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https://issues.apache.org/jira/browse/ARROW-10517?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17235277#comment-17235277
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Lance Dacey commented on ARROW-10517:
-------------------------------------
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.
{code:python}
fs.mkdir("dev/test7/2020/01/28", create_parents=True)
{code}
If I run the same code on my production environment it fails. I am using this
environment with read_table and write_to_dataset often though.
{code:python}
name: old
channels:
- conda-forge
- defaults
dependencies:
- python=3.8
- azure-storage-blob=2
- pandas=1.1
- pyarrow=2
- pip=20.2
- pip:
- adlfs==0.2.5
- fsspec==0.7.4
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-7-b95d56a11f83> in <module>
----> 1 fs.mkdir("dev/test8/2020/01/28", create_parents=True)
/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/test8/2020/01/28.
{code}
However, the dataset read function now works and it supports the row level
filtering which is great (the dataset below is over 65 million rows and I am
able to filter quickly for specific IDs across multiple files in under 2
seconds):
{code:java}
dataset = ds.dataset(source=ds_path,
format="parquet",
partitioning="hive",
exclude_invalid_files=False,
filesystem=fs)
len(dataset.files)
1050
table = dataset.to_table(columns=None, filter=
(ds.field("year") == "2020") &
(ds.field("month") == "11") &
(ds.field("day") > "10") &
(ds.field("id") == "102648"))
{code}
But I cannot use write_dataset (along with the new partitioning features),
unfortunately.
> [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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