NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter



Using Probabilistically Bounded Staleness in Cassandra 1.2.0

Peter Bailis:

With the help of the Cassandra community, we recently released PBS consistency predictions as a feature in the official Cassandra 1.2.0 stable release. In case you aren’t familiar, PBS (Probabilistically Bounded Staleness) predictions help answer questions like: how eventual is eventual consistency? how consistent is eventual consistency? These predictions help you profile your existing Cassandra cluster and determine which configuration of N,R, and W are the best fit for your application, expressed quantitatively in terms of latency, consistency, and durability (see output below).

If I get this right, this tool should become a must-run-before-going-into-production and then also a good start for investigating WTFs like what am I suppose to do to avoid getting stale data.

Original title and link: Using Probabilistically Bounded Staleness in Cassandra 1.2.0 (NoSQL database©myNoSQL)