Source code for pyarrow.parquet.core

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#   http://www.apache.org/licenses/LICENSE-2.0
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# under the License.


from collections import defaultdict
from contextlib import nullcontext
from functools import reduce

import inspect
import json
import os
import re
import operator
import warnings

import pyarrow as pa

try:
    import pyarrow._parquet as _parquet
except ImportError as exc:
    raise ImportError(
        "The pyarrow installation is not built with support "
        f"for the Parquet file format ({str(exc)})"
    ) from None

from pyarrow._parquet import (ParquetReader, Statistics,  # noqa
                              FileMetaData, RowGroupMetaData,
                              ColumnChunkMetaData,
                              ParquetSchema, ColumnSchema,
                              ParquetLogicalType,
                              FileEncryptionProperties,
                              FileDecryptionProperties,
                              SortingColumn)
from pyarrow.fs import (LocalFileSystem, FileSystem, FileType,
                        _resolve_filesystem_and_path, _ensure_filesystem)
from pyarrow.util import guid, _is_path_like, _stringify_path, _deprecate_api


def _check_contains_null(val):
    if isinstance(val, bytes):
        for byte in val:
            if isinstance(byte, bytes):
                compare_to = chr(0)
            else:
                compare_to = 0
            if byte == compare_to:
                return True
    elif isinstance(val, str):
        return '\x00' in val
    return False


def _check_filters(filters, check_null_strings=True):
    """
    Check if filters are well-formed.
    """
    if filters is not None:
        if len(filters) == 0 or any(len(f) == 0 for f in filters):
            raise ValueError("Malformed filters")
        if isinstance(filters[0][0], str):
            # We have encountered the situation where we have one nesting level
            # too few:
            #   We have [(,,), ..] instead of [[(,,), ..]]
            filters = [filters]
        if check_null_strings:
            for conjunction in filters:
                for col, op, val in conjunction:
                    if (
                        isinstance(val, list) and
                        all(_check_contains_null(v) for v in val) or
                        _check_contains_null(val)
                    ):
                        raise NotImplementedError(
                            "Null-terminated binary strings are not supported "
                            "as filter values."
                        )
    return filters


_DNF_filter_doc = """Predicates are expressed using an ``Expression`` or using
    the disjunctive normal form (DNF), like ``[[('x', '=', 0), ...], ...]``.
    DNF allows arbitrary boolean logical combinations of single column predicates.
    The innermost tuples each describe a single column predicate. The list of inner
    predicates is interpreted as a conjunction (AND), forming a more selective and
    multiple column predicate. Finally, the most outer list combines these filters
    as a disjunction (OR).

    Predicates may also be passed as List[Tuple]. This form is interpreted
    as a single conjunction. To express OR in predicates, one must
    use the (preferred) List[List[Tuple]] notation.

    Each tuple has format: (``key``, ``op``, ``value``) and compares the
    ``key`` with the ``value``.
    The supported ``op`` are:  ``=`` or ``==``, ``!=``, ``<``, ``>``, ``<=``,
    ``>=``, ``in`` and ``not in``. If the ``op`` is ``in`` or ``not in``, the
    ``value`` must be a collection such as a ``list``, a ``set`` or a
    ``tuple``.

    Examples:

    Using the ``Expression`` API:

    .. code-block:: python

        import pyarrow.compute as pc
        pc.field('x') = 0
        pc.field('y').isin(['a', 'b', 'c'])
        ~pc.field('y').isin({'a', 'b'})

    Using the DNF format:

    .. code-block:: python

        ('x', '=', 0)
        ('y', 'in', ['a', 'b', 'c'])
        ('z', 'not in', {'a','b'})

    """


def filters_to_expression(filters):
    """
    Check if filters are well-formed and convert to an ``Expression``.

    Parameters
    ----------
    filters : List[Tuple] or List[List[Tuple]]

    Notes
    -----
    See internal ``pyarrow._DNF_filter_doc`` attribute for more details.

    Examples
    --------

    >>> filters_to_expression([('foo', '==', 'bar')])
    <pyarrow.compute.Expression (foo == "bar")>

    Returns
    -------
    pyarrow.compute.Expression
        An Expression representing the filters
    """
    import pyarrow.dataset as ds

    if isinstance(filters, ds.Expression):
        return filters

    filters = _check_filters(filters, check_null_strings=False)

    def convert_single_predicate(col, op, val):
        field = ds.field(col)

        if op == "=" or op == "==":
            return field == val
        elif op == "!=":
            return field != val
        elif op == '<':
            return field < val
        elif op == '>':
            return field > val
        elif op == '<=':
            return field <= val
        elif op == '>=':
            return field >= val
        elif op == 'in':
            return field.isin(val)
        elif op == 'not in':
            return ~field.isin(val)
        else:
            raise ValueError(
                '"{0}" is not a valid operator in predicates.'.format(
                    (col, op, val)))

    disjunction_members = []

    for conjunction in filters:
        conjunction_members = [
            convert_single_predicate(col, op, val)
            for col, op, val in conjunction
        ]

        disjunction_members.append(reduce(operator.and_, conjunction_members))

    return reduce(operator.or_, disjunction_members)


_filters_to_expression = _deprecate_api(
    "_filters_to_expression", "filters_to_expression",
    filters_to_expression, "10.0.0", DeprecationWarning)


