November 10, 2014 Many data needs are better served by data stores that are optimized for maximum availability and scalability…
The third in a series of posts on how Riak differs from traditional relational databases.
The first in a series of posts on how Riak differs from traditional relational databases.
An in-depth look at the addition of counters to Riak 1.4.
Basho Technologies announces the public availability of Riak 1.4.
A look at Riak’s architectural design for high availability, and the benefits of Riak over relational databases.
Using the example of averaging many values, we explore some concerns that we need to address in a distributed, eventually consistent system like Riak.
Basho Technologies today announced the immediate availability of the second edition of Riak Handbook.
Here at Basho we want to make sure that your Riak implementations are set up from the beginning to succeed. While you can use the Riak Fast Track to quickly set up a 3-node dev/test environment, we recommend that all production deployments use a minimum of 5 nodes, ensuring you benefit from the architectural principles that underpin Riak’s availability, fault-tolerance and scaling properties.
Peter Bailis is Graduate Student in the much-heralded Berkeley CS department. He and some colleagues have been working on something called Probabilistically Bounded Staleness for Practical Partial Quorums (PBS). In short, PBS aims to define just how eventual “eventual consistency” is, and their research produced some fascinating findings that should affect how people view and deploy distributed databases like Riak, Cassandra and Voldemort.