While I was googling for some content on the Scalability patterns, I’ve found an interesting blog post written by a person named Jeppe at http://thebigsoftwareblog.blogspot.com/2010/08/scalability-fundamentals-and.html.
Here’s the content in brief:
Load distribution – Spread the system load across multiple processing units
Load balancing / load sharing – Spreading the load across many components with equal properties for handling the request
Partitioning – Spreading the load across many components by routing an individual request to a component that owns that data specific
Vertical partitioning – Spreading the load across the functional boundaries of a problem space, separate functions being handled by different processing units
Horizontal partitioning – Spreading a single type of data element across many instances, according to some partitioning key, e.g. hashing the player id and doing a modulus operation, etc. Quite often referred to as sharding.
Queuing and batch – Achieve efficiencies of scale by processing batches of data, usually because the overhead of an operation is amortized across multiple request
Relaxing of data constraints – Many different techniques and trade-offs with regards to the immediacy of processing / storing / access to data fall in this strategy
Parallelization – Work on the same task in parallel on multiple processing units
For those who’re looking for some real-life scalability articles: