Topic 5 Hashmap
Last updated
Last updated
The HashMap is a commonly used data structure in computer science and software engineering due to its efficiency and simplicity. It provides a way to store and retrieve data using a key-value pair system, enabling constant time complexity (O(1)) for most operations such as insertion, deletion, and lookup (in ideal cases).
A HashMap is a part of Java's java.util package and is an implementation of the Map interface. It stores elements in a hash table, where the data is organized using a hash function.
Key-Value Pair Storage: Data is stored as a pair of keys and values.
Hashing: Keys are hashed using a hash function, which determines where they will be stored in memory.
Non-Sorted: Elements in a HashMap are not stored in a specific order.
Allows Nulls: It permits one null key and multiple null values.
Efficiently retrieves data using a key.
Example: Implementing a dictionary for word definitions or a phone book for contact numbers.
Used to count the occurrence of elements in a dataset.
Example: Find the most frequent number in an array.
Temporary storage for frequently accessed data.
Example: Memoization in dynamic programming.
Represents adjacency lists for graphs.
Example: Store edges and weights in a graph.
Quickly determine if duplicates exist in a dataset.
Example: Identify duplicate emails in a list.
Fast Access: (O(1)) time complexity for basic operations like get and put.
Flexible Storage: Allows various data types as keys and values.
Scalable: Performs efficiently with large datasets.
Collision Handling: Multiple keys may hash to the same location, requiring resolution strategies like chaining or open addressing.
Non-Thread-Safe: By default, HashMap is not synchronized, so it requires additional mechanisms in multi-threaded environments (e.g., ConcurrentHashMap).
Unordered: Does not maintain the order of insertion.
Order Maintained
No
Sorted by key
Insertion order
Null Keys Allowed
Yes (1 key)
No
Yes (1 key)
Performance
(O(1))
(O(\log n))
(O(1))
Efficiency: HashMap is highly efficient for problems involving fast data lookup and storage.
Use Cases: Ideal for frequency counting, caching, and adjacency list representation.
Caveats: Requires careful handling of collisions and thread safety.
Happy Coding !!!