# ----------------------------------------------------------------------
# Reading a single Parquet file


[docs]class ParquetFile: """ Reader interface for a single Parquet file. Parameters ---------- source : str, pathlib.Path, pyarrow.NativeFile, or file-like object Readable source. For passing bytes or buffer-like file containing a Parquet file, use pyarrow.BufferReader. metadata : FileMetaData, default None Use existing metadata object, rather than reading from file. common_metadata : FileMetaData, default None Will be used in reads for pandas schema metadata if not found in the main file's metadata, no other uses at the moment. read_dictionary : list List of column names to read directly as DictionaryArray. memory_map : bool, default False If the source is a file path, use a memory map to read file, which can improve performance in some environments. buffer_size : int, default 0 If positive, perform read buffering when deserializing individual column chunks. Otherwise IO calls are unbuffered. pre_buffer : bool, default False Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3). If True, Arrow will use a background I/O thread pool. coerce_int96_timestamp_unit : str, default None Cast timestamps that are stored in INT96 format to a particular resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 timestamps will be inferred as timestamps in nanoseconds. decryption_properties : FileDecryptionProperties, default None File decryption properties for Parquet Modular Encryption. thrift_string_size_limit : int, default None If not None, override the maximum total string size allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. thrift_container_size_limit : int, default None If not None, override the maximum total size of containers allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. page_checksum_verification : bool, default False If True, verify the checksum for each page read from the file. Examples -------- Generate an example PyArrow Table and write it to Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') Create a ``ParquetFile`` object from the Parquet file: >>> parquet_file = pq.ParquetFile('example.parquet') Read the data: >>> parquet_file.read() pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[2,2,4,4,5,100]] animal: [["Flamingo","Parrot","Dog","Horse","Brittle stars","Centipede"]] Create a ParquetFile object with "animal" column as DictionaryArray: >>> parquet_file = pq.ParquetFile('example.parquet', ... read_dictionary=["animal"]) >>> parquet_file.read() pyarrow.Table n_legs: int64 animal: dictionary<values=string, indices=int32, ordered=0> ---- n_legs: [[2,2,4,4,5,100]] animal: [ -- dictionary: ["Flamingo","Parrot",...,"Brittle stars","Centipede"] -- indices: [0,1,2,3,4,5]] """
[docs] def __init__(self, source, *, metadata=None, common_metadata=None, read_dictionary=None, memory_map=False, buffer_size=0, pre_buffer=False, coerce_int96_timestamp_unit=None, decryption_properties=None, thrift_string_size_limit=None, thrift_container_size_limit=None, filesystem=None, page_checksum_verification=False): self._close_source = getattr(source, 'closed', True) filesystem, source = _resolve_filesystem_and_path( source, filesystem, memory_map=memory_map) if filesystem is not None: source = filesystem.open_input_file(source) self._close_source = True # We opened it here, ensure we close it. self.reader = ParquetReader() self.reader.open( source, use_memory_map=memory_map, buffer_size=buffer_size, pre_buffer=pre_buffer, read_dictionary=read_dictionary, metadata=metadata, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, decryption_properties=decryption_properties, thrift_string_size_limit=thrift_string_size_limit, thrift_container_size_limit=thrift_container_size_limit, page_checksum_verification=page_checksum_verification, ) self.common_metadata = common_metadata self._nested_paths_by_prefix = self._build_nested_paths()
def __enter__(self): return self def __exit__(self, *args, **kwargs): self.close() def _build_nested_paths(self): paths = self.reader.column_paths result = defaultdict(list) for i, path in enumerate(paths): key = path[0] rest = path[1:] while True: result[key].append(i) if not rest: break key = '.'.join((key, rest[0])) rest = rest[1:] return result @property def metadata(self): """ Return the Parquet metadata. """ return self.reader.metadata @property def schema(self): """ Return the Parquet schema, unconverted to Arrow types """ return self.metadata.schema @property def schema_arrow(self): """ Return the inferred Arrow schema, converted from the whole Parquet file's schema Examples -------- Generate an example Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') Read the Arrow schema: >>> parquet_file.schema_arrow n_legs: int64 animal: string """ return self.reader.schema_arrow @property def num_row_groups(self): """ Return the number of row groups of the Parquet file. Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.num_row_groups 1 """ return self.reader.num_row_groups
[docs] def close(self, force: bool = False): if self._close_source or force: self.reader.close()
@property def closed(self) -> bool: return self.reader.closed
[docs] def read_row_group(self, i, columns=None, use_threads=True, use_pandas_metadata=False): """ Read a single row group from a Parquet file. Parameters ---------- i : int Index of the individual row group that we want to read. columns : list If not None, only these columns will be read from the row group. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.table.Table Content of the row group as a table (of columns) Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.read_row_group(0) pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[2,2,4,4,5,100]] animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]] """ column_indices = self._get_column_indices( columns, use_pandas_metadata=use_pandas_metadata) return self.reader.read_row_group(i, column_indices=column_indices, use_threads=use_threads)
[docs] def read_row_groups(self, row_groups, columns=None, use_threads=True, use_pandas_metadata=False): """ Read a multiple row groups from a Parquet file. Parameters ---------- row_groups : list Only these row groups will be read from the file. columns : list If not None, only these columns will be read from the row group. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.table.Table Content of the row groups as a table (of columns). Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.read_row_groups([0,0]) pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[2,2,4,4,5,...,2,4,4,5,100]] animal: [["Flamingo","Parrot","Dog",...,"Brittle stars","Centipede"]] """ column_indices = self._get_column_indices( columns, use_pandas_metadata=use_pandas_metadata) return self.reader.read_row_groups(row_groups, column_indices=column_indices, use_threads=use_threads)
[docs] def iter_batches(self, batch_size=65536, row_groups=None, columns=None, use_threads=True, use_pandas_metadata=False): """ Read streaming batches from a Parquet file. Parameters ---------- batch_size : int, default 64K Maximum number of records to yield per batch. Batches may be smaller if there aren't enough rows in the file. row_groups : list Only these row groups will be read from the file. columns : list If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : boolean, default True Perform multi-threaded column reads. use_pandas_metadata : boolean, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Yields ------ pyarrow.RecordBatch Contents of each batch as a record batch Examples -------- Generate an example Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> for i in parquet_file.iter_batches(): ... print("RecordBatch") ... print(i.to_pandas()) ... RecordBatch n_legs animal 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede """ if row_groups is None: row_groups = range(0, self.metadata.num_row_groups) column_indices = self._get_column_indices( columns, use_pandas_metadata=use_pandas_metadata) batches = self.reader.iter_batches(batch_size, row_groups=row_groups, column_indices=column_indices, use_threads=use_threads) return batches
