Source code for pyarrow.dataset

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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 )