My case of datasets stored is specific. I have large strings (1-100MB each).
Let's take for example a single row.
43mb.csv is a 1-row CSV with 10 columns. One column a 43mb string.
When I read this csv with pandas and then dump to parquet, my script consumes 10x of the 43mb.
With increasing amount of such rows memory footprint overhead diminishes, but I want to focus on this specific case.
Here's the footprint after running using memory profiler:
Line # Mem usage Increment Line Contents
================================================
4 48.9 MiB 48.9 MiB @profile
5 def test():
6 143.7 MiB 94.7 MiB data = pd.read_csv('43mb.csv')
7 498.6 MiB 354.9 MiB data.to_parquet('out.parquet')
Is this typical for parquet in case of big strings?
Environment: Mac OSX
Reporter: Bogdan Klichuk
Related issues:
Original Issue Attachments:
Note: This issue was originally created as ARROW-7305. Please see the migration documentation for further details.
My case of datasets stored is specific. I have large strings (1-100MB each).
Let's take for example a single row.
43mb.csv is a 1-row CSV with 10 columns. One column a 43mb string.
When I read this csv with pandas and then dump to parquet, my script consumes 10x of the 43mb.
With increasing amount of such rows memory footprint overhead diminishes, but I want to focus on this specific case.
Here's the footprint after running using memory profiler:
Is this typical for parquet in case of big strings?
Environment: Mac OSX
Reporter: Bogdan Klichuk
Related issues:
Original Issue Attachments:
Note: This issue was originally created as ARROW-7305. Please see the migration documentation for further details.