Patent Document

TECHNICAL FIELD 
     The present invention relates in general to hash tables and in particular to minimally disruptive hash tables. 
     BACKGROUND 
     Generally speaking, hash tables are data structures used by computing devices to store elements of data. In one example, hash tables store elements in hash table buckets. Elements are stored in the hash table buckets and are accessed and inserted using a hash function. A hash function takes as input in one example a key associated with a particular element. The output of the hash function is a hash table index associated with a hash table bucket. The hash table index can be used to identify which hash table bucket to read an element from or insert an element into. 
     SUMMARY 
     Disclosed herein are embodiments of methods, apparatuses, and systems relating to hash tables and in particular to minimally disruptive hash tables. 
     One aspect of the disclosed embodiments is a method for storing elements in a hash table. The method includes creating the hash table in at least one memory, the hash table having a plurality of hash table buckets, wherein a count of the plurality of hash table buckets is greater than or equal to a maximum count of elements to be stored in the hash table over a period in which the hash table is used, storing a plurality of elements in the plurality of hash table buckets such that each hash table bucket has one of the plurality of elements, and adding a new element to the hash table with a processor. Adding the new element includes determining, using a hash function, a plurality of hash table indexes identifying at least some of the hash table buckets in which the new element will be inserted and inserting the new element in the identified hash table buckets by replacing existing elements stored in the hash table buckets. 
     Another aspect of the disclosed embodiments is an article of manufacture. The article of manufacture includes at least one non-transitory computer-readable medium having stored thereon a hash table data structure, a plurality of hash table buckets included within the hash table data structure, wherein a count of the plurality of hash table buckets is greater than or equal to a maximum count of elements to be stored in the hash table data structure over a period in which the hash table data structure is used, and a plurality of elements stored in the plurality of hash table buckets so that each of the plurality of hash table buckets has one of the plurality of elements, wherein the hash table data structure is capable of storing one or more new elements in the plurality of hash table buckets without refactoring the hash table data structure. 
     Another aspect of the disclosed embodiments is a method that includes identifying a hash table data structure defined above that is stored in a non-transitory memory and performing an operation on the hash table data structure using a processor without refactoring the hash table data structure, wherein the operation is one of add, remove, or replace. 
     These and other embodiments will be described in additional detail hereafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The description herein makes reference to the accompanying drawings wherein like reference numerals refer to like parts throughout the several views, and wherein: 
         FIG. 1  is a diagram of a computing scheme according to embodiments of the disclosed subject matter; 
         FIGS. 2A and 2B  are block diagrams of one exemplary hash table that requires refactoring when elements are added or removed; 
         FIGS. 3A and 3B  are block diagrams of one exemplary minimally disruptive hash table that does not require refactoring when elements are added or removed according to embodiments of the disclosed subject matter; 
         FIG. 4  is a flowchart of a technique of searching for an element in a minimally disruptive hash table according to embodiments of the disclosed subject matter; 
         FIG. 5  is a flowchart of one exemplary technique of adding an element to a minimally disruptive hash table according to embodiments of the disclosed subject matter; 
         FIG. 6  is a flowchart of another exemplary technique of adding an element to a minimally disruptive hash table according to embodiments of the disclosed subject matter; 
         FIG. 7  is a flowchart of one exemplary technique of removing an element from a minimally disruptive hash table according to embodiments of the disclosed subject matter; and 
         FIG. 8  is a flowchart of another exemplary technique of removing an element from a minimally disruptive hash table according to embodiments of the disclosed subject matter. 
     
    
    
     DETAILED DESCRIPTION 
     Hash tables have various uses in computing systems. In one exemplary use, a hash table is utilized to associate a key value to an element stored in a hash bucket in the hash table. For example, this association can be used in networking routers and switches for routing packets in a network. In many such applications, it is desirable for a particular key to remain associated with the same element after the initial association. 
     In typical hash tables, a particular key does not always remain associated with the same hash bucket. In order to map a key value to a hash bucket, a hash function is used. The hash function is configured to have a number of output values equal to the number of hash buckets included in the hash table so that any possible key value can be associated with a hash bucket using the hash function. In the case where the hash table is refactored (i.e. the number of hash buckets change), a significant number of associations between hash buckets and key values will change. The change is because the hash function will have to be adjusted so that the total possible outputs of the hash function will be equal to the new number of hash buckets in the hash table. 
