jorisvandenbossche commented on issue #37989: URL: https://github.com/apache/arrow/issues/37989#issuecomment-1745488061
@RizzoV thanks for the report and nice reproducer! I can reproduce this running your example with memray:  From the memray stats, it looks like the memory being held at the end is mostly coming from the list with strings, so somehow the conversion to arrow seems to keep those list object alive (haven't yet looked at how that is possible, though). And also the pandas metadata conversion (the json dump) seems to accumulate memory, although that's a bit strange (but I don't see that in the smaller reproducer below). It seems it is specifically happens when having a list that is nested inside another column (eg struct of list), so I can reproduce the observation as well with this simplified example: ```python import string from random import choice import pandas as pd import pyarrow as pa sample_schema = pa.struct( [ ( "a", pa.struct([("aa", pa.list_(pa.string()))])), ] ) def generate_random_string(str_length: int) -> str: return "".join( [choice(string.ascii_lowercase + string.digits) for n in range(str_length)] ) def generate_random_data(): return { "a": [{"aa": [generate_random_string(128) for i in range(50)]}], } def main(): for i in range(10000): df = pd.DataFrame.from_dict(generate_random_data()) # pa.jemalloc_set_decay_ms(0) table = pa.Table.from_pandas(df, schema=pa.schema(sample_schema)) if __name__ == "__main__": main() ``` -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
