Distributed Hash Table

Algorithms/Data Structures
Updated on:
April 16, 2024

What is a distributed hash table?

A distributed hash table (DHT) is a distributed system that provides a lookup service similar to a hash table. DHTs partition a key space across a decentralized network and use hash functions to assign ownership and locate values.

DHTs coordinate between untrusted distributed nodes to route key-value queries securely and efficiently using overlay networks. Nodes locally store values for a subset of keys and collaborate to correctly route queries.

DHTs provide a scalable way to distribute load and redundancy across machines and offer guaranteed lookup times within the network diameter. They handle churn from nodes joining and leaving.

Popular DHTs like Chord leverage interval arithmetic techniques used in data structures like B-trees and Bloom filters to map keys to nodes. DHTs are used to build distributed databases, file systems, and peer-to-peer systems.

How does it work?

In a DHT, nodes self-organize into a network overlay. Keys map to nodes via hash functions. Routing uses these hashes to forward lookups toward the owner through overlay links. Nodes periodically stabilize and repair the overlay.

DHTs handle node joins and failures gracefully using techniques like redundant replicas and distributed control.

Why is it important? Where is it used?

DHTs enable decentralized distributed systems with provable guarantees, overcoming single points of failure. DHTs are used in peer-to-peer file sharing, distributed databases, name resolution, and blockchain architectures.

DHTs exemplify robust distributed system design by transforming centralized hash tables into decentralized alternatives with remarkable fault tolerance.


How does a DHT contrast with client-server architecture?

DHT nodes all act as equal peers rather than clients of centralized servers. Data and control is distributed across nodes. DHTs have no single point of failure.

What are some examples of DHT implementations?

Popular DHTs include Chord, Pastry, Tapestry, Kademlia, and proprietary DHTs in BitTorrent, Coral CDN, and blockchain platforms.

What are the key components of a DHT?

  • Decentralized nodes organized into an overlay network.
  • A hash function that maps keys to nodes.
  • Routing to resolve lookups via the overlay.
  • Replication to provide redundancy and fault tolerance.

What are the main challenges with building DHTs?

  • Maintaining routing tables as nodes join and leave.
  • Cryptographic security, access control and privacy.
  • Hot spots and uneven load distribution.
  • Complex failure modes and system dynamics.


Related Entries

Bloom Filter

A Bloom filter is a probabilistic data structure used to test set membership that is space-efficient compared to storing the full set.

Probabilistic Data Structures

Probabilistic data structures are space and time efficient data structures that use randomized algorithms to provide approximate results to queries with strong guarantees.

Hash Functions

Hash functions are algorithms that map data of arbitrary size to fixed-size values called hashes in a deterministic, one-way manner for purposes like data integrity and database lookup.


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