In computing, a hash table, also known as hash map, is a data structure that implements a set abstract data type, a structure that can map keys to values. A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value can be found. During lookup, the key is hashed and the resulting hash indicates where the corresponding value is stored.
Hash table  

Type  Unordered associative array  
Invented  1953  
Time complexity in big O notation  

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 in some way.
In a welldimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation.^{[2]}^{[3]}^{[4]}
Hashing is an example of a spacetime tradeoff. If memory is infinite, the entire key can be used directly as an index to locate its value with a single memory access. On the other hand, if time is infinite, values can be stored without regard for their keys, and a binary search or linear search can be used to retrieve the element.^{[5]}^{: 458 }
In many situations, hash tables turn out to be on average more efficient than search trees or any other table lookup structure. For this reason, they are widely used in many kinds of computer software, particularly for associative arrays, database indexing, caches, and sets.
The idea of hashing arose independently in different places. In January 1953, Hans Peter Luhn wrote an internal IBM memorandum that used hashing with chaining. Open addressing was later proposed by A. D. Linh building on Luhn's paper.^{[6]}^{: 15 } Around the same time, Gene Amdahl, Elaine M. McGraw, Nathaniel Rochester, and Arthur Samuel of IBM Research implemented hashing for the IBM 701 assembler.^{[7]}^{: 124 } Open addressing with linear probing is credited to Amdahl, although Ershov independently had the same idea.^{[7]}^{: 124125 } The term "open addressing" was coined by W. Wesley Peterson on his article which discusses the problem of search in large files.^{[6]}^{: 15 }
The first published work on hashing with chaining is credited to Arnold Dumey, who discussed the idea of using remainder module a prime as a hash function.^{[6]}^{: 15 } The word "hashing" was first published by an article by Robert Morris.^{[7]}^{: 126 } A theoretical analysis of linear probing was submitted originally by Konheim and Weiss.^{[6]}^{: 15 }
A hash table is an implementation of set abstract data type, which is used for storing a set of elements in an associative array of length , where , with better time complexity bounds on search, delete, and insert operations in comparison to selfbalancing binary search trees.^{[6]}^{: 1 } A value gets stored at an index location , where is a hash function, and .^{[6]}^{: 2 }
A load factor is a critical statistic of a hash table, and is defined as follows:^{[1]}
where
The performance of the hash table deteriorates in relation to the load factor .^{[6]}^{: 2 } Therefore a hash table is resized or rehashed if the load factor approaches 1.^{[8]} A table is also resized if the load factor drops below .^{[8]} Acceptable figures of load factor include 0.6 and 0.75.^{[9]}^{[10]}^{: 110 }
A hash function maps the universe of keys to array indices or slots within the table for each where and . The conventional implementations of hash functions are based on the integer universe assumption that all elements of the table stem from the universe , where the bit length of is confined within the word size of a computer architecture.^{[6]}^{: 2 }
A perfect hash function is defined as an injective function such that each element in maps to a unique value in .^{[11]}^{[12]} A perfect hash function can be created if all the keys are known ahead of time.^{[11]}
The schemes of hashing used in integer universe assumption include hashing by division, hashing by multiplication, universal hashing, dynamic perfect hashing, and static perfect hashing.^{[6]}^{: 2 } However, hashing by division is the commonly used scheme.^{[13]}^{: 264 }^{[10]}^{: 110 }
The scheme in hashing by division is as follows:^{[6]}^{: 2 }
Where is the hash digest of and is the size of the table.
