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# 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 concurrent import futures
from functools import partial, reduce
import json
from collections.abc import Collection
import numpy as np
import os
import re
import operator
import urllib.parse
import pyarrow as pa
import pyarrow.lib as lib
import pyarrow._parquet as _parquet
from pyarrow._parquet import (ParquetReader, Statistics, # noqa
FileMetaData, RowGroupMetaData,
ColumnChunkMetaData,
ParquetSchema, ColumnSchema)
from pyarrow.fs import (LocalFileSystem, FileSystem,
_resolve_filesystem_and_path, _ensure_filesystem)
from pyarrow import filesystem as legacyfs
from pyarrow.util import guid, _is_path_like, _stringify_path
_URI_STRIP_SCHEMES = ('hdfs',)
def _parse_uri(path):
path = _stringify_path(path)
parsed_uri = urllib.parse.urlparse(path)
if parsed_uri.scheme in _URI_STRIP_SCHEMES:
return parsed_uri.path
else:
# ARROW-4073: On Windows returning the path with the scheme
# stripped removes the drive letter, if any
return path
def _get_filesystem_and_path(passed_filesystem, path):
if passed_filesystem is None:
return legacyfs.resolve_filesystem_and_path(path, passed_filesystem)
else:
passed_filesystem = legacyfs._ensure_filesystem(passed_filesystem)
parsed_path = _parse_uri(path)
return passed_filesystem, parsed_path
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 in 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:
.. 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.
See _DNF_filter_doc above for more details.
"""
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)
# ----------------------------------------------------------------------
# Reading a single Parquet file
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.
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.
"""
def __init__(self, source, metadata=None, common_metadata=None,
read_dictionary=None, memory_map=False, buffer_size=0,
pre_buffer=False):
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)
self.common_metadata = common_metadata
self._nested_paths_by_prefix = self._build_nested_paths()
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 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
"""
return self.reader.schema_arrow
@property
def num_row_groups(self):
return self.reader.num_row_groups
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
----------
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)
"""
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)
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).
"""
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)
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.
Returns
-------
iterator of pyarrow.RecordBatch
Contents of each batch as a record batch
"""
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
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).
"""
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)
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 : number of rows in file
"""
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", "2.0"}, default "1.0"
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 format version 2.0.0 and after. Note that files written with
version='2.0' may not be readable in all Parquet implementations, so
version='1.0' is likely the choice that maximizes file compatibility. Some
features, such as lossless storage of nanosecond timestamps as INT64
physical storage, are only available with version='2.0'. The Parquet 2.0.0
format version also introduced a new serialized data page format; this can
be enabled separately using the data_page_version option.
use_dictionary : bool or list
Specify if we should use dictionary encoding in general 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 a particular resolution. The defaults depends on `version`.
For ``version='1.0'`` (the default), nanoseconds will be cast to
microseconds ('us'), and seconds to milliseconds ('ms') by default. For
``version='2.0'``, the original resolution is preserved and no casting
is done by default. The casting might result in loss of data, in which
case ``allow_truncated_timestamps=True`` can be used to suppress the
raised exception.
Valid values: {None, 'ms', 'us'}
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.
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.
compression : str or dict
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
Specify if we should write statistics in general (default is True) or only
for some columns.
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.
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 False
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 ``False``.
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;
}
}
"""
class ParquetWriter:
__doc__ = """
Class for incrementally building a Parquet file for Arrow tables.
Parameters
----------
where : path or file-like object
schema : arrow Schema
{}
**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.
""".format(_parquet_writer_arg_docs)
def __init__(self, where, schema, filesystem=None,
flavor=None,
version='1.0',
use_dictionary=True,
compression='snappy',
write_statistics=True,
use_deprecated_int96_timestamps=None,
compression_level=None,
use_byte_stream_split=False,
writer_engine_version=None,
data_page_version='1.0',
use_compliant_nested_type=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, allow_legacy_filesystem=True
)
if filesystem is not None:
if isinstance(filesystem, legacyfs.FileSystem):
# legacy filesystem (eg custom subclass)
# TODO deprecate
sink = self.file_handle = filesystem.open(path, 'wb')
else:
# 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,
writer_engine_version=engine_version,
data_page_version=data_page_version,
use_compliant_nested_type=use_compliant_nested_type,
**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
def write_table(self, table, row_group_size=None):
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)
def close(self):
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'])
# ----------------------------------------------------------------------
# Metadata container providing instructions about reading a single Parquet
# file, possibly part of a partitioned dataset
class ParquetDatasetPiece:
"""
A single chunk of a potentially larger Parquet dataset to read.
The arguments will indicate to read either a single row group or all row
groups, and whether to add partition keys to the resulting pyarrow.Table.
