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# to you under the Apache License, Version 2.0 (the
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#
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#
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"""Dataset is currently unstable. APIs subject to change without notice."""
import pyarrow as pa
from pyarrow.util import _is_iterable, _stringify_path, _is_path_like
try:
from pyarrow._dataset import ( # noqa
CsvFileFormat,
CsvFragmentScanOptions,
JsonFileFormat,
JsonFragmentScanOptions,
Dataset,
DatasetFactory,
DirectoryPartitioning,
FeatherFileFormat,
FilenamePartitioning,
FileFormat,
FileFragment,
FileSystemDataset,
FileSystemDatasetFactory,
FileSystemFactoryOptions,
FileWriteOptions,
Fragment,
FragmentScanOptions,
HivePartitioning,
IpcFileFormat,
IpcFileWriteOptions,
InMemoryDataset,
Partitioning,
PartitioningFactory,
Scanner,
TaggedRecordBatch,
UnionDataset,
UnionDatasetFactory,
WrittenFile,
get_partition_keys,
get_partition_keys as _get_partition_keys, # keep for backwards compatibility
_filesystemdataset_write,
)
except ImportError as exc:
raise ImportError(
f"The pyarrow installation is not built with support for 'dataset' ({str(exc)})"
) from None
# keep Expression functionality exposed here for backwards compatibility
from pyarrow.compute import Expression, scalar, field # noqa
_orc_available = False
_orc_msg = (
"The pyarrow installation is not built with support for the ORC file "
"format."
)
try:
from pyarrow._dataset_orc import OrcFileFormat
_orc_available = True
except ImportError:
pass
_parquet_available = False
_parquet_msg = (
"The pyarrow installation is not built with support for the Parquet file "
"format."
)
try:
from pyarrow._dataset_parquet import ( # noqa
ParquetDatasetFactory,
ParquetFactoryOptions,
ParquetFileFormat,
ParquetFileFragment,
ParquetFileWriteOptions,
ParquetFragmentScanOptions,
ParquetReadOptions,
RowGroupInfo,
)
_parquet_available = True
except ImportError:
pass
def __getattr__(name):
if name == "OrcFileFormat" and not _orc_available:
raise ImportError(_orc_msg)
if name == "ParquetFileFormat" and not _parquet_available:
raise ImportError(_parquet_msg)
raise AttributeError(
"module 'pyarrow.dataset' has no attribute '{0}'".format(name)
)
[docs]def partitioning(schema=None, field_names=None, flavor=None,
dictionaries=None):
"""
Specify a partitioning scheme.
The supported schemes include:
- "DirectoryPartitioning": this scheme expects one segment in the file path
for each field in the specified schema (all fields are required to be
present). For example given schema<year:int16, month:int8> the path
"/2009/11" would be parsed to ("year"_ == 2009 and "month"_ == 11).
- "HivePartitioning": a scheme for "/$key=$value/" nested directories as
found in Apache Hive. This is a multi-level, directory based partitioning
scheme. Data is partitioned by static values of a particular column in
the schema. Partition keys are represented in the form $key=$value in
directory names. Field order is ignored, as are missing or unrecognized
field names.
For example, given schema<year:int16, month:int8, day:int8>, a possible
path would be "/year=2009/month=11/day=15" (but the field order does not
need to match).
- "FilenamePartitioning": this scheme expects the partitions will have
filenames containing the field values separated by "_".
For example, given schema<year:int16, month:int8, day:int8>, a possible
partition filename "2009_11_part-0.parquet" would be parsed
to ("year"_ == 2009 and "month"_ == 11).
Parameters
----------
schema : pyarrow.Schema, default None
The schema that describes the partitions present in the file path.
