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https://issues.apache.org/jira/browse/ARROW-15716?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17630046#comment-17630046
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Lance Dacey commented on ARROW-15716:
-------------------------------------
I wanted to check if this is something which might be possible eventually. It
would reduce a lot of ugly custom code that I use to achieve the result that I
am looking for.
Write dataset, collect the fragment paths:
{code:python}
collector = []
ds.write_dataset(
table,
base_dir="dev/staging",
partitioning=["date"],
partitioning_flavor="hive",
file_visitor=lambda x: collector.append(x)
)
{code}
Next my hope would be parse those paths into a consolidate filter expression
which I could use to query the original dataset. This ensures that I read in
the entire partition since it is possible that other files already existed
before the write step above.
{code:python}
paths = [file.path for file in collector]
partitioning = ds.partitioning(flavor="hive")
filter_expression = partitioning.parse(paths) #parse a list of paths, ideally
using the "hive" shortcut
dataset = ds.dataset(source="dev/staging", partitioning=partitioning)
new_table = dataset.to_table(filter=filter_expression)
ds.write_dataset(new_table, base_dir="dev/final",
existing_data_behavior="delete_matching")
{code}
> [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:python}
> # 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}
> My hope would be to do something like filt_exp = partitioning.parse(paths)
> which would return a dataset expression.
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