diff --git "a/llmeval-env/lib/python3.10/site-packages/pyarrow/_dataset.pyx" "b/llmeval-env/lib/python3.10/site-packages/pyarrow/_dataset.pyx" new file mode 100644--- /dev/null +++ "b/llmeval-env/lib/python3.10/site-packages/pyarrow/_dataset.pyx" @@ -0,0 +1,4052 @@ +# 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. + +# cython: language_level = 3 + +"""Dataset is currently unstable. APIs subject to change without notice.""" + +from cython.operator cimport dereference as deref + +import codecs +import collections +from libcpp cimport bool + +import pyarrow as pa +from pyarrow.lib cimport * +from pyarrow.lib import ArrowTypeError, frombytes, tobytes, _pac +from pyarrow.includes.libarrow_dataset cimport * +from pyarrow._acero cimport ExecNodeOptions +from pyarrow._compute cimport Expression, _bind +from pyarrow._compute import _forbid_instantiation +from pyarrow._fs cimport FileSystem, FileSelector, FileInfo +from pyarrow._csv cimport ( + ConvertOptions, ParseOptions, ReadOptions, WriteOptions) +from pyarrow.util import _is_iterable, _is_path_like, _stringify_path +from pyarrow._json cimport ParseOptions as JsonParseOptions +from pyarrow._json cimport ReadOptions as JsonReadOptions + + +_DEFAULT_BATCH_SIZE = 2**17 +_DEFAULT_BATCH_READAHEAD = 16 +_DEFAULT_FRAGMENT_READAHEAD = 4 + + +# Initialise support for Datasets in ExecPlan +Initialize() + + +_orc_fileformat = None +_orc_imported = False + + +def _get_orc_fileformat(): + """ + Import OrcFileFormat on first usage (to avoid circular import issue + when `pyarrow._dataset_orc` would be imported first) + """ + global _orc_fileformat + global _orc_imported + if not _orc_imported: + try: + from pyarrow._dataset_orc import OrcFileFormat + _orc_fileformat = OrcFileFormat + except ImportError as e: + _orc_fileformat = None + finally: + _orc_imported = True + return _orc_fileformat + + +_dataset_pq = False + + +def _get_parquet_classes(): + """ + Import Parquet class files on first usage (to avoid circular import issue + when `pyarrow._dataset_parquet` would be imported first) + """ + global _dataset_pq + if _dataset_pq is False: + try: + import pyarrow._dataset_parquet as _dataset_pq + except ImportError: + _dataset_pq = None + + +def _get_parquet_symbol(name): + """ + Get a symbol from pyarrow.parquet if the latter is importable, otherwise + return None. + """ + _get_parquet_classes() + return _dataset_pq and getattr(_dataset_pq, name) + + +cdef CFileSource _make_file_source(object file, FileSystem filesystem=None, object file_size=None): + + cdef: + CFileSource c_source + shared_ptr[CFileSystem] c_filesystem + CFileInfo c_info + c_string c_path + shared_ptr[CRandomAccessFile] c_file + shared_ptr[CBuffer] c_buffer + int64_t c_size + + if isinstance(file, Buffer): + c_buffer = pyarrow_unwrap_buffer(file) + c_source = CFileSource(move(c_buffer)) + elif _is_path_like(file): + if filesystem is None: + raise ValueError("cannot construct a FileSource from " + "a path without a FileSystem") + c_filesystem = filesystem.unwrap() + c_path = tobytes(_stringify_path(file)) + + if file_size is not None: + c_size = file_size + c_info = FileInfo(c_path, size=c_size).unwrap() + c_source = CFileSource(move(c_info), move(c_filesystem)) + else: + c_source = CFileSource(move(c_path), move(c_filesystem)) + elif hasattr(file, 'read'): + # Optimistically hope this is file-like + c_file = get_native_file(file, False).get_random_access_file() + c_source = CFileSource(move(c_file)) + + else: + raise TypeError("cannot construct a FileSource " + "from " + str(file)) + + return c_source + + +cdef CSegmentEncoding _get_segment_encoding(str segment_encoding): + if segment_encoding == "none": + return CSegmentEncoding_None + elif segment_encoding == "uri": + return CSegmentEncoding_Uri + raise ValueError(f"Unknown segment encoding: {segment_encoding}") + + +cdef str _wrap_segment_encoding(CSegmentEncoding segment_encoding): + if segment_encoding == CSegmentEncoding_None: + return "none" + elif segment_encoding == CSegmentEncoding_Uri: + return "uri" + raise ValueError("Unknown segment encoding") + + +cdef Expression _true = Expression._scalar(True) + + +cdef class Dataset(_Weakrefable): + """ + Collection of data fragments and potentially child datasets. + + Arrow Datasets allow you to query against data that has been split across + multiple files. This sharding of data may indicate partitioning, which + can accelerate queries that only touch some partitions (files). + """ + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef void init(self, const shared_ptr[CDataset]& sp): + self.wrapped = sp + self.dataset = sp.get() + self._scan_options = dict() + + @staticmethod + cdef wrap(const shared_ptr[CDataset]& sp): + type_name = frombytes(sp.get().type_name()) + + classes = { + 'union': UnionDataset, + 'filesystem': FileSystemDataset, + 'in-memory': InMemoryDataset, + } + + class_ = classes.get(type_name, None) + if class_ is None: + raise TypeError(type_name) + + cdef Dataset self = class_.__new__(class_) + self.init(sp) + return self + + cdef shared_ptr[CDataset] unwrap(self) nogil: + return self.wrapped + + @property + def partition_expression(self): + """ + An Expression which evaluates to true for all data viewed by this + Dataset. + """ + return Expression.wrap(self.dataset.partition_expression()) + + def replace_schema(self, Schema schema not None): + """ + Return a copy of this Dataset with a different schema. + + The copy will view the same Fragments. If the new schema is not + compatible with the original dataset's schema then an error will + be raised. + + Parameters + ---------- + schema : Schema + The new dataset schema. + """ + cdef shared_ptr[CDataset] copy = GetResultValue( + self.dataset.ReplaceSchema(pyarrow_unwrap_schema(schema)) + ) + + d = Dataset.wrap(move(copy)) + if self._scan_options: + # Preserve scan options if set. + d._scan_options = self._scan_options.copy() + return d + + def get_fragments(self, Expression filter=None): + """Returns an iterator over the fragments in this dataset. + + Parameters + ---------- + filter : Expression, default None + Return fragments matching the optional filter, either using the + partition_expression or internal information like Parquet's + statistics. + + Returns + ------- + fragments : iterator of Fragment + """ + if self._scan_options.get("filter") is not None: + # Accessing fragments of a filtered dataset is not supported. + # It would be unclear if you wanted to filter the fragments + # or the rows in those fragments. + raise ValueError( + "Retrieving fragments of a filtered or projected " + "dataset is not allowed. Remove the filtering." + ) + + return self._get_fragments(filter) + + def _get_fragments(self, Expression filter): + cdef: + CExpression c_filter + + if filter is None: + c_fragments = move(GetResultValue(self.dataset.GetFragments())) + else: + c_filter = _bind(filter, self.schema) + c_fragments = move(GetResultValue( + self.dataset.GetFragments(c_filter))) + + for maybe_fragment in c_fragments: + yield Fragment.wrap(GetResultValue(move(maybe_fragment))) + + def _scanner_options(self, options): + """Returns the default options to create a new Scanner. + + This is automatically invoked by :meth:`Dataset.scanner` + and there is no need to use it. + """ + new_options = options.copy() + + # at the moment only support filter + requested_filter = options.get("filter") + current_filter = self._scan_options.get("filter") + if requested_filter is not None and current_filter is not None: + new_options["filter"] = current_filter & requested_filter + elif current_filter is not None: + new_options["filter"] = current_filter + + return new_options + + def scanner(self, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Build a scan operation against the dataset. + + Data is not loaded immediately. Instead, this produces a Scanner, + which exposes further operations (e.g. loading all data as a + table, counting rows). + + See the :meth:`Scanner.from_dataset` method for further information. + + Parameters + ---------- + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + scanner : Scanner + + Examples + -------- + >>> import pyarrow as pa + >>> table = pa.table({'year': [2020, 2022, 2021, 2022, 2019, 2021], + ... 