# Distributed hash table

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A distributed hash table (DHT) is a class of a decentralized distributed system that provides a lookup service similar to a hash table: (key, value) pairs are stored in a DHT, and any participating node can efficiently retrieve the value associated with a given key. Responsibility for maintaining the mapping from keys to values is distributed among the nodes, in such a way that a change in the set of participants causes a minimal amount of disruption. This allows a DHT to scale to extremely large numbers of nodes and to handle continual node arrivals, departures, and failures.

DHTs form an infrastructure that can be used to build more complex services, such as anycast, cooperative Web caching, distributed file systems, domain name services, instant messaging, multicast, and also peer-to-peer file sharing and content distribution systems. Notable distributed networks that use DHTs include BitTorrent's distributed tracker, the Coral Content Distribution Network, the Kad network, the Storm botnet, the Tox instant messenger, Freenet, the YaCy search engine, and the InterPlanetary File System.

#### Locality-preserving hashing

Locality-preserving hashing ensures that similar keys are assigned to similar objects. This can enable a more efficient execution of range queries. Self-Chord decouples object keys from peer IDs and sorts keys along the ring with a statistical approach based on the swarm intelligence paradigm. Sorting ensures that similar keys are stored by neighbour nodes and that discovery procedures, including range queries, can be performed in logarithmic time.

### Overlay network

Each node maintains a set of links to other nodes (its neighbors or routing table) . Together, these links form the overlay network. A node picks its neighbors according to a certain structure, called the network's topology.

All DHT topologies share some variant of the most essential property: for any key , each node either has a node ID that owns or has a link to a node whose node ID is closer to , in terms of the keyspace distance defined above. It is then easy to route a message to the owner of any key using the following greedy algorithm (that is not necessarily globally optimal): at each step, forward the message to the neighbor whose ID is closest to . When there is no such neighbor, then we must have arrived at the closest node, which is the owner of as defined above. This style of routing is sometimes called key-based routing.

Beyond basic routing correctness, two important constraints on the topology are to guarantee that the maximum number of hops in any route (route length) is low, so that requests complete quickly; and that the maximum number of neighbors of any node (maximum node degree) is low, so that maintenance overhead is not excessive. Of course, having shorter routes requires higher maximum degree. Some common choices for maximum degree and route length are as follows, where is the number of nodes in the DHT, using Big O notation:

Max. degree Route length Used in Note
$O(1)$ $O(n)$
$O(\log n)$ $O(\log n/\log (\log n))$ Koorde
$O(\log n)$ $O(\log n)$ Chord most common, but not optimal (degree/route length)
$O(1)$ $O(\log n)$
$O(\sqrt{n})$ $O(1)$

The most common choice, $O(\log n)$ degree/route length, is not optimal in terms of degree/route length tradeoff, but such topologies typically allow more flexibility in choice of neighbors. Many DHTs use that flexibility to pick neighbors that are close in terms of latency in the physical underlying network.

Maximum route length is closely related to diameter: the maximum number of hops in any shortest path between nodes. Clearly, the network's worst case route length is at least as large as its diameter, so DHTs are limited by the degree/diameter tradeoff that is fundamental in graph theory. Route length can be greater than diameter, since the greedy routing algorithm may not find shortest paths.

### Algorithms for overlay networks

Aside from routing, there exist many algorithms that exploit the structure of the overlay network for sending a message to all nodes, or a subset of nodes, in a DHT. These algorithms are used by applications to do overlay multicast, range queries, or to collect statistics. Two systems that are based on this approach are Structella, which implements flooding and random walks on a Pastry overlay, and DQ-DHT, which implements a dynamic querying search algorithm over a Chord network.

## Security

Because of the decentralization, fault tolerance, and scalability of DHTs, they are inherently more resilient against a hostile attacker than a typical centralized system.

Open systems for distributed data storage that are robust against massive hostile attackers are feasible.

A DHT system that is carefully designed to have Byzantine fault tolerance can defend against a Sybil attack.

## DHT implementations

Most notable differences encountered in practical instances of DHT implementations include at least the following:

• The address space is a parameter of DHT. Several real world DHTs use 128-bit or 160-bit key space
• Some real-world DHTs use hash functions other than SHA-1.
• In the real world the key $k$ could be a hash of a file's content rather than a hash of a file's name to provide content-addressable storage, so that renaming of the file does not prevent users from finding it.
• Some DHTs may also publish objects of different types. For example, key $k$ could be the node $ID$ and associated data could describe how to contact this node. This allows publication-of-presence information and often used in IM applications, etc. In the simplest case, $ID$ is just a random number that is directly used as key $k$ (so in a 160-bit DHT $ID$ will be a 160-bit number, usually randomly chosen). In some DHTs, publishing of nodes' IDs is also used to optimize DHT operations.
• Redundancy can be added to improve reliability. The $(k, data)$ key pair can be stored in more than one node corresponding to the key. Usually, rather than selecting just one node, real world DHT algorithms select $i$ suitable nodes, with $i$ being an implementation-specific parameter of the DHT. In some DHT designs, nodes agree to handle a certain keyspace range, the size of which may be chosen dynamically, rather than hard-coded.
• Some advanced DHTs like Kademlia perform iterative lookups through the DHT first in order to select a set of suitable nodes and send $put(k, data)$ messages only to those nodes, thus drastically reducing useless traffic, since published messages are only sent to nodes that seem suitable for storing the key $k$; and iterative lookups cover just a small set of nodes rather than the entire DHT, reducing useless forwarding. In such DHTs, forwarding of $put(k, data)$ messages may only occur as part of a self-healing algorithm: if a target node receives a $put(k, data)$ message, but believes that $k$ is out of its handled range and a closer node (in terms of DHT keyspace) is known, the message is forwarded to that node. Otherwise, data are indexed locally. This leads to a somewhat self-balancing DHT behavior. Of course, such an algorithm requires nodes to publish their presence data in the DHT so the iterative lookups can be performed.

## Examples

• Applications employing DHTs

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