Parameters
----------
path : str or pathlib.Path
Path to file in the file system where this piece is located.
open_file_func : callable
Function to use for obtaining file handle to dataset piece.
partition_keys : list of tuples
Two-element tuples of ``(column name, ordinal index)``.
row_group : int, default None
Row group to load. By default, reads all row groups.
"""
def __init__(self, path, open_file_func=partial(open, mode='rb'),
file_options=None, row_group=None, partition_keys=None):
self.path = _stringify_path(path)
self.open_file_func = open_file_func
self.row_group = row_group
self.partition_keys = partition_keys or []
self.file_options = file_options or {}
def __eq__(self, other):
if not isinstance(other, ParquetDatasetPiece):
return False
return (self.path == other.path and
self.row_group == other.row_group and
self.partition_keys == other.partition_keys)
def __repr__(self):
return ('{}({!r}, row_group={!r}, partition_keys={!r})'
.format(type(self).__name__, self.path,
self.row_group,
self.partition_keys))
def __str__(self):
result = ''
if len(self.partition_keys) > 0:
partition_str = ', '.join('{}={}'.format(name, index)
for name, index in self.partition_keys)
result += 'partition[{}] '.format(partition_str)
result += self.path
if self.row_group is not None:
result += ' | row_group={}'.format(self.row_group)
return result
def get_metadata(self):
"""
Return the file's metadata.
Returns
-------
metadata : FileMetaData
"""
f = self.open()
return f.metadata
def open(self):
"""
Return instance of ParquetFile.
"""
reader = self.open_file_func(self.path)
if not isinstance(reader, ParquetFile):
reader = ParquetFile(reader, **self.file_options)
return reader
def read(self, columns=None, use_threads=True, partitions=None,
file=None, use_pandas_metadata=False):
"""
Read this piece as a pyarrow.Table.
Parameters
----------
columns : list of column names, default None
use_threads : bool, default True
Perform multi-threaded column reads.
partitions : ParquetPartitions, default None
file : file-like object
Passed to ParquetFile.
Returns
-------
table : pyarrow.Table
"""
if self.open_file_func is not None:
reader = self.open()
elif file is not None:
reader = ParquetFile(file, **self.file_options)
else:
# try to read the local path
reader = ParquetFile(self.path, **self.file_options)
options = dict(columns=columns,
use_threads=use_threads,
use_pandas_metadata=use_pandas_metadata)
if self.row_group is not None:
table = reader.read_row_group(self.row_group, **options)
else:
table = reader.read(**options)
if len(self.partition_keys) > 0:
if partitions is None:
raise ValueError('Must pass partition sets')
# Here, the index is the categorical code of the partition where
# this piece is located. Suppose we had
#
# /foo=a/0.parq
# /foo=b/0.parq
# /foo=c/0.parq
#
# Then we assign a=0, b=1, c=2. And the resulting Table pieces will
# have a DictionaryArray column named foo having the constant index
# value as indicated. The distinct categories of the partition have
# been computed in the ParquetManifest
for i, (name, index) in enumerate(self.partition_keys):
# The partition code is the same for all values in this piece
indices = np.full(len(table), index, dtype='i4')
# This is set of all partition values, computed as part of the
# manifest, so ['a', 'b', 'c'] as in our example above.
dictionary = partitions.levels[i].dictionary
arr = pa.DictionaryArray.from_arrays(indices, dictionary)
table = table.append_column(name, arr)
return table
class PartitionSet:
"""
A data structure for cataloguing the observed Parquet partitions at a
particular level. So if we have
/foo=a/bar=0
/foo=a/bar=1
/foo=a/bar=2
/foo=b/bar=0
/foo=b/bar=1
/foo=b/bar=2
Then we have two partition sets, one for foo, another for bar. As we visit
levels of the partition hierarchy, a PartitionSet tracks the distinct
values and assigns categorical codes to use when reading the pieces
"""
def __init__(self, name, keys=None):
self.name = name
self.keys = keys or []
self.key_indices = {k: i for i, k in enumerate(self.keys)}
self._dictionary = None
def get_index(self, key):
"""
Get the index of the partition value if it is known, otherwise assign
one
"""
if key in self.key_indices:
return self.key_indices[key]
else:
index = len(self.key_indices)
self.keys.append(key)
self.key_indices[key] = index
return index
@property
def dictionary(self):
if self._dictionary is not None:
return self._dictionary
if len(self.keys) == 0:
raise ValueError('No known partition keys')
# Only integer and string partition types are supported right now
try:
integer_keys = [int(x) for x in self.keys]
dictionary = lib.array(integer_keys)
except ValueError:
dictionary = lib.array(self.keys)
self._dictionary = dictionary
return dictionary
@property
def is_sorted(self):
return list(self.keys) == sorted(self.keys)
class ParquetPartitions:
def __init__(self):
self.levels = []
self.partition_names = set()
def __len__(self):
return len(self.levels)
def __getitem__(self, i):
return self.levels[i]
def equals(self, other):
if not isinstance(other, ParquetPartitions):
raise TypeError('`other` must be an instance of ParquetPartitions')
return (self.levels == other.levels and
self.partition_names == other.partition_names)
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def get_index(self, level, name, key):
"""
Record a partition value at a particular level, returning the distinct
code for that value at that level.
