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Posted to jira@arrow.apache.org by "Lance Dacey (Jira)" <ji...@apache.org> on 2020/12/01 20:08:00 UTC
[jira] [Commented] (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=17241833#comment-17241833 ]
Lance Dacey commented on ARROW-10517:
-------------------------------------
Thanks - since the \{i} increments each time a new file is written, I am not sure if this can work for my use case unless I am designing this incorrectly.
I am using the partition_filename_cb similar to how I would create a materialized view in a database to ensure that there is only one row per unique ID based on the latest update timestamp. I can then connect this parquet dataset to our visualization tool, or I can export it to CSV format and email it to another team, etc.
{code:java}
#the historical dataset includes all rows, the number of files will depend on the frequency of scheduled downloads. it is possible to have multiple rows per unique ID
historical_dataset = [
'dev/test/report_date=2018-01-01/part-0.parquet',
'dev/test/report_date=2018-01-01/part-1.parquet',
'dev/test/report_date=2018-01-01/part-2.parquet',
'dev/test/report_date=2018-01-01/part-3.parquet',
'dev/test/report_date=2018-01-01/part-4.parquet',
'dev/test/report_date=2018-01-01/part-5.parquet',
]
#read the historical dataset and filter for the partition. in this case, report_date = 2018-01-01, so all data from that date is read into a table
#convert to pandas dataframe, sort based on "id" and "updated_at" fields
#drop duplicates based on "id" field, retaining the latest version
#write to a new dataset which is just the latest version of each "id". The 6 parts are now in a single file which will be continuously overwritten if any new data is added to the historical_dataset. Our visualization tool connects to these finalized files, and sometimes I send the data through email for reporting purposes
latest_dataset = [
'dev/test/report_date=2018-01-01/2018-01-01.parquet',
]
{code}
Perhaps there is a better way to go about this? With a database, I would just create view which selects distinct on the ID column based on the latest update timestamp. This seems to be a common use case, so I am not sure how people would go about it with Parquet.
> [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
> Fix For: 2.0.0
>
> Attachments: ss.PNG, ss2.PNG
>
>
>
> {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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