The scheme in hashing by multiplication is as follows:^{[6]}^{: 23 }
Where is a realvalued constant. An advantage of the hashing by multiplication is that the is not critical.^{[6]}^{: 23 } Although any value real value produces a hash function, Donald Knuth suggests using the golden ratio.^{[6]}^{: 3 }
A search algorithm that uses hashing consists of two parts. The first part is computing a hash function which transforms the search key into an array index. The ideal case is such that no two search keys hashes to the same array index, however, this is not always the case, and is impossible to preclude for unseen given data.^{[14]}^{: 515 } Hence the second part of the algorithm is collision resolution. The two common methods for collision resolution are separate chaining and open addressing.^{[5]}^{: 458 }
In separate chaining, the process involves building a linked list with keyvalue pair for each search array index. The collided items are chained together through a single linked list, which can be traversed to access the item with a unique search key.^{[5]}^{: 464 } Collision resolution through chaining with linked list is a common method of implementation of hash tables. Let and be the hash table and the node respectively, the operation involves as follows:^{[13]}^{: 258 }
ChainedHashInsert(T, x) insert at the head of linked list 
If the element is comparable either numerically or lexically, and inserted into the list by maintaining the total order, it results in faster termination of the unsuccessful searches.^{[14]}^{: 520521 }
If the keys are ordered, it could be efficient to use "selforganizing" concepts such as using a selfbalancing binary search tree, through which the theoretical worst case could be brought down to , although it introduces additional complexities.^{[14]}^{: 521 }
In dynamic perfect hashing, two level hash tables are used to reduce the lookup complexity to be a guaranteed in the worst case. In this technique, the buckets of entries are organized as perfect hash tables with slots providing constant worstcase lookup time, and low amortized time for insertion.^{[15]} A study shows array based separate chaining to be 97% more performant when compared to the standard linked list method under heavy load.^{[16]}^{: 99 }
Techniques such as using fusion tree for each buckets also result in constant time for all operations with high probability.^{[17]}
The linked list of separate chaining implementation may not be cacheconscious due to spatial locality—locality of reference—when the nodes of the linked list are scattered across memory, thus the list traversal during insert and search may entail CPU cache inefficiencies.^{[16]}^{: 91 }
In cacheconscious variants, a dynamic array found to be more cachefriendly is used in the place where a linked list or selfbalancing binary search trees is usually deployed for collision resolution through separate chaining, since the contiguous allocation pattern of the array could be exploited by hardwarecache prefetchers—such as translation lookaside buffer—resulting in reduced access time and memory consumption.^{[18]}^{[19]}^{[20]}
Open addressing is another collision resolution technique in which every entry records are stored in the bucket array itself, and the hash resolution is performed through probing. When a new entry has to be inserted, the buckets are examined, starting with the hashedto slot and proceeding in some probe sequence, until an unoccupied slot is found. When searching for an entry, the buckets are scanned in the same sequence, until either the target record is found, or an unused array slot is found, which indicates an unsuccessful search.^{[21]}
Wellknown probe sequences include:
The performance of open addressing may be slower compared to separate chaining since the probe sequence increases when the load factor approaches 1.^{[8]}^{[16]}^{: 93 } The probing results in an infinite loop if the load factor reaches 1, in the case of a completely filled table.^{[5]}^{: 471 } The average cost of linear probing depends on the hash function's ability to distribute the elements uniformly throughout the table to avoid clustering, since formation of clusters would result in increased search time.^{[5]}^{: 472 }
Since the slots are located in successive locations, linear probing could lead to better utilization of CPU cache due to locality of references resulting in reduced memory latency.^{[22]}
Coalesced hashing is a hybrid of both separate chaining and open addressing in which the buckets or nodes link within the table.^{[24]}^{: 6–8 } The algorithm is ideally suited for fixed memory allocation.^{[24]}^{: 4 } The collision in coalesced hashing is resolved by identifying the largestindexed empty slot on the hash table, then the colliding value is inserted into that slot. The bucket is also linked to the inserted node's slot which contains its colliding hash address.^{[24]}^{: 8 }
Cuckoo hashing is a form of open addressing collision resolution technique which guarantees worstcase lookup complexity and constant amortized time for insertions. The collision is resolved through maintaining two hash tables, each having its own hashing function, and collided slot gets replaced with the given item, and the preoccupied element of the slot gets displaced into the other hash table. The process continues until every key has its own spot in the empty buckets of the tables; if the procedure enters into infinite loop—which is identified through maintaining a threshold loop counter—both hash tables get rehashed with newer hash functions and the procedure continues.^{[25]}^{: 124–125 }
Hopscotch hashing is an open addressing based algorithm which combines the elements of cuckoo hashing, linear probing and chaining through the notion of a neighbourhood of buckets—the subsequent buckets around any given occupied bucket, also called a "virtual" bucket.^{[26]}^{: 351–352 } The algorithm is designed to deliver better performance when the load factor of the hash table grows beyond 90%; it also provides high throughput in concurrent settings, thus well suited for implementing resizable concurrent hash table.^{[26]}^{: 350 } The neighbourhood characteristic of hopscotch hashing guarantees a property that, the cost of finding the desired item from any given buckets within the neighbourhood is very close to the cost of finding it in the bucket itself; the algorithm attempts to be an item into its neighbourhood—with a possible cost involved in displacing other items.^{[26]}^{: 352 }
