Voldemort (distributed data store)
|Original author(s)||LinkedIn / Microsoft|
1.10.25 / July 25, 2017
|Type||Distributed data store|
|License||Apache License 2.0|
Voldemort does not try to satisfy arbitrary relations and the ACID properties, but rather is a big, distributed, persistent hash table. A 2012 study comparing systems for storing application performance management data reported that Voldemort, Apache Cassandra, and HBase all offered linear scalability in most cases, with Voldemort having the lowest latency and Cassandra having the highest throughput.
In the parlance of Eric Brewer's CAP theorem, Voldemort is an AP type system.
Voldemort's creator and primary corporate contributor, LinkedIn, has migrated all of their systems off of Voldemort as of approximately August 2018, with no replacement sponsor as of October 2018[update].
Voldemort uses in-memory caching to eliminate a separate caching tier. It has a storage layer that is possible to emulate. Voldemort reads and writes scale horizontally. The API decides data replication and placement and accommodates a wide range of application-specific strategies.
The Voldemort distributed data store supports pluggable placement strategies for distribution across data centers. Data is automatically replicated across servers. Data is partitioned meaning a single server contains only a portion of the total data. Each data node is independent to avoid central point of failure. Pluggable serialization allows rich keys and values including lists and tuples with named fields, as well as the integration with common serialisation frameworks such as Avro, Java Serialization, Protocol Buffers, and Thrift. Server failures are handled transparently. Data items are versioned, which maximizes data integrity.
- "Voldemort is a distributed key-value storage system". Project Voldemort - A distributed database. Retrieved 2015-04-20.
- "Comparison to relational databases". Project Voldemort - A distributed database. Archived from the original on 2011-04-23. Retrieved 2011-04-05.
- Rabl, Tilmann; Sadoghi, Mohammad; Jacobsen, Hans-Arno; Gómez-Villamor, Sergio; Muntés-Mulero, Victor; Mankovskii, Serge (August 2012). "Solving Big Data Challenges for Enterprise Application Performance Management" (PDF). Proceedings of the VLDB Endowment. 5 (12): 1724–1735. arXiv:1208.4167. doi:10.14778/2367502.2367512. S2CID 15591565.
- project-voldemort mailing list post. 2018-08-16. Retrieved 2018-10-06.
- Serving Large-scale Batch Computed Data with Project Voldemort