If not specified, and `field_names` and/or `flavor` are specified,
the schema will be inferred from the file path (and a
PartitioningFactory is returned).
field_names : list of str, default None
A list of strings (field names). If specified, the schema's types are
inferred from the file paths (only valid for DirectoryPartitioning).
flavor : str, default None
The default is DirectoryPartitioning. Specify ``flavor="hive"`` for
a HivePartitioning, and ``flavor="filename"`` for a
FilenamePartitioning.
dictionaries : dict[str, Array]
If the type of any field of `schema` is a dictionary type, the
corresponding entry of `dictionaries` must be an array containing
every value which may be taken by the corresponding column or an
error will be raised in parsing. Alternatively, pass `infer` to have
Arrow discover the dictionary values, in which case a
PartitioningFactory is returned.
Returns
-------
Partitioning or PartitioningFactory
The partioning scheme
Examples
--------
Specify the Schema for paths like "/2009/June":
>>> import pyarrow as pa
>>> import pyarrow.dataset as ds
>>> part = ds.partitioning(pa.schema([("year", pa.int16()),
... ("month", pa.string())]))
or let the types be inferred by only specifying the field names:
>>> part = ds.partitioning(field_names=["year", "month"])
For paths like "/2009/June", the year will be inferred as int32 while month
will be inferred as string.
Specify a Schema with dictionary encoding, providing dictionary values:
>>> part = ds.partitioning(
... pa.schema([
... ("year", pa.int16()),
... ("month", pa.dictionary(pa.int8(), pa.string()))
... ]),
... dictionaries={
... "month": pa.array(["January", "February", "March"]),
... })
Alternatively, specify a Schema with dictionary encoding, but have Arrow
infer the dictionary values:
>>> part = ds.partitioning(
... pa.schema([
... ("year", pa.int16()),
... ("month", pa.dictionary(pa.int8(), pa.string()))
... ]),
... dictionaries="infer")
Create a Hive scheme for a path like "/year=2009/month=11":
>>> part = ds.partitioning(
... pa.schema([("year", pa.int16()), ("month", pa.int8())]),
... flavor="hive")
A Hive scheme can also be discovered from the directory structure (and
types will be inferred):
>>> part = ds.partitioning(flavor="hive")
"""
if flavor is None:
# default flavor
if schema is not None:
if field_names is not None:
raise ValueError(
"Cannot specify both 'schema' and 'field_names'")
if dictionaries == 'infer':
return DirectoryPartitioning.discover(schema=schema)
return DirectoryPartitioning(schema, dictionaries)
elif field_names is not None:
if isinstance(field_names, list):
return DirectoryPartitioning.discover(field_names)
else:
raise ValueError(
"Expected list of field names, got {}".format(
type(field_names)))
else:
raise ValueError(
"For the default directory flavor, need to specify "
"a Schema or a list of field names")
if flavor == "filename":
if schema is not None:
if field_names is not None:
raise ValueError(
"Cannot specify both 'schema' and 'field_names'")
if dictionaries == 'infer':
return FilenamePartitioning.discover(schema=schema)
return FilenamePartitioning(schema, dictionaries)
elif field_names is not None:
if isinstance(field_names, list):
return FilenamePartitioning.discover(field_names)
else:
raise ValueError(
"Expected list of field names, got {}".format(
type(field_names)))
else:
raise ValueError(
"For the filename flavor, need to specify "
"a Schema or a list of field names")
elif flavor == 'hive':
if field_names is not None:
raise ValueError("Cannot specify 'field_names' for flavor 'hive'")
elif schema is not None:
if isinstance(schema, pa.Schema):
if dictionaries == 'infer':
return HivePartitioning.discover(schema=schema)
return HivePartitioning(schema, dictionaries)
else:
raise ValueError(
"Expected Schema for 'schema', got {}".format(
type(schema)))
else:
return HivePartitioning.discover()
else:
raise ValueError("Unsupported flavor")
def _ensure_partitioning(scheme):
"""
Validate input and return a Partitioning(Factory).
It passes None through if no partitioning scheme is defined.