[docs] def read(self, columns=None, use_threads=True, use_pandas_metadata=False): """ Read a Table from Parquet format. Parameters ---------- columns : list If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.table.Table Content of the file as a table (of columns). Examples -------- Generate an example Parquet file: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') Read a Table: >>> parquet_file.read(columns=["animal"]) pyarrow.Table animal: string ---- animal: [["Flamingo","Parrot",...,"Brittle stars","Centipede"]] """ column_indices = self._get_column_indices( columns, use_pandas_metadata=use_pandas_metadata) return self.reader.read_all(column_indices=column_indices, use_threads=use_threads)
[docs] def scan_contents(self, columns=None, batch_size=65536): """ Read contents of file for the given columns and batch size. Notes ----- This function's primary purpose is benchmarking. The scan is executed on a single thread. Parameters ---------- columns : list of integers, default None Select columns to read, if None scan all columns. batch_size : int, default 64K Number of rows to read at a time internally. Returns ------- num_rows : int Number of rows in file Examples -------- >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') >>> parquet_file = pq.ParquetFile('example.parquet') >>> parquet_file.scan_contents() 6 """ column_indices = self._get_column_indices(columns) return self.reader.scan_contents(column_indices, batch_size=batch_size)
def _get_column_indices(self, column_names, use_pandas_metadata=False): if column_names is None: return None indices = [] for name in column_names: if name in self._nested_paths_by_prefix: indices.extend(self._nested_paths_by_prefix[name]) if use_pandas_metadata: file_keyvalues = self.metadata.metadata common_keyvalues = (self.common_metadata.metadata if self.common_metadata is not None else None) if file_keyvalues and b'pandas' in file_keyvalues: index_columns = _get_pandas_index_columns(file_keyvalues) elif common_keyvalues and b'pandas' in common_keyvalues: index_columns = _get_pandas_index_columns(common_keyvalues) else: index_columns = [] if indices is not None and index_columns: indices += [self.reader.column_name_idx(descr) for descr in index_columns if not isinstance(descr, dict)] return indices
_SPARK_DISALLOWED_CHARS = re.compile('[ ,;{}()\n\t=]') def _sanitized_spark_field_name(name): return _SPARK_DISALLOWED_CHARS.sub('_', name) def _sanitize_schema(schema, flavor): if 'spark' in flavor: sanitized_fields = [] schema_changed = False for field in schema: name = field.name sanitized_name = _sanitized_spark_field_name(name) if sanitized_name != name: schema_changed = True sanitized_field = pa.field(sanitized_name, field.type, field.nullable, field.metadata) sanitized_fields.append(sanitized_field) else: sanitized_fields.append(field) new_schema = pa.schema(sanitized_fields, metadata=schema.metadata) return new_schema, schema_changed else: return schema, False def _sanitize_table(table, new_schema, flavor): # TODO: This will not handle prohibited characters in nested field names if 'spark' in flavor: column_data = [table[i] for i in range(table.num_columns)] return pa.Table.from_arrays(column_data, schema=new_schema) else: return table _parquet_writer_arg_docs = """version : {"1.0", "2.4", "2.6"}, default "2.6" Determine which Parquet logical types are available for use, whether the reduced set from the Parquet 1.x.x format or the expanded logical types added in later format versions. Files written with version='2.4' or '2.6' may not be readable in all Parquet implementations, so version='1.0' is likely the choice that maximizes file compatibility. UINT32 and some logical types are only available with version '2.4'. Nanosecond timestamps are only available with version '2.6'. Other features such as compression algorithms or the new serialized data page format must be enabled separately (see 'compression' and 'data_page_version'). use_dictionary : bool or list, default True Specify if we should use dictionary encoding in general or only for some columns. When encoding the column, if the dictionary size is too large, the column will fallback to ``PLAIN`` encoding. Specially, ``BOOLEAN`` type doesn't support dictionary encoding. compression : str or dict, default 'snappy' Specify the compression codec, either on a general basis or per-column. Valid values: {'NONE', 'SNAPPY', 'GZIP', 'BROTLI', 'LZ4', 'ZSTD'}. write_statistics : bool or list, default True Specify if we should write statistics in general (default is True) or only for some columns. use_deprecated_int96_timestamps : bool, default None Write timestamps to INT96 Parquet format. Defaults to False unless enabled by flavor argument. This take priority over the coerce_timestamps option. coerce_timestamps : str, default None Cast timestamps to a particular resolution. If omitted, defaults are chosen depending on `version`. By default, for ``version='1.0'`` (the default) and ``version='2.4'``, nanoseconds are cast to microseconds ('us'), while for other `version` values, they are written natively without loss of resolution. Seconds are always cast to milliseconds ('ms') by default, as Parquet does not have any temporal type with seconds resolution. If the casting results in loss of data, it will raise an exception unless ``allow_truncated_timestamps=True`` is given. Valid values: {None, 'ms', 'us'} allow_truncated_timestamps : bool, default False Allow loss of data when coercing timestamps to a particular resolution. E.g. if microsecond or nanosecond data is lost when coercing to 'ms', do not raise an exception. Passing ``allow_truncated_timestamp=True`` will NOT result in the truncation exception being ignored unless ``coerce_timestamps`` is not None. data_page_size : int, default None Set a target threshold for the approximate encoded size of data pages within a column chunk (in bytes). If None, use the default data page size of 1MByte. flavor : {'spark'}, default None Sanitize schema or set other compatibility options to work with various target systems. filesystem : FileSystem, default None If nothing passed, will be inferred from `where` if path-like, else `where` is already a file-like object so no filesystem is needed. compression_level : int or dict, default None Specify the compression level for a codec, either on a general basis or per-column. If None is passed, arrow selects the compression level for the compression codec in use. The compression level has a different meaning for each codec, so you have to read the documentation of the codec you are using. An exception is thrown if the compression codec does not allow specifying a compression level. use_byte_stream_split : bool or list, default False Specify if the byte_stream_split encoding should be used in general or only for some columns. If both dictionary and byte_stream_stream are enabled, then dictionary is preferred. The byte_stream_split encoding is valid only for floating-point data types and should be combined with a compression codec. column_encoding : string or dict, default None Specify the encoding scheme on a per column basis. Can only be used when ``use_dictionary`` is set to False, and cannot be used in combination with ``use_byte_stream_split``. Currently supported values: {'PLAIN', 'BYTE_STREAM_SPLIT', 'DELTA_BINARY_PACKED', 'DELTA_LENGTH_BYTE_ARRAY', 'DELTA_BYTE_ARRAY'}. Certain encodings are only compatible with certain data types. Please refer to the encodings section of `Reading and writing Parquet files <https://arrow.apache.org/docs/cpp/parquet.html#encodings>`_. data_page_version : {"1.0", "2.0"}, default "1.0" The serialized Parquet data page format version to write, defaults to 1.0. This does not impact the file schema logical types and Arrow to Parquet type casting behavior; for that use the "version" option. use_compliant_nested_type : bool, default True Whether to write compliant Parquet nested type (lists) as defined `here <https://github.com/apache/parquet-format/blob/master/ LogicalTypes.md#nested-types>`_, defaults to ``True``. For ``use_compliant_nested_type=True``, this will write into a list with 3-level structure where the middle level, named ``list``, is a repeated group with a single field named ``element``:: <list-repetition> group <name> (LIST) { repeated group list { <element-repetition> <element-type> element; } } For ``use_compliant_nested_type=False``, this will also write into a list with 3-level structure, where the name of the single field of the middle level ``list`` is taken from the element name for nested columns in Arrow, which defaults to ``item``:: <list-repetition> group <name> (LIST) { repeated group list { <element-repetition> <element-type> item; } } encryption_properties : FileEncryptionProperties, default None File encryption properties for Parquet Modular Encryption. If None, no encryption will be done. The encryption properties can be created using: ``CryptoFactory.file_encryption_properties()``. write_batch_size : int, default None Number of values to write to a page at a time. If None, use the default of 1024. ``write_batch_size`` is complementary to ``data_page_size``. If pages are exceeding the ``data_page_size`` due to large column values, lowering the batch size can help keep page sizes closer to the intended size. dictionary_pagesize_limit : int, default None Specify the dictionary page size limit per row group. If None, use the default 1MB. store_schema : bool, default True By default, the Arrow schema is serialized and stored in the Parquet file metadata (in the "ARROW:schema" key). When reading the file, if this key is available, it will be used to more faithfully recreate the original Arrow data. For example, for tz-aware timestamp columns it will restore the timezone (Parquet only stores the UTC values without timezone), or columns with duration type will be restored from the int64 Parquet column. write_page_index : bool, default False Whether to write a page index in general for all columns. Writing statistics to the page index disables the old method of writing statistics to each data page header. The page index makes statistics-based filtering more efficient than the page header, as it gathers all the statistics for a Parquet file in a single place, avoiding scattered I/O. Note that the page index is not yet used on the read size by PyArrow. write_page_checksum : bool, default False Whether to write page checksums in general for all columns. Page checksums enable detection of data corruption, which might occur during transmission or in the storage. sorting_columns : Sequence of SortingColumn, default None Specify the sort order of the data being written. The writer does not sort the data nor does it verify that the data is sorted. The sort order is written to the row group metadata, which can then be used by readers. """ _parquet_writer_example_doc = """\ Generate an example PyArrow Table and RecordBatch: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> batch = pa.record_batch([[2, 2, 4, 4, 5, 100], ... ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]], ... names=['n_legs', 'animal']) create a ParquetWriter object: >>> import pyarrow.parquet as pq >>> writer = pq.ParquetWriter('example.parquet', table.schema) and write the Table into the Parquet file: >>> writer.write_table(table) >>> writer.close() >>> pq.read_table('example.parquet').to_pandas() n_legs animal 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede create a ParquetWriter object for the RecordBatch: >>> writer2 = pq.ParquetWriter('example2.parquet', batch.schema) and write the RecordBatch into the Parquet file: >>> writer2.write_batch(batch) >>> writer2.close() >>> pq.read_table('example2.parquet').to_pandas() n_legs animal 0 2 Flamingo 1 2 Parrot 2 4 Dog 3 4 Horse 4 5 Brittle stars 5 100 Centipede """
[docs]class ParquetWriter: __doc__ = """ Class for incrementally building a Parquet file for Arrow tables. Parameters ---------- where : path or file-like object schema : pyarrow.Schema {} writer_engine_version : unused **options : dict If options contains a key `metadata_collector` then the corresponding value is assumed to be a list (or any object with `.append` method) that will be filled with the file metadata instance of the written file. Examples -------- {} """.format(_parquet_writer_arg_docs, _parquet_writer_example_doc)
[docs] def __init__(self, where, schema, filesystem=None, flavor=None, version='2.6', use_dictionary=True, compression='snappy', write_statistics=True, use_deprecated_int96_timestamps=None, compression_level=None, use_byte_stream_split=False, column_encoding=None, writer_engine_version=None, data_page_version='1.0', use_compliant_nested_type=True, encryption_properties=None, write_batch_size=None, dictionary_pagesize_limit=None, store_schema=True, write_page_index=False, write_page_checksum=False, sorting_columns=None, **options): if use_deprecated_int96_timestamps is None: # Use int96 timestamps for Spark if flavor is not None and 'spark' in flavor: use_deprecated_int96_timestamps = True else: use_deprecated_int96_timestamps = False self.flavor = flavor if flavor is not None: schema, self.schema_changed = _sanitize_schema(schema, flavor) else: self.schema_changed = False self.schema = schema self.where = where # If we open a file using a filesystem, store file handle so we can be # sure to close it when `self.close` is called. self.file_handle = None filesystem, path = _resolve_filesystem_and_path(where, filesystem) if filesystem is not None: # ARROW-10480: do not auto-detect compression. While # a filename like foo.parquet.gz is nonconforming, it # shouldn't implicitly apply compression. sink = self.file_handle = filesystem.open_output_stream( path, compression=None) else: sink = where self._metadata_collector = options.pop('metadata_collector', None) engine_version = 'V2' self.writer = _parquet.ParquetWriter( sink, schema, version=version, compression=compression, use_dictionary=use_dictionary, write_statistics=write_statistics, use_deprecated_int96_timestamps=use_deprecated_int96_timestamps, compression_level=compression_level, use_byte_stream_split=use_byte_stream_split, column_encoding=column_encoding, writer_engine_version=engine_version, data_page_version=data_page_version, use_compliant_nested_type=use_compliant_nested_type, encryption_properties=encryption_properties, write_batch_size=write_batch_size, dictionary_pagesize_limit=dictionary_pagesize_limit, store_schema=store_schema, write_page_index=write_page_index, write_page_checksum=write_page_checksum, sorting_columns=sorting_columns, **options) self.is_open = True
def __del__(self): if getattr(self, 'is_open', False): self.close() def __enter__(self): return self def __exit__(self, *args, **kwargs): self.close() # return false since we want to propagate exceptions return False
[docs] def write(self, table_or_batch, row_group_size=None): """ Write RecordBatch or Table to the Parquet file. Parameters ---------- table_or_batch : {RecordBatch, Table} row_group_size : int, default None Maximum number of rows in each written row group. If None, the row group size will be the minimum of the input table or batch length and 1024 * 1024. """ if isinstance(table_or_batch, pa.RecordBatch): self.write_batch(table_or_batch, row_group_size) elif isinstance(table_or_batch, pa.Table): self.write_table(table_or_batch, row_group_size) else: raise TypeError(type(table_or_batch))
[docs] def write_batch(self, batch, row_group_size=None): """ Write RecordBatch to the Parquet file. Parameters ---------- batch : RecordBatch row_group_size : int, default None Maximum number of rows in written row group. If None, the row group size will be the minimum of the RecordBatch size and 1024 * 1024. If set larger than 64Mi then 64Mi will be used instead. """ table = pa.Table.from_batches([batch], batch.schema) self.write_table(table, row_group_size)
[docs] def write_table(self, table, row_group_size=None): """ Write Table to the Parquet file. Parameters ---------- table : Table row_group_size : int, default None Maximum number of rows in each written row group. If None, the row group size will be the minimum of the Table size and 1024 * 1024. If set larger than 64Mi then 64Mi will be used instead. """ if self.schema_changed: table = _sanitize_table(table, self.schema, self.flavor) assert self.is_open if not table.schema.equals(self.schema, check_metadata=False): msg = ('Table schema does not match schema used to create file: ' '\ntable:\n{!s} vs. \nfile:\n{!s}' .format(table.schema, self.schema)) raise ValueError(msg) self.writer.write_table(table, row_group_size=row_group_size)
[docs] def close(self): """ Close the connection to the Parquet file. """ if self.is_open: self.writer.close() self.is_open = False if self._metadata_collector is not None: self._metadata_collector.append(self.writer.metadata) if self.file_handle is not None: self.file_handle.close()