'n_legs': [2, 2, 4, 4, 5, 100], + ... 'animal': ["Flamingo", "Parrot", "Dog", "Horse", + ... "Brittle stars", "Centipede"]}) + >>> + >>> import pyarrow.parquet as pq + >>> pq.write_table(table, "dataset_scanner.parquet") + + >>> import pyarrow.dataset as ds + >>> dataset = ds.dataset("dataset_scanner.parquet") + + Selecting a subset of the columns: + + >>> dataset.scanner(columns=["year", "n_legs"]).to_table() + pyarrow.Table + year: int64 + n_legs: int64 + ---- + year: [[2020,2022,2021,2022,2019,2021]] + n_legs: [[2,2,4,4,5,100]] + + Projecting selected columns using an expression: + + >>> dataset.scanner(columns={ + ... "n_legs_uint": ds.field("n_legs").cast("uint8"), + ... }).to_table() + pyarrow.Table + n_legs_uint: uint8 + ---- + n_legs_uint: [[2,2,4,4,5,100]] + + Filtering rows while scanning: + + >>> dataset.scanner(filter=ds.field("year") > 2020).to_table() + pyarrow.Table + year: int64 + n_legs: int64 + animal: string + ---- + year: [[2022,2021,2022,2021]] + n_legs: [[2,4,4,100]] + animal: [["Parrot","Dog","Horse","Centipede"]] + """ + return Scanner.from_dataset( + self, + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ) + + def to_batches(self, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Read the dataset as materialized record batches. + + Parameters + ---------- + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + record_batches : iterator of RecordBatch + """ + return self.scanner( + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).to_batches() + + def to_table(self, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Read the dataset to an Arrow table. + + Note that this method reads all the selected data from the dataset + into memory. + + Parameters + ---------- + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + table : Table + """ + return self.scanner( + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).to_table() + + def take(self, + object indices, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Select rows of data by index. + + Parameters + ---------- + indices : Array or array-like + indices of rows to select in the dataset. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + table : Table + """ + return self.scanner( + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).take(indices) + + def head(self, + int num_rows, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Load the first N rows of the dataset. + + Parameters + ---------- + num_rows : int + The number of rows to load. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + table : Table + """ + return self.scanner( + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).head(num_rows) + + def count_rows(self, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Count rows matching the scanner filter. + + Parameters + ---------- + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + count : int + """ + return self.scanner( + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).count_rows() + + @property + def schema(self): + """The common schema of the full Dataset""" + return pyarrow_wrap_schema(self.dataset.schema()) + + def filter(self, expression not None): + """ + Apply a row filter to the dataset. + + Parameters + ---------- + expression : Expression + The filter that should be applied to the dataset. + + Returns + ------- + Dataset + """ + cdef: + Dataset filtered_dataset + + new_filter = expression + current_filter = self._scan_options.get("filter") + if current_filter is not None and new_filter is not None: + new_filter = current_filter & new_filter + + filtered_dataset = self.__class__.__new__(self.__class__) + filtered_dataset.init(self.wrapped) + filtered_dataset._scan_options = dict(filter=new_filter) + return filtered_dataset + + def sort_by(self, sorting, **kwargs): + """ + Sort the Dataset by one or multiple columns. + + Parameters + ---------- + sorting : str or list[tuple(name, order)] + Name of the column to use to sort (ascending), or + a list of multiple sorting conditions where + each entry is a tuple with column name + and sorting order ("ascending" or "descending") + **kwargs : dict, optional + Additional sorting options. + As allowed by :class:`SortOptions` + + Returns + ------- + InMemoryDataset + A new dataset sorted according to the sort keys. + """ + if isinstance(sorting, str): + sorting = [(sorting, "ascending")] + + res = _pac()._sort_source( + self, output_type=InMemoryDataset, sort_keys=sorting, **kwargs + ) + return res + + def join(self, right_dataset, keys, right_keys=None, join_type="left outer", + left_suffix=None, right_suffix=None, coalesce_keys=True, + use_threads=True): + """ + Perform a join between this dataset and another one. + + Result of the join will be a new dataset, where further + operations can be applied. + + Parameters + ---------- + right_dataset : dataset + The dataset to join to the current one, acting as the right dataset + in the join operation. + keys : str or list[str] + The columns from current dataset that should be used as keys + of the join operation left side. + right_keys : str or list[str], default None + The columns from the right_dataset that should be used as keys + on the join operation right side. + When ``None`` use the same key names as the left dataset. + join_type : str, default "left outer" + The kind of join that should be performed, one of + ("left semi", "right semi", "left anti", "right anti", + "inner", "left outer", "right outer", "full outer") + left_suffix : str, default None + Which suffix to add to right column names. This prevents confusion + when the columns in left and right datasets have colliding names. + right_suffix : str, default None + Which suffix to add to the left column names. This prevents confusion + when the columns in left and right datasets have colliding names. + coalesce_keys : bool, default True + If the duplicated keys should be omitted from one of the sides + in the join result. + use_threads : bool, default True + Whenever to use multithreading or not. + + Returns + ------- + InMemoryDataset + """ + if right_keys is None: + right_keys = keys + return _pac()._perform_join( + join_type, self, keys, right_dataset, right_keys, + left_suffix=left_suffix, right_suffix=right_suffix, + use_threads=use_threads, coalesce_keys=coalesce_keys, + output_type=InMemoryDataset + ) + + def join_asof(self, right_dataset, on, by, tolerance, right_on=None, right_by=None): + """ + Perform an asof join between this dataset and another one. + + This is similar to a left-join except that we match on nearest key rather + than equal keys. Both datasets must be sorted by the key. This type of join + is most useful for time series data that are not perfectly aligned. + + Optionally match on equivalent keys with "by" before searching with "on". + + Result of the join will be a new Dataset, where further + operations can be applied. + + Parameters + ---------- + right_dataset : dataset + The dataset to join to the current one, acting as the right dataset + in the join operation. + on : str + The column from current dataset that should be used as the "on" key + of the join operation left side. + + An inexact match is used on the "on" key, i.e. a row is considered a + match if and only if left_on - tolerance <= right_on <= left_on. + + The input table must be sorted by the "on" key. Must be a single + field of a common type. + + Currently, the "on" key must be an integer, date, or timestamp type. + by : str or list[str] + The columns from current dataset that should be used as the keys + of the join operation left side. The join operation is then done + only for the matches in these columns. + tolerance : int + The tolerance for inexact "on" key matching. A right row is considered + a match with the left row `right.on - left.on <= tolerance`. The + `tolerance` may be: + + - negative, in which case a past-as-of-join occurs; + - or positive, in which case a future-as-of-join occurs; + - or