Tsvika Shapira created ARROW-7706:
-------------------------------------
Summary: saving a dataframe to the same partitioned location
silently doubles the data
Key: ARROW-7706
URL: https://issues.apache.org/jira/browse/ARROW-7706
Project: Apache Arrow
Issue Type: Bug
Components: Python
Affects Versions: 0.15.1
Reporter: Tsvika Shapira
When a user saves a dataframe:
{code:python}
df1.to_parquet('/tmp/table', partition_cols=['col_a'], engine='pyarrow')
{code}
it will create sub-directories named "{{a=val1}}", "{{a=val2}}" in
{{/tmp/table}}. Each of them will contain one (or more?) parquet files with
random filenames.
If a user runs the same command again, the code will use the existing
sub-directories, but with different (random) filenames. As a result, any data
loaded from this folder will be wrong - each row will be present twice.
For example, when using
{code:python}
df1.to_parquet('/tmp/table', partition_cols=['col_a'], engine='pyarrow') #
second time
df2 = pd.read_parquet('/tmp/table', engine='pyarrow')
assert len(df1) == len(df2) # raise an error{code}
This is a subtle change in the data that can pass unnoticed.
I would expect that the code will prevent the user from using an non-empty
destination as partitioned target. an overwrite flag can also be useful.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
