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Posted to jira@arrow.apache.org by "Lance Dacey (Jira)" <ji...@apache.org> on 2020/11/19 07:45:00 UTC
[jira] [Comment Edited] (ARROW-10517) [Python] Unable to read/write
Parquet datasets with fsspec on Azure Blob
[ https://issues.apache.org/jira/browse/ARROW-10517?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17235084#comment-17235084 ]
Lance Dacey edited comment on ARROW-10517 at 11/19/20, 7:44 AM:
----------------------------------------------------------------
Added an edit with the results of pure fsspec and adlfs find() commands against a dataset I created with pyarrow. For some reason, a list is being output although I am using the latest version of each library.
I checked the versions by doing a conda list, and then inside of the notebook I ran:
{code:java}
print('\n'.join(f'{m.__name__} {m.__version__}' for m in globals().values() if getattr(m, '__version__', None)))
{code}
A separate attempt on my laptop locally using a fresh env file:
{code:java}
name: airflow
channels:
- conda-forge
- defaults
dependencies:
- python=3.8
- azure-storage-blob=12
- pandas=1.1
- pyarrow=2
- adlfs=0.5
~/miniconda3/envs/airflow/lib/python3.8/site-packages/pyarrow/dataset.py in _filesystem_dataset(source, schema, filesystem, partitioning, format, partition_base_dir, exclude_invalid_files, selector_ignore_prefixes)
434 selector_ignore_prefixes=selector_ignore_prefixes
435 )
--> 436 factory = FileSystemDatasetFactory(fs, paths_or_selector, format, options)
437
438 return factory.finish(schema)
~/miniconda3/envs/airflow/lib/python3.8/site-packages/pyarrow/_dataset.pyx in pyarrow._dataset.FileSystemDatasetFactory.__init__()
~/miniconda3/envs/airflow/lib/python3.8/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()
~/miniconda3/envs/airflow/lib/python3.8/site-packages/pyarrow/_fs.pyx in pyarrow._fs._cb_get_file_info_selector()
~/miniconda3/envs/airflow/lib/python3.8/site-packages/pyarrow/fs.py in get_file_info_selector(self, selector)
219 selector.base_dir, maxdepth=maxdepth, withdirs=True, detail=True
220 )
--> 221 for path, info in selected_files.items():
222 infos.append(self._create_file_info(path, info))
223
AttributeError: 'list' object has no attribute 'items'
{code}
was (Author: ldacey):
Added an edit with the results of pure fsspec and adlfs find() commands against a dataset I created with pyarrow. For some reason, a list is being output although I am using the latest version of each library.
I checked the versions by doing a conda list, and then inside of the notebook I ran:
{code:java}
print('\n'.join(f'{m.__name__} {m.__version__}' for m in globals().values() if getattr(m, '__version__', None)))
{code}
> [Python] Unable to read/write Parquet datasets with fsspec on Azure Blob
> ------------------------------------------------------------------------
>
> Key: ARROW-10517
> URL: https://issues.apache.org/jira/browse/ARROW-10517
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 2.0.0
> Environment: Ubuntu 18.04
> Reporter: Lance Dacey
> Priority: Major
> Labels: azureblob, dataset, dataset-parquet-read, dataset-parquet-write, fsspec
>
>
> {code:python}
> # adal==1.2.5
> # adlfs==0.2.5
> # fsspec==0.7.4
> # pandas==1.1.3
> # pyarrow==2.0.0
> # azure-storage-blob==2.1.0
> # azure-storage-common==2.1.0
> import pyarrow.dataset as ds
> import fsspec
> from pyarrow.dataset import DirectoryPartitioning
> fs = fsspec.filesystem(protocol='abfs',
> account_name=base.login,
> account_key=base.password)
> ds.write_dataset(data=table,
> base_dir="dev/test7",
> basename_template=None,
> format="parquet",
> partitioning=DirectoryPartitioning(pa.schema([("year", pa.string()), ("month", pa.string()), ("day", pa.string())])),
> schema=table.schema,
> filesystem=fs,
> )
> {code}
> I think this is due to early versions of adlfs having mkdir(). Although I use write_to_dataset and write_table all of the time, so I am not sure why this would be an issue.