zero, in which case an exact-as-of-join occurs. + + The tolerance is interpreted in the same units as the "on" key. + right_on : str or list[str], default None + The columns from the right_dataset that should be used as the on key + on the join operation right side. + When ``None`` use the same key name as the left dataset. + right_by : str or list[str], default None + The columns from the right_dataset that should be used as by keys + on the join operation right side. + When ``None`` use the same key names as the left dataset. + + Returns + ------- + InMemoryDataset + """ + if right_on is None: + right_on = on + if right_by is None: + right_by = by + return _pac()._perform_join_asof(self, on, by, + right_dataset, right_on, right_by, + tolerance, output_type=InMemoryDataset) + + +cdef class InMemoryDataset(Dataset): + """ + A Dataset wrapping in-memory data. + + Parameters + ---------- + source : RecordBatch, Table, list, tuple + The data for this dataset. Can be a RecordBatch, Table, list of + RecordBatch/Table, iterable of RecordBatch, or a RecordBatchReader + If an iterable is provided, the schema must also be provided. + schema : Schema, optional + Only required if passing an iterable as the source + """ + + cdef: + CInMemoryDataset* in_memory_dataset + + def __init__(self, source, Schema schema=None): + cdef: + shared_ptr[CInMemoryDataset] in_memory_dataset + + if isinstance(source, (pa.RecordBatch, pa.Table)): + source = [source] + + if isinstance(source, (list, tuple)): + batches = [] + for item in source: + if isinstance(item, pa.RecordBatch): + batches.append(item) + elif isinstance(item, pa.Table): + batches.extend(item.to_batches()) + else: + raise TypeError( + 'Expected a list of tables or batches. The given list ' + 'contains a ' + type(item).__name__) + if schema is None: + schema = item.schema + elif not schema.equals(item.schema): + raise ArrowTypeError( + f'Item has schema\n{item.schema}\nwhich does not ' + f'match expected schema\n{schema}') + if not batches and schema is None: + raise ValueError('Must provide schema to construct in-memory ' + 'dataset from an empty list') + table = pa.Table.from_batches(batches, schema=schema) + in_memory_dataset = make_shared[CInMemoryDataset]( + pyarrow_unwrap_table(table)) + else: + raise TypeError( + 'Expected a table, batch, or list of tables/batches ' + 'instead of the given type: ' + + type(source).__name__ + ) + + self.init( in_memory_dataset) + + cdef void init(self, const shared_ptr[CDataset]& sp): + Dataset.init(self, sp) + self.in_memory_dataset = sp.get() + + +cdef class UnionDataset(Dataset): + """ + A Dataset wrapping child datasets. + + Children's schemas must agree with the provided schema. + + Parameters + ---------- + schema : Schema + A known schema to conform to. + children : list of Dataset + One or more input children + """ + + cdef: + CUnionDataset* union_dataset + + def __init__(self, Schema schema not None, children): + cdef: + Dataset child + CDatasetVector c_children + shared_ptr[CUnionDataset] union_dataset + + for child in children: + c_children.push_back(child.wrapped) + + union_dataset = GetResultValue(CUnionDataset.Make( + pyarrow_unwrap_schema(schema), move(c_children))) + self.init( union_dataset) + + cdef void init(self, const shared_ptr[CDataset]& sp): + Dataset.init(self, sp) + self.union_dataset = sp.get() + + def __reduce__(self): + return UnionDataset, (self.schema, self.children) + + @property + def children(self): + cdef CDatasetVector children = self.union_dataset.children() + return [Dataset.wrap(children[i]) for i in range(children.size())] + + +cdef class FileSystemDataset(Dataset): + """ + A Dataset of file fragments. + + A FileSystemDataset is composed of one or more FileFragment. + + Parameters + ---------- + fragments : list[Fragments] + List of fragments to consume. + schema : Schema + The top-level schema of the Dataset. + format : FileFormat + File format of the fragments, currently only ParquetFileFormat, + IpcFileFormat, CsvFileFormat, and JsonFileFormat are supported. + filesystem : FileSystem + FileSystem of the fragments. + root_partition : Expression, optional + The top-level partition of the DataDataset. + """ + + cdef: + CFileSystemDataset* filesystem_dataset + + def __init__(self, fragments, Schema schema, FileFormat format, + FileSystem filesystem=None, root_partition=None): + cdef: + FileFragment fragment=None + vector[shared_ptr[CFileFragment]] c_fragments + CResult[shared_ptr[CDataset]] result + shared_ptr[CFileSystem] c_filesystem + + if root_partition is None: + root_partition = _true + elif not isinstance(root_partition, Expression): + raise TypeError( + "Argument 'root_partition' has incorrect type (expected " + "Expression, got {0})".format(type(root_partition)) + ) + + for fragment in fragments: + c_fragments.push_back( + static_pointer_cast[CFileFragment, CFragment]( + fragment.unwrap())) + + if filesystem is None: + filesystem = fragment.filesystem + + if filesystem is not None: + c_filesystem = filesystem.unwrap() + + result = CFileSystemDataset.Make( + pyarrow_unwrap_schema(schema), + ( root_partition).unwrap(), + format.unwrap(), + c_filesystem, + c_fragments + ) + self.init(GetResultValue(result)) + + @property + def filesystem(self): + return FileSystem.wrap(self.filesystem_dataset.filesystem()) + + @property + def partitioning(self): + """ + The partitioning of the Dataset source, if discovered. + + If the FileSystemDataset is created using the ``dataset()`` factory + function with a partitioning specified, this will return the + finalized Partitioning object from the dataset discovery. In all + other cases, this returns None. + """ + c_partitioning = self.filesystem_dataset.partitioning() + if c_partitioning.get() == nullptr: + return None + try: + return Partitioning.wrap(c_partitioning) + except TypeError: + # e.g. type_name "default" + return None + + cdef void init(self, const shared_ptr[CDataset]& sp): + Dataset.init(self, sp) + self.filesystem_dataset = sp.get() + + def __reduce__(self): + return FileSystemDataset, ( + list(self.get_fragments()), + self.schema, + self.format, + self.filesystem, + self.partition_expression + ) + + @classmethod + def from_paths(cls, paths, schema=None, format=None, + filesystem=None, partitions=None, root_partition=None): + """ + A Dataset created from a list of paths on a particular filesystem. + + Parameters + ---------- + paths : list of str + List of file paths to create the fragments from. + schema : Schema + The top-level schema of the DataDataset. + format : FileFormat + File format to create fragments from, currently only + ParquetFileFormat, IpcFileFormat, CsvFileFormat, and JsonFileFormat are supported. + filesystem : FileSystem + The filesystem which files are from. + partitions : list[Expression], optional + Attach additional partition information for the file paths. + root_partition : Expression, optional + The top-level partition of the DataDataset. + """ + if root_partition is None: + root_partition = _true + + for arg, class_, name in [ + (schema, Schema, 'schema'), + (format, FileFormat, 'format'), + (filesystem, FileSystem, 'filesystem'), + (root_partition, Expression, 'root_partition') + ]: + if not isinstance(arg, class_): + raise TypeError( + "Argument '{0}' has incorrect type (expected {1}, " + "got {2})".format(name, class_.__name__, type(arg)) + ) + + partitions = partitions or [_true] * len(paths) + + if len(paths) != len(partitions): + raise ValueError( + 'The number of files resulting from paths_or_selector ' + 'must be equal to the number of partitions.' + ) + + fragments = [ + format.make_fragment(path, filesystem, partitions[i]) + for i, path in enumerate(paths) + ] + return FileSystemDataset(fragments, schema, format, + filesystem, root_partition) + + @property + def files(self): + """List of the files""" + cdef vector[c_string] files = self.filesystem_dataset.files() + return [frombytes(f) for f in files] + + @property + def format(self): + """The FileFormat of this source.""" + return FileFormat.wrap(self.filesystem_dataset.format()) + + +cdef class FileWriteOptions(_Weakrefable): + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef void init(self, const shared_ptr[CFileWriteOptions]& sp): + self.wrapped = sp + self.c_options = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CFileWriteOptions]& sp): + type_name = frombytes(sp.get().type_name()) + + classes = { + 'csv': CsvFileWriteOptions, + 'ipc': IpcFileWriteOptions, + 'parquet': _get_parquet_symbol('ParquetFileWriteOptions'), + } + + class_ = classes.get(type_name, None) + if class_ is None: + raise TypeError(type_name) + + cdef FileWriteOptions self = class_.