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)