     Described herein is a minimally disruptive hash table where changing a number of elements stored within the hash table does not require refactoring the hash table. A minimally disruptive hash table is created with a number of hash buckets greater than or equal to a maximum expected count of elements to be stored in the hash table over a period in which the hash table is used. The maximum expected count can be application dependent and, for example, can be determined automatically when the hash table is created or can be pre-determined. Elements added to the hash table are distributed throughout the available hash buckets. The distribution of elements within the hash table can be equally or unequally weighted. For example, if equally weighted, each element will be targeted to occupy an equal number of hash buckets. As another example, if unequally weighted, each element can have an associated weight that controls a number of hash buckets that each element is targeted to occupy. 
       FIG. 1  is a diagram of a computing scheme  10  according to embodiments of the disclosed subject matter. An exemplary computing device  12  can be, for example, a computer having an internal configuration of hardware including a processor such as a central processing unit (CPU)  14  and a memory  16 . CPU  14  can be a controller for controlling the operations of computing device  12 . The CPU  14  is connected to memory  16  by, for example, a memory bus. Memory  16  can include random access memory (RAM) or any other suitable memory device. Memory  16  can store data and program instructions which are used by the CPU  14 . Computing device  12  can also take other forms, such as network routers or switches. Other suitable implementations of computing device  12  are possible. 
     The computing device  12  can be the only computing device or can be one of a group of computing devices  17  that includes additional computing devices  18 . The group of computing devices  17  can be implemented as a computing cluster whereby the computing device  12  and additional computing devices  18  share resources, such as storage memory, and load-balance the processing of requests to the computing device. The group of computing devices  17  can alternatively be a cloud computing service. For example, a cloud computing service can include hundreds or thousands of computing devices configured to provide scalable computing resources. In a cloud computing service, computing tasks can be performed on one or more computing devices or other computing devices included within the cloud computing service. 
     The above are only exemplary implementations of the group of computing devices  17 , and any distributed computing model can be used in their place. As used herein, the term computing device is understood to include any combination or implementation of computing devices, computing device groups, or any other configuration of computing devices. 
     Other implementations of the computing scheme  10  are possible, and components can be added, removed, modified, or replaced in various implementations. In one exemplary implementation, the group of computing devices  17  and additional computing devices  18  are omitted and a single computing device  12  is used. In another exemplary implementation, single computing device  12  and/or additional computing devices  18  include one or more network ports operable to connect device  12  and devices  18  to one or more networks (such as a local area network (LAN), wide area network (WAN), or the Internet) and/or other devices. 
     Although  FIG. 1  depicts that processor  14  and memory  16  are integrated into single units, this depiction is exemplary only. The operations of computing device  12  and/or additional computing devices  18  can be distributed across multiple processors on the same machine or different machines or across a network such as a local area network, wide area network or the Internet and the term “computing device” can encompass such multi-machine systems. Memory  16  can also be integral units (such as the RAM within a computer) or can be distributed across multiple machines such as a network-based memory or memory in multiple machines performing the operations of computing device  12  and/or additional computing devices  18 . 
       FIGS. 2A and 2B  are block diagrams of one exemplary hash table that requires refactoring when elements are added or removed according to embodiments of the disclosed subject matter.  FIGS. 2A and 2B  depict a hash table used for (Un)Equal Cost Multiple Path (ECMP) IP routing. In an IP network, network packets can sometimes be routed to a destination over a number of different network paths. ECMP IP routing can be used to select a network path for each packet sent through a device by selecting adjacent network connections (i.e. adjacency) to transmit each packet through. Network packets are typically associated with a packet flow, which is, for example, a sequence of packets sent from a source application to a destination application. In a typical ECMP implementation, it is desirable for the transmission of each packet flow to be maintained on a consistent network path to avoid reordering of packets within the packet flow. For example, the performance of some applications and protocols (such as TCP) can be degraded when packets in a flow are reordered. 
     With respect to  FIG. 2A , a hash table  30   a  is shown having two hash buckets. The hash buckets include elements associated with “Adjacency  1 ” and “Adjacency  2 .” The hash buckets of hash table  30   a  are accessed by way of hash table indexes  32   a . In this example, hash table indexes  32   a  include “Hash table index  0 ” and “Hash table index  1 ” which are each associated with one of the hash buckets. A hash function  34   a  is used to convert a key value  36  to one of the hash table indexes  32   a . In this example, the key value  36  is a destination IP address of a packet to be routed using ECMP. However, in other examples, other key values may be used, such as a combination of source and destination IP addresses. 
