Distributed Database Question Bank with Answers 02

Question:

List two ways to measure the time improvement when processing a query using parallel architecture? Why is linearity important?

Answer:

1. Speed up: the time required in the original setting/ the time required in the new parallel setting 
2. Scale up: the time required in the original setting for a small problem / the time required in the new parallel setting for a similar but larger problem.

These two can be different because not all small problems can be easily made parallel.

Linearity in speed up/scale up is important since it means we are getting a corresponding return on the investment: increasing the number of cores decreases correspondingly the processing time, so the system can be grown incrementally.

Linear speed up: The same workload can be executed in half the time (at twice the speed) if the amount of hardware (e.g. cores) is doubled.

As for linear scale up, we can have linear scale up with respect to our data (relations/DB) or the number of transactions. These are known respectively as linear data scale up and linear transaction scale up:

Linear data scale up: The same level of performance can be maintained on a database twice the size of the original database provided the amount of hardware (e.g. cores) is also doubled. This is also known sometimes as linear batch scale up.

Linear transaction scale up: The same level of performance can be maintained when the number of arriving transactions is doubled provided the amount of hardware (e.g. cores) is also doubled.

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why speed up is important in parallel database architecture

why scale up is important in parallel database architecture

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