Example:
partitions.get_index(1, 'foo', 'a') returns 0
partitions.get_index(1, 'foo', 'b') returns 1
partitions.get_index(1, 'foo', 'c') returns 2
partitions.get_index(1, 'foo', 'a') returns 0
Parameters
----------
level : int
The nesting level of the partition we are observing
name : str
The partition name
key : str or int
The partition value
"""
if level == len(self.levels):
if name in self.partition_names:
raise ValueError('{} was the name of the partition in '
'another level'.format(name))
part_set = PartitionSet(name)
self.levels.append(part_set)
self.partition_names.add(name)
return self.levels[level].get_index(key)
def filter_accepts_partition(self, part_key, filter, level):
p_column, p_value_index = part_key
f_column, op, f_value = filter
if p_column != f_column:
return True
f_type = type(f_value)
if op in {'in', 'not in'}:
if not isinstance(f_value, Collection):
raise TypeError(
"'%s' object is not a collection", f_type.__name__)
if not f_value:
raise ValueError("Cannot use empty collection as filter value")
if len({type(item) for item in f_value}) != 1:
raise ValueError("All elements of the collection '%s' must be"
" of same type", f_value)
f_type = type(next(iter(f_value)))
elif not isinstance(f_value, str) and isinstance(f_value, Collection):
raise ValueError(
"Op '%s' not supported with a collection value", op)
p_value = f_type(self.levels[level]
.dictionary[p_value_index].as_py())
if op == "=" or op == "==":
return p_value == f_value
elif op == "!=":
return p_value != f_value
elif op == '<':
return p_value < f_value
elif op == '>':
return p_value > f_value
elif op == '<=':
return p_value <= f_value
elif op == '>=':
return p_value >= f_value
elif op == 'in':
return p_value in f_value
elif op == 'not in':
return p_value not in f_value
else:
raise ValueError("'%s' is not a valid operator in predicates.",
filter[1])
class ParquetManifest:
def __init__(self, dirpath, open_file_func=None, filesystem=None,
pathsep='/', partition_scheme='hive', metadata_nthreads=1):
filesystem, dirpath = _get_filesystem_and_path(filesystem, dirpath)
self.filesystem = filesystem
self.open_file_func = open_file_func
self.pathsep = pathsep
self.dirpath = _stringify_path(dirpath)
self.partition_scheme = partition_scheme
self.partitions = ParquetPartitions()
self.pieces = []
self._metadata_nthreads = metadata_nthreads
self._thread_pool = futures.ThreadPoolExecutor(
max_workers=metadata_nthreads)
self.common_metadata_path = None
self.metadata_path = None
self._visit_level(0, self.dirpath, [])
# Due to concurrency, pieces will potentially by out of order if the
# dataset is partitioned so we sort them to yield stable results
self.pieces.sort(key=lambda piece: piece.path)
if self.common_metadata_path is None:
# _common_metadata is a subset of _metadata
self.common_metadata_path = self.metadata_path
self._thread_pool.shutdown()
def _visit_level(self, level, base_path, part_keys):
fs = self.filesystem
_, directories, files = next(fs.walk(base_path))
filtered_files = []
for path in files:
full_path = self.pathsep.join((base_path, path))
if path.endswith('_common_metadata'):
self.common_metadata_path = full_path
elif path.endswith('_metadata'):
self.metadata_path = full_path
elif self._should_silently_exclude(path):
continue
else:
filtered_files.append(full_path)
# ARROW-1079: Filter out "private" directories starting with underscore
filtered_directories = [self.pathsep.join((base_path, x))
for x in directories
if not _is_private_directory(x)]
filtered_files.sort()
filtered_directories.sort()
if len(filtered_files) > 0 and len(filtered_directories) > 0:
raise ValueError('Found files in an intermediate '
'directory: {}'.format(base_path))
elif len(filtered_directories) > 0:
self._visit_directories(level, filtered_directories, part_keys)
else:
self._push_pieces(filtered_files, part_keys)
def _should_silently_exclude(self, file_name):
return (file_name.endswith('.crc') or # Checksums
file_name.endswith('_$folder$') or # HDFS directories in S3
file_name.startswith('.') or # Hidden files starting with .
