Faster create #2
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avoids the expensive transformation of random station names from numpy array of strings to polars column of strings
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Hi @fizmat , that is really cool, thank you very much for your contribution |
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It takes 1130s on my machine (Apple M1) to generate the data.
I hoped generating the measurements with numpy in batches would be enough for a good speedup. But writing records in a loop is also very slow. Total time ~ 760s
I tried using pandas for csv output, it was slower.
Writing csv in polars is very fast, but converting the array of randomly selected station names into a polars column is slow. Total time ~ 480s
By using polars instead of numpy to sample the stations, this slow conversion can be avoided, so finally creating the 1brc dataset takes just 71s.