[Cassandra Wiki] Update of "AntiEntropy" by JonathanEllis

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The "AntiEntropy" page has been changed by JonathanEllis.
http://wiki.apache.org/cassandra/AntiEntropy

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New page:
Quoting from [[http://www.allthingsdistributed.com/2007/10/amazons_dynamo.html|Amazon's Dynamo]] section 4.7,

 . To detect the inconsistencies between replicas faster and to minimize the amount of transferred data, Dynamo uses Merkle trees [13]. A Merkle tree is a hash tree where leaves are hashes of the values of  individual keys. Parent nodes higher in the tree are hashes of their respective  children. The principal advantage of Merkle tree is that each branch of the tree  can be checked independently without requiring nodes to download the entire  tree or the entire data set. Moreover, Merkle trees help in reducing the amount of  data that needs to be transferred while checking for inconsistencies among  replicas. For instance, if the hash values of the root of two trees are equal,  then the values of the leaf nodes in the tree are equal and the nodes require no synchronization. If not, it implies that the values of some replicas are different. In such cases, the nodes may exchange the hash values of  children and the process continues until it reaches the leaves of the trees, at which  point the hosts can identify the keys that are “out of sync”. Merkle trees  minimize the amount of data that needs to be transferred for synchronization and  reduce the number of disk reads performed during the anti-entropy process.

 . Dynamo uses Merkle trees for anti-entropy as follows: Each node maintains a separate Merkle tree for each key range (the set of keys  covered by a virtual node) it hosts. This allows nodes to compare whether the keys  within a key range are up-to-date. In this scheme, two nodes exchange the root  of the Merkle tree corresponding to the key ranges that they host in common.  Subsequently, using the tree traversal scheme described above the nodes determine if  they have any differences and perform the appropriate synchronization action.