Number Of Slots In Hash Table
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–Probing –examining slots in the table. Number of probes = number of slots examined. –h(k,i,M) where i gives the number of probes/attempts: i=0,1,2, until successful hash. –Linear probing: h(k,i,M) = (h 1 (k) + i)% M,. If the slot where the key is hashed is taken, use the next available slot and wrap around the table. For this reason, chained hash tables remain effective even when the number of table entries (N) is much higher than the number of slots. For separate chaining, the worst-case scenario is when all the entries are inserted into the same linked list.
*Number Of Slots In Hash Tablespoon
*Number Of Slots In Hash Tables
*Number Of Slots In Hash Table Search
*Number Of Slots In Hash Tableau
Though using an Array, we can search an element with time complexity O(1), but the array has its limitation such as it stores similar data types, each cell of array occupies the same amount of space and to find an element we require its index value. To find the Index value of an element itself can take a time complexity of O(n) or O(log n).
Using the concept of Hashing we can build a data structure that can search elements with constant time complexity.What is Hashing?
Hashing is a Technique in which we store data, in an array, at specific indices using some methods, rather than then storing it in ascending order, descending order or randomly. Suppose if we want to store 4 in an array, we perform some methods or operations on 4 and calculate the perfect index value for it, and if we want to retrieve 4 from the array, we just reverse that method or operation and get 4 with constant time complexity.Hashing Table
Hashing Table or Hash Table is a collection of elements which are stored in a data structure using a Hashing method, which makes it easy to find them later. The Hash table consists of key and index or slot, here key represents the value which will store in the table and index or slot represent the index location of that key.
Each position of the hash table, slots, can hold an item and is named by an integer value starting at 0. We can use an Array to implement a hash table and initially all the elements of the array would be None.
For Example:
Insert 1, 3 ,5 , 7 , 8, 10, 11 in a hash table using hashing.
Create an array arbitrary sizeKey-ValueHashing (key value % size of array)Array Index11 % 7 =133 % 7 =355 % 7 =577 % 7 =099 % 7 =21010 % 7 =3 (collision) 3 is already occupied
Collision resolution = 3+1= 4 1111 % 7 =4 (Collison) = 4+1=5(collision)= 5+1 = 6
Array will be arr = [7, 1, 9, 3, 10, 5, 11, None, None, None, None, None, None]Hashing Function
Hash function, are also known as Hashing methods and they are used to map each element or key to a unique slot or index. Hash is also used to minimize the number of collisions and using some easy methods it computes and evenly distributes the items in the hash table.
There are various hashing methods we can use to map a key to its slot:
*Remainder Method
*Folding Method
*Mid Square Method1. Reminder method
In the reminder method, we use the divide the key value with the total size of the table or array and use it remainder to specify the index or slot value of that key. Free slots win real money no deposit required canada.
For example, if we want to insert 9 in an array of size 20, so it would be placed at 9%20 = 9th index if 9th index is free.2. Folding Method
The folding method for constructing hash functions begins by dividing the item into equal-size pieces (the last piece may not be of equal size).
These pieces are then added together to give the resulting hash value.
This hashing method applies to large digit numbers, for example, if we want a hash table in which key elements are the mobile numbers of the customers, this reminder method would not be efficient.
For instance:
If our key was the phone number 436-555-4601Number Of Slots In Hash Tablespoon
We would take the digits and divide them into groups of 2 (43,65,55,46,01).
After the addition, 43+65+55+46+01, we get 210.
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If we assume our hash table has 11 slots, then we need to perform the extra step of dividing by 11 and keeping the remainder.
210 % 11 is 1, so the phone number 436-555-4601 hashes to slot 1.3. Mid Square Method
In the mid-square method, we to compute the key slot number or index location we first square the item and then extract some portion of the resulting digits.
For example:
if the item were 44, we would first compute 442=1,936.
