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HashMap uses an array of buckets and computes an index from the hash of the key. Each bucket may hold one or more entries. When collisions happen, Java stores entries in a linked list and later in a tree when the bucket grows large enough. The table resizes when the load factor is exceeded so the map keeps operations efficient. The key idea is balancing fast lookup with controlled memory use.

Modern Java ConcurrentHashMap does not use the fixed lock segments from older implementations. It uses non-blocking reads and coordinates updates with CAS and synchronization at individual bins when needed. This allows unrelated keys to make progress concurrently. It protects map operations, not a multi-key transaction or mutable values stored inside it.

Modern Java implementations do not use the old fixed segment array commonly described in older explanations. Reads are generally non-blocking; initialization and some updates use CAS, while contended updates coordinate at a bin. Map operations are thread-safe, but multi-key invariants and mutable values still need their own synchronization design.

A hash helps select a bucket but does not establish key identity. HashMap checks equality among entries in the bucket; collisions can be stored in a list and, in sufficiently large bins, treeified under implementation conditions. Correct equals/hashCode behavior is essential, and resizing changes bucket placement without making hashes unique.

HashMap uses hashCode to choose a bucket and equals to identify a key within it. Equal values must share a hash, and hash-relevant state must not change while the object is used as a key. Check equality in both directions, inspect hash values, and prefer immutable key objects.

Sets and dictionary keys rely on a stable hash and equality relationship while a value is stored. A list is mutable, so changing it could invalidate the collection’s lookup structure; Python therefore makes lists unhashable. Use a tuple if its members are also hashable and the value should be immutable.