"""
if scheme is None:
pass
elif isinstance(scheme, str):
scheme = partitioning(flavor=scheme)
elif isinstance(scheme, list):
scheme = partitioning(field_names=scheme)
elif isinstance(scheme, (Partitioning, PartitioningFactory)):
pass
else:
ValueError("Expected Partitioning or PartitioningFactory, got {}"
.format(type(scheme)))
return scheme
def _ensure_format(obj):
if isinstance(obj, FileFormat):
return obj
elif obj == "parquet":
if not _parquet_available:
raise ValueError(_parquet_msg)
return ParquetFileFormat()
elif obj in {"ipc", "arrow"}:
return IpcFileFormat()
elif obj == "feather":
return FeatherFileFormat()
elif obj == "csv":
return CsvFileFormat()
elif obj == "orc":
if not _orc_available:
raise ValueError(_orc_msg)
return OrcFileFormat()
elif obj == "json":
return JsonFileFormat()
else:
raise ValueError("format '{}' is not supported".format(obj))
def _ensure_multiple_sources(paths, filesystem=None):
"""
Treat a list of paths as files belonging to a single file system
If the file system is local then also validates that all paths
are referencing existing *files* otherwise any non-file paths will be
silently skipped (for example on a remote filesystem).
Parameters
----------
paths : list of path-like
Note that URIs are not allowed.
filesystem : FileSystem or str, optional
If an URI is passed, then its path component will act as a prefix for
the file paths.
Returns
-------
(FileSystem, list of str)
File system object and a list of normalized paths.
Raises
------
TypeError
If the passed filesystem has wrong type.
IOError
If the file system is local and a referenced path is not available or
not a file.
"""
from pyarrow.fs import (
LocalFileSystem, SubTreeFileSystem, _MockFileSystem, FileType,
_ensure_filesystem
)
if filesystem is None:
# fall back to local file system as the default
filesystem = LocalFileSystem()
else:
# construct a filesystem if it is a valid URI
filesystem = _ensure_filesystem(filesystem)
is_local = (
isinstance(filesystem, (LocalFileSystem, _MockFileSystem)) or
(isinstance(filesystem, SubTreeFileSystem) and
isinstance(filesystem.base_fs, LocalFileSystem))
)
# allow normalizing irregular paths such as Windows local paths
paths = [filesystem.normalize_path(_stringify_path(p)) for p in paths]
# validate that all of the paths are pointing to existing *files*
# possible improvement is to group the file_infos by type and raise for
# multiple paths per error category
if is_local:
for info in filesystem.get_file_info(paths):
file_type = info.type
if file_type == FileType.File:
continue
elif file_type == FileType.NotFound:
raise FileNotFoundError(info.path)
elif file_type == FileType.Directory:
raise IsADirectoryError(
'Path {} points to a directory, but only file paths are '
'supported. To construct a nested or union dataset pass '
'a list of dataset objects instead.'.format(info.path)
)
else:
raise IOError(
'Path {} exists but its type is unknown (could be a '
'special file such as a Unix socket or character device, '
'or Windows NUL / CON / ...)'.format(info.path)
)
return filesystem, paths
def _ensure_single_source(path, filesystem=None):
"""
Treat path as either a recursively traversable directory or a single file.
Parameters
----------
path : path-like
filesystem : FileSystem or str, optional
If an URI is passed, then its path component will act as a prefix for
the file paths.
Returns
-------
(FileSystem, list of str or fs.Selector)
File system object and either a single item list pointing to a file or
an fs.Selector object pointing to a directory.
Raises
------
TypeError
If the passed filesystem has wrong type.
FileNotFoundError
If the referenced file or directory doesn't exist.
"""
from pyarrow.fs import FileType, FileSelector, _resolve_filesystem_and_path
# at this point we already checked that `path` is a path-like
filesystem, path = _resolve_filesystem_and_path(path, filesystem)
# ensure that the path is normalized before passing to dataset discovery
path = filesystem.normalize_path(path)
# retrieve the file descriptor
file_info = filesystem.get_file_info(path)
# depending on the path type either return with a recursive
# directory selector or as a list containing a single file
if file_info.type == FileType.Directory:
paths_or_selector = FileSelector(path, recursive=True)
elif file_info.type == FileType.File:
paths_or_selector = [path]
else:
raise FileNotFoundError(path)
return filesystem, paths_or_selector
def _filesystem_dataset(source, schema=None, filesystem=None,
partitioning=None, format=None,
partition_base_dir=None, exclude_invalid_files=None,
selector_ignore_prefixes=None):
"""
Create a FileSystemDataset which can be used to build a Dataset.