By extracting the middle two digits, 93, and performing the remainder step, we get 93%11 = 5Implementation of Hash tablePython
Output:
In computer science, consistent hashing[1][2] is a special kind of hashing such that when a hash table is resized, only n/m{displaystyle n/m} keys need to be remapped on average where n{displaystyle n} is the number of keys and m{displaystyle m} is the number of slots. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped because the mapping between the keys and the slots is defined by a modular operation. Consistent hashing is a particular case of rendezvous hashing, which has a conceptually simpler algorithm, and was first described in 1996. Consistent hashing first appeared in 1997, and uses a different algorithm.[1]History[edit]
The term ’consistent hashing’ was introduced by David Kargeret al. at MIT for use in distributed caching. This academic paper from 1997 introduced the term ’consistent hashing’ as a way of distributing requests among a changing population of web servers. Each slot is then represented by a server in a distributed system. The addition of a server and the removal of server (say, due to failure) requires only num_keys/num_slots{displaystyle num_keys/num_slots} items to be re-shuffled when the number of slots (i.e., servers) change. The authors mention linear hashing and its ability to handle sequential server addition and removal, while consistent hashing allows servers to be added and removed in arbitrary order.[1]Number Of Slots In Hash Tables
Teradata used this technique in their distributed database, released in 1986, although they did not use this term. Teradata still uses the concept of a hash table to fulfill exactly this purpose. Akamai Technologies was founded in 1998 by the scientists Daniel Lewin and F. Thomson Leighton (co-authors of the article coining ’consistent hashing’). In Akamai’s content delivery network,[3] consistent hashing is used to balance the load within a cluster of servers, while a stable marriage algorithm is used to balance load across clusters.[2]
Consistent hashing has also been used to reduce the impact of partial system failures in large web applications to provide robust caching without incurring the system-wide fallout of a failure.[4] Consistent hashing is also the cornerstone of distributed hash tables (DHTs), which employ hash values to partition a keyspace across a distributed set of nodes, then construct an overlay network of connected nodes that provide efficient node retrieval by key. Rendezvous hashing, designed in 1996, is a simpler and more general technique. It achieves the goals of consistent hashing using the very different highest random weight (HRW) algorithm.Basic Technique[edit]
Consider the problem of load balancing where a set of objects (say, web pages or video segments) need to be assigned to a set of n{displaystyle n} servers. One way of distributing objects evenly across the n{displaystyle n} servers is to use a standard hash function and place object o{displaystyle o} in server with id hash(o)(mod n){displaystyle {text{hash}}(o);left({text{mod }}nright)}, However, if a server is added or removed (i.e., n{displaystyle n} changes), the server assignment of nearly every object in the system may change. This is problematic since servers often go up or down and each such event would require nearly all objects to be reassigned and moved to new servers. Consistent hashing first maps both objects and servers to the unit circle. An object is then mapped to the next server that appears on the circle in clockwise order.[2]
Consistent hashing was designed to avoid the problem of having to change the server assignment of every object when a server is added or removed. The main idea is to use a hash function to randomly map both the objects and the servers to a unit circle. Each object is then assigned to the next server that appears on the circle in clockwise order. This provides an even distribution of objects to servers. But, more importantly, if a server fails and is removed from the circle, only the objects that were mapped to the failed server need to be reassigned to the next server in clockwise order. Likewise, if a new server is added, it is added to the unit circle, and only the objects mapped to that server need to be reassigned. Importantly, when a server is added or removed, the vast majority of the objects maintain their prior server assignments.Practical Extensions[edit]
A number of extensions to the basic technique are needed for effectively using consistent hashing for load balancing in practice.[2] In the basic scheme above, if a server fails, all its objects are reassigned to the next server in clockwise order, potentially doubling the load of that server. This may not be desirable. To ensure a more even re-distribution objects on server failure, each server can be hashed to multiple locations on the unit circle.[2] When a server fails, the objects assigned to each of its replicas on the unit circle will get reassigned to a different server in clockwise order, thus redistributing the objects more evenly. Another extension concerns a flash crowd situation where a single object gets ’hot’ and is accessed a large number of times and will have to be hosted in multiple servers. In this situation, the object may be assigned to multiple contiguous servers by traversing the unit circle in clockwise order.[2] A more complex practical consideration arises when two objects that are hashed near each other in the unit circle and both get ’hot’ at the same time. In this case, both objects will use the same set of contiguous servers in the unit circle. This situation can be ameliorated by each object choosing a different hash function for mapping servers to the unit circle.[2]Comparison with Rendezvous Hashing and other alternatives[edit]
Rendezvous hashing, designed in 1996, is a simpler and more general technique, and permits fully distributed agreement on a set of k{displaystyle k} options out of a possible set of n{displaystyle n} options. It can in fact be shown that consistent hashing is a special case of rendezvous hashing. Because of its simplicity and generality, Rendezvous Hashing is now being used in place of Consistent Hashing in many applications.
