Scaling a relational database

Below are the highlights I saw from the valuable article given in the following link.

http://java-persistence-performance.blogspot.com/2011/05/data-partitioning-scaling-database.html

 So hats off to the author who outlined these valuable things.

You can take 5 steps to scale a database

1. optimizing the number and types of queries hitting the database, using parametrized SQL, using batch writing, using lazy, join and batch fetching, a significant load can be removed from the database.
2. ensuring your database is configured optimally, has the correct indexes, queries are using the optimal query plan, and the disk access optimally, its performance, and thus scalability can be improved
3. caching objects and data in the mid-tier, you can offload a lot of the queries hitting the database, and improve your application's performance to boot. Most JPA providers support caching, and some such as EclipseLink offer quite advanced caching functionality including invalidation, and coordinated clustered caches. JPA 2.0 defines some basic caching annotations to enable and access the cache.
4. scale the database through clustering the database across multiple machines. This could be a real clustered database, such as Oracle RAC, or just multiple regular database instances. Clustered database are good, and can improve your scalability without much work, but depending on your application you may also have to partition your data across the database nodes for optimal scalability. Without partitioning, if you write a row on one node, then access it on another, the other node must request the latest copy of the data from the other node, this can potentially make performance worse.
5. partitioning data across each of the database nodes

Data partitioning can be done in 2 major ways

• Vertical partitioning
• Horizontal partitioning