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 # MemusageIncrementLineContents
================================================
448.9MiB48.9MiB@profile5deftest():
6143.7MiB94.7MiBdata = pd.read_csv('43mb.csv')
7498.6MiB354.9MiBdata.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.