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Comment · 4 years ago
Comment · 4 years ago
Thanks! Didn't know this existed
Comment · 4 years ago
Fair, fair. I do have a decent network locally. It's just that I'm interested in landing a fully remote role at some of these global distributed startups where I can't leverage my network.
Comment · 4 years ago
Thanks, that's very useful. Have been trying to customize my applications. Will definitely continue to do so! :)
Comment · 4 years ago
I just skimmed through the article tbh, but I saw 14 GB and what looked like a predicate pushdown optimization. I think DuckDB could handle that on my 16GB mac.
Comment · 4 years ago
Comment · 4 years ago
Fair point about the "recentness". I guess it seemed recent to me since I learned about it only a few months back :)

Also, yes, 100%. Columnar storage fits well with vectorized execution! Thanks for sharing

Comment · 4 years ago
I can't seem to understand why vectorization wouldn't help, say if you read after a sort. Irrespective of whether it fits in memory, or you perform some sort of an external sort, any operation that you want to perform on top of that sorted vector, be it an aggregation to reduce it, or an arithmetic operation with another column, you could still leverage vectorization and would end up using fewer CPU cycles, no?

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