Parameters are documented in the dataset function.
Returns
-------
FileSystemDataset
"""
format = _ensure_format(format or 'parquet')
partitioning = _ensure_partitioning(partitioning)
if isinstance(source, (list, tuple)):
fs, paths_or_selector = _ensure_multiple_sources(source, filesystem)
else:
fs, paths_or_selector = _ensure_single_source(source, filesystem)
options = FileSystemFactoryOptions(
partitioning=partitioning,
partition_base_dir=partition_base_dir,
exclude_invalid_files=exclude_invalid_files,
selector_ignore_prefixes=selector_ignore_prefixes
)
factory = FileSystemDatasetFactory(fs, paths_or_selector, format, options)
return factory.finish(schema)
def _in_memory_dataset(source, schema=None, **kwargs):
if any(v is not None for v in kwargs.values()):
raise ValueError(
"For in-memory datasets, you cannot pass any additional arguments")
return InMemoryDataset(source, schema)
def _union_dataset(children, schema=None, **kwargs):
if any(v is not None for v in kwargs.values()):
raise ValueError(
"When passing a list of Datasets, you cannot pass any additional "
"arguments"
)
if schema is None:
# unify the children datasets' schemas
schema = pa.unify_schemas([child.schema for child in children])
for child in children:
if getattr(child, "_scan_options", None):
raise ValueError(
"Creating an UnionDataset from filtered or projected Datasets "
"is currently not supported. Union the unfiltered datasets "
"and apply the filter to the resulting union."
)
# create datasets with the requested schema
children = [child.replace_schema(schema) for child in children]
return UnionDataset(schema, children)
[docs]def parquet_dataset(metadata_path, schema=None, filesystem=None, format=None,
partitioning=None, partition_base_dir=None):
"""
Create a FileSystemDataset from a `_metadata` file created via
`pyarrrow.parquet.write_metadata`.
Parameters
----------
metadata_path : path,
Path pointing to a single file parquet metadata file
schema : Schema, optional
Optionally provide the Schema for the Dataset, in which case it will
not be inferred from the source.
filesystem : FileSystem or URI string, default None
If a single path is given as source and filesystem is None, then the
filesystem will be inferred from the path.
If an URI string is passed, then a filesystem object is constructed
using the URI's optional path component as a directory prefix. See the
examples below.
Note that the URIs on Windows must follow 'file:///C:...' or
'file:/C:...' patterns.
format : ParquetFileFormat
An instance of a ParquetFileFormat if special options needs to be
passed.
partitioning : Partitioning, PartitioningFactory, str, list of str
The partitioning scheme specified with the ``partitioning()``
function. A flavor string can be used as shortcut, and with a list of
field names a DirectionaryPartitioning will be inferred.
partition_base_dir : str, optional
For the purposes of applying the partitioning, paths will be
stripped of the partition_base_dir. Files not matching the
partition_base_dir prefix will be skipped for partitioning discovery.
The ignored files will still be part of the Dataset, but will not
have partition information.
Returns
-------
FileSystemDataset
The dataset corresponding to the given metadata
"""
from pyarrow.fs import LocalFileSystem, _ensure_filesystem
if format is None:
format = ParquetFileFormat()
elif not isinstance(format, ParquetFileFormat):
raise ValueError("format argument must be a ParquetFileFormat")
if filesystem is None:
filesystem = LocalFileSystem()
else:
filesystem = _ensure_filesystem(filesystem)
metadata_path = filesystem.normalize_path(_stringify_path(metadata_path))
options = ParquetFactoryOptions(
partition_base_dir=partition_base_dir,
partitioning=_ensure_partitioning(partitioning)
)
factory = ParquetDatasetFactory(
metadata_path, filesystem, format, options=options)
return factory.finish(schema)
[docs]def dataset(source, schema=None, format=None, filesystem=None,
partitioning=None, partition_base_dir=None,
exclude_invalid_files=None, ignore_prefixes=None):
"""
Open a dataset.