If key values will always increase monotonically, an alternative approach using a hash table with monotonic keys may be more suitable than consistent hashing.[citation needed]Complexity[edit]Asymptotic time complexities for N{displaystyle N} nodes (or slots) and K{displaystyle K} keysClassic hash tableConsistent hashingadd a nodeO(K){displaystyle O(K)}O(K/N+logN){displaystyle O(K/N+log N)}remove a nodeO(K){displaystyle O(K)}O(K/N+logN){displaystyle O(K/N+log N)}add a keyO(1){displaystyle O(1)}O(logN){displaystyle O(log N)}remove a keyO(1){displaystyle O(1)}O(logN){displaystyle O(log N)}
The O(K/N){displaystyle O(K/N)} is an average cost for redistribution of keys and the O(logN){displaystyle O(log N)} complexity for consistent hashing comes from the fact that a binary search among nodes angles is required to find the next node on the ring.[citation needed]Examples[edit]
No deposit bonus codes for silver oak casino 2019. Known examples of consistent hashing use include:
*Couchbase automated data partitioning [5]
*OpenStack’s Object Storage Service Swift[6]
*Partitioning component of Amazon’s storage system Dynamo[7]
*Data partitioning in Apache Cassandra[8]
*Data partitioning in Voldemort[9]
*Akka’s consistent hashing router[10]
*Riak, a distributed key-value database[11]
*Gluster, a network-attached storage file system[12]
*Akamai content delivery network[13]
*Discord chat application[14]
*Maglev network load balancer[15]
*Data partitioning in Azure Cosmos DBNumber Of Slots In Hash Table SearchReferences[edit]Number Of Slots In Hash Tableau
*^ abcKarger, D.; Lehman, E.; Leighton, T.; Panigrahy, R.; Levine, M.; Lewin, D. (1997). Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web. Proceedings of the Twenty-ninth Annual ACM Symposium on Theory of Computing. ACM Press New York, NY, USA. pp. 654–663. doi:10.1145/258533.258660.
*^ abcdefgBruce Maggs and Ramesh Sitaraman (2015). ’Algorithmic nuggets in content delivery’(PDF). ACM SIGCOMM Computer Communication Review. 45 (3).
*^Nygren., E.; Sitaraman R. K.; Sun, J. (2010). ’The Akamai Network: A Platform for High-Performance Internet Applications’(PDF). ACM SIGOPS Operating Systems Review. 44 (3): 2–19. doi:10.1145/1842733.1842736. S2CID207181702. Archived(PDF) from the original on September 13, 2012. Retrieved November 19, 2012.
*^Karger, D.; Sherman, A.; Berkheimer, A.; Bogstad, B.; Dhanidina, R.; Iwamoto, K.; Kim, B.; Matkins, L.; Yerushalmi, Y. (1999). ’Web Caching with Consistent Hashing’. Computer Networks. 31 (11): 1203–1213. doi:10.1016/S1389-1286(99)00055-9. Archived from the original on 2008-07-21. Retrieved 2008-02-05.
*^’What Exactly Is Membase?’. Retrieved 2020-10-29.
*^Holt, Greg (February 2011). ’Building a Consistent Hashing Ring’. openstack.org. Retrieved 2019-11-17.
*^DeCandia, G.; Hastorun, D.; Jampani, M.; Kakulapati, G.; Lakshman, A.; Pilchin, A.; Sivasubramanian, S.; Vosshall, P.; Vogels, Werner (2007). ’Dynamo: Amazon’s Highly Available Key-Value Store’(PDF). Proceedings of the 21st ACM Symposium on Operating Systems Principles. 41 (6): 205–220. doi:10.1145/1323293.1294281. Retrieved 2018-06-07.