def _get_pandas_index_columns(keyvalues): return (json.loads(keyvalues[b'pandas'].decode('utf8')) ['index_columns']) EXCLUDED_PARQUET_PATHS = {'_SUCCESS'} _read_docstring_common = """\ read_dictionary : list, default None List of names or column paths (for nested types) to read directly as DictionaryArray. Only supported for BYTE_ARRAY storage. To read a flat column as dictionary-encoded pass the column name. For nested types, you must pass the full column "path", which could be something like level1.level2.list.item. Refer to the Parquet file's schema to obtain the paths. memory_map : bool, default False If the source is a file path, use a memory map to read file, which can improve performance in some environments. buffer_size : int, default 0 If positive, perform read buffering when deserializing individual column chunks. Otherwise IO calls are unbuffered. partitioning : pyarrow.dataset.Partitioning or str or list of str, \ default "hive" The partitioning scheme for a partitioned dataset. The default of "hive" assumes directory names with key=value pairs like "/year=2009/month=11". In addition, a scheme like "/2009/11" is also supported, in which case you need to specify the field names or a full schema. See the ``pyarrow.dataset.partitioning()`` function for more details.""" _parquet_dataset_example = """\ Generate an example PyArrow Table and write it to a partitioned dataset: >>> import pyarrow as pa >>> 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"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2', ... partition_cols=['year']) create a ParquetDataset object from the dataset source: >>> dataset = pq.ParquetDataset('dataset_v2/') and read the data: >>> dataset.read().to_pandas() n_legs animal year 0 5 Brittle stars 2019 1 2 Flamingo 2020 2 4 Dog 2021 3 100 Centipede 2021 4 2 Parrot 2022 5 4 Horse 2022 create a ParquetDataset object with filter: >>> dataset = pq.ParquetDataset('dataset_v2/', ... filters=[('n_legs','=',4)]) >>> dataset.read().to_pandas() n_legs animal year 0 4 Dog 2021 1 4 Horse 2022 """
[docs]class ParquetDataset: __doc__ = """ Encapsulates details of reading a complete Parquet dataset possibly consisting of multiple files and partitions in subdirectories. Parameters ---------- path_or_paths : str or List[str] A directory name, single file name, or list of file names. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. schema : pyarrow.parquet.Schema Optionally provide the Schema for the Dataset, in which case it will not be inferred from the source. filters : pyarrow.compute.Expression or List[Tuple] or List[List[Tuple]], default None Rows which do not match the filter predicate will be removed from scanned data. Partition keys embedded in a nested directory structure will be exploited to avoid loading files at all if they contain no matching rows. Within-file level filtering and different partitioning schemes are supported. {1} {0} 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. pre_buffer : bool, default True Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3, GCS). If True, Arrow will use a background I/O thread pool. If using a filesystem layer that itself performs readahead (e.g. fsspec's S3FS), disable readahead for best results. Set to False if you want to prioritize minimal memory usage over maximum speed. coerce_int96_timestamp_unit : str, default None Cast timestamps that are stored in INT96 format to a particular resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 timestamps will be inferred as timestamps in nanoseconds. decryption_properties : FileDecryptionProperties or None File-level decryption properties. The decryption properties can be created using ``CryptoFactory.file_decryption_properties()``. thrift_string_size_limit : int, default None If not None, override the maximum total string size allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. thrift_container_size_limit : int, default None If not None, override the maximum total size of containers allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. page_checksum_verification : bool, default False If True, verify the page checksum for each page read from the file. use_legacy_dataset : bool, optional Deprecated and has no effect from PyArrow version 15.0.0. Examples -------- {2} """.format(_read_docstring_common, _DNF_filter_doc, _parquet_dataset_example)
[docs] def __init__(self, path_or_paths, filesystem=None, schema=None, *, filters=None, read_dictionary=None, memory_map=False, buffer_size=None, partitioning="hive", ignore_prefixes=None, pre_buffer=True, coerce_int96_timestamp_unit=None, decryption_properties=None, thrift_string_size_limit=None, thrift_container_size_limit=None, page_checksum_verification=False, use_legacy_dataset=None): if use_legacy_dataset is not None: warnings.warn( "Passing 'use_legacy_dataset' is deprecated as of pyarrow 15.0.0 " "and will be removed in a future version.", FutureWarning, stacklevel=2) import pyarrow.dataset as ds # map format arguments read_options = { "pre_buffer": pre_buffer, "coerce_int96_timestamp_unit": coerce_int96_timestamp_unit, "thrift_string_size_limit": thrift_string_size_limit, "thrift_container_size_limit": thrift_container_size_limit, "page_checksum_verification": page_checksum_verification, } if buffer_size: read_options.update(use_buffered_stream=True, buffer_size=buffer_size) if read_dictionary is not None: read_options.update(dictionary_columns=read_dictionary) if decryption_properties is not None: read_options.update(decryption_properties=decryption_properties) self._filter_expression = None if filters is not None: self._filter_expression = filters_to_expression(filters) # map old filesystems to new one if filesystem is not None: filesystem = _ensure_filesystem( filesystem, use_mmap=memory_map) elif filesystem is None and memory_map: # if memory_map is specified, assume local file system (string # path can in principle be URI for any filesystem) filesystem = LocalFileSystem(use_mmap=memory_map) # This needs to be checked after _ensure_filesystem, because that # handles the case of an fsspec LocalFileSystem if ( hasattr(path_or_paths, "__fspath__") and filesystem is not None and not isinstance(filesystem, LocalFileSystem) ): raise TypeError( "Path-like objects with __fspath__ must only be used with " f"local file systems, not {type(filesystem)}" ) # check for single fragment dataset or dataset directory single_file = None self._base_dir = None if not isinstance(path_or_paths, list): if _is_path_like(path_or_paths): path_or_paths = _stringify_path(path_or_paths) if filesystem is None: # path might be a URI describing the FileSystem as well try: filesystem, path_or_paths = FileSystem.from_uri( path_or_paths) except ValueError: filesystem = LocalFileSystem(use_mmap=memory_map) finfo = filesystem.get_file_info(path_or_paths) if finfo.type == FileType.Directory: self._base_dir = path_or_paths else: single_file = path_or_paths parquet_format = ds.ParquetFileFormat(**read_options) if single_file is not None: fragment = parquet_format.make_fragment(single_file, filesystem) self._dataset = ds.FileSystemDataset( [fragment], schema=schema or fragment.physical_schema, format=parquet_format, filesystem=fragment.filesystem ) return # check partitioning to enable dictionary encoding if partitioning == "hive": partitioning = ds.HivePartitioning.discover( infer_dictionary=True) self._dataset = ds.dataset(path_or_paths, filesystem=filesystem, schema=schema, format=parquet_format, partitioning=partitioning, ignore_prefixes=ignore_prefixes)
[docs] def equals(self, other): if not isinstance(other, ParquetDataset): raise TypeError('`other` must be an instance of ParquetDataset') return (self.schema == other.schema and self._dataset.format == other._dataset.format and self.filesystem == other.filesystem and # self.fragments == other.fragments and self.files == other.files)
def __eq__(self, other): try: return self.equals(other) except TypeError: return NotImplemented @property def schema(self): """ Schema of the Dataset. Examples -------- Generate an example dataset: >>> import pyarrow as pa >>> 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"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_schema', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_schema/') Read the schema: >>> dataset.schema n_legs: int64 animal: string year: dictionary<values=int32, indices=int32, ordered=0> """ return self._dataset.schema