Datasets provides functionality to efficiently work with tabular,
potentially larger than memory and multi-file dataset.
- A unified interface for different sources, like Parquet and Feather
- Discovery of sources (crawling directories, handle directory-based
partitioned datasets, basic schema normalization)
- Optimized reading with predicate pushdown (filtering rows), projection
(selecting columns), parallel reading or fine-grained managing of tasks.
Note that this is the high-level API, to have more control over the dataset
construction use the low-level API classes (FileSystemDataset,
FilesystemDatasetFactory, etc.)
Parameters
----------
source : path, list of paths, dataset, list of datasets, (list of) \
RecordBatch or Table, iterable of RecordBatch, RecordBatchReader, or URI
Path pointing to a single file:
Open a FileSystemDataset from a single file.
Path pointing to a directory:
The directory gets discovered recursively according to a
partitioning scheme if given.
List of file paths:
Create a FileSystemDataset from explicitly given files. The files
must be located on the same filesystem given by the filesystem
parameter.
Note that in contrary of construction from a single file, passing
URIs as paths is not allowed.
List of datasets:
A nested UnionDataset gets constructed, it allows arbitrary
composition of other datasets.
Note that additional keyword arguments are not allowed.
(List of) batches or tables, iterable of batches, or RecordBatchReader:
Create an InMemoryDataset. If an iterable or empty list is given,
a schema must also be given. If an iterable or RecordBatchReader
is given, the resulting dataset can only be scanned once; further
attempts will raise an error.
schema : Schema, optional
Optionally provide the Schema for the Dataset, in which case it will
not be inferred from the source.
format : FileFormat or str
Currently "parquet", "ipc"/"arrow"/"feather", "csv", "json", and "orc" are
supported. For Feather, only version 2 files are supported.
filesystem : FileSystem or URI string, default None
If a single path is given as source and filesystem is None, then the
filesystem will be inferred from the path.
If an URI string is passed, then a filesystem object is constructed
using the URI's optional path component as a directory prefix. See the
examples below.
Note that the URIs on Windows must follow 'file:///C:...' or
'file:/C:...' patterns.
partitioning : Partitioning, PartitioningFactory, str, list of str
The partitioning scheme specified with the ``partitioning()``
function. A flavor string can be used as shortcut, and with a list of
field names a DirectionaryPartitioning will be inferred.
partition_base_dir : str, optional
For the purposes of applying the partitioning, paths will be
stripped of the partition_base_dir. Files not matching the
partition_base_dir prefix will be skipped for partitioning discovery.
The ignored files will still be part of the Dataset, but will not
have partition information.
exclude_invalid_files : bool, optional (default True)
If True, invalid files will be excluded (file format specific check).
This will incur IO for each files in a serial and single threaded
fashion. Disabling this feature will skip the IO, but unsupported
files may be present in the Dataset (resulting in an error at scan
time).
ignore_prefixes : list, optional
Files matching any of these prefixes will be ignored by the
discovery process. This is matched to the basename of a path.
By default this is ['.', '_'].
Note that discovery happens only if a directory is passed as source.
Returns
-------
dataset : Dataset
Either a FileSystemDataset or a UnionDataset depending on the source
parameter.