file_name.startswith('_') or # Hidden files starting with _
file_name in EXCLUDED_PARQUET_PATHS)
def _visit_directories(self, level, directories, part_keys):
futures_list = []
for path in directories:
head, tail = _path_split(path, self.pathsep)
name, key = _parse_hive_partition(tail)
index = self.partitions.get_index(level, name, key)
dir_part_keys = part_keys + [(name, index)]
# If you have less threads than levels, the wait call will block
# indefinitely due to multiple waits within a thread.
if level < self._metadata_nthreads:
future = self._thread_pool.submit(self._visit_level,
level + 1,
path,
dir_part_keys)
futures_list.append(future)
else:
self._visit_level(level + 1, path, dir_part_keys)
if futures_list:
futures.wait(futures_list)
def _parse_partition(self, dirname):
if self.partition_scheme == 'hive':
return _parse_hive_partition(dirname)
else:
raise NotImplementedError('partition schema: {}'
.format(self.partition_scheme))
def _push_pieces(self, files, part_keys):
self.pieces.extend([
ParquetDatasetPiece(path, partition_keys=part_keys,
open_file_func=self.open_file_func)
for path in files
])
def _parse_hive_partition(value):
if '=' not in value:
raise ValueError('Directory name did not appear to be a '
'partition: {}'.format(value))
return value.split('=', 1)
def _is_private_directory(x):
_, tail = os.path.split(x)
return (tail.startswith('_') or tail.startswith('.')) and '=' not in tail
def _path_split(path, sep):
i = path.rfind(sep) + 1
head, tail = path[:i], path[i:]
head = head.rstrip(sep)
return head, tail
EXCLUDED_PARQUET_PATHS = {'_SUCCESS'}
class _ParquetDatasetMetadata:
__slots__ = ('fs', 'memory_map', 'read_dictionary', 'common_metadata',
'buffer_size')
def _open_dataset_file(dataset, path, meta=None):
if (dataset.fs is not None and
not isinstance(dataset.fs, legacyfs.LocalFileSystem)):
path = dataset.fs.open(path, mode='rb')
return ParquetFile(
path,
metadata=meta,
memory_map=dataset.memory_map,
read_dictionary=dataset.read_dictionary,
common_metadata=dataset.common_metadata,
buffer_size=dataset.buffer_size
)
_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 : 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."""
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, paths assumed to be found in the local on-disk
filesystem.
metadata : pyarrow.parquet.FileMetaData
Use metadata obtained elsewhere to validate file schemas.
schema : pyarrow.parquet.Schema
Use schema obtained elsewhere to validate file schemas. Alternative to
metadata parameter.
split_row_groups : bool, default False
Divide files into pieces for each row group in the file.
validate_schema : bool, default True
Check that individual file schemas are all the same / compatible.
filters : List[Tuple] or List[List[Tuple]] or None (default)
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.
If `use_legacy_dataset` is True, filters can only reference partition
keys and only a hive-style directory structure is supported. When
setting `use_legacy_dataset` to False, also within-file level filtering
and different partitioning schemes are supported.
{1}
metadata_nthreads: int, default 1
How many threads to allow the thread pool which is used to read the
dataset metadata. Increasing this is helpful to read partitioned
datasets.
{0}
use_legacy_dataset : bool, default True
Set to False to enable the new code path (experimental, using the
new Arrow Dataset API). Among other things, this allows to pass
`filters` for all columns and not only the partition keys, enables
different partitioning schemes, etc.
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. This option is only supported for
use_legacy_dataset=False. If using a filesystem layer that itself
performs readahead (e.g. fsspec's S3FS), disable readahead for best
results.
