{"id":1428,"date":"2025-05-26T13:38:44","date_gmt":"2025-05-26T13:38:44","guid":{"rendered":"https:\/\/www.wordpress-prod.sharpener.tech\/?p=1428"},"modified":"2025-05-27T04:31:33","modified_gmt":"2025-05-27T04:31:33","slug":"hashing-in-data-structure","status":"publish","type":"post","link":"https:\/\/www.sharpener.tech\/blog\/hashing-in-data-structure\/","title":{"rendered":"Hashing in data Structure"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

While dealing with huge amounts of data, data storage and quick retrieval are very important. Hashing in data structures accomplishes this. It allows for constant time complexity for search, insert, and delete operations on average. This is considered a fundamental technique in computer science and is often used in competitive programming.<\/p>\n\n\n\n

In this blog post, we will discuss the methods of hashing and its real life applications. Also, we will check what functions does it have, its properties, qualities of a good hash function, functions of hash and its collision resolution techniques. This will suit well for people who are just starting out as well as people preparing for interviews. It will provide insights into the core principles of hashing.<\/p>\n\n\n\n

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How do you define Hashing for data structures?<\/strong><\/p>\n\n\n\n

A hashing technique is a process which condenses and restructures information into a more manageable form. It is often used in fast data access scenarios. The main concept is to use a hash function that transforms the input (key) into its corresponding hash code which is used to store and retrieve values from a hash table or a hash map.<\/p>\n\n\n\n

Key Terms:<\/strong><\/h3>\n\n\n\n
    \n
  • Hash Function<\/strong>: Converts input data (key) into a specific hash code or index.
    <\/li>\n\n\n\n
  • Hash Table<\/strong>: A data structure that stores key-value pairs at indexed positions determined by the hash function.
    <\/li>\n\n\n\n
  • Hash Code<\/strong>: The returned integer value from executing a hash function.
    <\/li>\n\n\n\n
  • Collision<\/strong>: When two keys produce the same hash code.
    <\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Why Use Hashing?<\/strong><\/h2>\n\n\n\n

    Hashing is widely used because it offers:<\/p>\n\n\n\n

      \n
    • Constant Time Complexity (O(1))<\/strong> on average for insertions, deletions, and lookups.
      <\/li>\n\n\n\n
    • Efficient memory usage.
      <\/li>\n\n\n\n
    • Scalability for large datasets.
      <\/li>\n<\/ul>\n\n\n\n

      Some of the most common applications of hashing include:<\/p>\n\n\n\n

        \n
      • Implementing hash maps<\/strong> and sets<\/strong>
        <\/strong><\/li>\n\n\n\n
      • Database indexing<\/strong>
        <\/strong><\/li>\n\n\n\n
      • Caching<\/strong>
        <\/strong><\/li>\n\n\n\n
      • Password encryption<\/strong>
        <\/strong><\/li>\n\n\n\n
      • Compiler symbol tables<\/strong>
        <\/strong><\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

        How Hashing Works<\/strong><\/h2>\n\n\n\n

        Here\u2019s a simple breakdown of how hashing works:<\/p>\n\n\n\n

          \n
        1. A key is passed through a hash function<\/strong>.
          <\/li>\n\n\n\n
        2. The hash function returns an index within the bounds of the hash table.
          <\/li>\n\n\n\n
        3. The data is stored at the computed index.
          <\/li>\n\n\n\n
        4. Data can be accessed by applying the same key to the hash function and fetching it from the appropriate index.
          <\/li>\n<\/ol>\n\n\n\n

          Example:<\/strong><\/h3>\n\n\n\n

          hash(“apple”) % table_size = index<\/p>\n\n\n\n

          Using a hash function will give the value 3 while scanning the string \u201capple\u201d with a table of size 10. Thus the value of the key \u201capple\u201d will be stored in index 3.<\/p>\n\n\n\n

          \n\n\n\n

          Properties of a Good Hash Function<\/strong><\/h2>\n\n\n\n

          For best performance, a hash function should:<\/p>\n\n\n\n

            \n
          • Distribute keys uniformly<\/strong> across the hash table.
            <\/li>\n\n\n\n
          • Be fast and efficient<\/strong> to compute.
            <\/li>\n\n\n\n
          • Minimize collisions<\/strong>.
            <\/li>\n\n\n\n
          • Always produce the same output given the same input.<\/li>\n<\/ul>\n\n\n\n

            Popular hash functions include:<\/p>\n\n\n\n

              \n
            • Division Method<\/strong>: h(k) = k % m
              <\/li>\n\n\n\n
            • Multiplication Method<\/strong>: Uses fractional parts of the key multiplied by a constant.
              <\/li>\n\n\n\n
            • Universal Hashing: <\/strong>Selects at random from a set of hash functions.
              <\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              Collision Handling in Hashing<\/strong><\/h2>\n\n\n\n

              Due to the size limitation of the table and the practically infinite input, collisions are unavoidable. Handling them properly is critical.<\/p>\n\n\n\n

              1. Chaining (Separate Chaining)<\/strong><\/h3>\n\n\n\n

              Each index stores a linked list (or dynamic array) of all elements that hash to the same index.<\/p>\n\n\n\n

              Pros:<\/strong><\/p>\n\n\n\n

                \n
              • Easy to implement.
                <\/li>\n\n\n\n
              • Can store multiple values at a single index.
                <\/li>\n<\/ul>\n\n\n\n

                Cons:<\/strong><\/p>\n\n\n\n

                  \n
                • Extra memory for pointers.
                  <\/li>\n\n\n\n
                • Slower lookup in case of long chains.
                  <\/li>\n<\/ul>\n\n\n\n

                  2. Open Addressing<\/strong><\/h3>\n\n\n\n

                  Due to the size limitation of the table and the practically infinite input, collisions are unavoidable. When a collision occurs, it probes for the next empty slot.<\/p>\n\n\n\n

                  Techniques:<\/strong><\/h4>\n\n\n\n
                    \n
                  • Linear Probing<\/strong>: Check next slots linearly.
                    <\/li>\n\n\n\n
                  • Quadratic Probing<\/strong>: Check slots in a quadratic manner.
                    <\/li>\n\n\n\n
                  • Double Hashing:<\/strong> The second hash function is used to determine the increment.
                    <\/li>\n<\/ul>\n\n\n\n

                    Pros:<\/strong><\/h3>\n\n\n\n
                      \n
                    • Saves space by avoiding linked lists.
                      <\/li>\n\n\n\n
                    • Cache-friendly.
                      <\/li>\n<\/ul>\n\n\n\n

                      Cons:<\/strong><\/h3>\n\n\n\n
                        \n
                      • Clustering issues.
                        <\/li>\n\n\n\n
                      • Table needs to be resized as it fills up.
                        <\/li>\n<\/ul>\n\n\n\n
                        \n\n\n\n

                        Load Factor and Rehashing<\/strong><\/h2>\n\n\n\n

                        The load factor is defined as the ratio of the number of elements stored in the hash table to the total number of slots provided. A high load factor increases the chance of collisions.<\/p>\n\n\n\n

                          \n
                        • Load Factor (\u03b1) = Number of Elements \/ Table Size<\/strong>
                          <\/strong><\/li>\n<\/ul>\n\n\n\n

                          When the load factor crosses a certain threshold (usually 0.7), rehashing<\/strong> is done. This involves:<\/p>\n\n\n\n

                            \n
                          • Creating a larger table (usually double the size).
                            <\/li>\n\n\n\n
                          • Re-inserting all elements using the new hash function.
                            <\/li>\n<\/ul>\n\n\n\n
                            \n\n\n\n