[docs] def read(self, columns=None, use_threads=True, use_pandas_metadata=False): """ Read (multiple) Parquet files as a single pyarrow.Table. Parameters ---------- columns : List[str] Names of columns to read from the dataset. The partition fields are not automatically included. use_threads : bool, default True Perform multi-threaded column reads. use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded. Returns ------- pyarrow.Table Content of the file as a table (of columns). Examples -------- Generate an example dataset: >>> import pyarrow as pa >>> 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"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_read', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_read/') Read the dataset: >>> dataset.read(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[5],[2],[4,100],[2,4]] """ # if use_pandas_metadata, we need to include index columns in the # column selection, to be able to restore those in the pandas DataFrame metadata = self.schema.metadata or {} if use_pandas_metadata: # if the dataset schema metadata itself doesn't have pandas # then try to get this from common file (for backwards compat) if b"pandas" not in metadata: common_metadata = self._get_common_pandas_metadata() if common_metadata: metadata = common_metadata if columns is not None and use_pandas_metadata: if metadata and b'pandas' in metadata: # RangeIndex can be represented as dict instead of column name index_columns = [ col for col in _get_pandas_index_columns(metadata) if not isinstance(col, dict) ] columns = ( list(columns) + list(set(index_columns) - set(columns)) ) table = self._dataset.to_table( columns=columns, filter=self._filter_expression, use_threads=use_threads ) # if use_pandas_metadata, restore the pandas metadata (which gets # lost if doing a specific `columns` selection in to_table) if use_pandas_metadata: if metadata and b"pandas" in metadata: new_metadata = table.schema.metadata or {} new_metadata.update({b"pandas": metadata[b"pandas"]}) table = table.replace_schema_metadata(new_metadata) return table
def _get_common_pandas_metadata(self): if not self._base_dir: return None metadata = None for name in ["_common_metadata", "_metadata"]: metadata_path = os.path.join(str(self._base_dir), name) finfo = self.filesystem.get_file_info(metadata_path) if finfo.is_file: pq_meta = read_metadata( metadata_path, filesystem=self.filesystem) metadata = pq_meta.metadata if metadata and b'pandas' in metadata: break return metadata
[docs] def read_pandas(self, **kwargs): """ Read dataset including pandas metadata, if any. Other arguments passed through to :func:`read`, see docstring for further details. Parameters ---------- **kwargs : optional Additional options for :func:`read` Examples -------- Generate an example parquet file: >>> import pyarrow as pa >>> import pandas as pd >>> df = pd.DataFrame({'year': [2020, 2022, 2021, 2022, 2019, 2021], ... 'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) >>> table = pa.Table.from_pandas(df) >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'table_V2.parquet') >>> dataset = pq.ParquetDataset('table_V2.parquet') Read the dataset with pandas metadata: >>> dataset.read_pandas(columns=["n_legs"]) pyarrow.Table n_legs: int64 ---- n_legs: [[2,2,4,4,5,100]] >>> dataset.read_pandas(columns=["n_legs"]).schema.pandas_metadata {'index_columns': [{'kind': 'range', 'name': None, 'start': 0, ...} """ return self.read(use_pandas_metadata=True, **kwargs)
@property def fragments(self): """ A list of the Dataset source fragments or pieces with absolute file paths. Examples -------- Generate an example dataset: >>> import pyarrow as pa >>> 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"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_fragments', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_fragments/') List the fragments: >>> dataset.fragments [<pyarrow.dataset.ParquetFileFragment path=dataset_v2_fragments/... """ return list(self._dataset.get_fragments()) @property def files(self): """ A list of absolute Parquet file paths in the Dataset source. Examples -------- Generate an example dataset: >>> import pyarrow as pa >>> 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"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_v2_files', ... partition_cols=['year']) >>> dataset = pq.ParquetDataset('dataset_v2_files/') List the files: >>> dataset.files ['dataset_v2_files/year=2019/...-0.parquet', ... """ return self._dataset.files @property def filesystem(self): """ The filesystem type of the Dataset source. """ return self._dataset.filesystem @property def partitioning(self): """ The partitioning of the Dataset source, if discovered. """ return self._dataset.partitioning
_read_table_docstring = """ {0} Parameters ---------- source : str, pyarrow.NativeFile, or file-like object If a string passed, can be a single file name or directory name. For file-like objects, only read a single file. Use pyarrow.BufferReader to read a file contained in a bytes or buffer-like object. columns : list If not None, only these columns will be read from the file. A column name may be a prefix of a nested field, e.g. 'a' will select 'a.b', 'a.c', and 'a.d.e'. If empty, no columns will be read. Note that the table will still have the correct num_rows set despite having no columns. use_threads : bool, default True Perform multi-threaded column reads. schema : Schema, optional Optionally provide the Schema for the parquet dataset, in which case it will not be inferred from the source. {1} filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. filters : pyarrow.compute.Expression or List[Tuple] or List[List[Tuple]], default None Rows which do not match the filter predicate will be removed from scanned data. Partition keys embedded in a nested directory structure will be exploited to avoid loading files at all if they contain no matching rows. Within-file level filtering and different partitioning schemes are supported. {3} use_legacy_dataset : bool, optional Deprecated and has no effect from PyArrow version 15.0.0. 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. pre_buffer : bool, default True Coalesce and issue file reads in parallel to improve performance on high-latency filesystems (e.g. S3). If True, Arrow will use a background I/O thread pool. If using a filesystem layer that itself performs readahead (e.g. fsspec's S3FS), disable readahead for best results. coerce_int96_timestamp_unit : str, default None Cast timestamps that are stored in INT96 format to a particular resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 timestamps will be inferred as timestamps in nanoseconds. decryption_properties : FileDecryptionProperties or None File-level decryption properties. The decryption properties can be created using ``CryptoFactory.file_decryption_properties()``. thrift_string_size_limit : int, default None If not None, override the maximum total string size allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. thrift_container_size_limit : int, default None If not None, override the maximum total size of containers allocated when decoding Thrift structures. The default limit should be sufficient for most Parquet files. page_checksum_verification : bool, default False If True, verify the checksum for each page read from the file. Returns ------- {2} {4} """ _read_table_example = """\ Examples -------- Generate an example PyArrow Table and write it to a partitioned dataset: >>> import pyarrow as pa >>> 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"]}) >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_name_2', ... partition_cols=['year']) Read the data: >>> pq.read_table('dataset_name_2').to_pandas() n_legs animal year 0 5 Brittle stars 2019 1 2 Flamingo 2020 2 4 Dog 2021 3 100 Centipede 2021 4 2 Parrot 2022 5 4 Horse 2022 Read only a subset of columns: >>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"]) pyarrow.Table n_legs: int64 animal: string ---- n_legs: [[5],[2],[4,100],[2,4]] animal: [["Brittle stars"],["Flamingo"],["Dog","Centipede"],["Parrot","Horse"]] Read a subset of columns and read one column as DictionaryArray: >>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"], ... read_dictionary=["animal"]) pyarrow.Table n_legs: int64 animal: dictionary<values=string, indices=int32, ordered=0> ---- n_legs: [[5],[2],[4,100],[2,4]] animal: [ -- dictionary: ["Brittle stars"] -- indices: [0], -- dictionary: ["Flamingo"] -- indices: [0], -- dictionary: ["Dog","Centipede"] -- indices: [0,1], -- dictionary: ["Parrot","Horse"] -- indices: [0,1]] Read the table with filter: >>> pq.read_table('dataset_name_2', columns=["n_legs", "animal"], ... filters=[('n_legs','<',4)]).to_pandas() n_legs animal 0 2 Flamingo 1 2 Parrot Read data from a single Parquet file: >>> pq.write_table(table, 'example.parquet') >>> pq.read_table('dataset_name_2').to_pandas() n_legs animal year 0 5 Brittle stars 2019 1 2 Flamingo 2020 2 4 Dog 2021 3 100 Centipede 2021 4 2 Parrot 2022 5 4 Horse 2022 """