__new__(class_) + self.init(sp) + return self + + @property + def format(self): + return FileFormat.wrap(self.c_options.format()) + + cdef inline shared_ptr[CFileWriteOptions] unwrap(self): + return self.wrapped + + +cdef class FileFormat(_Weakrefable): + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef void init(self, const shared_ptr[CFileFormat]& sp): + self.wrapped = sp + self.format = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CFileFormat]& sp): + type_name = frombytes(sp.get().type_name()) + + classes = { + 'ipc': IpcFileFormat, + 'csv': CsvFileFormat, + 'json': JsonFileFormat, + 'parquet': _get_parquet_symbol('ParquetFileFormat'), + 'orc': _get_orc_fileformat(), + } + + class_ = classes.get(type_name, None) + if class_ is None: + raise TypeError(type_name) + + cdef FileFormat self = class_.__new__(class_) + self.init(sp) + return self + + cdef WrittenFile _finish_write(self, path, base_dir, + CFileWriter* file_writer): + parquet_metadata = None + size = GetResultValue(file_writer.GetBytesWritten()) + return WrittenFile(path, parquet_metadata, size) + + cdef inline shared_ptr[CFileFormat] unwrap(self): + return self.wrapped + + def inspect(self, file, filesystem=None): + """ + Infer the schema of a file. + + Parameters + ---------- + file : file-like object, path-like or str + The file or file path to infer a schema from. + filesystem : Filesystem, optional + If `filesystem` is given, `file` must be a string and specifies + the path of the file to read from the filesystem. + + Returns + ------- + schema : Schema + The schema inferred from the file + """ + cdef: + CFileSource c_source = _make_file_source(file, filesystem, file_size=None) + CResult[shared_ptr[CSchema]] c_result + with nogil: + c_result = self.format.Inspect(c_source) + c_schema = GetResultValue(c_result) + return pyarrow_wrap_schema(move(c_schema)) + + def make_fragment(self, file, filesystem=None, + Expression partition_expression=None, + *, file_size=None): + """ + Make a FileFragment from a given file. + + Parameters + ---------- + file : file-like object, path-like or str + The file or file path to make a fragment from. + filesystem : Filesystem, optional + If `filesystem` is given, `file` must be a string and specifies + the path of the file to read from the filesystem. + partition_expression : Expression, optional + An expression that is guaranteed true for all rows in the fragment. Allows + fragment to be potentially skipped while scanning with a filter. + file_size : int, optional + The size of the file in bytes. Can improve performance with high-latency filesystems + when file size needs to be known before reading. + + Returns + ------- + fragment : Fragment + The file fragment + """ + if partition_expression is None: + partition_expression = _true + c_source = _make_file_source(file, filesystem, file_size) + c_fragment = GetResultValue( + self.format.MakeFragment(move(c_source), + partition_expression.unwrap(), + nullptr)) + return Fragment.wrap(move(c_fragment)) + + def make_write_options(self): + sp_write_options = self.format.DefaultWriteOptions() + if sp_write_options.get() == nullptr: + # DefaultWriteOptions() may return `nullptr` which means that + # the format does not yet support writing datasets. + raise NotImplementedError( + "Writing datasets not yet implemented for this file format." + ) + return FileWriteOptions.wrap(sp_write_options) + + @property + def default_extname(self): + return frombytes(self.format.type_name()) + + @property + def default_fragment_scan_options(self): + dfso = FragmentScanOptions.wrap( + self.wrapped.get().default_fragment_scan_options) + # CsvFileFormat stores a Python-specific encoding field that needs + # to be restored because it does not exist in the C++ struct + if isinstance(self, CsvFileFormat): + if self._read_options_py is not None: + dfso.read_options = self._read_options_py + return dfso + + @default_fragment_scan_options.setter + def default_fragment_scan_options(self, FragmentScanOptions options): + if options is None: + self.wrapped.get().default_fragment_scan_options =\ + nullptr + else: + self._set_default_fragment_scan_options(options) + + cdef _set_default_fragment_scan_options(self, FragmentScanOptions options): + raise ValueError(f"Cannot set fragment scan options for " + f"'{options.type_name}' on {self.__class__.__name__}") + + def __eq__(self, other): + try: + return self.equals(other) + except TypeError: + return False + + +cdef class Fragment(_Weakrefable): + """Fragment of data from a Dataset.""" + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef void init(self, const shared_ptr[CFragment]& sp): + self.wrapped = sp + self.fragment = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CFragment]& sp): + type_name = frombytes(sp.get().type_name()) + + classes = { + # IpcFileFormat, CsvFileFormat, JsonFileFormat and OrcFileFormat do not have + # corresponding subclasses of FileFragment + 'ipc': FileFragment, + 'csv': FileFragment, + 'json': FileFragment, + 'orc': FileFragment, + 'parquet': _get_parquet_symbol('ParquetFileFragment'), + } + + class_ = classes.get(type_name, None) + if class_ is None: + class_ = Fragment + + cdef Fragment self = class_.__new__(class_) + self.init(sp) + return self + + cdef inline shared_ptr[CFragment] unwrap(self): + return self.wrapped + + @property + def physical_schema(self): + """Return the physical schema of this Fragment. This schema can be + different from the dataset read schema.""" + cdef: + CResult[shared_ptr[CSchema]] maybe_schema + with nogil: + maybe_schema = self.fragment.ReadPhysicalSchema() + return pyarrow_wrap_schema(GetResultValue(maybe_schema)) + + @property + def partition_expression(self): + """An Expression which evaluates to true for all data viewed by this + Fragment. + """ + return Expression.wrap(self.fragment.partition_expression()) + + def scanner(self, + Schema schema=None, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Build a scan operation against the fragment. + + Data is not loaded immediately. Instead, this produces a Scanner, + which exposes further operations (e.g. loading all data as a + table, counting rows). + + Parameters + ---------- + schema : Schema + Schema to use for scanning. This is used to unify a Fragment to + its Dataset's schema. If not specified this will use the + Fragment's physical schema which might differ for each Fragment. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + scanner : Scanner + """ + return Scanner.from_fragment( + self, + schema=schema, + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ) + + def to_batches(self, + Schema schema=None, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Read the fragment as materialized record batches. + + Parameters + ---------- + schema : Schema, optional + Concrete schema to use for scanning. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + record_batches : iterator of RecordBatch + """ + return Scanner.from_fragment( + self, + schema=schema, + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).to_batches() + + def to_table(self, + Schema schema=None, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Convert this Fragment into a Table. + + Use this convenience utility with care. This will serially materialize + the Scan result in memory before creating the Table. + + Parameters + ---------- + schema : Schema, optional + Concrete schema to use for scanning. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + table : Table + """ + return self.scanner( + schema=schema, + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).to_table() + + def take(self, + object indices, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Select rows of data by index. + + Parameters + ---------- + indices : Array or array-like + The indices of row to select in the dataset. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + Table + """ + return self.scanner( + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).take(indices) + + def head(self, + int num_rows, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Load the first N rows of the fragment. + + Parameters + ---------- + num_rows : int + The number of rows to load. + columns : list of str, default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + Table + """ + return self.scanner( + columns=columns, + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).head(num_rows) + + def count_rows(self, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, + MemoryPool memory_pool=None): + """ + Count rows matching the scanner filter. + + Parameters + ---------- + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + + Returns + ------- + count : int + """ + return self.scanner( + filter=filter, + batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + fragment_scan_options=fragment_scan_options, + use_threads=use_threads, + memory_pool=memory_pool + ).count_rows() + + +cdef class FileFragment(Fragment): + """A Fragment representing a data file.""" + + cdef void init(self, const shared_ptr[CFragment]& sp): + Fragment.init(self, sp) + self.file_fragment = sp.get() + + def __repr__(self): + type_name = frombytes(self.fragment.type_name()) + if type_name != "parquet": + typ = f" type={type_name}" + else: + # parquet has a subclass -> type embedded in class name + typ = "" + partition_dict = get_partition_keys(self.partition_expression) + partition = ", ".join( + [f"{key}={val}" for key, val in partition_dict.items()] + ) + if partition: + partition = f" partition=[{partition}]" + return "".format( + self.__class__.__name__, typ, self.path, partition + ) + + def __reduce__(self): + buffer = self.buffer + return self.format.make_fragment, ( + self.path if buffer is None else buffer, + self.filesystem, + self.partition_expression + ) + + def open(self): + """ + Open a NativeFile of the buffer or file viewed by this fragment. + """ + cdef: + shared_ptr[CFileSystem] c_filesystem + shared_ptr[CRandomAccessFile] opened + c_string c_path + NativeFile out = NativeFile() + + if self.buffer is not None: + return pa.BufferReader(self.buffer) + + c_path = tobytes(self.file_fragment.source().path()) + with nogil: + c_filesystem = self.file_fragment.source().filesystem() + opened = GetResultValue(c_filesystem.get().OpenInputFile(c_path)) + + out.set_random_access_file(opened) + out.is_readable = True + return out + + @property + def path(self): + """ + The path of the data file viewed by this fragment, if it views a + file. If instead it views a buffer, this will be "". + """ + return frombytes(self.file_fragment.source().path()) + + @property + def filesystem(self): + """ + The FileSystem containing the data file viewed by this fragment, if + it views a file. If instead it views a buffer, this will be None. + """ + cdef: + shared_ptr[CFileSystem] c_fs + c_fs = self.file_fragment.source().filesystem() + + if c_fs.get() == nullptr: + return None + + return FileSystem.wrap(c_fs) + + @property + def buffer(self): + """ + The buffer viewed by this fragment, if it views a buffer. If + instead it views a file, this will be None. + """ + cdef: + shared_ptr[CBuffer] c_buffer + c_buffer = self.file_fragment.source().buffer() + + if c_buffer.get() == nullptr: + return None + + return pyarrow_wrap_buffer(c_buffer) + + @property + def format(self): + """ + The format of the data file viewed by this fragment. + """ + return FileFormat.wrap(self.file_fragment.format()) + + +cdef class FragmentScanOptions(_Weakrefable): + """Scan options specific to a particular fragment and scan operation.""" + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef void init(self, const shared_ptr[CFragmentScanOptions]& sp): + self.wrapped = sp + + @staticmethod + cdef wrap(const shared_ptr[CFragmentScanOptions]& sp): + if not sp: + return None + + type_name = frombytes(sp.get().type_name()) + + classes = { + 'csv': CsvFragmentScanOptions, + 'json': JsonFragmentScanOptions, + 'parquet': _get_parquet_symbol('ParquetFragmentScanOptions'), + } + + class_ = classes.get(type_name, None) + if class_ is None: + raise TypeError(type_name) + + cdef FragmentScanOptions self = class_.__new__(class_) + self.init(sp) + return self + + @property + def type_name(self): + return frombytes(self.wrapped.get().type_name()) + + def __eq__(self, other): + try: + return self.equals(other) + except TypeError: + return False + + +cdef class IpcFileWriteOptions(FileWriteOptions): + cdef: + CIpcFileWriteOptions* ipc_options + + def __init__(self): + _forbid_instantiation(self.__class__) + + @property + def write_options(self): + out = IpcWriteOptions() + out.c_options = CIpcWriteOptions(deref(self.ipc_options.options)) + return out + + @write_options.setter + def write_options(self, IpcWriteOptions write_options not None): + self.ipc_options.options.reset( + new CIpcWriteOptions(write_options.c_options)) + + cdef void init(self, const shared_ptr[CFileWriteOptions]& sp): + FileWriteOptions.init(self, sp) + self.ipc_options = sp.get() + + +cdef class IpcFileFormat(FileFormat): + + def __init__(self): + self.init(shared_ptr[CFileFormat](new CIpcFileFormat())) + + def equals(self, IpcFileFormat other): + """ + Parameters + ---------- + other : pyarrow.dataset.IpcFileFormat + + Returns + ------- + True + """ + return True + + def make_write_options(self, **kwargs): + """ + Parameters + ---------- + **kwargs : dict + + Returns + ------- + pyarrow.ipc.IpcWriteOptions + """ + cdef IpcFileWriteOptions opts = \ + FileFormat.make_write_options(self) + opts.write_options = IpcWriteOptions(**kwargs) + return opts + + @property + def default_extname(self): + return "arrow" + + def __reduce__(self): + return IpcFileFormat, tuple() + + +cdef class FeatherFileFormat(IpcFileFormat): + + @property + def default_extname(self): + return "feather" + + +cdef class CsvFileFormat(FileFormat): + """ + FileFormat for CSV files. + + Parameters + ---------- + parse_options : pyarrow.csv.ParseOptions + Options regarding CSV parsing. + default_fragment_scan_options : CsvFragmentScanOptions + Default options for fragments scan. + convert_options : pyarrow.csv.ConvertOptions + Options regarding value conversion. + read_options : pyarrow.csv.ReadOptions + General read options. + """ + cdef: + CCsvFileFormat* csv_format + # The encoding field in ReadOptions does not exist in the C++ struct. + # We need to store it here and override it when reading + # default_fragment_scan_options.read_options + public ReadOptions _read_options_py + + # Avoid mistakingly creating attributes + __slots__ = () + + def __init__(self, ParseOptions parse_options=None, + default_fragment_scan_options=None, + ConvertOptions convert_options=None, + ReadOptions read_options=None): + self.init(shared_ptr[CFileFormat](new CCsvFileFormat())) + if parse_options is not None: + self.parse_options = parse_options + if convert_options is not None or read_options is not None: + if default_fragment_scan_options: + raise ValueError('If `default_fragment_scan_options` is ' + 'given, cannot specify convert_options ' + 'or read_options') + self.default_fragment_scan_options = CsvFragmentScanOptions( + convert_options=convert_options, read_options=read_options) + elif isinstance(default_fragment_scan_options, dict): + self.default_fragment_scan_options = CsvFragmentScanOptions( + **default_fragment_scan_options) + elif isinstance(default_fragment_scan_options, CsvFragmentScanOptions): + self.default_fragment_scan_options = default_fragment_scan_options + elif default_fragment_scan_options is not None: + raise TypeError('`default_fragment_scan_options` must be either ' + 'a dictionary