Examples
--------
Creating an example Table:
>>> import pyarrow as pa
>>> import pyarrow.parquet as pq
>>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021],
... 'n_legs': [2, 2, 4, 4, 5, 100],
... 'animal': ["Flamingo", "Parrot", "Dog", "Horse",
... "Brittle stars", "Centipede"]})
>>> pq.write_table(table, "file.parquet")
Opening a single file:
>>> import pyarrow.dataset as ds
>>> dataset = ds.dataset("file.parquet", format="parquet")
>>> dataset.to_table()
pyarrow.Table
year: int64
n_legs: int64
animal: string
----
year: [[2020,2022,2021,2022,2019,2021]]
n_legs: [[2,2,4,4,5,100]]
animal: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]
Opening a single file with an explicit schema:
>>> myschema = pa.schema([
... ('n_legs', pa.int64()),
... ('animal', pa.string())])
>>> dataset = ds.dataset("file.parquet", schema=myschema, format="parquet")
>>> dataset.to_table()
pyarrow.Table
n_legs: int64
animal: string
----
n_legs: [[2,2,4,4,5,100]]
animal: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]]
Opening a dataset for a single directory:
>>> ds.write_dataset(table, "partitioned_dataset", format="parquet",
... partitioning=['year'])
>>> dataset = ds.dataset("partitioned_dataset", format="parquet")
>>> dataset.to_table()
pyarrow.Table
n_legs: int64
animal: string
----
n_legs: [[5],[2],[4,100],[2,4]]
animal: [["Brittle stars"],["Flamingo"],...["Parrot","Horse"]]
For a single directory from a S3 bucket:
>>> ds.dataset("s3://mybucket/nyc-taxi/",
... format="parquet") # doctest: +SKIP
Opening a dataset from a list of relatives local paths:
>>> dataset = ds.dataset([
... "partitioned_dataset/2019/part-0.parquet",
... "partitioned_dataset/2020/part-0.parquet",
... "partitioned_dataset/2021/part-0.parquet",
... ], format='parquet')
>>> dataset.to_table()
pyarrow.Table
n_legs: int64
animal: string
----
n_legs: [[5],[2],[4,100]]
animal: [["Brittle stars"],["Flamingo"],["Dog","Centipede"]]
With filesystem provided:
>>> paths = [
... 'part0/data.parquet',
... 'part1/data.parquet',
... 'part3/data.parquet',
... ]
>>> ds.dataset(paths, filesystem='file:///directory/prefix,
... format='parquet') # doctest: +SKIP
Which is equivalent with:
>>> fs = SubTreeFileSystem("/directory/prefix",
... LocalFileSystem()) # doctest: +SKIP
>>> ds.dataset(paths, filesystem=fs, format='parquet') # doctest: +SKIP
With a remote filesystem URI:
>>> paths = [
... 'nested/directory/part0/data.parquet',
... 'nested/directory/part1/data.parquet',
... 'nested/directory/part3/data.parquet',
... ]
>>> ds.dataset(paths, filesystem='s3://bucket/',
... format='parquet') # doctest: +SKIP
Similarly to the local example, the directory prefix may be included in the
filesystem URI:
>>> ds.dataset(paths, filesystem='s3://bucket/nested/directory',
... format='parquet') # doctest: +SKIP
Construction of a nested dataset:
>>> ds.dataset([
... dataset("s3://old-taxi-data", format="parquet"),
... dataset("local/path/to/data", format="ipc")
... ]) # doctest: +SKIP
"""
# collect the keyword arguments for later reuse
kwargs = dict(
schema=schema,
filesystem=filesystem,
partitioning=partitioning,
format=format,
partition_base_dir=partition_base_dir,
exclude_invalid_files=exclude_invalid_files,
selector_ignore_prefixes=ignore_prefixes
)
if _is_path_like(source):
return _filesystem_dataset(source, **kwargs)
elif isinstance(source, (tuple, list)):
if all(_is_path_like(elem) for elem in source):
return _filesystem_dataset(source, **kwargs)
elif all(isinstance(elem, Dataset) for elem in source):
return _union_dataset(source, **kwargs)
elif all(isinstance(elem, (pa.RecordBatch, pa.Table))
for elem in source):
return _in_memory_dataset(source, **kwargs)
else:
unique_types = set(type(elem).__name__ for elem in source)
type_names = ', '.join('{}'.format(t) for t in unique_types)
raise TypeError(
'Expected a list of path-like or dataset objects, or a list '
'of batches or tables. The given list contains the following '
'types: {}'.format(type_names)
)
elif isinstance(source, (pa.RecordBatch, pa.Table)):
return _in_memory_dataset(source, **kwargs)
else:
raise TypeError(
'Expected a path-like, list of path-likes or a list of Datasets '
'instead of the given type: {}'.format(type(source).__name__)
)
def _ensure_write_partitioning(part, schema, flavor):
if isinstance(part, PartitioningFactory):
raise ValueError("A PartitioningFactory cannot be used. "
"Did you call the partitioning function "
"without supplying a schema?")
