Thank you for the response Neal. That is helpful to know there is an open issue for this to support from R and I will watch the issue for updates.
From: Neal Richardson <[email protected]> Reply-To: "[email protected]" <[email protected]> Date: Wednesday, December 23, 2020 at 8:50 AM To: "[email protected]" <[email protected]> Subject: Re: [R][Dataset] how to speed up creating FileSystemDatasetFactory from a large partitioned dataset? Thanks for the report. The R bindings to the C++ methods that pyarrow is using in the docs you linked haven't been written yet. https://issues.apache.org/jira/browse/ARROW-9657 is the open issue for that. I agree that it would be good to support from R. A couple of minutes also seems a bit slow even for the case where you don't provide the file paths, so that would be worth investigating as well. Neal On Tue, Dec 22, 2020 at 9:08 PM Charlton Callender <[email protected]<mailto:[email protected]>> wrote: Hi I am starting to use arrow in a workflow where I have a dataset partitioned by a couple variables (like location and year) that leads to > 100,000 parquet files. I have been using `arrow::open_dataset(sources = FILEPATH, unify_schemas = FALSE)` but found this is taking a couple minutes to run. I can see that almost all the time is spent on this line creating the FileSystemDatasetFactory. https://github.com/apache/arrow/blob/master/r/R/dataset-factory.R#L135 In my use case I know all the partition file paths and I know the schema (and that it is consistent across partitions). Is there any way to use that information to more quickly create the Dataset object with a highly partitioned dataset? I found this section in the Python docs about creating a dataset from filepaths, is this possible to do from R? https://arrow.apache.org/docs/python/dataset.html#manual-specification-of-the-dataset Thank you! I’ve been finding arrow/parquet really useful as an alternative to hdf5 and csv.
