hash table

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hash table

hash table

A lookup table that is designed to efficiently store non-contiguous keys (account numbers, part numbers, etc.) that may have wide gaps in their alphabetic or numeric sequences.

Hash tables are created with an algorithm that stores the keys into hash buckets, which contain key-value pairs. Since different keys may hash to the same bucket, the goal of hash table design is to spread out the key-value pairs evenly with each bucket containing as few key-value pairs as possible. When an item is looked up, its key is hashed to find the appropriate bucket, and the bucket is then compared to find the right key-value pair. See key-value pair, cryptographic hash function, DHT and hash total.


A Hash Table
When an item is looked up, its key is hashed into a bucket value, and the bucket is compared to find the key.
References in periodicals archive ?
A hashtable is created which keeps track of a number of PropertyChangeListeners for Figs.
Finally, the value which included the hashtable was inserted into the database system using the SQL query.
Some basic operations involved in this algorithm (and in Algorithm 2 and 3) include operation over stack, operation over hashtable and operation over vector.
For each query edge, if the twig is of P-C relationship, the parent and child details will be updated in the twigPC (a hashtable to store parent and child) repository as depicted in Figure 4.
Hashtable to store the relationship between customers and countries.
IDictionaryEnumerator to iterate through Hashtable keys.
n] [element of] i) [conjunction] (0 [is less than or equal to] x [is less than or equal to][is less than or equal to] maxspan)} 2 EH-Apriori: set all the buckets of HashTable to 0; 3 foreach extended transaction [[Delta].
As shown, although the execution time of the first pass of EH-Apriori is slightly larger than that of E-Apriori, due to the extra overhead required for building HashTable, EH-Apriori incurs significantly smaller execution time than E-Apriori in the latter Pass 2, as |[C.
type [Type definitions for elements and segments are the same as for linear hashing] hashtable = record y0: integer; [See the text for an explanation] x0: real; [of the use of these fields] lowaddr, [First address and] highaddr: integer; [last address in current address space] keycount: integer [Number of records stored] minloadfctr, [Lower and] maxloadfctr: real; [upper bound on the load factor] directory array [0 .
Similar to a traditional hashtable data structure, other operations supported in the DHT include put and get.
The hash table itself, hashtable, is an array of pointers to word structures: #define HASHSIZE 07777 /* hash table size */ struct word [ char *word; /* the word */ int count; /* frequency count */ struct word *next; /* link to next entry */ ] *hashtable [HASHSIZE+1];
s = buf; for (len = 0; *s; len++) h += *s++; for (wp = hashtable [h&HASHSIZE]; wp; wp = wp->next) if (strcmp(wp->word, buf) == 0) break; if (wp == NULL) [ /* a new word */ wp = (struct word *) alloc(1, sizeof *wp); wp->word = alloc(len + 1, sizeof(char)); strcpy(wp->word, buf); wp->count = 0; wp->next = hashtable [h&HASHSIZE]; hashtable [h&HASHSIZE] = wp; total++; ] wp->count++; ] addword also increments a global integer, total, which counts the number of distinct words in the table.