[docs]def read_table(source, *, columns=None, use_threads=True, schema=None, use_pandas_metadata=False, read_dictionary=None, memory_map=False, buffer_size=0, partitioning="hive", filesystem=None, filters=None, use_legacy_dataset=None, ignore_prefixes=None, pre_buffer=True, coerce_int96_timestamp_unit=None, decryption_properties=None, thrift_string_size_limit=None, thrift_container_size_limit=None, page_checksum_verification=False): if use_legacy_dataset is not None: warnings.warn( "Passing 'use_legacy_dataset' is deprecated as of pyarrow 15.0.0 " "and will be removed in a future version.", FutureWarning, stacklevel=2) try: dataset = ParquetDataset( source, schema=schema, filesystem=filesystem, partitioning=partitioning, memory_map=memory_map, read_dictionary=read_dictionary, buffer_size=buffer_size, filters=filters, ignore_prefixes=ignore_prefixes, pre_buffer=pre_buffer, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, decryption_properties=decryption_properties, thrift_string_size_limit=thrift_string_size_limit, thrift_container_size_limit=thrift_container_size_limit, page_checksum_verification=page_checksum_verification, ) except ImportError: # fall back on ParquetFile for simple cases when pyarrow.dataset # module is not available if filters is not None: raise ValueError( "the 'filters' keyword is not supported when the " "pyarrow.dataset module is not available" ) if partitioning != "hive": raise ValueError( "the 'partitioning' keyword is not supported when the " "pyarrow.dataset module is not available" ) if schema is not None: raise ValueError( "the 'schema' argument is not supported when the " "pyarrow.dataset module is not available" ) filesystem, path = _resolve_filesystem_and_path(source, filesystem) if filesystem is not None: source = filesystem.open_input_file(path) # TODO test that source is not a directory or a list dataset = ParquetFile( source, read_dictionary=read_dictionary, memory_map=memory_map, buffer_size=buffer_size, pre_buffer=pre_buffer, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, decryption_properties=decryption_properties, thrift_string_size_limit=thrift_string_size_limit, thrift_container_size_limit=thrift_container_size_limit, page_checksum_verification=page_checksum_verification, ) return dataset.read(columns=columns, use_threads=use_threads, use_pandas_metadata=use_pandas_metadata)
read_table.__doc__ = _read_table_docstring.format( """Read a Table from Parquet format""", "\n".join(("""use_pandas_metadata : bool, default False If True and file has custom pandas schema metadata, ensure that index columns are also loaded.""", _read_docstring_common)), """pyarrow.Table Content of the file as a table (of columns)""", _DNF_filter_doc, _read_table_example)
[docs]def read_pandas(source, columns=None, **kwargs): return read_table( source, columns=columns, use_pandas_metadata=True, **kwargs )
read_pandas.__doc__ = _read_table_docstring.format( 'Read a Table from Parquet format, also reading DataFrame\n' 'index values if known in the file metadata', "\n".join((_read_docstring_common, """**kwargs additional options for :func:`read_table`""")), """pyarrow.Table Content of the file as a Table of Columns, including DataFrame indexes as columns""", _DNF_filter_doc, "")
[docs]def write_table(table, where, row_group_size=None, version='2.6', use_dictionary=True, compression='snappy', write_statistics=True, use_deprecated_int96_timestamps=None, coerce_timestamps=None, allow_truncated_timestamps=False, data_page_size=None, flavor=None, filesystem=None, compression_level=None, use_byte_stream_split=False, column_encoding=None, data_page_version='1.0', use_compliant_nested_type=True, encryption_properties=None, write_batch_size=None, dictionary_pagesize_limit=None, store_schema=True, write_page_index=False, write_page_checksum=False, sorting_columns=None, **kwargs): # Implementor's note: when adding keywords here / updating defaults, also # update it in write_to_dataset and _dataset_parquet.pyx ParquetFileWriteOptions row_group_size = kwargs.pop('chunk_size', row_group_size) use_int96 = use_deprecated_int96_timestamps try: with ParquetWriter( where, table.schema, filesystem=filesystem, version=version, flavor=flavor, use_dictionary=use_dictionary, write_statistics=write_statistics, coerce_timestamps=coerce_timestamps, data_page_size=data_page_size, allow_truncated_timestamps=allow_truncated_timestamps, compression=compression, use_deprecated_int96_timestamps=use_int96, compression_level=compression_level, use_byte_stream_split=use_byte_stream_split, column_encoding=column_encoding, data_page_version=data_page_version, use_compliant_nested_type=use_compliant_nested_type, encryption_properties=encryption_properties, write_batch_size=write_batch_size, dictionary_pagesize_limit=dictionary_pagesize_limit, store_schema=store_schema, write_page_index=write_page_index, write_page_checksum=write_page_checksum, sorting_columns=sorting_columns, **kwargs) as writer: writer.write_table(table, row_group_size=row_group_size) except Exception: if _is_path_like(where): try: os.remove(_stringify_path(where)) except os.error: pass raise
_write_table_example = """\ Generate an example PyArrow Table: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) and write the Table into Parquet file: >>> import pyarrow.parquet as pq >>> pq.write_table(table, 'example.parquet') Defining row group size for the Parquet file: >>> pq.write_table(table, 'example.parquet', row_group_size=3) Defining row group compression (default is Snappy): >>> pq.write_table(table, 'example.parquet', compression='none') Defining row group compression and encoding per-column: >>> pq.write_table(table, 'example.parquet', ... compression={'n_legs': 'snappy', 'animal': 'gzip'}, ... use_dictionary=['n_legs', 'animal']) Defining column encoding per-column: >>> pq.write_table(table, 'example.parquet', ... column_encoding={'animal':'PLAIN'}, ... use_dictionary=False) """ write_table.__doc__ = """ Write a Table to Parquet format. Parameters ---------- table : pyarrow.Table where : string or pyarrow.NativeFile row_group_size : int Maximum number of rows in each written row group. If None, the row group size will be the minimum of the Table size and 1024 * 1024. {} **kwargs : optional Additional options for ParquetWriter Examples -------- {} """.format(_parquet_writer_arg_docs, _write_table_example)
[docs]def write_to_dataset(table, root_path, partition_cols=None, filesystem=None, use_legacy_dataset=None, schema=None, partitioning=None, basename_template=None, use_threads=None, file_visitor=None, existing_data_behavior=None, **kwargs): """Wrapper around dataset.write_dataset for writing a Table to Parquet format by partitions. For each combination of partition columns and values, a subdirectories are created in the following manner: root_dir/ group1=value1 group2=value1 <uuid>.parquet group2=value2 <uuid>.parquet group1=valueN group2=value1 <uuid>.parquet group2=valueN <uuid>.parquet Parameters ---------- table : pyarrow.Table root_path : str, pathlib.Path The root directory of the dataset. partition_cols : list, Column names by which to partition the dataset. Columns are partitioned in the order they are given. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. use_legacy_dataset : bool, optional Deprecated and has no effect from PyArrow version 15.0.0. schema : Schema, optional This Schema of the dataset. partitioning : Partitioning or list[str], optional The partitioning scheme specified with the ``pyarrow.dataset.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. 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 "guid-{i}.parquet". 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. 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 : 'overwrite_or_ignore' | 'error' | \ 'delete_matching' Controls how the dataset will handle data that already exists in the destination. The default behaviour is 'overwrite_or_ignore'. '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. 