[ARROW-8964] [Python][Parquet] improve reading of partitioned parquet datasets whose schema changed

• Type: Improvement
• Status: Closed
• Priority: Major
• Resolution: Duplicate
• Affects Version/s: 0.17.1
• Component/s: Python
• Environment: Ubuntu 18.04, latest miniconda with python 3.7, pyarrow 0.17.1
• External issue URL: https://github.com/apache/arrow/issues/25089
• Assignee: Unassigned
• Reporter: Ira Saktor
• Created: 27/May/20 12:19
• Updated: 11/Jan/23 08:03
• Resolved: 09/Jun/20 08:52

Description

Hi there, i'm encountering the following issue when reading from HDFS:

 

My situation:

I have a paritioned parquet dataset in HDFS, whose recent partitions contain parquet files with more columns than the older ones. When i try to read data using pyarrow.dataset.dataset and filter on recent data, i still get only the columns that are also contained in the old parquet files. I'd like to somehow merge the schema or use the schema from parquet files from which data ends up being loaded.

when using:

`pyarrow.dataset.dataset(path_to_hdfs_directory, paritioning = 'hive', filters = my_filter_expression).to_table().to_pandas()`

Is there please a way to handle schema changes in a way, that the read data would contain all columns?

everything works fine when i copy the needed parquet files into a separate folder, however it is very inconvenient way of working. 

 

Issue Links

• is duplicated by: ARROW-8221 [Python][Dataset] Expose schema inference / validation options in the factory