# 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 warnings 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. 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 infered as timestamps in nanoseconds. """ 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): 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 ) 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 `_, 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``:: group (LIST) { repeated group list { 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``:: group (LIST) { repeated group list { 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: """ DEPRECATED: 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. .. deprecated:: 5.0 Directly constructing a ``ParquetDatasetPiece`` is deprecated, as well as accessing the pieces of a ``ParquetDataset`` object. Specify ``use_legacy_dataset=False`` when constructing the ``ParquetDataset`` and use the ``ParquetDataset.fragments`` attribute instead. 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): warnings.warn( "ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will " "be removed in a future version.", DeprecationWarning, stacklevel=2) self._init( path, open_file_func, file_options, row_group, partition_keys) @staticmethod def _create(path, open_file_func=partial(open, mode='rb'), file_options=None, row_group=None, partition_keys=None): self = ParquetDatasetPiece.__new__(ParquetDatasetPiece) self._init( path, open_file_func, file_options, row_group, partition_keys) return self def _init(self, path, open_file_func, file_options, row_group, partition_keys): 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._create(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 ) _DEPR_MSG = ( "'{}' attribute is deprecated as of pyarrow 5.0.0 and will be removed " "in a future version.{}" ) _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. 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 infered as timestamps in nanoseconds. """.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, coerce_int96_timestamp_unit=None): 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, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, # 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, coerce_int96_timestamp_unit=None): 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', '_pieces', '_partitions', 'common_metadata_path', 'metadata_path', 'common_metadata', 'metadata', 'schema', 'split_row_groups'): if getattr(self, prop) != getattr(other, prop): return False for prop in ('memory_map', 'buffer_size'): if getattr(self._metadata, prop) != getattr(other._metadata, 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)] @property def pieces(self): warnings.warn( _DEPR_MSG.format( "ParquetDataset.pieces", " Specify 'use_legacy_dataset=False' while constructing the " "ParquetDataset, and then use the '.fragments' attribute " "instead."), DeprecationWarning, stacklevel=2) return self._pieces @property def partitions(self): warnings.warn( _DEPR_MSG.format("ParquetDataset.partitions", ""), DeprecationWarning, stacklevel=2) return self._partitions @property def memory_map(self): warnings.warn( _DEPR_MSG.format("ParquetDataset.memory_map", ""), DeprecationWarning, stacklevel=2) return self._metadata.memory_map @property def read_dictionary(self): warnings.warn( _DEPR_MSG.format("ParquetDataset.read_dictionary", ""), DeprecationWarning, stacklevel=2) return self._metadata.read_dictionary @property def buffer_size(self): warnings.warn( _DEPR_MSG.format("ParquetDataset.buffer_size", ""), DeprecationWarning, stacklevel=2) return self._metadata.buffer_size _fs = property( operator.attrgetter('_metadata.fs') ) @property def fs(self): warnings.warn( _DEPR_MSG.format( "ParquetDataset.fs", " Specify 'use_legacy_dataset=False' while constructing the " "ParquetDataset, and then use the '.filesystem' attribute " "instead."), DeprecationWarning, stacklevel=2) return self._metadata.fs common_metadata = property( operator.attrgetter('_metadata.common_metadata') ) 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._create( 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, coerce_int96_timestamp_unit=None, **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, "coerce_int96_timestamp_unit": coerce_int96_timestamp_unit } 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): warnings.warn( _DEPR_MSG.format("ParquetDataset.pieces", " Use the '.fragments' attribute instead"), DeprecationWarning, stacklevel=2) return list(self._dataset.get_fragments()) @property def fragments(self): return list(self._dataset.get_fragments()) @property def files(self): return self._dataset.files @property def filesystem(self): return self._dataset.filesystem _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, coerce_int96_timestamp_unit=None): 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, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit ) 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, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit ) 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, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit ) else: pf = ParquetFile( source, metadata=metadata, read_dictionary=read_dictionary, memory_map=memory_map, buffer_size=buffer_size, coerce_int96_timestamp_unit=coerce_int96_timestamp_unit ) 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 .parquet group2=value2 .parquet group1=valueN group2=value1 .parquet group2=valueN .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) file_visitor = None if metadata_collector is not None: def file_visitor(written_file): metadata_collector.append(written_file.metadata) 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, file_visitor=file_visitor) 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()