if isinstance(part, Partitioning) and flavor:
raise ValueError(
"Providing a partitioning_flavor with "
"a Partitioning object is not supported"
)
elif isinstance(part, (tuple, list)):
# Name of fields were provided instead of a partitioning object.
# Create a partitioning factory with those field names.
part = partitioning(
schema=pa.schema([schema.field(f) for f in part]),
flavor=flavor
)
elif part is None:
part = partitioning(pa.schema([]), flavor=flavor)
if not isinstance(part, Partitioning):
raise ValueError(
"partitioning must be a Partitioning object or "
"a list of column names"
)
return part
[docs]def write_dataset(data, base_dir, *, basename_template=None, format=None,
partitioning=None, partitioning_flavor=None, schema=None,
filesystem=None, file_options=None, use_threads=True,
max_partitions=None, max_open_files=None,
max_rows_per_file=None, min_rows_per_group=None,
max_rows_per_group=None, file_visitor=None,
existing_data_behavior='error', create_dir=True):
"""
Write a dataset to a given format and partitioning.
Parameters
----------
data : Dataset, Table/RecordBatch, RecordBatchReader, list of \
Table/RecordBatch, or iterable of RecordBatch
The data to write. This can be a Dataset instance or
in-memory Arrow data. If an iterable is given, the schema must
also be given.
base_dir : str
The root directory where to write the dataset.
basename_template : str, optional
A template string used to generate basenames of written data files.
The token '{i}' will be replaced with an automatically incremented
integer. If not specified, it defaults to
"part-{i}." + format.default_extname
format : FileFormat or str
The format in which to write the dataset. Currently supported:
"parquet", "ipc"/"arrow"/"feather", and "csv". If a FileSystemDataset
is being written and `format` is not specified, it defaults to the
same format as the specified FileSystemDataset. When writing a
Table or RecordBatch, this keyword is required.
partitioning : Partitioning or list[str], optional
The partitioning scheme specified with the ``partitioning()``
function or a list of field names. When providing a list of
field names, you can use ``partitioning_flavor`` to drive which
partitioning type should be used.
partitioning_flavor : str, optional
One of the partitioning flavors supported by
``pyarrow.dataset.partitioning``. If omitted will use the
default of ``partitioning()`` which is directory partitioning.
schema : Schema, optional
filesystem : FileSystem, optional
file_options : pyarrow.dataset.FileWriteOptions, optional
FileFormat specific write options, created using the
``FileFormat.make_write_options()`` function.
use_threads : bool, default True
Write files in parallel. If enabled, then maximum parallelism will be
used determined by the number of available CPU cores.
max_partitions : int, default 1024
Maximum number of partitions any batch may be written into.
max_open_files : int, default 1024
If greater than 0 then this will limit the maximum number of
files that can be left open. If an attempt is made to open
too many files then the least recently used file will be closed.
