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https://issues.apache.org/jira/browse/ARROW-10517?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17235297#comment-17235297
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Joris Van den Bossche commented on ARROW-10517:
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bq. This works on my local conda environment (dependencies posted on my last
edit, using the latest version of fsspec and adlfs). The "28" partition was a
file instead of a folder in this case.
What do you mean exactly with "28" being a file? Because the command is
"mkdir", so it should create directories, not files .. (unless this is related
to Azure Blob details of its filesystem / directories that I am not familiar
with)
bq. If I run the same code on my production environment it fails.
Any idea why it would fail creating directories there? The correct rights to
create directories? Does the directory already exist? (just some suggestions to
start looking, no experience with Azure myself)
If the {{mkdir}} directly is failing, this seems an issue with {{adlfs}}, so I
would report an issue there.
> [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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