*^Lakshman, Avinash; Malik, Prashant (2010). ’Cassandra: a decentralized structured storage system’. ACM SIGOPS Operating Systems Review. 44 (2): 35–40. doi:10.1145/1773912.1773922.
*^’Design -- Voldemort’. www.project-voldemort.com/. Archived from the original on 9 February 2015. Retrieved 9 February 2015. Consistent hashing is a technique that avoids these problems, and we use it to compute the location of each key on the cluster.
*^’Akka Routing’. akka.io. Retrieved 2019-11-16.
*^’Riak Concepts’. Archived from the original on 2015-09-19. Retrieved 2016-12-06.
*^’GlusterFS Algorithms: Distribution’. gluster.org. 2012-03-01. Retrieved 2019-11-16.
*^Roughgarden, Tim; Valiant, Gregory (2016-03-28). ’Modern Algorithmic Toolbox’(PDF). stanford.edu. Retrieved 2019-11-17.
*^Vishnevskiy, Stanislav (2017-07-06). ’How Discord Scaled Elixir to 5,000,000 Concurrent Users’. Retrieved 2019-11-17.
*^Eisenbud, Daniel E.; Yi, Cheng; Contavalli, Carlo; Smith, Cody; Kononov, Roman; Mann-Hielscher, Eric; Cilingiroglu, Ardas; Cheyney, Bin; Shang, Wentao; Hosein, Jinnah Dylan. ’Maglev: A Fast and Reliable Software Network Load Balancer’(PDF). Retrieved 2019-11-17.External links[edit]
*Implementations in various languages:Retrieved from ’https://en.wikipedia.org/w/index.php?title=Consistent_hashing&oldid=990444416’
Register here: http://gg.gg/v33c2
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–Probing –examining slots in the table. Number of probes = number of slots examined. –h(k,i,M) where i gives the number of probes/attempts: i=0,1,2, until successful hash. –Linear probing: h(k,i,M) = (h 1 (k) + i)% M,. If the slot where the key is hashed is taken, use the next available slot and wrap around the table. For this reason, chained hash tables remain effective even when the number of table entries (N) is much higher than the number of slots. For separate chaining, the worst-case scenario is when all the entries are inserted into the same linked list.
*Number Of Slots In Hash Tablespoon
*Number Of Slots In Hash Tables
*Number Of Slots In Hash Table Search
*Number Of Slots In Hash Tableau
Though using an Array, we can search an element with time complexity O(1), but the array has its limitation such as it stores similar data types, each cell of array occupies the same amount of space and to find an element we require its index value. To find the Index value of an element itself can take a time complexity of O(n) or O(log n).
Using the concept of Hashing we can build a data structure that can search elements with constant time complexity.What is Hashing?
Hashing is a Technique in which we store data, in an array, at specific indices using some methods, rather than then storing it in ascending order, descending order or randomly. Suppose if we want to store 4 in an array, we perform some methods or operations on 4 and calculate the perfect index value for it, and if we want to retrieve 4 from the array, we just reverse that method or operation and get 4 with constant time complexity.Hashing Table
Hashing Table or Hash Table is a collection of elements which are stored in a data structure using a Hashing method, which makes it easy to find them later. The Hash table consists of key and index or slot, here key represents the value which will store in the table and index or slot represent the index location of that key.
Each position of the hash table, slots, can hold an item and is named by an integer value starting at 0. We can use an Array to implement a hash table and initially all the elements of the array would be None.
For Example:
Insert 1, 3 ,5 , 7 , 8, 10, 11 in a hash table using hashing.
