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https://issues.apache.org/jira/browse/ARROW-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Jacob Wujciak-Jens updated ARROW-15716:
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Component/s: Python
> [Dataset][Python] Parse a list of fragment paths to gather filters
> ------------------------------------------------------------------
>
> Key: ARROW-15716
> URL: https://issues.apache.org/jira/browse/ARROW-15716
> Project: Apache Arrow
> Issue Type: Wish
> Components: Python
> Affects Versions: 7.0.0
> Reporter: Lance Dacey
> Priority: Minor
>
> Is it possible for partitioning.parse() to be updated to parse a list of
> paths instead of just a single path?
> I am passing the .paths from file_visitor to downstream tasks to process data
> which was recently saved, but I can run into problems with this if I
> overwrite data with delete_matching in order to consolidate small files since
> the paths won't exist.
> Here is the output of my current approach to use filters instead of reading
> the paths directly:
> {code:java}
> # Fragments saved during write_dataset
> ['dev/dataset/fragments/date_id=20210813/data-0.parquet',
> 'dev/dataset/fragments/date_id=20210114/data-2.parquet',
> 'dev/dataset/fragments/date_id=20210114/data-1.parquet',
> 'dev/dataset/fragments/date_id=20210114/data-0.parquet']
> # Run partitioning.parse() on each fragment
> [<pyarrow.compute.Expression (date_id == 20210813)>,
> <pyarrow.compute.Expression (date_id == 20210114)>,
> <pyarrow.compute.Expression (date_id == 20210114)>,
> <pyarrow.compute.Expression (date_id == 20210114)>]
> # Format those expressions into a list of tuples
> [('date_id', 'in', [20210114, 20210813])]
> # Convert to an expression which is used as a filter in .to_table()
> is_in(date_id, {value_set=int64:[
> 20210114,
> 20210813
> ], skip_nulls=false})
> {code}
> And here is how I am creating the filter from a list of .paths (perhaps there
> is a better way?):
> {code:python}
> partitioning = ds.HivePartitioning(partition_schema)
> expressions = []
> for file in paths:
> expressions.append(partitioning.parse(file))
> values = []
> filters = []
> for expression in expressions:
> partitions = ds._get_partition_keys(expression)
> if len(partitions.keys()) > 1:
> element = [(k, "==", v) for k, v in partitions.items()]
> if element not in filters:
> filters.append(element)
> else:
> for k, v in partitions.items():
> if v not in values:
> values.append(v)
> filters = [(k, "in", sorted(values))]
> filt_exp = pa.parquet._filters_to_expression(filters)
> dataset.to_table(filter=filt_exp)
> {code}
> My hope would be to do something like filt_exp = partitioning.parse(paths)
> which would return a dataset expression.
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