     Routing table  38   a  contains exemplary key values, hash table indexes  32   a , and hash bucket elements from hash table  30   a  associated with the hash table indexes  32   a  based on hash function  34   a . For example, key (destination IP) 10.1.0.1 is associated with hash table index  0  and hash bucket (adjacency)  1 , and key (destination IP) 10.1.0.2 is associated with hash table index  1  and hash bucket (adjacency)  2 . The remaining keys similarly alternate association with the two hash buckets. 
     Now with respect to  FIG. 2B , a hash table  30   b  is shown having four hash buckets. Hash table  30   b  is an example of hash table  30   a  being refactored (i.e. resized) from two hash buckets to four hash buckets. This can be done, for example, when a number of adjacencies changes. In this example, hash table indexes  32   b  include in addition to  32   a  a “Hash table index  2 ” and “Hash table index  3 .” Hash table indexes  32   b  are each associated with one of the hash buckets. A hash function  34   b  is used to convert a key value  36  to one of the hash table indexes  32   b . Hash function  34   b  can be a modification of hash function  34   a  configured to produce four output values (hash table indexes) instead of two output values (hash table indexes). 
     Routing table  38   b  contains exemplary key values, hash table indexes  32   b , and hash bucket elements from hash table  30   b  associated with the hash table indexes  32   b  based on hash function  34   b . For example, key (destination IP) 10.1.0.1 is now associated with hash table index  1  and hash bucket (adjacency)  2 , and key (destination IP) 10.1.0.2 is associated with hash table index  2  and hash bucket (adjacency)  3 . The remaining keys alternate association with the four hash buckets now included in hash table  30   b.    
     Notably, in routing table  38   b , the adjacency associated with each key (destination IP) has changed because of the refactoring of the hash table. Generally speaking, when a typical hash table is refactored, nearly all key to element associations will be lost. The associations that are maintained (if any) will depend on numerous implementation specific factors, including the size of the hash table, number of hash buckets added or deleted, and the hash function utilized. For example, a hash function having a poor output distribution over the hash buckets may retain more associations than another hash function having a good output distribution. 
       FIGS. 3A and 3B  are block diagrams of one exemplary minimally disruptive hash table that does not require refactoring when elements are added or removed according to embodiments of the disclosed subject matter. Similar to  FIGS. 2A and 2B ,  FIGS. 3A and 3B  depict a hash table usable for ECMP IP routing. However, unlike  FIGS. 2A and 2B ,  FIGS. 3A and 3B  illustrate the use of a minimally disruptive hash table to avoid unnecessary changes in the associations between keys and hash buckets when adding or removing elements from the hash table. 
     With respect to  FIG. 3A , a minimally disruptive hash table  50   a  is shown having eight hash buckets. The hash buckets include two distinct elements associated with “Adjacency  1 ” and “Adjacency  2 .” The two distinct elements are distributed throughout the hash buckets. The hash buckets of minimally disruptive hash table  50   a  are accessed by way of hash table indexes  52 . In this example, hash table indexes  52  include indexes from 0 to 7 which are each associated with one of the hash buckets. A hash function  54  is used to convert a key value  56  to one of the hash table indexes  52 . In this example, the key value  56  is a destination IP address of a packet to be routed using ECMP. However, in other examples, other key values may be used, such as a combination of source and destination IP addresses. 
     The distinct elements included in hash table  50   a  are distributed throughout the table using techniques such as those described later with respect to  FIGS. 5-8 . In this example, the “Adjacency  1 ” element is stored in hash buckets associated with hash table indexes  0  and  5 - 7 . Correspondingly, the “Adjacency  2 ” element is stored in hash buckets associated with hash table indexes  1 - 4 . 
     Routing table  58   a  contains exemplary key values, hash table indexes  52 , and hash bucket elements from hash table  50   a  associated with the hash table indexes  52  based on hash function  54 . For example, key (destination IP) 10.1.0.1 is associated with hash table index  0  and hash bucket  0  (adjacency  1 ), and key (destination IP) 10.1.0.2 is associated with hash table index  1  and hash bucket  1  (adjacency  2 ). The remaining keys are similarly associated with one of the eight hash buckets and one of the two adjacencies. 
