<story><title>Looking into the future with Cassandra</title><url>http://blog.digg.com/?p=966</url></story><parent_chain></parent_chain><comment><author>rarrrrrr</author><text>In SQL systems, you can easily shift the computation cost from at read time to at write time.  That&apos;s what triggers are for.  When new data comes in that changes a result you know you need quickly, a trigger can automatically add to a work queue to update a result table.&lt;p&gt;Since the trigger has automatic access to the contents of the new data (and old data in case of an update or delete) the computation to update the results table can often be made much faster.&lt;p&gt;Every situation is different of course, but it&apos;s  overreaching to say that SQL systems have no options beyond read-time computation of results.</text></comment>
<story><title>Looking into the future with Cassandra</title><url>http://blog.digg.com/?p=966</url></story><parent_chain></parent_chain><comment><author>mcav</author><text>Wow:&lt;p&gt;&amp;#62; &lt;i&gt;For this feature, the fully denormalized Cassandra dataset weighs in at 3 terabytes and 76 billion columns.&lt;/i&gt;</text></comment>