or an instance of ' + 'CsvFragmentScanOptions') + if read_options is not None: + self._read_options_py = read_options + + cdef void init(self, const shared_ptr[CFileFormat]& sp): + FileFormat.init(self, sp) + self.csv_format = sp.get() + + def make_write_options(self, **kwargs): + """ + Parameters + ---------- + **kwargs : dict + + Returns + ------- + pyarrow.csv.WriteOptions + """ + cdef CsvFileWriteOptions opts = \ + FileFormat.make_write_options(self) + opts.write_options = WriteOptions(**kwargs) + return opts + + @property + def parse_options(self): + return ParseOptions.wrap(self.csv_format.parse_options) + + @parse_options.setter + def parse_options(self, ParseOptions parse_options not None): + self.csv_format.parse_options = deref(parse_options.options) + + cdef _set_default_fragment_scan_options(self, FragmentScanOptions options): + if options.type_name == 'csv': + self.csv_format.default_fragment_scan_options = options.wrapped + self.default_fragment_scan_options.read_options = options.read_options + self._read_options_py = options.read_options + else: + super()._set_default_fragment_scan_options(options) + + def equals(self, CsvFileFormat other): + """ + Parameters + ---------- + other : pyarrow.dataset.CsvFileFormat + + Returns + ------- + bool + """ + return ( + self.parse_options.equals(other.parse_options) and + self.default_fragment_scan_options == + other.default_fragment_scan_options) + + def __reduce__(self): + return CsvFileFormat, (self.parse_options, + self.default_fragment_scan_options) + + def __repr__(self): + return f"" + + +cdef class CsvFragmentScanOptions(FragmentScanOptions): + """ + Scan-specific options for CSV fragments. + + Parameters + ---------- + convert_options : pyarrow.csv.ConvertOptions + Options regarding value conversion. + read_options : pyarrow.csv.ReadOptions + General read options. + """ + + cdef: + CCsvFragmentScanOptions* csv_options + # The encoding field in ReadOptions does not exist in the C++ struct. + # We need to store it here and override it when reading read_options + ReadOptions _read_options_py + + # Avoid mistakingly creating attributes + __slots__ = () + + def __init__(self, ConvertOptions convert_options=None, + ReadOptions read_options=None): + self.init(shared_ptr[CFragmentScanOptions]( + new CCsvFragmentScanOptions())) + if convert_options is not None: + self.convert_options = convert_options + if read_options is not None: + self.read_options = read_options + self._read_options_py = read_options + + cdef void init(self, const shared_ptr[CFragmentScanOptions]& sp): + FragmentScanOptions.init(self, sp) + self.csv_options = sp.get() + + @property + def convert_options(self): + return ConvertOptions.wrap(self.csv_options.convert_options) + + @convert_options.setter + def convert_options(self, ConvertOptions convert_options not None): + self.csv_options.convert_options = deref(convert_options.options) + + @property + def read_options(self): + read_options = ReadOptions.wrap(self.csv_options.read_options) + if self._read_options_py is not None: + read_options.encoding = self._read_options_py.encoding + return read_options + + @read_options.setter + def read_options(self, ReadOptions read_options not None): + self.csv_options.read_options = deref(read_options.options) + self._read_options_py = read_options + if codecs.lookup(read_options.encoding).name != 'utf-8': + self.csv_options.stream_transform_func = deref( + make_streamwrap_func(read_options.encoding, 'utf-8')) + + def equals(self, CsvFragmentScanOptions other): + """ + Parameters + ---------- + other : pyarrow.dataset.CsvFragmentScanOptions + + Returns + ------- + bool + """ + return ( + other and + self.convert_options.equals(other.convert_options) and + self.read_options.equals(other.read_options)) + + def __reduce__(self): + return CsvFragmentScanOptions, (self.convert_options, + self.read_options) + + +cdef class CsvFileWriteOptions(FileWriteOptions): + cdef: + CCsvFileWriteOptions* csv_options + object _properties + + def __init__(self): + _forbid_instantiation(self.__class__) + + @property + def write_options(self): + return WriteOptions.wrap(deref(self.csv_options.write_options)) + + @write_options.setter + def write_options(self, WriteOptions write_options not None): + self.csv_options.write_options.reset( + new CCSVWriteOptions(deref(write_options.options))) + + cdef void init(self, const shared_ptr[CFileWriteOptions]& sp): + FileWriteOptions.init(self, sp) + self.csv_options = sp.get() + + +cdef class JsonFileFormat(FileFormat): + """ + FileFormat for JSON files. + + Parameters + ---------- + default_fragment_scan_options : JsonFragmentScanOptions + Default options for fragments scan. + parse_options : pyarrow.json.ParseOptions + Options regarding json parsing. + read_options : pyarrow.json.ReadOptions + General read options. + """ + cdef: + CJsonFileFormat* json_format + + # Avoid mistakingly creating attributes + __slots__ = () + + def __init__(self, default_fragment_scan_options=None, + JsonParseOptions parse_options=None, + JsonReadOptions read_options=None): + self.init(shared_ptr[CFileFormat](new CJsonFileFormat())) + if parse_options is not None or read_options is not None: + if default_fragment_scan_options is not None: + raise ValueError('If `default_fragment_scan_options` is ' + 'given, cannot specify read_options') + self.default_fragment_scan_options = JsonFragmentScanOptions( + parse_options=parse_options, + read_options=read_options) + elif isinstance(default_fragment_scan_options, dict): + self.default_fragment_scan_options = JsonFragmentScanOptions( + **default_fragment_scan_options) + elif isinstance(default_fragment_scan_options, JsonFragmentScanOptions): + self.default_fragment_scan_options = default_fragment_scan_options + elif default_fragment_scan_options is not None: + raise TypeError('`default_fragment_scan_options` must be either ' + 'a dictionary or an instance of ' + 'JsonFragmentScanOptions') + + cdef void init(self, const shared_ptr[CFileFormat]& sp): + FileFormat.init(self, sp) + self.json_format = sp.get() + + cdef _set_default_fragment_scan_options(self, FragmentScanOptions options): + if options.type_name == 'json': + self.json_format.default_fragment_scan_options = options.wrapped + self.default_fragment_scan_options.read_options = options.read_options + self.default_fragment_scan_options.parse_options = options.parse_options + else: + super()._set_default_fragment_scan_options(options) + + def equals(self, JsonFileFormat other): + """ + Parameters + ---------- + other : pyarrow.dataset.JsonFileFormat + + Returns + ------- + bool + """ + return (other and + self.default_fragment_scan_options == + other.default_fragment_scan_options) + + def __reduce__(self): + return JsonFileFormat, (self.default_fragment_scan_options,) + + def __repr__(self): + return "" + + +cdef class JsonFragmentScanOptions(FragmentScanOptions): + """ + Scan-specific options for JSON fragments. + + Parameters + ---------- + parse_options : pyarrow.json.ParseOptions + Options regarding JSON parsing. + read_options : pyarrow.json.ReadOptions + General read options. + """ + cdef: + CJsonFragmentScanOptions* json_options + + # Avoid mistakingly creating attributes + __slots__ = () + + def __init__(self, JsonParseOptions parse_options=None, + JsonReadOptions read_options=None): + self.init(shared_ptr[CFragmentScanOptions]( + new CJsonFragmentScanOptions())) + if parse_options is not None: + self.parse_options = parse_options + if read_options is not None: + self.read_options = read_options + + cdef void init(self, const shared_ptr[CFragmentScanOptions]& sp): + FragmentScanOptions.init(self, sp) + self.json_options = sp.get() + + @property + def parse_options(self): + return JsonParseOptions.wrap(self.json_options.parse_options) + + @parse_options.setter + def parse_options(self, JsonParseOptions parse_options not None): + self.json_options.parse_options = parse_options.options + + @property + def read_options(self): + return JsonReadOptions.wrap(self.json_options.read_options) + + @read_options.setter + def read_options(self, JsonReadOptions read_options not None): + self.json_options.read_options = read_options.options + + def equals(self, JsonFragmentScanOptions other): + """ + Parameters + ---------- + other : pyarrow.dataset.JsonFragmentScanOptions + + Returns + ------- + bool + """ + return ( + other and + self.read_options.equals(other.read_options) and + self.parse_options.equals(other.parse_options)) + + def __reduce__(self): + return JsonFragmentScanOptions, (self.parse_options, self.read_options) + + +cdef class Partitioning(_Weakrefable): + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef init(self, const shared_ptr[CPartitioning]& sp): + self.wrapped = sp + self.partitioning = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CPartitioning]& sp): + type_name = frombytes(sp.get().type_name()) + + classes = { + 'directory': DirectoryPartitioning, + 'hive': HivePartitioning, + 'filename': FilenamePartitioning, + } + + class_ = classes.get(type_name, None) + if class_ is None: + raise TypeError(type_name) + + cdef Partitioning self = class_.