     Now with respect to  FIG. 3B , a minimally disruptive hash table  50   b  is shown having eight hash buckets, similar to minimally disruptive hash table  50   a . Hash table  50   b  is an illustrative example of adding an element to hash table  50   a  without refactoring hash table  50   a . Techniques for adding an element to a minimally disruptive hash table are described further with respect to  FIGS. 5-6 . 
     Similar to hash table  50   a , the three distinct elements of hash table  50   b  are distributed throughout the hash buckets. The hash buckets of minimally disruptive hash table  50   b  are accessed by way of hash table indexes  52 . In this example, hash table indexes  52  include indexes from 0 to 7 which are each associated with one of the hash buckets. A hash function  54  is used to convert a key value  56  to one of the hash table indexes  52 . 
     The distinct elements included in hash table  50   a  are distributed throughout the table using techniques such as those described later with respect to  FIGS. 5-8 . In this example, the “Adjacency  1 ” element is stored in hash buckets associated with hash table indexes  0  and  6 - 7 . The “Adjacency  2 ” element is stored in hash buckets associated with hash table indexes  1 - 3 . 
     The new “Adjacency  3 ” element is stored in hash buckets associated with hash table indexes  4  and  5 . Notably (unlike hash tables  30   a - b ), the addition of the new “Adjacency  3 ” element only disturbs the key to element associations for the hash buckets in which the new element is added (hash table indexes  4  and  5 ). 
     Routing table  58   b  contains exemplary key values, hash table indexes  52 , and hash bucket elements from hash table  50   a  associated with the hash table indexes  52  based on hash function  54 . For example, key (destination IP) 10.1.0.1 is still associated with hash table index  0  and hash bucket  0  (adjacency  1 ), and key (destination IP) 10.1.0.2 is still associated with hash table index  1  and hash bucket  1  (adjacency  2 ). The only key to element associations that are changed are those associated with hash table indexes  4  and  5 . The remaining key to element associations are maintained. 
     The number of key to element associations impacted by a change to a minimally disruptive hash table can be expressed mathematically. For example, a number of hash buckets x changed when adding y elements can be expressed using formula 1: 
                   x   =       ∑     i   =   e       e   +   y       ⁢     {               N     e   +   i       ,           i   &gt;     (     N   ⁢           ⁢   %   ⁢     (     e   +   i     )       )                     N     e   +   i       +   1     ,           i   ≤     (     N   ⁢           ⁢   %   ⁢     (     e   +   i     )       )             ;   wherein                 (   1   )               
e is a number of elements stored in the hash table before adding the y new elements;
 
% is the modulo operator; and
 
N is a total number of hash buckets in the hash table.
 
       FIG. 4  is a flowchart of a technique of searching for an element in a minimally disruptive hash table according to embodiments of the disclosed subject matter. First, at stage  72 , a key value is identified for the hash table lookup. For example, in many networking use-cases, the key value can be any value associated with a packet or other piece of data to be associated with an element stored in a minimally disruptive hash table. One exemplary key value is an IP address. However, other values and combinations of values can be used for the key value. 
     Once the key value is identified, a hash for the key value is determined at stage  74 . The hash can be determined using a hash function, such as hash function  54  described earlier. After the hash is determined, it is used to lookup an element from the hash table at stage  76 . 
       FIG. 5  is a flowchart of one exemplary technique  90  of adding an element to a minimally disruptive hash table according to embodiments of the disclosed subject matter. At stage  92 , a key value associated with a new element E n  to be inserted is identified. Similar to the search techniques described above, the key value can be any value associated with the new element E n  or any combination of values. For example, with respect to  FIG. 3A  above, the key value for an adjacency could be the index number of that adjacency (i.e. 1 or 2). 
     Next, a hash I n  is determined at stage  94  for the identified key value. Hash I n  can be determined using any hash function. At stage  96 , variables needed for technique  90  are initialized. For example, the variable STORED_COUNT can be initialized to zero. The variable STORED_COUNT represents a total count of hash table indexes associated with hash buckets in which the new element E n  is inserted. 
     At stage  98 , an original element E n  is read from the hash table from hash table index I n . At stage  100 , element E c  is read from the hash table from hash table index I n . E n  is maintained throughout technique  90  as an indication of the first element replaced in the hash table. E c  is updated in each iteration of technique  90  as E n  is inserted in hash buckets of the hash table and as I n  is incremented. 
