Strong Consistency

Please Note:
Riak KV's strong consistency is an experimental feature and may be removed from the product in the future. Strong consistency is not commercially supported or production-ready. Strong consistency is incompatible with Multi-Datacenter Replication, Riak Search, Bitcask Expiration, LevelDB Secondary Indexes, Riak Data Types and Commit Hooks. We do not recommend its usage in any production environment.

Riak was originally designed as an eventually consistent system, fundamentally geared toward providing partition (i.e. fault) tolerance and high read and write availability.

While this focus on high availability is a great fit for many data storage needs, there are also many use cases for which strong data consistency is more important than availability. Basho introduced a new strong consistency option in version 2.0 to address these use cases. In Riak, strong consistency is applied using bucket types, which enables developers to apply strong consistency guarantees on a per-key basis.

Elsewhere in the documentation there are instructions for enabling and using strong consistency, as well as a guide for operators looking to manage, configure, and monitor strong consistency.

Strong vs. Eventual Consistency

If you successfully write a value to a key in a strongly consistent system, the next successful read of that key is guaranteed to show that write. A client will never see out-of-date values. The drawback is that some operations may fail if an insufficient number of object replicas are available. More on this in the section on trade-offs.

In an eventually consistent system, on the other hand, a read may return an out-of-date value, particularly during system or network failures. The advantage of this approach is that reads and writes can succeed even when a cluster is experiencing significant service degradation.


Building on the example presented in the eventual consistency doc, imagine that information about who manages Manchester United is stored in Riak, in the key manchester-manager. In the eventual consistency example, the value associated with this key was originally David Moyes, meaning that that was the first successful write to that key. But then Louis van Gaal became Man U’s manager, and a write was executed to change the value of manchester-manager.

Now imagine that this write failed on one node in a multi-node cluster. Thus, all nodes report that the value of manchester-manager is Louis van Gaal except for one. On the errant node, the value of the manchester-manager key is still David Moyes. An eventually consistent system is one in which a get request will most likely return Louis van Gaal but could return the outdated value David Moyes.

In a strongly consistent system, conversely, any successful read on manchester-manager will return Louis van Gaal and never David Moyes. Reads will return Louis van Gaal every single time until Man U gets a new manager and someone performs a successful write to manchester-manager to change its value.

It might also be useful to imagine it a bit more abstractly. The following causal sequence would characterize a strongly consistent system:

  1. The value of the key k is set to v
  2. All successful reads on k return v
  3. The value of k is changed to v2
  4. All successful reads on k return v2
  5. And so forth

At no point in time does this system return an out-of-date value.

The following sequence could characterize an eventually consistent system:

  1. A write is made that sets the value of the key k to v
  2. Nearly all reads to k return v, but a small percentage return not found
  3. A write to k changes the value to v2
  4. Nearly all reads to k now return v2, but a small number return the outdated v (or even not found) because the newer value hasn’t yet been replicated to all nodes