""".format(_read_docstring_common, _DNF_filter_doc)
def __new__(cls, path_or_paths=None, filesystem=None, schema=None,
metadata=None, split_row_groups=False, validate_schema=True,
filters=None, metadata_nthreads=1, read_dictionary=None,
memory_map=False, buffer_size=0, partitioning="hive",
use_legacy_dataset=None, pre_buffer=True):
if use_legacy_dataset is None:
# if a new filesystem is passed -> default to new implementation
if isinstance(filesystem, FileSystem):
use_legacy_dataset = False
# otherwise the default is still True
else:
use_legacy_dataset = True
if not use_legacy_dataset:
return _ParquetDatasetV2(path_or_paths, filesystem=filesystem,
filters=filters,
partitioning=partitioning,
read_dictionary=read_dictionary,
memory_map=memory_map,
buffer_size=buffer_size,
pre_buffer=pre_buffer,
# unsupported keywords
schema=schema, metadata=metadata,
split_row_groups=split_row_groups,
validate_schema=validate_schema,
metadata_nthreads=metadata_nthreads)
self = object.__new__(cls)
return self
def __init__(self, path_or_paths, filesystem=None, schema=None,
metadata=None, split_row_groups=False, validate_schema=True,
filters=None, metadata_nthreads=1, read_dictionary=None,
memory_map=False, buffer_size=0, partitioning="hive",
use_legacy_dataset=True, pre_buffer=True):
if partitioning != "hive":
raise ValueError(
'Only "hive" for hive-like partitioning is supported when '
'using use_legacy_dataset=True')
self._metadata = _ParquetDatasetMetadata()
a_path = path_or_paths
if isinstance(a_path, list):
a_path = a_path[0]
self._metadata.fs, _ = _get_filesystem_and_path(filesystem, a_path)
if isinstance(path_or_paths, list):
self.paths = [_parse_uri(path) for path in path_or_paths]
else:
self.paths = _parse_uri(path_or_paths)
self._metadata.read_dictionary = read_dictionary
self._metadata.memory_map = memory_map
self._metadata.buffer_size = buffer_size
(self.pieces,
self.partitions,
self.common_metadata_path,
self.metadata_path) = _make_manifest(
path_or_paths, self.fs, metadata_nthreads=metadata_nthreads,
open_file_func=partial(_open_dataset_file, self._metadata)
)
if self.common_metadata_path is not None:
with self.fs.open(self.common_metadata_path) as f:
self._metadata.common_metadata = read_metadata(
f,
memory_map=memory_map
)
else:
self._metadata.common_metadata = None
if metadata is None and self.metadata_path is not None:
with self.fs.open(self.metadata_path) as f:
self.metadata = read_metadata(f, memory_map=memory_map)
else:
self.metadata = metadata
self.schema = schema
self.split_row_groups = split_row_groups
if split_row_groups:
raise NotImplementedError("split_row_groups not yet implemented")
if filters is not None:
filters = _check_filters(filters)
self._filter(filters)
if validate_schema:
self.validate_schemas()
def equals(self, other):
if not isinstance(other, ParquetDataset):
raise TypeError('`other` must be an instance of ParquetDataset')
if self.fs.__class__ != other.fs.__class__:
return False
for prop in ('paths', 'memory_map', 'pieces', 'partitions',
'common_metadata_path', 'metadata_path',
'common_metadata', 'metadata', 'schema',
'buffer_size', 'split_row_groups'):
if getattr(self, prop) != getattr(other, prop):
return False
return True
def __eq__(self, other):
try:
return self.equals(other)
except TypeError:
return NotImplemented
def validate_schemas(self):
if self.metadata is None and self.schema is None:
if self.common_metadata is not None:
self.schema = self.common_metadata.schema
else:
self.schema = self.pieces[0].get_metadata().schema
elif self.schema is None:
self.schema = self.metadata.schema
# Verify schemas are all compatible
dataset_schema = self.schema.to_arrow_schema()
# Exclude the partition columns from the schema, they are provided
# by the path, not the DatasetPiece
if self.partitions is not None:
for partition_name in self.partitions.partition_names:
if dataset_schema.get_field_index(partition_name) != -1:
field_idx = dataset_schema.get_field_index(partition_name)
dataset_schema = dataset_schema.remove(field_idx)
for piece in self.pieces:
file_metadata = piece.get_metadata()
file_schema = file_metadata.schema.to_arrow_schema()
if not dataset_schema.equals(file_schema, check_metadata=False):
raise ValueError('Schema in {!s} was different. \n'
'{!s}\n\nvs\n\n{!s}'
.format(piece, file_schema,
dataset_schema))
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 file.
use_threads : bool, default True
Perform multi-threaded column reads
use_pandas_metadata : bool, default False
Passed through to each dataset piece.
Returns
-------
pyarrow.Table
Content of the file as a table (of columns).
"""
tables = []
for piece in self.pieces:
table = piece.read(columns=columns, use_threads=use_threads,
partitions=self.partitions,
use_pandas_metadata=use_pandas_metadata)
tables.append(table)
all_data = lib.concat_tables(tables)
if use_pandas_metadata:
# We need to ensure that this metadata is set in the Table's schema
# so that Table.to_pandas will construct pandas.DataFrame with the
# right index
common_metadata = self._get_common_pandas_metadata()
current_metadata = all_data.schema.metadata or {}
if common_metadata and b'pandas' not in current_metadata:
all_data = all_data.replace_schema_metadata({
b'pandas': common_metadata})
return all_data
def read_pandas(self, **kwargs):
"""
Read dataset including pandas metadata, if any. Other arguments passed
through to ParquetDataset.read, see docstring for further details.
Returns
-------
pyarrow.Table
Content of the file as a table (of columns).