Create an array arbitrary sizeKey-ValueHashing (key value % size of array)Array Index11 % 7 =133 % 7 =355 % 7 =577 % 7 =099 % 7 =21010 % 7 =3 (collision) 3 is already occupied
Collision resolution = 3+1= 4 1111 % 7 =4 (Collison) = 4+1=5(collision)= 5+1 = 6
Array will be arr = [7, 1, 9, 3, 10, 5, 11, None, None, None, None, None, None]Hashing Function
Hash function, are also known as Hashing methods and they are used to map each element or key to a unique slot or index. Hash is also used to minimize the number of collisions and using some easy methods it computes and evenly distributes the items in the hash table.
There are various hashing methods we can use to map a key to its slot:
*Remainder Method
*Folding Method
*Mid Square Method1. Reminder method
In the reminder method, we use the divide the key value with the total size of the table or array and use it remainder to specify the index or slot value of that key. Free slots win real money no deposit required canada.
For example, if we want to insert 9 in an array of size 20, so it would be placed at 9%20 = 9th index if 9th index is free.2. Folding Method
The folding method for constructing hash functions begins by dividing the item into equal-size pieces (the last piece may not be of equal size).
These pieces are then added together to give the resulting hash value.
This hashing method applies to large digit numbers, for example, if we want a hash table in which key elements are the mobile numbers of the customers, this reminder method would not be efficient.
For instance:
If our key was the phone number 436-555-4601Number Of Slots In Hash Tablespoon
We would take the digits and divide them into groups of 2 (43,65,55,46,01).
After the addition, 43+65+55+46+01, we get 210.
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If we assume our hash table has 11 slots, then we need to perform the extra step of dividing by 11 and keeping the remainder.
210 % 11 is 1, so the phone number 436-555-4601 hashes to slot 1.3. Mid Square Method
In the mid-square method, we to compute the key slot number or index location we first square the item and then extract some portion of the resulting digits.
For example:
if the item were 44, we would first compute 442=1,936.
By extracting the middle two digits, 93, and performing the remainder step, we get 93%11 = 5Implementation of Hash tablePython
Output:
In computer science, consistent hashing[1][2] is a special kind of hashing such that when a hash table is resized, only n/m{displaystyle n/m} keys need to be remapped on average where n{displaystyle n} is the number of keys and m{displaystyle m} is the number of slots. In contrast, in most traditional hash tables, a change in the number of array slots causes nearly all keys to be remapped because the mapping between the keys and the slots is defined by a modular operation. Consistent hashing is a particular case of rendezvous hashing, which has a conceptually simpler algorithm, and was first described in 1996. Consistent hashing first appeared in 1997, and uses a different algorithm.[1]History[edit]
The term ’consistent hashing’ was introduced by David Kargeret al. at MIT for use in distributed caching. This academic paper from 1997 introduced the term ’consistent hashing’ as a way of distributing requests among a changing population of web servers. Each slot is then represented by a server in a distributed system. The addition of a server and the removal of server (say, due to failure) requires only num_keys/num_slots{displaystyle num_keys/num_slots} items to be re-shuffled when the number of slots (i.e., servers) change. The authors mention linear hashing and its ability to handle sequential server addition and removal, while consistent hashing allows servers to be added and removed in arbitrary order.[1]Number Of Slots In Hash Tables
Teradata used this technique in their distributed database, released in 1986, although they did not use this term. Teradata still uses the concept of a hash table to fulfill exactly this purpose. Akamai Technologies was founded in 1998 by the scientists Daniel Lewin and F. Thomson Leighton (co-authors of the article coining ’consistent hashing’). In Akamai’s content delivery network,[3] consistent hashing is used to balance the load within a cluster of servers, while a stable marriage algorithm is used to balance load across clusters.[2]
Consistent hashing has also been used to reduce the impact of partial system failures in large web applications to provide robust caching without incurring the system-wide fallout of a failure.[4] Consistent hashing is also the cornerstone of distributed hash tables (DHTs), which employ hash values to partition a keyspace across a distributed set of nodes, then construct an overlay network of connected nodes that provide efficient node retrieval by key. Rendezvous hashing, designed in 1996, is a simpler and more general technique. It achieves the goals of consistent hashing using the very different highest random weight (HRW) algorithm.Basic Technique[edit]
