lelandroling opened a new issue, #1571: URL: https://github.com/apache/iceberg-python/issues/1571
### Question Checking through the GitHub issues, I noticed very few examples and I did see the open requests for improved documentation. Understandably, I understand that I can use MERGE INTO using Pyspark. My specific example is attempting to avoid the large overhead of Pyspark, but if that's the solution... ok. But before I walk down that path, I'm trying to understand how the use case looks for the ```.overwrite()``` and ```overwrite_filter```. ``` conditions = [] for row in values: row_condition = And(*[EqualTo(k, v) for k, v in zip(newKeys, row)]) conditions.append(row_condition) filter_condition = Or(*conditions) ``` I'm using this code to build out the filter_condition, then assigning that to overwrite_filter. What I've noticed is that if I have 1000 records, I'm hitting a maximum recursion error. My assumption is that I'm not understanding how to structure the filter_condition. Or the process can't handle this right now and I should move to MERGE INTO and Pyspark. -- 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: issues-unsubscr...@iceberg.apache.org.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@iceberg.apache.org For additional commands, e-mail: issues-h...@iceberg.apache.org