'error' will raise an error if any data exists in the destination. '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. **kwargs : dict, Used as additional kwargs for :func:`pyarrow.dataset.write_dataset` function for matching kwargs, and remainder to :func:`pyarrow.dataset.ParquetFileFormat.make_write_options`. See the docstring of :func:`write_table` and :func:`pyarrow.dataset.write_dataset` for the available options. Using `metadata_collector` in kwargs allows one to collect the file metadata instances of dataset pieces. The file paths in the ColumnChunkMetaData will be set relative to `root_path`. Examples -------- Generate an example PyArrow Table: >>> import pyarrow as pa >>> 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"]}) and write it to a partitioned dataset: >>> import pyarrow.parquet as pq >>> pq.write_to_dataset(table, root_path='dataset_name_3', ... partition_cols=['year']) >>> pq.ParquetDataset('dataset_name_3').files ['dataset_name_3/year=2019/...-0.parquet', ... Write a single Parquet file into the root folder: >>> pq.write_to_dataset(table, root_path='dataset_name_4') >>> pq.ParquetDataset('dataset_name_4/').files ['dataset_name_4/...-0.parquet'] """ if use_legacy_dataset is not None: warnings.warn( "Passing 'use_legacy_dataset' is deprecated as of pyarrow 15.0.0 " "and will be removed in a future version.", FutureWarning, stacklevel=2) metadata_collector = kwargs.pop('metadata_collector', None) # Check for conflicting keywords msg_confl = ( "The '{1}' argument is not supported. " "Use only '{0}' instead." ) if partition_cols is not None and partitioning is not None: raise ValueError(msg_confl.format("partitioning", "partition_cols")) if metadata_collector is not None and file_visitor is not None: raise ValueError(msg_confl.format("file_visitor", "metadata_collector")) import pyarrow.dataset as ds # extract write_dataset specific options # reset assumed to go to make_write_options write_dataset_kwargs = dict() for key in inspect.signature(ds.write_dataset).parameters: if key in kwargs: write_dataset_kwargs[key] = kwargs.pop(key) write_dataset_kwargs['max_rows_per_group'] = kwargs.pop( 'row_group_size', kwargs.pop("chunk_size", None) ) if metadata_collector is not None: def file_visitor(written_file): metadata_collector.append(written_file.metadata) # map format arguments parquet_format = ds.ParquetFileFormat() write_options = parquet_format.make_write_options(**kwargs) # map old filesystems to new one if filesystem is not None: filesystem = _ensure_filesystem(filesystem) if partition_cols: part_schema = table.select(partition_cols).schema partitioning = ds.partitioning(part_schema, flavor="hive") if basename_template is None: basename_template = guid() + '-{i}.parquet' if existing_data_behavior is None: existing_data_behavior = 'overwrite_or_ignore' ds.write_dataset( table, root_path, filesystem=filesystem, format=parquet_format, file_options=write_options, schema=schema, partitioning=partitioning, use_threads=use_threads, file_visitor=file_visitor, basename_template=basename_template, existing_data_behavior=existing_data_behavior, **write_dataset_kwargs) return
[docs]def write_metadata(schema, where, metadata_collector=None, filesystem=None, **kwargs): """ Write metadata-only Parquet file from schema. This can be used with `write_to_dataset` to generate `_common_metadata` and `_metadata` sidecar files. Parameters ---------- schema : pyarrow.Schema where : string or pyarrow.NativeFile metadata_collector : list where to collect metadata information. filesystem : FileSystem, default None If nothing passed, will be inferred from `where` if path-like, else `where` is already a file-like object so no filesystem is needed. **kwargs : dict, Additional kwargs for ParquetWriter class. See docstring for `ParquetWriter` for more information. Examples -------- Generate example data: >>> import pyarrow as pa >>> table = pa.table({'n_legs': [2, 2, 4, 4, 5, 100], ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", ... "Brittle stars", "Centipede"]}) Write a dataset and collect metadata information. >>> metadata_collector = [] >>> import pyarrow.parquet as pq >>> pq.write_to_dataset( ... table, 'dataset_metadata', ... metadata_collector=metadata_collector) Write the `_common_metadata` parquet file without row groups statistics. >>> pq.write_metadata( ... table.schema, 'dataset_metadata/_common_metadata') Write the `_metadata` parquet file with row groups statistics. >>> pq.write_metadata( ... table.schema, 'dataset_metadata/_metadata', ... metadata_collector=metadata_collector) """ filesystem, where = _resolve_filesystem_and_path(where, filesystem) if hasattr(where, "seek"): # file-like cursor_position = where.tell() writer = ParquetWriter(where, schema, filesystem, **kwargs) writer.close() if metadata_collector is not None: # ParquetWriter doesn't expose the metadata until it's written. Write # it and read it again. metadata = read_metadata(where, filesystem=filesystem) if hasattr(where, "seek"): where.seek(cursor_position) # file-like, set cursor back. for m in metadata_collector: metadata.append_row_groups(m) if filesystem is not None: with filesystem.open_output_stream(where) as f: metadata.write_metadata_file(f) else: metadata.write_metadata_file(where)
[docs]def read_metadata(where, memory_map=False, decryption_properties=None, filesystem=None): """ Read FileMetaData from footer of a single Parquet file. Parameters ---------- where : str (file path) or file-like object memory_map : bool, default False Create memory map when the source is a file path. decryption_properties : FileDecryptionProperties, default None Decryption properties for reading encrypted Parquet files. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. Returns ------- metadata : FileMetaData The metadata of the Parquet file Examples -------- >>> import pyarrow as pa >>> import pyarrow.parquet as pq >>> table = pa.table({'n_legs': [4, 5, 100], ... 'animal': ["Dog", "Brittle stars", "Centipede"]}) >>> pq.write_table(table, 'example.parquet') >>> pq.read_metadata('example.parquet') <pyarrow._parquet.FileMetaData object at ...> created_by: parquet-cpp-arrow version ... num_columns: 2 num_rows: 3 num_row_groups: 1 format_version: 2.6 serialized_size: ... """ filesystem, where = _resolve_filesystem_and_path(where, filesystem) file_ctx = nullcontext() if filesystem is not None: file_ctx = where = filesystem.open_input_file(where) with file_ctx: file = ParquetFile(where, memory_map=memory_map, decryption_properties=decryption_properties) return file.metadata
[docs]def read_schema(where, memory_map=False, decryption_properties=None, filesystem=None): """ Read effective Arrow schema from Parquet file metadata. Parameters ---------- where : str (file path) or file-like object memory_map : bool, default False Create memory map when the source is a file path. decryption_properties : FileDecryptionProperties, default None Decryption properties for reading encrypted Parquet files. filesystem : FileSystem, default None If nothing passed, will be inferred based on path. Path will try to be found in the local on-disk filesystem otherwise it will be parsed as an URI to determine the filesystem. Returns ------- schema : pyarrow.Schema The schema of the Parquet file Examples -------- >>> import pyarrow as pa >>> import pyarrow.parquet as pq >>> table = pa.table({'n_legs': [4, 5, 100], ... 'animal': ["Dog", "Brittle stars", "Centipede"]}) >>> pq.write_table(table, 'example.parquet') >>> pq.read_schema('example.parquet') n_legs: int64 animal: string """ filesystem, where = _resolve_filesystem_and_path(where, filesystem) file_ctx = nullcontext() if filesystem is not None: file_ctx = where = filesystem.open_input_file(where) with file_ctx: file = ParquetFile( where, memory_map=memory_map, decryption_properties=decryption_properties) return file.schema.to_arrow_schema()
__all__ = ( "ColumnChunkMetaData", "ColumnSchema", "FileDecryptionProperties", "FileEncryptionProperties", "FileMetaData", "ParquetDataset", "ParquetFile", "ParquetLogicalType", "ParquetReader", "ParquetSchema", "ParquetWriter", "RowGroupMetaData", "SortingColumn", "Statistics", "read_metadata", "read_pandas", "read_schema", "read_table", "write_metadata", "write_table", "write_to_dataset", "_filters_to_expression", "filters_to_expression", )