> {code:python}
> ---------------------------------------------------------------------------
> RuntimeError Traceback (most recent call last)
> <ipython-input-40-bb38d83f896e> in <module>
> 13
> 14
> ---> 15 ds.write_dataset(data=table,
> 16 base_dir="dev/test7",
> 17 basename_template=None,
> /opt/conda/lib/python3.8/site-packages/pyarrow/dataset.py in write_dataset(data, base_dir, basename_template, format, partitioning, schema, filesystem, file_options, use_threads)
> 771 filesystem, _ = _ensure_fs(filesystem)
> 772
> --> 773 _filesystemdataset_write(
> 774 data, base_dir, basename_template, schema,
> 775 filesystem, partitioning, file_options, use_threads,
> /opt/conda/lib/python3.8/site-packages/pyarrow/_dataset.pyx in pyarrow._dataset._filesystemdataset_write()
> /opt/conda/lib/python3.8/site-packages/pyarrow/_fs.pyx in pyarrow._fs._cb_create_dir()
> /opt/conda/lib/python3.8/site-packages/pyarrow/fs.py in create_dir(self, path, recursive)
> 226 def create_dir(self, path, recursive):
> 227 # mkdir also raises FileNotFoundError when base directory is not found
> --> 228 self.fs.mkdir(path, create_parents=recursive)
> 229
> 230 def delete_dir(self, path):
> /opt/conda/lib/python3.8/site-packages/adlfs/core.py in mkdir(self, path, delimiter, exists_ok, **kwargs)
> 561 else:
> 562 ## everything else
> --> 563 raise RuntimeError(f"Cannot create {container_name}{delimiter}{path}.")
> 564 else:
> 565 if container_name in self.ls("") and path:
> RuntimeError: Cannot create dev/test7/2020/01/28.
> {code}
>
> Next, if I try to read a dataset (keep in mind that this works with read_table and ParquetDataset):
> {code:python}
> ds.dataset(source="dev/staging/evaluations",
> format="parquet",
> partitioning="hive",
> exclude_invalid_files=False,
> filesystem=fs
> )
> {code}
>
> This doesn't seem to respect the filesystem connected to Azure Blob.
> {code:python}
> ---------------------------------------------------------------------------
> FileNotFoundError Traceback (most recent call last)
> <ipython-input-41-4de65fe95db7> in <module>
> ----> 1 ds.dataset(source="dev/staging/evaluations",
> 2 format="parquet",
> 3 partitioning="hive",
> 4 exclude_invalid_files=False,
> 5 filesystem=fs
> /opt/conda/lib/python3.8/site-packages/pyarrow/dataset.py in dataset(source, schema, format, filesystem, partitioning, partition_base_dir, exclude_invalid_files, ignore_prefixes)
> 669 # TODO(kszucs): support InMemoryDataset for a table input
> 670 if _is_path_like(source):
> --> 671 return _filesystem_dataset(source, **kwargs)
> 672 elif isinstance(source, (tuple, list)):
> 673 if all(_is_path_like(elem) for elem in source):
> /opt/conda/lib/python3.8/site-packages/pyarrow/dataset.py in _filesystem_dataset(source, schema, filesystem, partitioning, format, partition_base_dir, exclude_invalid_files, selector_ignore_prefixes)
> 426 fs, paths_or_selector = _ensure_multiple_sources(source, filesystem)
> 427 else:
> --> 428 fs, paths_or_selector = _ensure_single_source(source, filesystem)
> 429
> 430 options = FileSystemFactoryOptions(
> /opt/conda/lib/python3.8/site-packages/pyarrow/dataset.py in _ensure_single_source(path, filesystem)
> 402 paths_or_selector = [path]
> 403 else:
> --> 404 raise FileNotFoundError(path)
> 405
> 406 return filesystem, paths_or_selector
> FileNotFoundError: dev/staging/evaluations
> {code}
> This *does* work though when I list the blobs before passing them to ds.dataset:
> {code:python}
> blobs = wasb.list_blobs(container_name="dev", prefix="staging/evaluations")
> dataset = ds.dataset(source=["dev/" + blob.name for blob in blobs],
> format="parquet",
> partitioning="hive",
> exclude_invalid_files=False,
> filesystem=fs)
> {code}
> Next, if I downgrade to pyarrow 1.0.1, I am able to read datasets (but there is no write_datasets):
> {code:python}
> # adal==1.2.5
> # adlfs==0.2.5
> # azure-storage-blob==2.1.0
> # azure-storage-common==2.1.0
> # fsspec==0.7.4
> # pandas==1.1.3
> # pyarrow==1.0.1
> dataset = ds.dataset("dev/staging/evaluations", format="parquet", filesystem=fs)
> dataset.to_table().to_pandas()
> {code}
> edit:
> pyarrow 2.0.0
> fsspec 0.8.4
> adlfs v0.5.5
> pandas 1.1.4
> numpy 1.19.4
> azure.storage.blob 12.6.0
> {code:python}
> x = adlfs.AzureBlobFileSystem(account_name=name, account_key=key)
> type(x.find("dev/test", detail=True))
> list
> fs = fsspec.filesystem(protocol="abfs", account_name=name, account_key=key)
> type(fs.find("dev/test", detail=True))
> list
> {code}
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