4 General Core Scalabilty Patterns (by Jesper Söderlund)

 Jesper Söderlund put together an excellent list of four general scalability patterns and four subpatterns in his post Scalability patterns and an interesting story:

  • 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 more details and explanations see the original post.

Scalability patterns and an interesting story

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