                            Hashing vs Other Data Structures<\/strong><\/h2>\n\n\n\n
                            Feature<\/strong><\/td>Hash Table<\/strong><\/td>Array<\/strong><\/td>Linked List<\/strong><\/td>Binary Search Tree<\/strong><\/td><\/tr>
                            Search Time<\/td>O(1)<\/td>O(n)<\/td>O(n)<\/td>O(log n)<\/td><\/tr>
                            Insertion Time<\/td>O(1)<\/td>O(1)<\/td>O(1)<\/td>O(log n)<\/td><\/tr>
                            Deletion Time<\/td>O(1)<\/td>O(n)<\/td>O(n)<\/td>O(log n)<\/td><\/tr>
                            Ordered?<\/td>No<\/td>Yes<\/td>Yes<\/td>Yes<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
                            \n\n\n\n

                            Real-World Applications of Hashing<\/strong><\/h2>\n\n\n\n
                              \n
                            1. Databases<\/strong>: Fast indexing and data retrieval.
                              <\/li>\n\n\n\n
                            2. Compilers<\/strong>: Symbol tables for variable\/function tracking.
                              <\/li>\n\n\n\n
                            3. Cryptography<\/strong>: Password hashing (MD5, SHA-256).
                              <\/li>\n\n\n\n
                            4. Caching<\/strong>: Storing recently accessed data using hash maps.
                              <\/li>\n\n\n\n
                            5. Blockchain<\/strong>: Uses SHA-256 hash for block integrity.
                              <\/li>\n<\/ol>\n\n\n\n
                              \n\n\n\n

                              Pros and Cons of Hashing<\/strong><\/h2>\n\n\n\n

                              Pros:<\/strong><\/h3>\n\n\n\n
                                \n
                              • Extremely fast data access.
                                <\/li>\n\n\n\n
                              • Efficient memory usage.
                                <\/li>\n\n\n\n
                              • Easy implementation.
                                <\/li>\n<\/ul>\n\n\n\n

                                Cons:<\/strong><\/h3>\n\n\n\n
                                  \n
                                • Not suitable for ordered data traversal.
                                  <\/li>\n\n\n\n
                                • Performance degrades with poor hash functions or high load factors.
                                  <\/li>\n\n\n\n
                                • Difficult to implement in concurrent environments.
                                  <\/li>\n<\/ul>\n\n\n\n
                                  \n\n\n\n

                                  Conclusion<\/strong><\/h2>\n\n\n\n

                                  \u200b\u200bHashing allows for quick storage and retrieval of data pertaining to data structures. Developers and computer science students need to know how to deal with hash functions, collisions, and rehashing.<\/p>\n\n\n\n

                                  Mastering the concepts of hashing will ease the development of scalable, high-performance applications\u2014regardless if they are built as part of system software, web applications, or during coding interview challenges.<\/p>\n\n\n\n

                                  \n\n\n\n

                                  Common Questions (CQs)<\/p>\n\n\n\n

                                  Q1. What is the difference between a hash table and a hash map?<\/h3>\n\n\n\n

                                  A: Legally, both hold collections of key-values pairs which are stored by index via hashing. In Java, HashMap is part of the Collection Framework, while hash tables refer to the original data structure concept.<\/p>\n\n\n\n

                                  Q2. Can we use hashing in sorting?<\/strong><\/h3>\n\n\n\n

                                  A:<\/strong> Hashing is not suitable for sorting, as it does not preserve order. It\u2019s optimized for quick access and lookup.<\/p>\n\n\n\n

                                  Q3.<\/strong>What is the time complexity of hashing?<\/strong><\/h3>\n\n\n\n

                                  A: Typically, it is O(1) in average for setup, removing, and looking up. However, it may reach O(n) in the worst case due to collisions.<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  While dealing with huge amounts of data, data storage and quick retrieval are very important. Hashing in data structures accomplishes this. It allows for constant time complexity for search, insert,…<\/p>\n","protected":false},"author":3,"featured_media":1429,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[23],"tags":[],"class_list":["post-1428","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-science"],"yoast_head":"\nHashing in Data Structures: Concepts, Techniques & Applications in 2025<\/title>\n<meta name=\"description\" content=\"Learn what hashing is in data structures, how it works, and where it's used. 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