April 16, 2014

Designing one to many relations – MongoDB vs MySQL

We already discussed one to one relations in MongoDB, and the main conclusion was that you should design your collections according to the most frequent access pattern. With one to many relations, this is still valid, but other factors may come into play. Let’s look at a simple problem: we are a shop and we […]

Distributed Set Processing with Shard-Query

Can Shard-Query scale to 20 nodes? Peter asked this question in comments to to my previous Shard-Query benchmark. Actually he asked if it could scale to 50, but testing 20 was all I could due to to EC2 and time limits. I think the results at 20 nodes are very useful to understand the performance: […]

Flexviews – part 3 – improving query performance using materialized views

Combating “data drift” In my first post in this series, I described materialized views (MVs). An MV is essentially a cached result set at one point in time. The contents of the MV will become incorrect (out of sync) when the underlying data changes. This loss of synchronization is sometimes called drift. This is conceptually […]

Faster MySQL failover with SELECT mirroring

One of my favorite MySQL configurations for high availability is master-master replication, which is just like normal master-slave replication except that you can fail over in both directions. Aside from MySQL Cluster, which is more special-purpose, this is probably the best general-purpose way to get fast failover and a bunch of other benefits (non-blocking ALTER […]

What would make MySQL Multiple Queries Usable ?

MySQL Has API to run Multiple Queries at once. This feature was designed mainly with saving network round trip in mind and got a little traction due to associated security risks and not significant gains in most cases. What would make MySQL Multiple Queries API more usable ?