# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# 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`. For ``version='1.0'`` and ``version='2.4'``,
nanoseconds are cast to microseconds ('us'), while for
``version='2.6'`` (the default), 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 for integer, floating-point
and fixed-size binary data types (including decimals); it should be
combined with a compression codec so as to achieve size reduction.
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.
store_decimal_as_integer : bool, default False
Allow decimals with 1 <= precision <= 18 to be stored as integers.
In Parquet, DECIMAL can be stored in any of the following physical types:
- int32: for 1 <= precision <= 9.
- int64: for 10 <= precision <= 18.
- fixed_len_byte_array: precision is limited by the array size.
Length n can store <= floor(log_10(2^(8*n - 1) - 1)) base-10 digits.
- binary: precision is unlimited. The minimum number of bytes to store the
unscaled value is used.
By default, this is DISABLED and all decimal types annotate fixed_len_byte_array.
When enabled, the writer will use the following physical types to store decimals:
- int32: for 1 <= precision <= 9.
- int64: for 10 <= precision <= 18.
- fixed_len_byte_array: for precision > 18.
As a consequence, decimal columns stored in integer types are more compact.
"""
_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,
store_decimal_as_integer=False,
**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,
store_decimal_as_integer=store_decimal_as_integer,
**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,
store_decimal_as_integer=False,
**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,
store_decimal_as_integer=store_decimal_as_integer,
**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 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",
)