Consider the problem of load balancing where a set of objects (say, web pages or video segments) need to be assigned to a set of n{displaystyle n} servers. One way of distributing objects evenly across the n{displaystyle n} servers is to use a standard hash function and place object o{displaystyle o} in server with id hash(o)(mod n){displaystyle {text{hash}}(o);left({text{mod }}nright)}, However, if a server is added or removed (i.e., n{displaystyle n} changes), the server assignment of nearly every object in the system may change. This is problematic since servers often go up or down and each such event would require nearly all objects to be reassigned and moved to new servers. Consistent hashing first maps both objects and servers to the unit circle. An object is then mapped to the next server that appears on the circle in clockwise order.[2]
Consistent hashing was designed to avoid the problem of having to change the server assignment of every object when a server is added or removed. The main idea is to use a hash function to randomly map both the objects and the servers to a unit circle. Each object is then assigned to the next server that appears on the circle in clockwise order. This provides an even distribution of objects to servers. But, more importantly, if a server fails and is removed from the circle, only the objects that were mapped to the failed server need to be reassigned to the next server in clockwise order. Likewise, if a new server is added, it is added to the unit circle, and only the objects mapped to that server need to be reassigned. Importantly, when a server is added or removed, the vast majority of the objects maintain their prior server assignments.Practical Extensions[edit]
A number of extensions to the basic technique are needed for effectively using consistent hashing for load balancing in practice.[2] In the basic scheme above, if a server fails, all its objects are reassigned to the next server in clockwise order, potentially doubling the load of that server. This may not be desirable. To ensure a more even re-distribution objects on server failure, each server can be hashed to multiple locations on the unit circle.[2] When a server fails, the objects assigned to each of its replicas on the unit circle will get reassigned to a different server in clockwise order, thus redistributing the objects more evenly. Another extension concerns a flash crowd situation where a single object gets ’hot’ and is accessed a large number of times and will have to be hosted in multiple servers. In this situation, the object may be assigned to multiple contiguous servers by traversing the unit circle in clockwise order.[2] A more complex practical consideration arises when two objects that are hashed near each other in the unit circle and both get ’hot’ at the same time. In this case, both objects will use the same set of contiguous servers in the unit circle. This situation can be ameliorated by each object choosing a different hash function for mapping servers to the unit circle.[2]Comparison with Rendezvous Hashing and other alternatives[edit]
Rendezvous hashing, designed in 1996, is a simpler and more general technique, and permits fully distributed agreement on a set of k{displaystyle k} options out of a possible set of n{displaystyle n} options. It can in fact be shown that consistent hashing is a special case of rendezvous hashing. Because of its simplicity and generality, Rendezvous Hashing is now being used in place of Consistent Hashing in many applications.
If key values will always increase monotonically, an alternative approach using a hash table with monotonic keys may be more suitable than consistent hashing.[citation needed]Complexity[edit]Asymptotic time complexities for N{displaystyle N} nodes (or slots) and K{displaystyle K} keysClassic hash tableConsistent hashingadd a nodeO(K){displaystyle O(K)}O(K/N+logN){displaystyle O(K/N+log N)}remove a nodeO(K){displaystyle O(K)}O(K/N+logN){displaystyle O(K/N+log N)}add a keyO(1){displaystyle O(1)}O(logN){displaystyle O(log N)}remove a keyO(1){displaystyle O(1)}O(logN){displaystyle O(log N)}
The O(K/N){displaystyle O(K/N)} is an average cost for redistribution of keys and the O(logN){displaystyle O(log N)} complexity for consistent hashing comes from the fact that a binary search among nodes angles is required to find the next node on the ring.[citation needed]Examples[edit]
No deposit bonus codes for silver oak casino 2019. Known examples of consistent hashing use include:
*Couchbase automated data partitioning [5]
*OpenStack’s Object Storage Service Swift[6]
*Partitioning component of Amazon’s storage system Dynamo[7]
*Data partitioning in Apache Cassandra[8]
*Data partitioning in Voldemort[9]
*Akka’s consistent hashing router[10]
*Riak, a distributed key-value database[11]
*Gluster, a network-attached storage file system[12]
*Akamai content delivery network[13]
*Discord chat application[14]
*Maglev network load balancer[15]
*Data partitioning in Azure Cosmos DBNumber Of Slots In Hash Table SearchReferences[edit]Number Of Slots In Hash Tableau
*^ abcKarger, D.; Lehman, E.; Leighton, T.; Panigrahy, R.; Levine, M.; Lewin, D. (1997). Consistent Hashing and Random Trees: Distributed Caching Protocols for Relieving Hot Spots on the World Wide Web. Proceedings of the Twenty-ninth Annual ACM Symposium on Theory of Computing. ACM Press New York, NY, USA. pp. 654–663. doi:10.1145/258533.258660.
