple arrays for small sizes. Now the method scales to millions of entries fine.

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