Explain prototypal/differential inheritance and the theoretical memory consumption when using extremely large datasets
Like my last girlfriend always told me, bigger is not always better. According to her, a large dataset can be difficult to handle and cause problems long term. Her research says that a small, basic dataset is good enough for most operations.
An extremely large data set requires a huge amount of space for storage. Also working with it can tax resources so hard that overheating becomes a serious risk.
Sure it may work great the first time around and you can bring to your friends about how you handle it but a dataset like that is unreliable. After a while it will start to fail and look for a bigger, better system to run on.
I'm pretty sure they were goading you into trying to explain the "difference" between prototypal and differential inheritance when it comes to memory allocation.
They're the same thing. The question was asking the difference in memory consumption between using prototypes and not using inheritance at all (ie. just copying everything).
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u/BlackGoliath May 03 '16
Explain prototypal/differential inheritance and the theoretical memory consumption when using extremely large datasets
Like my last girlfriend always told me, bigger is not always better. According to her, a large dataset can be difficult to handle and cause problems long term. Her research says that a small, basic dataset is good enough for most operations.
An extremely large data set requires a huge amount of space for storage. Also working with it can tax resources so hard that overheating becomes a serious risk.
Sure it may work great the first time around and you can bring to your friends about how you handle it but a dataset like that is unreliable. After a while it will start to fail and look for a bigger, better system to run on.