"""
return self.read(use_pandas_metadata=True, **kwargs)
def _get_common_pandas_metadata(self):
if self.common_metadata is None:
return None
keyvalues = self.common_metadata.metadata
return keyvalues.get(b'pandas', None)
def _filter(self, filters):
accepts_filter = self.partitions.filter_accepts_partition
def one_filter_accepts(piece, filter):
return all(accepts_filter(part_key, filter, level)
for level, part_key in enumerate(piece.partition_keys))
def all_filters_accept(piece):
return any(all(one_filter_accepts(piece, f) for f in conjunction)
for conjunction in filters)
self.pieces = [p for p in self.pieces if all_filters_accept(p)]
fs = property(operator.attrgetter('_metadata.fs'))
memory_map = property(operator.attrgetter('_metadata.memory_map'))
read_dictionary = property(
operator.attrgetter('_metadata.read_dictionary')
)
common_metadata = property(
operator.attrgetter('_metadata.common_metadata')
)
buffer_size = property(operator.attrgetter('_metadata.buffer_size'))
def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1,
open_file_func=None):
partitions = None
common_metadata_path = None
metadata_path = None
if isinstance(path_or_paths, list) and len(path_or_paths) == 1:
# Dask passes a directory as a list of length 1
path_or_paths = path_or_paths[0]
if _is_path_like(path_or_paths) and fs.isdir(path_or_paths):
manifest = ParquetManifest(path_or_paths, filesystem=fs,
open_file_func=open_file_func,
pathsep=getattr(fs, "pathsep", "/"),
metadata_nthreads=metadata_nthreads)
common_metadata_path = manifest.common_metadata_path
metadata_path = manifest.metadata_path
pieces = manifest.pieces
partitions = manifest.partitions
else:
if not isinstance(path_or_paths, list):
path_or_paths = [path_or_paths]
# List of paths
if len(path_or_paths) == 0:
raise ValueError('Must pass at least one file path')
pieces = []
for path in path_or_paths:
if not fs.isfile(path):
raise OSError('Passed non-file path: {}'
.format(path))
piece = ParquetDatasetPiece(path, open_file_func=open_file_func)
pieces.append(piece)
return pieces, partitions, common_metadata_path, metadata_path
def _is_local_file_system(fs):
return isinstance(fs, LocalFileSystem) or isinstance(
fs, legacyfs.LocalFileSystem
)
class _ParquetDatasetV2:
"""
ParquetDataset shim using the Dataset API under the hood.
"""
def __init__(self, path_or_paths, filesystem=None, filters=None,
partitioning="hive", read_dictionary=None, buffer_size=None,
memory_map=False, ignore_prefixes=None, pre_buffer=True,
**kwargs):
import pyarrow.dataset as ds
# Raise error for not supported keywords
for keyword, default in [
("schema", None), ("metadata", None),
("split_row_groups", False), ("validate_schema", True),
("metadata_nthreads", 1)]:
if keyword in kwargs and kwargs[keyword] is not default:
raise ValueError(
"Keyword '{0}' is not yet supported with the new "
"Dataset API".format(keyword))
# map format arguments
read_options = {"pre_buffer": pre_buffer}
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)
# map filters to Expressions
self._filters = filters
self._filter_expression = filters and _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 _is_local_file_system(filesystem)
):
raise TypeError(
"Path-like objects with __fspath__ must only be used with "
f"local file systems, not {type(filesystem)}"
)
# check for single fragment dataset
single_file = None
if isinstance(path_or_paths, list):
if len(path_or_paths) == 1:
single_file = path_or_paths[0]
else:
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)
if filesystem.get_file_info(path_or_paths).is_file:
single_file = path_or_paths
else:
single_file = path_or_paths
if single_file is not None:
self._enable_parallel_column_conversion = True
read_options.update(enable_parallel_column_conversion=True)
parquet_format = ds.ParquetFileFormat(**read_options)
fragment = parquet_format.make_fragment(single_file, filesystem)
self._dataset = ds.FileSystemDataset(
[fragment], schema=fragment.physical_schema,
format=parquet_format,
filesystem=fragment.filesystem
)
return
else:
self._enable_parallel_column_conversion = False
parquet_format = ds.ParquetFileFormat(**read_options)
# 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,
format=parquet_format,
partitioning=partitioning,
ignore_prefixes=ignore_prefixes)
@property
def schema(self):
return self._dataset.schema
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 (in contrast to when setting
``use_legacy_dataset=True``).
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).
"""
# 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
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))
)
if self._enable_parallel_column_conversion:
if use_threads:
# Allow per-column parallelism; would otherwise cause
# contention in the presence of per-file parallelism.
use_threads = False
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 read_pandas(self, **kwargs):
"""
Read dataset including pandas metadata, if any. Other arguments passed
through to ParquetDataset.read, see docstring for further details.
