aws/aws-sdk-pandas

`athena.to_parquet` fails when `mode=overwrite_partitions` and `partition_cols` contains something like `hour(timestamp_col)`.

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#2,845 opened on Jun 4, 2024

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Description

Describe the bug

When using s3.to_parquet to update a parquet file that is partitioned by a time interval or a timestamp "attribute" (such as year, month, hour, etc.), the function fails because for this mode the implementation assumes that the values of partition_cols are names of the parquet / table columns, and it does not find something like hour(column) in the dataframe columns.

I think the problem is this line, which uses the function delete_from_iceberg_table, which expects column names.

How to Reproduce

Expected behavior

I expect the partition_cols option to accept anything that can be used to partition a parquet. In particular, anything that is accepted when the argument mode is append or overwrite instead of overwrite_partitions.

Your project

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Screenshots

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OS

Ubuntu 22.04

Python version

3.10

AWS SDK for pandas version

3.7.3

Additional context

No response

Contributor guide

Research direction
Inspect the function `delete from iceberg table` in `awswrangler/athena/ write iceberg.py` around line 452 to understand how it processes `partition cols`. Determine if the issue can be fixed by parsing expressions like `hour(column)` to extract the column name for deletion, or by modifying the logic to handle transformed partition columns.
Tech stack
python
Domain
backend
Issue type
Bug
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-3 hours
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Mostly clear
Prerequisites
Python
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
70