Bogdan Klichuk created ARROW-7305:
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Summary: High memory usage writing pyarrow.Table to parquet
Key: ARROW-7305
URL: https://issues.apache.org/jira/browse/ARROW-7305
Project: Apache Arrow
Issue Type: Task
Components: Python
Affects Versions: 0.15.1
Environment: Mac OSX
Reporter: Bogdan Klichuk
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:
{code:java}
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')
{code}
Is this typical for parquet in case of big strings?
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