Each bucket within the hash table includes an additional "hopinformation"—an Hbit bit array for indicating the relative distance of the item which was originally hashed into the current virtual bucket within H1 entries.^{[26]}^{: 352 } Let and be the key to be inserted and bucket to which the key is hashed into respectively; several cases are involved in the insertion procedure such that the neighbourhood property of the algorithm is vowed:^{[26]}^{: 352353 } if is empty, the element is inserted, and the leftmost bit of bitmap is set to 1; if not empty, linear probing is used for finding an empty slot in the table, the bitmap of the bucket gets updated followed by the insertion; if the empty slot is not within the range of the neighbourhood, i.e. H1, subsequent swap and hopinfo bit array manipulation of each bucket is performed in accordance with its neighbourhood invariant properties.^{[26]}^{: 353 }
Robin hood hashing is an open addressing based collision resolution algorithm; the collisions are resolved through favouring the displacement of the element that is farthest—or longest probe sequence length (PSL)—from its "home location" i.e. the bucket to which the item was hashed into.^{[27]}^{: 12 } Although robin hood hashing does not change the theoretical search cost, it significantly affects the variance of the distribution of the items on the buckets,^{[28]}^{: 2 } i.e. dealing with cluster formation in the hash table.^{[29]} Each node within the hash table that uses robin hood hashing should be augmented to store an extra PSL value.^{[30]} Let be the key to be inserted, be the (incremental) PSL length of , be the hash table and be the index, the insertion procedure is as follows:^{[27]}^{: 1213 }^{[31]}^{: 5 }
Repeated insertions cause the number of entries in a hash table to grow, which consequently increases the load factor; to maintain the amortized performance of the lookup and insertion operations, a hash table is dynamically resized and the items of the tables are rehashed into the buckets of the new hash table,^{[8]} since the items cannot be copied over as varying table sizes results in different hash value due to modulo operation.^{[32]} Resizing may be performed on hash tables with fewer entries compared to its size to avoid excessive memory usage.^{[33]}
Generally, a new hash table with a size double that of the original hash table gets allocated privately and every item in the original hash table gets moved to the newly allocated one by computing the hash values of the items followed by the insertion operation. Rehashing is computationally expensive despite its simplicity.^{[34]}^{: 478–479 }
Some hash table implementations, notably in realtime systems, cannot pay the price of enlarging the hash table all at once, because it may interrupt timecritical operations. If one cannot avoid dynamic resizing, a solution is to perform the resizing gradually to avoid storage blip—typically of size 50% of new table's size—during rehashing and to avoid memory fragmentation that triggers heap compaction due to deallocation of large memory blocks caused by the old hash table.^{[35]}^{: 2–3 } In such case, the rehashing operation is done incrementally through extending prior memory block allocated for the old hash table such that the buckets of the hash table remain unaltered. A common approach for amortized rehashing involves maintaining two hash functions and . The process of rehashing a bucket's items in accordance with the new hash function is termed as cleaning, which is implemented through command pattern by encapsulating the operations such as , and through a wrapper such that each element in the bucket gets rehashed and its procedure involve as follows:^{[35]}^{: 3 }
Linear hashing is an implementation of the hash table which enables dynamic growths or shrinks of the table one bucket at a time.^{[36]}
The performance of a hash table is dependent on the hash function's ability in generating quasirandom numbers ( ) for entries in the hash table where , and denotes the key, number of buckets and the hash function such that . If the hash function generates same for distinct keys ( ), this results in collision, which should be dealt with in several ways. The constant time complexity ( ) of the operation in a hash table is presupposed on the condition that the hash function doesn't generate colliding indices; thus, the performance of the hash table is directly proportional to the chosen hash function ability to disperse the indice.^{[37]}^{: 1 } However, construction of such a hash function is practically unfeasible, that being so, implementations depend on casespecific collision resolution techniques in achieving higher performance.^{[37]}^{: 2 }
Hash tables are commonly used to implement many types of inmemory tables. They are used to implement associative arrays.^{[23]}
Hash tables may also be used as diskbased data structures and database indices (such as in dbm) although Btrees are more popular in these applications.^{[38]}
Hash tables can be used to implement caches, auxiliary data tables that are used to speed up the access to data that is primarily stored in slower media. In this application, hash collisions can be handled by discarding one of the two colliding entries—usually erasing the old item that is currently stored in the table and overwriting it with the new item, so every item in the table has a unique hash value.^{[39]}^{[40]}
Hash tables can be used in the implementation of set data structure, which can store unique values without any particular order; set is typically used in testing the membership of a value in the collection, rather than element retrieval.^{[41]}
A transposition table to a complex Hash Table which stores information about each section that has been searched.^{[42]}
Many programming languages provide hash table functionality, either as builtin associative arrays or as standard library modules.
C++11 includes unordered_map
in its standard library for storing keys and values of arbitrary types.^{[43]}
Java programming language includes the HashSet
, HashMap
, LinkedHashSet
, and LinkedHashMap
generic collections.^{[44]}
Python's builtin dict
implements a hash table in the form of a type.^{[45]}
Ruby the hash table uses the open addressing model from Ruby 2.4 onwards.^{[46]}
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