ClickHouse cost-efficiency in action: analyzing 500B rows on an Intel NUC

Re:ClickHouse cost-efficiency in action: analyzing 500B rows... by delusional on Monday January 06, 2020 @03:57PM (#21973222)

That's what you wanted to show, but what you ended up showing is that if you have different data, then the query performance can be quite good.

I get the desire to critique the temperature profile, but completely changing it makes the comparison worthless. From a data perspective it's like saying "if all the sensors just report 1 for temperature every reading, computing the min, max, and average is super fast". No shit, that wasn't the task though.

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Re:ClickHouse cost-efficiency in action: analyzing 500B rows... by jayleeg on Tuesday January 07, 2020 @05:43AM (#21978754)

But they didn't set the temperature reading to anything that would advantage their tests. Without access to the original data they simply generated a dataset as close to the original dataset and volume as possible. The fact they took a few sentences talking about the temperature doesn't equate to invalidating the test.

Looking at this your way - Scylla used an INT, Altinity used a Decimal type with specialized compression (T64). I can tell you that this would have hampered ClickHouse and advantaged Scylla. It's the opposite of what you're saying. They actually performed this benchmark with one arm tied behind their back.

It's a funny benchmark anyway because the two systems have very different use cases but it doesn't invalidate the result.

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