*^ abcdefgBruce Maggs and Ramesh Sitaraman (2015). ’Algorithmic nuggets in content delivery’(PDF). ACM SIGCOMM Computer Communication Review. 45 (3).
*^Nygren., E.; Sitaraman R. K.; Sun, J. (2010). ’The Akamai Network: A Platform for High-Performance Internet Applications’(PDF). ACM SIGOPS Operating Systems Review. 44 (3): 2–19. doi:10.1145/1842733.1842736. S2CID207181702. Archived(PDF) from the original on September 13, 2012. Retrieved November 19, 2012.
*^Karger, D.; Sherman, A.; Berkheimer, A.; Bogstad, B.; Dhanidina, R.; Iwamoto, K.; Kim, B.; Matkins, L.; Yerushalmi, Y. (1999). ’Web Caching with Consistent Hashing’. Computer Networks. 31 (11): 1203–1213. doi:10.1016/S1389-1286(99)00055-9. Archived from the original on 2008-07-21. Retrieved 2008-02-05.
*^’What Exactly Is Membase?’. Retrieved 2020-10-29.
*^Holt, Greg (February 2011). ’Building a Consistent Hashing Ring’. openstack.org. Retrieved 2019-11-17.
*^DeCandia, G.; Hastorun, D.; Jampani, M.; Kakulapati, G.; Lakshman, A.; Pilchin, A.; Sivasubramanian, S.; Vosshall, P.; Vogels, Werner (2007). ’Dynamo: Amazon’s Highly Available Key-Value Store’(PDF). Proceedings of the 21st ACM Symposium on Operating Systems Principles. 41 (6): 205–220. doi:10.1145/1323293.1294281. Retrieved 2018-06-07.
*^Lakshman, Avinash; Malik, Prashant (2010). ’Cassandra: a decentralized structured storage system’. ACM SIGOPS Operating Systems Review. 44 (2): 35–40. doi:10.1145/1773912.1773922.
*^’Design -- Voldemort’. www.project-voldemort.com/. Archived from the original on 9 February 2015. Retrieved 9 February 2015. Consistent hashing is a technique that avoids these problems, and we use it to compute the location of each key on the cluster.
*^’Akka Routing’. akka.io. Retrieved 2019-11-16.
*^’Riak Concepts’. Archived from the original on 2015-09-19. Retrieved 2016-12-06.
*^’GlusterFS Algorithms: Distribution’. gluster.org. 2012-03-01. Retrieved 2019-11-16.
*^Roughgarden, Tim; Valiant, Gregory (2016-03-28). ’Modern Algorithmic Toolbox’(PDF). stanford.edu. Retrieved 2019-11-17.
*^Vishnevskiy, Stanislav (2017-07-06). ’How Discord Scaled Elixir to 5,000,000 Concurrent Users’. Retrieved 2019-11-17.
*^Eisenbud, Daniel E.; Yi, Cheng; Contavalli, Carlo; Smith, Cody; Kononov, Roman; Mann-Hielscher, Eric; Cilingiroglu, Ardas; Cheyney, Bin; Shang, Wentao; Hosein, Jinnah Dylan. ’Maglev: A Fast and Reliable Software Network Load Balancer’(PDF). Retrieved 2019-11-17.External links[edit]
*Implementations in various languages:Retrieved from ’https://en.wikipedia.org/w/index.php?title=Consistent_hashing&oldid=990444416’
Register here: http://gg.gg/v33c2
https://diarynote-jp.indered.space
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