"""
return self.read(use_pandas_metadata=True, **kwargs)
@property
def pieces(self):
# TODO raise deprecation warning
return list(self._dataset.get_fragments())
_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'.
use_threads : bool, default True
Perform multi-threaded column reads.
metadata : FileMetaData
If separately computed
{1}
use_legacy_dataset : bool, default False
By default, `read_table` uses the new Arrow Datasets API since
pyarrow 1.0.0. Among other things, this allows to pass `filters`
for all columns and not only the partition keys, enables
different partitioning schemes, etc.
Set to True to use the legacy behaviour.
ignore_prefixes : list, optional
Files matching any of these prefixes will be ignored by the
discovery process if use_legacy_dataset=False.
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.
filesystem : FileSystem, default None
If nothing passed, paths assumed to be found in the local on-disk
filesystem.
filters : List[Tuple] or List[List[Tuple]] or None (default)
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.
If `use_legacy_dataset` is True, filters can only reference partition
keys and only a hive-style directory structure is supported. When
setting `use_legacy_dataset` to False, also within-file level filtering
and different partitioning schemes are supported.
{3}
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. This option is only supported for
use_legacy_dataset=False. If using a filesystem layer that itself
performs readahead (e.g. fsspec's S3FS), disable readahead for best
results.
Returns
-------
{2}
"""
def read_table(source, columns=None, use_threads=True, metadata=None,
use_pandas_metadata=False, memory_map=False,
read_dictionary=None, filesystem=None, filters=None,
buffer_size=0, partitioning="hive", use_legacy_dataset=False,
ignore_prefixes=None, pre_buffer=True):
if not use_legacy_dataset:
if metadata is not None:
raise ValueError(
"The 'metadata' keyword is no longer supported with the new "
"datasets-based implementation. Specify "
"'use_legacy_dataset=True' to temporarily recover the old "
"behaviour."
)
try:
dataset = _ParquetDatasetV2(
source,
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,
)
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"
)
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, metadata=metadata, read_dictionary=read_dictionary,
memory_map=memory_map, buffer_size=buffer_size,
pre_buffer=pre_buffer)
return dataset.read(columns=columns, use_threads=use_threads,
use_pandas_metadata=use_pandas_metadata)
if ignore_prefixes is not None:
raise ValueError(
"The 'ignore_prefixes' keyword is only supported when "
"use_legacy_dataset=False")
if _is_path_like(source):
pf = ParquetDataset(source, metadata=metadata, memory_map=memory_map,
read_dictionary=read_dictionary,
buffer_size=buffer_size,
filesystem=filesystem, filters=filters,
partitioning=partitioning)
else:
pf = ParquetFile(source, metadata=metadata,
read_dictionary=read_dictionary,
memory_map=memory_map,
buffer_size=buffer_size)
return pf.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
Note: starting with pyarrow 1.0, the default for `use_legacy_dataset` is
switched to False.""",
"\n".join((_read_docstring_common,
"""use_pandas_metadata : bool, default False
If True and file has custom pandas schema metadata, ensure that
index columns are also loaded.""")),
"""pyarrow.Table
Content of the file as a table (of columns)""",
_DNF_filter_doc)
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',
_read_docstring_common,
"""pyarrow.Table
Content of the file as a Table of Columns, including DataFrame
indexes as columns""",
_DNF_filter_doc)
def write_table(table, where, row_group_size=None, version='1.0',
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,
data_page_version='1.0',
use_compliant_nested_type=False,
**kwargs):
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,
data_page_version=data_page_version,
use_compliant_nested_type=use_compliant_nested_type,
**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.__doc__ = """
Write a Table to Parquet format.
Parameters
----------
table : pyarrow.Table
where: string or pyarrow.NativeFile
row_group_size: int
The number of rows per rowgroup
{}
""".format(_parquet_writer_arg_docs)
def _mkdir_if_not_exists(fs, path):
if fs._isfilestore() and not fs.exists(path):
try:
fs.mkdir(path)
except OSError:
assert fs.exists(path)
def write_to_dataset(table, root_path, partition_cols=None,
partition_filename_cb=None, filesystem=None,
use_legacy_dataset=None, **kwargs):
"""Wrapper around parquet.write_table 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
filesystem : FileSystem, default None
If nothing passed, paths assumed to be found in the local on-disk
filesystem
partition_cols : list,
Column names by which to partition the dataset
Columns are partitioned in the order they are given
partition_filename_cb : callable,
A callback function that takes the partition key(s) as an argument
and allow you to override the partition filename. If nothing is
passed, the filename will consist of a uuid.
use_legacy_dataset : bool
Default is True unless a ``pyarrow.fs`` filesystem is passed.
Set to False to enable the new code path (experimental, using the
new Arrow Dataset API). This is more efficient when using partition
columns, but does not (yet) support `partition_filename_cb` and
`metadata_collector` keywords.