     At stage  102 , E c  is tested to see if it is NULL (or has a similar property, such as being empty). If E c  is NULL, control passes to stage  108 , which is described later. If E c  is not NULL, control passes to stage  104 , where STORED_COUNT is compared to a threshold. The threshold controls how many hash buckets into which the new element E n  is inserted. 
     The threshold can be determined in a number of different ways. For example, the threshold can be calculated so that each element in the hash table preferably occupies an equal number of hash buckets. In this case, the threshold T E     i    for an element E i  can be determined by dividing the total number of hash buckets in the hash table (N) by the number of elements stored in the hash table (e). In an alternate implementation, the threshold can be determined using a weight w i . With a weight, each element can be targeted to occupy a different proportion of the hash buckets of the hash table. In one example of the alternative implementation, the threshold can be calculated as follows: 
                     T     E   i       =     {                 w   i       ∑   w       ,           i   ≥     (     N   ⁢           ⁢   %   ⁢           ⁢   e     )                       w   i       ∑   w       +   1     ,           i   &lt;     (     N   ⁢           ⁢   %   ⁢           ⁢   e     )             ;   wherein               (   2   )               
Σw is the sum of the weights of all of the elements stored in the hash table.
 
     If the STORED_COUNT is not greater than the threshold, E o  and E c  are compared at stage  106 . If E o  and E c  are the same element, control passes to stage  108 . At stage  108 , E n  is stored in the hash table at hash table index I n . Next, at stage  110 , I n  is incremented. Incrementing can mean any change in I n  that points the incremented I n  to the next hash bucket in the hash table. For example, the incrementing can include a modulus operation that loops the value I n  back to the first hash table index of the hash table once the last hash bucket of the hash table is reached. Once I n  is incremented, STORED_COUNT is incremented at stage  112 . Control then passes back to stage  100 . 
     Execution of stages  100  through  112  of technique  90  continues as described to determine additional hash table indexes identifying hash table buckets in which the new element E n  is to be added/inserted and to insert E n  into those hash table buckets. Technique  90  completes when the STORED_COUNT is greater than the threshold at stage  106  or if E o  and E c  are not the same element at stage  108 . 
       FIG. 6  is a flowchart of another exemplary technique  120  of adding an element to a minimally disruptive hash table according to embodiments of the disclosed subject matter. At stage  122 , the hash table is checked to see if it is empty. If it is empty, control passes to stage  124 , where a new element E n  is inserted into all hash buckets of the hash table. Technique  120  is then completed. 
     Referring back to stage  122 , if the hash table is not empty, control instead passes to stage  126 , where a key value associated with the new element E n  to be inserted is identified. Similar to the techniques described above, the key value can be any value associated with the new element E n  or any combination of values. For example, with respect to  FIG. 3A  above, the key value for an adjacency could be the index number of that adjacency (i.e. 1 or 2). 
     At stage  128 , variables needed for technique  90  are initialized. For example, the variable STORED_COUNT can be initialized to zero. The variable STORED_COUNT tracks the number of hash buckets in which the new element E n  is inserted. Next, STORED_COUNT is compared to a threshold at stage  130 . The threshold controls how many hash buckets into which the new element E n  is inserted. The threshold can be determined as previously described with respect to technique  90 . 
     If the STORED_COUNT is less than the threshold, control passes to stage  132 , where a hash I n  is determined for a (key value, STORED_COUNT) tuple. STORED_COUNT is added to the input of the hash function in order to distribute entries of the new element E n  throughout the hash table. Next, at stage  134 , E n  is stored in a hash bucket of the hash table associated with hash table index I n . At stage  136 , STORED_COUNT is incremented. Then, control returns to stage  130 . With respect to stage  130 , once the STORED_COUNT is greater than the threshold, technique  120  completes. 
     Techniques  90  and  120  as described and shown are only two possible implementations of adding elements to a minimally disruptive hash table. Other techniques are possible, including those that add, remove, modify, or replace stages of techniques  90  and  120 . For example, in an alternative implementation, stage  106  of technique  90  can be omitted. In another alternative implementation, stages  122  and  124  of technique  120  can be omitted. 
       FIG. 7  is a flowchart of one exemplary technique  150  of removing an element from a minimally disruptive hash table according to embodiments of the disclosed subject matter. Technique  150  generally can be used with a hash table that uses technique  90  for adding elements. 
