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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
from pyarrow._dataset import ( # noqa
CsvFileFormat,
CsvFragmentScanOptions,
Expression,
Dataset,
DatasetFactory,
DirectoryPartitioning,
FileFormat,
FileFragment,
FileSystemDataset,
FileSystemDatasetFactory,
FileSystemFactoryOptions,
FileWriteOptions,
Fragment,
HivePartitioning,
IpcFileFormat,
IpcFileWriteOptions,
InMemoryDataset,
ParquetDatasetFactory,
ParquetFactoryOptions,
ParquetFileFormat,
ParquetFileFragment,
ParquetFileWriteOptions,
ParquetFragmentScanOptions,
ParquetReadOptions,
Partitioning,
PartitioningFactory,
RowGroupInfo,
Scanner,
TaggedRecordBatch,
UnionDataset,
UnionDatasetFactory,
_get_partition_keys,
_filesystemdataset_write,
)
[docs]def field(name):
"""Reference a named column of the dataset.
Stores only the field's name. Type and other information is known only when
the expression is bound to a dataset having an explicit scheme.
Parameters
----------
name : string
The name of the field the expression references to.
Returns
-------
field_expr : Expression
"""
return Expression._field(name)
[docs]def scalar(value):
"""Expression representing a scalar value.
Parameters
----------
value : bool, int, float or string
Python value of the scalar. Note that only a subset of types are
currently supported.
Returns
-------
scalar_expr : Expression
"""
return Expression._scalar(value)
[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).
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.
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
Examples
--------
Specify the Schema for paths like "/2009/June":
>>> partitioning(pa.schema([("year", pa.int16()), ("month", pa.string())]))
or let the types be inferred by only specifying the field names:
>>> 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:
>>> 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:
>>> 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":
>>> 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):
>>> 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")
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":
return ParquetFileFormat()
elif obj in {"ipc", "arrow", "feather"}:
return IpcFileFormat()
elif obj == "csv":
return CsvFileFormat()
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])
# 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
"""
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)
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) batches\
or tables, iterable of batches, 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" and "ipc"/"arrow"/"feather" 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
--------
Opening a single file:
>>> dataset("path/to/file.parquet", format="parquet")
Opening a single file with an explicit schema:
>>> dataset("path/to/file.parquet", schema=myschema, format="parquet")
Opening a dataset for a single directory:
>>> dataset("path/to/nyc-taxi/", format="parquet")
>>> dataset("s3://mybucket/nyc-taxi/", format="parquet")
Opening a dataset from a list of relatives local paths:
>>> dataset([
... "part0/data.parquet",
... "part1/data.parquet",
... "part3/data.parquet",
... ], format='parquet')
With filesystem provided:
>>> paths = [
... 'part0/data.parquet',
... 'part1/data.parquet',
... 'part3/data.parquet',
... ]
>>> dataset(paths, filesystem='file:///directory/prefix, format='parquet')
Which is equivalent with:
>>> fs = SubTreeFileSystem("/directory/prefix", LocalFileSystem())
>>> dataset(paths, filesystem=fs, format='parquet')
With a remote filesystem URI:
>>> paths = [
... 'nested/directory/part0/data.parquet',
... 'nested/directory/part1/data.parquet',
... 'nested/directory/part3/data.parquet',
... ]
>>> dataset(paths, filesystem='s3://bucket/', format='parquet')
Similarly to the local example, the directory prefix may be included in the
filesystem URI:
>>> dataset(paths, filesystem='s3://bucket/nested/directory',
... format='parquet')
Construction of a nested dataset:
>>> dataset([
... dataset("s3://old-taxi-data", format="parquet"),
... dataset("local/path/to/data", format="ipc")
... ])
"""
# 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(scheme):
if scheme is None:
scheme = partitioning(pa.schema([]))
if not isinstance(scheme, Partitioning):
# TODO support passing field names, and get types from schema
raise ValueError("partitioning needs to be actual Partitioning object")
return scheme
[docs]def write_dataset(data, base_dir, basename_template=None, format=None,
partitioning=None, schema=None,
filesystem=None, file_options=None, use_threads=True,
use_async=False, max_partitions=None, file_visitor=None):
"""
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"/"feather". 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, optional
The partitioning scheme specified with the ``partitioning()``
function.
schema : Schema, optional
filesystem : FileSystem, optional
file_options : 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.
use_async : bool, default False
If enabled, an async scanner will be used that should offer
better performance with high-latency/highly-parallel filesystems
(e.g. S3)
max_partitions : int, default 1024
Maximum number of partitions any batch may be written into.
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)
"""
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
partitioning = _ensure_write_partitioning(partitioning)
filesystem, base_dir = _resolve_filesystem_and_path(base_dir, filesystem)
if isinstance(data, Dataset):
scanner = data.scanner(use_threads=use_threads, use_async=use_async)
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
)
