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Project Voldemort

Project Voldemort

Voldemort is an open source distributed data store that is designed as a key-value store used for high-scalability storage.

Voldemort is a big, distributed, fault-tolerant, persistent hash table.

It is used at LinkedIn for certain high-scalability storage problems where simple functional partitioning is not sufficient.

 Voldemort 0.96

Price
Free to download

Size
22.1MB
License

Apache License Version 2.0

Developer
LinkedIn

Website
www.project-voldemort.com

System Requirements

Support Sites:
Wiki, Mailing List, Project Voldemort: Scaling Simple Storage, Serving Large-scale Batch Computed Data with Project Voldemort

Selected Reviews:

Features include:

  • Data is automatically replicated over multiple servers
  • Data is automatically partitioned so each server contains only a subset of the total data
  • Server failure is handled transparently
  • Pluggable serialization is supported to allow rich keys and values including lists and tuples with named fields, as well as to integrate with common serialization frameworks like Protocol Buffers, Thrift, Avro and Java Serialization
  • Data items are versioned to maximize data integrity in failure scenarios without compromising availability of the system
  • Each node is independent of other nodes with no central point of failure or coordination
  • Good single node performance: you can expect 10-20k operations per second depending on the machines, the network, the disk system, and the data replication factor
  • Support for pluggable data placement strategies to support things like distribution across data centers that are geographically far apart
  • Combines in-memory caching with the storage system so that a separate caching tier is not required (instead the storage system itself is just fast)
  • Emulate the storage layer, as it is completely mockable. This makes the development and the unit testing easy, as it can be done against a throw-away in-memory storage system without the need for a real cluster or real storage system
  • Unlike MySQL replication, both reads and writes scale horizontally
  • Simple API: The API decides data replication and placement and accommodates a wide range of application-specific strategies
  • Transparent data partitioning: This allows for cluster expansion without rebalancing all data
  • Supports hashtable semantics
  • Embeddable
  • Integrity: Atomicity, Consistency, Durability, Revision Control, Optimistic Locking model
  • Distribution: Horizontal scalable, replication, symmetric replication, and sharing
  • Compression
  • TTL for entries
  • Unicoide support

Return to Key Value Stores Home Page

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Last Updated Sunday, April 14 2013 @ 09:28 AM EDT


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