     At stage  152 , a key value associated with the old element E o  to be removed is identified. Similar to the techniques described above, the key value can be any value associated with the old element E o  or any combination of values. For example, with respect to  FIG. 3A  above, the key value for an adjacency could be the index number of that adjacency (i.e. 1 or 2). 
     Next, a hash I o  is determined at stage  154  for the identified key value. Hash I o  can be determined using any hash function. At stage  156 , I o  is compared to zero. If I o  is not zero, control passes to stage  158 , where replacement element E n  is read from the hash bucket corresponding to hash table index I o −1. Otherwise, if I o  is zero, control passes to stage  160 , where replacement element E n  is read from the hash bucket corresponding to hash table index N−1. In other words, the replacement element E r  corresponds to the element stored before the element to be removed E o  in the hash table. 
     Next, at stage  162 , E r  is compared to E o . If they are equal, E r  is set to NULL (or other indication of an empty value) at stage  164 . If they are not equal control passes directly to stage  166 . At stage  166 , current element E c  is read from the hash bucket corresponding to hash table index I o . Next, at stage  168 , E c  is compared to E o . If they are equal, control passes to stage  170 , where E n  is stored in the hash table in the hash bucket corresponding to hash table index I o . Next, at stage  172 , hash table index I o  is incremented. As described above with respect to I n , the incrementing of I o  can include a modulus operation so that when I o  reaches the end of the hash table, the next value of I o  will correspond to the beginning of the hash table. 
     Control then passes back to stage  166 . Stages  166  through  172  are performed iteratively until E c  does not equal E n  at stage  168 , at which point technique  150  completes. 
       FIG. 8  is a flowchart of another exemplary technique  180  of removing an element from a minimally disruptive hash table according to embodiments of the disclosed subject matter. Technique  180  generally can be used with a hash table that uses technique  120  for adding elements. At stage  182 , variables needed for technique  90  are initialized. For example, the variable DELETE_COUNT can be initialized to zero. The variable DELETE_COUNT tracks the number of hash buckets from which the old element E n  is removed. 
     At stage  184 , a key value associated with the old element E n  to be removed is identified. Similar to the techniques described above, the key value can be any value associated with the new element E n  or any combination of values. For example, with respect to  FIG. 3A  above, the key value for an adjacency could be the index number of that adjacency (i.e. 1 or 2). 
     At stage  186 , STORED_COUNT is compared to DELETE_COUNT. STORED_COUNT can be an input to technique  180  and is an indication of a number of hash buckets in which E n  is stored. STORED_COUNT can be determined from values stored in memory that indicate a number of hash buckets that each element of the hash table occupies. For example, the values stored in memory can be updated on each add and remove operation on the hash table. In an alternative implementation, the STORED_COUNT of an element can be determined on demand when a remove operation is to be performed. 
     If STORED_COUNT is not equal to DELETE_COUNT, control passes to stage  188 . At stage  188 , a hash I o  is determined for a (key value, DELETE_COUNT) tuple. DELETE_COUNT is added to the input of the hash function in order to find the entries of the element E n  that were stored throughout the hash table when E o  was added to the hash table (for example, by use of technique  90 ). 
     Next, at stage  190 , current element E c  is read from the hash bucket associated with hash table index I o . At stage  192 , E c  is compared to E o . If E c  is equal to E o , control passes to stage  194 . Otherwise, stage  194  is skipped and control passes to stage  196 . At stage  194 , replacement element E r  is stored in the hash table in a hash bucket associated with index I o . 
     Replacement element E r  can be an input to technique  180 . In one example, the replacement element E r  can be selected based on a loading of the hash table. In this case, loading means a comparison between a number of hash table buckets in which a given element is stored in the hash table as compared to an ideal number of buckets that the given element would ideally be stored in based on a total number of hash table buckets and the weight associated with the given element. When selecting the replacement element E r  based on loading, the element stored in the hash table having the lowest loading (i.e. greatest deficiency in number of actual hash table buckets versus ideal number of hash table buckets) will be selected. 
     Next, at stage  196 , DELETE_COUNT is incremented. Control then returns to stage  186 . Technique  180  continues until STORED_COUNT is equal to DELETE_COUNT at stage  186 , at which point technique  180  completes. 
