Hash table

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Hash table 3 1 1 0 1 0 0 SP
Hash table 5 0 1 1 1 1 1 LL
Hash table 5 0 1 1 1 1 0 LL
Hash table 5 0 1 1 1 1 0 SP
Hash table average insertion time

Hash table is a data structure used to implement an associative array, a structure that can map keys to values. A hash table uses a hash function to compute an index into an array of buckets or slots, from which the desired value can be found. Ideally, the hash function will assign each key to a unique bucket, but most hash table designs employ an imperfect hash function, which might cause hash collisions where the hash function generates the same index for more than one key. Such collisions are typically accommodated by various collision resolution strategies, such as linear probing, quadratic probing, double hashing, or chaining.

Overview[edit | edit source]

The efficiency of mapping in a hash table is characterized by its ability to insert, delete, and search for elements in constant time, O(1), under the best-case scenario. However, in the worst-case scenario, such as when many elements are mapped to the same bucket, the performance can degrade to O(n), where n is the number of entries in the table. To minimize collision occurrences, a good hash function, table resizing (such as using dynamic resizing techniques), and a load factor—a measure of how full the hash table is allowed to get before its capacity is automatically increased—are crucial considerations.

Components[edit | edit source]

Hash Function[edit | edit source]

The hash function is a critical component of the hash table. It converts the key into a hash code, which is then transformed into an index to an array where the value is stored. The efficiency of a hash function is measured by its ability to distribute keys uniformly across the buckets, its speed, and how it minimizes collisions.

Buckets and Slots[edit | edit source]

Buckets or slots are the basic units of storage in a hash table. Each bucket can store one or more entries, depending on the collision resolution strategy employed.

Collision Resolution[edit | edit source]

Collision resolution is a method to handle situations where multiple keys hash to the same index. Common strategies include:

  • Chaining: Each bucket contains a list of all entries that hash to the same index.
  • Open Addressing: All entry records are stored within the array itself. When a new entry has to be inserted, the hash table checks the bucket at the calculated index and, if it is already occupied, searches the array sequentially until an empty bucket is found.

Applications[edit | edit source]

Hash tables are widely used in many kinds of computer software, particularly for tasks such as database indexing, caching, and sets management. They offer fast data retrieval and are a foundational element in the design of various data structures.

Advantages and Disadvantages[edit | edit source]

Advantages[edit | edit source]

  • Fast data access on average.
  • Efficient in terms of space when compared to other data structures with similar functionalities.

Disadvantages[edit | edit source]

  • Performance can significantly degrade in poor implementations or with unsuitable hash functions.
  • Deterministic data retrieval time cannot be guaranteed due to the possibility of collisions.

See Also[edit | edit source]

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Contributors: Prab R. Tumpati, MD