I don't want to optimize for reads or writes, I want to optimize for having the smallest gap possible between the time I write and the time I read. []s
From: user@cassandra.apache.org Subject: Re: to normalize or not to normalize - read penalty vs write penalty Roughly how often do you expect to update alerts? How often do you expect to read the alerts? I suspect you'll be doing 100x more reads (or more), in which case optimizing for reads is the definitely right choice. On Wed, Feb 4, 2015 at 9:50 AM, Marcelo Valle (BLOOMBERG/ LONDON) <mvallemil...@bloomberg.net> wrote: Hello everyone, I am thinking about the architecture of my application using Cassandra and I am asking myself if I should or shouldn't normalize an entity. I have users and alerts in my application and for each user, several alerts. The first model which came into my mind was creating an "alerts" CF with user-id as part of the partition key. This way, I can have fast writes and my reads will be fast too, as I will always read per partition. However, I received a requirement later that made my life more complicated. Alerts can be shared by 1000s of users and alerts can change. I am building a real time app and if I change an alert, all users related to it should see the change. Suppose I want to keep thing not normalized - always an alert changes I would need to do a write on 1000s of records. This way my write performance everytime I change an alert would be affected. On the other hand, I could have a CF for users-alerts and another for alert details. Then, at read time, I would need to query 1000s of alerts for a given user. In both situations, there is a gap between the time data is written and the time it's available to be read. I understand not normalizing will make me use more disk space, but once data is written once, I will be able to perform as many reads as I want to with no penalty in performance. Also, I understand writes are faster than reads in Cassandra, so the gap would be smaller in the first solution. I would be glad in hearing thoughts from the community. Best regards, Marcelo Valle. -- Tyler Hobbs DataStax
