Distributed Hash Tables: Design and Implementation, Slides of Computer Networks

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Lecture 12

Distributed Hash Tables

Hash Table

• Name-value pairs (or key-value pairs)
  • E.g,. “Mehmet Hadi Gunes” and [email protected]
  • E.g., “http://cse.unr.edu/” and the Web page
  • E.g., “HitSong.mp3” and “12.78.183.2”
• Hash table
  • Data structure that associates keys with values

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lookup(key) key^ value value

Distributed Hash Table

• Two key design decisions

• How do we map names on to nodes?
• How do we route a request to that node?

Hash Functions

• Hashing

• Transform the key into a number
• And use the number to index an array

• Example hash function

• Hash(x) = x mod 101, mapping to 0, 1, …, 100

• Challenges

• What if there are more than 101 nodes? Fewer?
• Which nodes correspond to each hash value?
• What if nodes come and go over time?

Consistent Hashing

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0

4

8

12 Bucket

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• Construction
  • Assign each of C hash buckets to random points on mod 2 n^ circle; hash key size = n
  • Map object to random position on circle
  • Hash of object = closest clockwise bucket
• Desired features
  • Balanced: No bucket responsible for large number of objects
  • Smoothness: Addition of bucket does not cause movement among existing buckets
  • Spread and load: Small set of buckets that lie near object
• Similar to that later used in P2P Distributed Hash Tables (DHTs)
  • In DHTs, each node only has partial view of neighbors Docsity.com

Consistent Hashing

• Large, sparse identifier space (e.g., 128 bits)

• Hash a set of keys x uniformly to large id space
• Hash nodes to the id space as well

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Hash(name)  object_id Hash(IP_address)  node_id

Id space represented as a ring

Joins and Leaves of Nodes

• Maintain a circularly linked list around the ring

• Every node has a predecessor and successor

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node

pred

succ

Joins and Leaves of Nodes

• When an existing node leaves

• Node copies its <key, value> pairs to its predecessor
• Predecessor points to node’s successor in the ring

• When a node joins

• Node does a lookup on its own id
• And learns the node responsible for that id
• This node becomes the new node’s successor
• And the node can learn that node’s predecessor
  • which will become the new node’s predecessor

How to Find the Nearest Node?

• Need to find the closest node

• To determine who should store (key, value) pair
• To direct a future lookup(key) query to the

node

• Strawman solution: walk through linked list

• Circular linked list of nodes in the ring
• O(n) lookup time when n nodes in the ring

• Alternative solution:

• Jump further around ring
• “Finger” table of additional overlay links (^) Docsity.com^13

Links in the Overlay Topology

• Trade-off between # of hops vs. # of neighbors
  • E.g., log(n) for both, where n is the number of nodes
  • E.g., such as overlay links 1/2, 1/4 1/8, … around the ring
  • Each hop traverses at least half of the remaining distance

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