     Exemplary hash functions usable within the techniques herein (including hash function  54 ) can include primitive polynomials such as cyclic redundancy check (CRC) functions or cryptographic functions such as MD5 functions. Specific functions include but are not limited to modulo, pseudo modulo, XOR, folded XOR, CRC4, CRC8, CRC16, CRC24, CRC32, MD5, Elliptic Curve Signing, SHA1, and SHA256. Suitable hash functions include those providing good distribution across all of the hash table buckets, those having uncorrelated distributions, fast execution times, and small memory requirements. However, any hash function can be utilized. 
     In one alternative implementation, a transformed hash function can be used to reduce the number of hash table buckets actually stored in memory. For example, a hash table can be created with 2 32  (4,294,967,295) hash table buckets. Such a hash table could utilize a CRC32 (32 bit CRC) hash function to generate hash table indexes associated with the hash table buckets. If only a relatively small number of elements are to be stored in the hash table at a given time, it can be advantageous to store only a portion of the hash table buckets in physical memory in order to improve processing efficiency and to reduce memory utilization. 
     In this instance, the hash function can be transformed so that the range of the hash function output is reduced to cover only a portion of the hash table buckets. For example, the output of the hash function can be truncated. In one implementation, the output function can be truncated to 8 bits. In one example, the most significant 24 bits of the hash function output are set to zero (discarded). In this case only 2 8  hash table buckets would be stored in memory. In this case, the hash table still logically has 2 32  hash table buckets, just 2 24  of those hash table buckets would not be stored in memory and the key values that would normally be associated with those non-stored hash table buckets instead are associated with (redirected to) stored hash table buckets. 
     In order to increase the number of hash table buckets stored in memory, the transformation is changed so that the range of the transformed hash function output is increased. The change in transformation maintains the association between key values and stored elements. In an extension of the implementation described above, the truncation of the output function could be changed to 9 bits instead of 8 bits. In this case, 2 9  hash table buckets would be now stored in memory. The additional hash table buckets stored in memory are interleaved with the previously stored hash table buckets, with each additional hash table bucket being associated with one previously stored hash table bucket. The additional hash table buckets are initialized with an element by copying the element stored in the previously stored hash table bucket associated with each additional hash table bucket. Accordingly, the change in the number of hash table buckets stored in memory does not affect the key value to element associations of the hash table. 
     The examples described above of transforming the hash function and reducing the number of hash table buckets stored in memory is only one implementation of these techniques. Other implementations are also available. For example, in other implementations, different transformation functions can be utilized instead of truncation and different hash functions can be used instead of CRC. 
     The embodiments of computing device  12  and/or computing devices  17  (and the algorithms, techniques, instructions etc. stored thereon and/or executed thereby) can be realized in hardware including, for example, computers, IP cores, application-specific integrated circuits (ASICs), programmable logic arrays, optical processors, programmable logic controllers, microcode, firmware, microcontrollers, computing devices, microprocessors, digital signal processors or any other suitable circuit. As used herein, the term “processor” should be understood as encompassing any the foregoing hardware, either singly or in combination. The terms “signal” and “data” are used interchangeably. Further, portions of computing device  12  and/or computing devices  17  do not necessarily have to be implemented in the same manner. 
     Further, in one example, computing device  12  and/or computing devices  17  can be implemented using a general purpose computer/processor with a computer program that, when executed, carries out any of the respective techniques, algorithms and/or instructions described herein. In addition or alternatively, for example, a special purpose computer/processor can be utilized which can contain specialized hardware for carrying out any of the techniques, algorithms, or instructions described herein. 
     Implementations or portions of implementations of the above disclosures can take the form of a computer program product accessible from, for example, a computer-usable or computer-readable medium. A computer-usable or computer-readable medium can be any device that can, for example, tangibly contain, store, communicate, or transport a program or data structure for use by or in connection with any processor. The medium can be, for example, an electronic, magnetic, optical, electromagnetic, or a semiconductor device. Other suitable mediums are also available. Such computer-usable or computer-readable media can be referred to as non-transitory memory or media, and may include RAM or other volatile memory or storage devices that may change over time. 
     The exemplary approaches herein have been described in order to allow easy understanding of the disclosed subject matter and do not limit the disclosed subject matter. On the contrary, the disclosed subject matter is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims, which scope is to be accorded the broadest interpretation so as to encompass all such modifications and equivalent structure as is permitted under the law.

Technology Category: 3