**kwargs : dict,
Additional kwargs for write_table function. See docstring for
`write_table` or `ParquetWriter` for more information.
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`.
"""
if use_legacy_dataset is None:
# if a new filesystem is passed -> default to new implementation
if isinstance(filesystem, FileSystem):
use_legacy_dataset = False
# otherwise the default is still True
else:
use_legacy_dataset = True
if not use_legacy_dataset:
import pyarrow.dataset as ds
# extract non-file format options
schema = kwargs.pop("schema", None)
use_threads = kwargs.pop("use_threads", True)
# raise for unsupported keywords
msg = (
"The '{}' argument is not supported with the new dataset "
"implementation."
)
metadata_collector = kwargs.pop('metadata_collector', None)
if metadata_collector is not None:
raise ValueError(msg.format("metadata_collector"))
if partition_filename_cb is not None:
raise ValueError(msg.format("partition_filename_cb"))
# 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)
partitioning = None
if partition_cols:
part_schema = table.select(partition_cols).schema
partitioning = ds.partitioning(part_schema, flavor="hive")
ds.write_dataset(
table, root_path, filesystem=filesystem,
format=parquet_format, file_options=write_options, schema=schema,
partitioning=partitioning, use_threads=use_threads)
return
fs, root_path = legacyfs.resolve_filesystem_and_path(root_path, filesystem)
_mkdir_if_not_exists(fs, root_path)
metadata_collector = kwargs.pop('metadata_collector', None)
if partition_cols is not None and len(partition_cols) > 0:
df = table.to_pandas()
partition_keys = [df[col] for col in partition_cols]
data_df = df.drop(partition_cols, axis='columns')
data_cols = df.columns.drop(partition_cols)
if len(data_cols) == 0:
raise ValueError('No data left to save outside partition columns')
subschema = table.schema
# ARROW-2891: Ensure the output_schema is preserved when writing a
# partitioned dataset
for col in table.schema.names:
if col in partition_cols:
subschema = subschema.remove(subschema.get_field_index(col))
for keys, subgroup in data_df.groupby(partition_keys):
if not isinstance(keys, tuple):
keys = (keys,)
subdir = '/'.join(
['{colname}={value}'.format(colname=name, value=val)
for name, val in zip(partition_cols, keys)])
subtable = pa.Table.from_pandas(subgroup, schema=subschema,
safe=False)
_mkdir_if_not_exists(fs, '/'.join([root_path, subdir]))
if partition_filename_cb:
outfile = partition_filename_cb(keys)
else:
outfile = guid() + '.parquet'
relative_path = '/'.join([subdir, outfile])
full_path = '/'.join([root_path, relative_path])
with fs.open(full_path, 'wb') as f:
write_table(subtable, f, metadata_collector=metadata_collector,
**kwargs)
if metadata_collector is not None:
metadata_collector[-1].set_file_path(relative_path)
else:
if partition_filename_cb:
outfile = partition_filename_cb(None)
else:
outfile = guid() + '.parquet'
full_path = '/'.join([root_path, outfile])
with fs.open(full_path, 'wb') as f:
write_table(table, f, metadata_collector=metadata_collector,
**kwargs)
if metadata_collector is not None:
metadata_collector[-1].set_file_path(outfile)
def write_metadata(schema, where, metadata_collector=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:
**kwargs : dict,
Additional kwargs for ParquetWriter class. See docstring for
`ParquetWriter` for more information.
Examples
--------
Write a dataset and collect metadata information.
>>> metadata_collector = []
>>> write_to_dataset(
... table, root_path,
... metadata_collector=metadata_collector, **writer_kwargs)
Write the `_common_metadata` parquet file without row groups statistics.
>>> write_metadata(
... table.schema, root_path / '_common_metadata', **writer_kwargs)
Write the `_metadata` parquet file with row groups statistics.
>>> write_metadata(
... table.schema, root_path / '_metadata',
... metadata_collector=metadata_collector, **writer_kwargs)
"""
writer = ParquetWriter(where, schema, **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)
for m in metadata_collector:
metadata.append_row_groups(m)
metadata.write_metadata_file(where)
def read_metadata(where, memory_map=False):
"""
Read FileMetadata from footer of a single Parquet file.
Parameters
----------
where : str (filepath) or file-like object
memory_map : bool, default False
Create memory map when the source is a file path.
Returns
-------
metadata : FileMetadata
"""
return ParquetFile(where, memory_map=memory_map).metadata
def read_schema(where, memory_map=False):
"""
Read effective Arrow schema from Parquet file metadata.
Parameters
----------
where : str (filepath) or file-like object
memory_map : bool, default False
Create memory map when the source is a file path.
Returns
-------
schema : pyarrow.Schema
"""
return ParquetFile(where, memory_map=memory_map).schema.to_arrow_schema()