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