If this setting is set too low you may end up fragmenting your
data into many small files.
max_rows_per_file : int, default 0
Maximum number of rows per file. If greater than 0 then this will
limit how many rows are placed in any single file. Otherwise there
will be no limit and one file will be created in each output
directory unless files need to be closed to respect max_open_files
min_rows_per_group : int, default 0
Minimum number of rows per group. When the value is greater than 0,
the dataset writer will batch incoming data and only write the row
groups to the disk when sufficient rows have accumulated.
max_rows_per_group : int, default 1024 * 1024
Maximum number of rows per group. If the value is greater than 0,
then the dataset writer may split up large incoming batches into
multiple row groups. If this value is set, then min_rows_per_group
should also be set. Otherwise it could end up with very small row
groups.
file_visitor : function
If set, this function will be called with a WrittenFile instance
for each file created during the call. This object will have both
a path attribute and a metadata attribute.
The path attribute will be a string containing the path to
the created file.
The metadata attribute will be the parquet metadata of the file.
This metadata will have the file path attribute set and can be used
to build a _metadata file. The metadata attribute will be None if
the format is not parquet.
Example visitor which simple collects the filenames created::
visited_paths = []
def file_visitor(written_file):
visited_paths.append(written_file.path)
existing_data_behavior : 'error' | 'overwrite_or_ignore' | \
'delete_matching'
Controls how the dataset will handle data that already exists in
the destination. The default behavior ('error') is to raise an error
if any data exists in the destination.
'overwrite_or_ignore' will ignore any existing data and will
overwrite files with the same name as an output file. Other
existing files will be ignored. This behavior, in combination
with a unique basename_template for each write, will allow for
an append workflow.
'delete_matching' is useful when you are writing a partitioned
dataset. The first time each partition directory is encountered
the entire directory will be deleted. This allows you to overwrite
old partitions completely.
create_dir : bool, default True
If False, directories will not be created. This can be useful for
filesystems that do not require directories.
"""
from pyarrow.fs import _resolve_filesystem_and_path
if isinstance(data, (list, tuple)):
schema = schema or data[0].schema
data = InMemoryDataset(data, schema=schema)
elif isinstance(data, (pa.RecordBatch, pa.Table)):
schema = schema or data.schema
data = InMemoryDataset(data, schema=schema)
elif isinstance(data, pa.ipc.RecordBatchReader) or _is_iterable(data):
data = Scanner.from_batches(data, schema=schema)
schema = None
elif not isinstance(data, (Dataset, Scanner)):
raise ValueError(
"Only Dataset, Scanner, Table/RecordBatch, RecordBatchReader, "
"a list of Tables/RecordBatches, or iterable of batches are "
"supported."
)
if format is None and isinstance(data, FileSystemDataset):
format = data.format
else:
format = _ensure_format(format)
if file_options is None:
file_options = format.make_write_options()
if format != file_options.format:
raise TypeError("Supplied FileWriteOptions have format {}, "
"which doesn't match supplied FileFormat {}".format(
format, file_options))
if basename_template is None:
basename_template = "part-{i}." + format.default_extname
if max_partitions is None:
max_partitions = 1024
if max_open_files is None:
max_open_files = 1024
if max_rows_per_file is None:
max_rows_per_file = 0
if max_rows_per_group is None:
max_rows_per_group = 1 << 20
if min_rows_per_group is None:
min_rows_per_group = 0
# at this point data is a Scanner or a Dataset, anything else
# was converted to one of those two. So we can grab the schema
# to build the partitioning object from Dataset.
if isinstance(data, Scanner):
partitioning_schema = data.projected_schema
else:
partitioning_schema = data.schema
partitioning = _ensure_write_partitioning(partitioning,
schema=partitioning_schema,
flavor=partitioning_flavor)
filesystem, base_dir = _resolve_filesystem_and_path(base_dir, filesystem)
if isinstance(data, Dataset):
scanner = data.scanner(use_threads=use_threads)
else:
# scanner was passed directly by the user, in which case a schema
# cannot be passed
if schema is not None:
raise ValueError("Cannot specify a schema when writing a Scanner")
scanner = data
_filesystemdataset_write(
scanner, base_dir, basename_template, filesystem, partitioning,
file_options, max_partitions, file_visitor, existing_data_behavior,
max_open_files, max_rows_per_file,
min_rows_per_group, max_rows_per_group, create_dir
)