__new__(class_) + self.init(sp) + return self + + cdef inline shared_ptr[CPartitioning] unwrap(self): + return self.wrapped + + def __eq__(self, other): + if isinstance(other, Partitioning): + return self.partitioning.Equals(deref((other).unwrap())) + return False + + def parse(self, path): + """ + Parse a path into a partition expression. + + Parameters + ---------- + path : str + + Returns + ------- + pyarrow.dataset.Expression + """ + cdef CResult[CExpression] result + result = self.partitioning.Parse(tobytes(path)) + return Expression.wrap(GetResultValue(result)) + + @property + def schema(self): + """The arrow Schema attached to the partitioning.""" + return pyarrow_wrap_schema(self.partitioning.schema()) + + +cdef class PartitioningFactory(_Weakrefable): + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef init(self, const shared_ptr[CPartitioningFactory]& sp): + self.wrapped = sp + self.factory = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CPartitioningFactory]& sp, + object constructor, object options): + cdef PartitioningFactory self = PartitioningFactory.__new__( + PartitioningFactory + ) + self.init(sp) + self.constructor = constructor + self.options = options + return self + + cdef inline shared_ptr[CPartitioningFactory] unwrap(self): + return self.wrapped + + def __reduce__(self): + return self.constructor, self.options + + @property + def type_name(self): + return frombytes(self.factory.type_name()) + + +cdef vector[shared_ptr[CArray]] _partitioning_dictionaries( + Schema schema, dictionaries) except *: + cdef: + vector[shared_ptr[CArray]] c_dictionaries + + dictionaries = dictionaries or {} + + for field in schema: + dictionary = dictionaries.get(field.name) + + if (isinstance(field.type, pa.DictionaryType) and + dictionary is not None): + c_dictionaries.push_back(pyarrow_unwrap_array(dictionary)) + else: + c_dictionaries.push_back( nullptr) + + return c_dictionaries + + +cdef class KeyValuePartitioning(Partitioning): + + cdef: + CKeyValuePartitioning* keyvalue_partitioning + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef init(self, const shared_ptr[CPartitioning]& sp): + Partitioning.init(self, sp) + self.keyvalue_partitioning = sp.get() + self.wrapped = sp + self.partitioning = sp.get() + + def __reduce__(self): + dictionaries = self.dictionaries + if dictionaries: + dictionaries = dict(zip(self.schema.names, dictionaries)) + segment_encoding = _wrap_segment_encoding( + deref(self.keyvalue_partitioning).segment_encoding() + ) + return self.__class__, (self.schema, dictionaries, segment_encoding) + + @property + def dictionaries(self): + """ + The unique values for each partition field, if available. + + Those values are only available if the Partitioning object was + created through dataset discovery from a PartitioningFactory, or + if the dictionaries were manually specified in the constructor. + If no dictionary field is available, this returns an empty list. + """ + cdef vector[shared_ptr[CArray]] c_arrays + c_arrays = self.keyvalue_partitioning.dictionaries() + res = [] + for arr in c_arrays: + if arr.get() == nullptr: + # Partitioning object has not been created through + # inspected Factory + res.append(None) + else: + res.append(pyarrow_wrap_array(arr)) + return res + + +def _constructor_directory_partitioning_factory(*args): + return DirectoryPartitioning.discover(*args) + + +cdef class DirectoryPartitioning(KeyValuePartitioning): + """ + A Partitioning based on a specified Schema. + + The DirectoryPartitioning expects one segment in the file path for each + field in the schema (all fields are required to be present). + For example given schema the path "/2009/11" would + be parsed to ("year"_ == 2009 and "month"_ == 11). + + Parameters + ---------- + schema : Schema + The schema that describes the partitions present in the file path. + dictionaries : dict[str, Array] + If the type of any field of `schema` is a dictionary type, the + corresponding entry of `dictionaries` must be an array containing + every value which may be taken by the corresponding column or an + error will be raised in parsing. + segment_encoding : str, default "uri" + After splitting paths into segments, decode the segments. Valid + values are "uri" (URI-decode segments) and "none" (leave as-is). + + Returns + ------- + DirectoryPartitioning + + Examples + -------- + >>> from pyarrow.dataset import DirectoryPartitioning + >>> partitioning = DirectoryPartitioning( + ... pa.schema([("year", pa.int16()), ("month", pa.int8())])) + >>> print(partitioning.parse("/2009/11/")) + ((year == 2009) and (month == 11)) + """ + + cdef: + CDirectoryPartitioning* directory_partitioning + + def __init__(self, Schema schema not None, dictionaries=None, + segment_encoding="uri"): + cdef: + shared_ptr[CDirectoryPartitioning] c_partitioning + CKeyValuePartitioningOptions c_options + + c_options.segment_encoding = _get_segment_encoding(segment_encoding) + c_partitioning = make_shared[CDirectoryPartitioning]( + pyarrow_unwrap_schema(schema), + _partitioning_dictionaries(schema, dictionaries), + c_options, + ) + self.init( c_partitioning) + + cdef init(self, const shared_ptr[CPartitioning]& sp): + KeyValuePartitioning.init(self, sp) + self.directory_partitioning = sp.get() + + @staticmethod + def discover(field_names=None, infer_dictionary=False, + max_partition_dictionary_size=0, + schema=None, segment_encoding="uri"): + """ + Discover a DirectoryPartitioning. + + Parameters + ---------- + field_names : list of str + The names to associate with the values from the subdirectory names. + If schema is given, will be populated from the schema. + infer_dictionary : bool, default False + When inferring a schema for partition fields, yield dictionary + encoded types instead of plain types. This can be more efficient + when materializing virtual columns, and Expressions parsed by the + finished Partitioning will include dictionaries of all unique + inspected values for each field. + max_partition_dictionary_size : int, default 0 + Synonymous with infer_dictionary for backwards compatibility with + 1.0: setting this to -1 or None is equivalent to passing + infer_dictionary=True. + schema : Schema, default None + Use this schema instead of inferring a schema from partition + values. Partition values will be validated against this schema + before accumulation into the Partitioning's dictionary. + segment_encoding : str, default "uri" + After splitting paths into segments, decode the segments. Valid + values are "uri" (URI-decode segments) and "none" (leave as-is). + + Returns + ------- + PartitioningFactory + To be used in the FileSystemFactoryOptions. + """ + cdef: + CPartitioningFactoryOptions c_options + vector[c_string] c_field_names + + if max_partition_dictionary_size in {-1, None}: + infer_dictionary = True + elif max_partition_dictionary_size != 0: + raise NotImplementedError("max_partition_dictionary_size must be " + "0, -1, or None") + + if infer_dictionary: + c_options.infer_dictionary = True + + if schema: + c_options.schema = pyarrow_unwrap_schema(schema) + c_field_names = [tobytes(f.name) for f in schema] + elif not field_names: + raise ValueError( + "Neither field_names nor schema was passed; " + "cannot infer field_names") + else: + c_field_names = [tobytes(s) for s in field_names] + + c_options.segment_encoding = _get_segment_encoding(segment_encoding) + + return PartitioningFactory.wrap( + CDirectoryPartitioning.MakeFactory(c_field_names, c_options), + _constructor_directory_partitioning_factory, + (field_names, infer_dictionary, max_partition_dictionary_size, + schema, segment_encoding) + ) + + +def _constructor_hive_partitioning_factory(*args): + return HivePartitioning.discover(*args) + + +cdef class HivePartitioning(KeyValuePartitioning): + """ + A Partitioning for "/$key=$value/" nested directories as found in + Apache Hive. + + Multi-level, directory based partitioning scheme originating from + Apache Hive with all data files stored in the leaf directories. Data is + partitioned by static values of a particular column in the schema. + Partition keys are represented in the form $key=$value in directory names. + Field order is ignored, as are missing or unrecognized field names. + + For example, given schema, a possible + path would be "/year=2009/month=11/day=15". + + Parameters + ---------- + schema : Schema + The schema that describes the partitions present in the file path. + dictionaries : dict[str, Array] + If the type of any field of `schema` is a dictionary type, the + corresponding entry of `dictionaries` must be an array containing + every value which may be taken by the corresponding column or an + error will be raised in parsing. + null_fallback : str, default "__HIVE_DEFAULT_PARTITION__" + If any field is None then this fallback will be used as a label + segment_encoding : str, default "uri" + After splitting paths into segments, decode the segments. Valid + values are "uri" (URI-decode segments) and "none" (leave as-is). + + Returns + ------- + HivePartitioning + + Examples + -------- + >>> from pyarrow.dataset import HivePartitioning + >>> partitioning = HivePartitioning( + ... pa.schema([("year", pa.int16()), ("month", pa.int8())])) + >>> print(partitioning.parse("/year=2009/month=11/")) + ((year == 2009) and (month == 11)) + + """ + + cdef: + CHivePartitioning* hive_partitioning + + def __init__(self, + Schema schema not None, + dictionaries=None, + null_fallback="__HIVE_DEFAULT_PARTITION__", + segment_encoding="uri"): + + cdef: + shared_ptr[CHivePartitioning] c_partitioning + CHivePartitioningOptions c_options + + c_options.null_fallback = tobytes(null_fallback) + c_options.segment_encoding = _get_segment_encoding(segment_encoding) + + c_partitioning = make_shared[CHivePartitioning]( + pyarrow_unwrap_schema(schema), + _partitioning_dictionaries(schema, dictionaries), + c_options, + ) + self.init( c_partitioning) + + cdef init(self, const shared_ptr[CPartitioning]& sp): + KeyValuePartitioning.init(self, sp) + self.hive_partitioning = sp.get() + + def __reduce__(self): + dictionaries = self.dictionaries + if dictionaries: + dictionaries = dict(zip(self.schema.names, dictionaries)) + segment_encoding = _wrap_segment_encoding( + deref(self.keyvalue_partitioning).segment_encoding() + ) + null_fallback = frombytes(deref(self.hive_partitioning).null_fallback()) + return HivePartitioning, ( + self.schema, dictionaries, null_fallback, segment_encoding + ) + + @staticmethod + def discover(infer_dictionary=False, + max_partition_dictionary_size=0, + null_fallback="__HIVE_DEFAULT_PARTITION__", + schema=None, + segment_encoding="uri"): + """ + Discover a HivePartitioning. + + Parameters + ---------- + infer_dictionary : bool, default False + When inferring a schema for partition fields, yield dictionary + encoded types instead of plain. This can be more efficient when + materializing virtual columns, and Expressions parsed by the + finished Partitioning will include dictionaries of all unique + inspected values for each field. + max_partition_dictionary_size : int, default 0 + Synonymous with infer_dictionary for backwards compatibility with + 1.0: setting this to -1 or None is equivalent to passing + infer_dictionary=True. + null_fallback : str, default "__HIVE_DEFAULT_PARTITION__" + When inferring a schema for partition fields this value will be + replaced by null. The default is set to __HIVE_DEFAULT_PARTITION__ + for compatibility with Spark + schema : Schema, default None + Use this schema instead of inferring a schema from partition + values. Partition values will be validated against this schema + before accumulation into the Partitioning's dictionary. + segment_encoding : str, default "uri" + After splitting paths into segments, decode the segments. Valid + values are "uri" (URI-decode segments) and "none" (leave as-is). + + Returns + ------- + PartitioningFactory + To be used in the FileSystemFactoryOptions. + """ + cdef: + CHivePartitioningFactoryOptions c_options + + if max_partition_dictionary_size in {-1, None}: + infer_dictionary = True + elif max_partition_dictionary_size != 0: + raise NotImplementedError("max_partition_dictionary_size must be " + "0, -1, or None") + + if infer_dictionary: + c_options.infer_dictionary = True + + c_options.null_fallback = tobytes(null_fallback) + + if schema: + c_options.schema = pyarrow_unwrap_schema(schema) + + c_options.segment_encoding = _get_segment_encoding(segment_encoding) + + return PartitioningFactory.wrap( + CHivePartitioning.MakeFactory(c_options), + _constructor_hive_partitioning_factory, + (infer_dictionary, max_partition_dictionary_size, null_fallback, + schema, segment_encoding), + ) + + +def _constructor_filename_partitioning_factory(*args): + return FilenamePartitioning.discover(*args) + + +cdef class FilenamePartitioning(KeyValuePartitioning): + """ + A Partitioning based on a specified Schema. + + The FilenamePartitioning expects one segment in the file name for each + field in the schema (all fields are required to be present) separated + by '_'. For example given schema the name + ``"2009_11_"`` would be parsed to ("year" == 2009 and "month" == 11). + + Parameters + ---------- + schema : Schema + The schema that describes the partitions present in the file path. + dictionaries : dict[str, Array] + If the type of any field of `schema` is a dictionary type, the + corresponding entry of `dictionaries` must be an array containing + every value which may be taken by the corresponding column or an + error will be raised in parsing. + segment_encoding : str, default "uri" + After splitting paths into segments, decode the segments. Valid + values are "uri" (URI-decode segments) and "none" (leave as-is). + + Returns + ------- + FilenamePartitioning + + Examples + -------- + >>> from pyarrow.dataset import FilenamePartitioning + >>> partitioning = FilenamePartitioning( + ... pa.schema([("year", pa.int16()), ("month", pa.int8())])) + >>> print(partitioning.parse("2009_11_data.parquet")) + ((year == 2009) and (month == 11)) + """ + + cdef: + CFilenamePartitioning* filename_partitioning + + def __init__(self, Schema schema not None, dictionaries=None, + segment_encoding="uri"): + cdef: + shared_ptr[CFilenamePartitioning] c_partitioning + CKeyValuePartitioningOptions c_options + + c_options.segment_encoding = _get_segment_encoding(segment_encoding) + c_partitioning = make_shared[CFilenamePartitioning]( + pyarrow_unwrap_schema(schema), + _partitioning_dictionaries(schema, dictionaries), + c_options, + ) + self.init( c_partitioning) + + cdef init(self, const shared_ptr[CPartitioning]& sp): + KeyValuePartitioning.init(self, sp) + self.filename_partitioning = sp.get() + + @staticmethod + def discover(field_names=None, infer_dictionary=False, + schema=None, segment_encoding="uri"): + """ + Discover a FilenamePartitioning. + + Parameters + ---------- + field_names : list of str + The names to associate with the values from the subdirectory names. + If schema is given, will be populated from the schema. + infer_dictionary : bool, default False + When inferring a schema for partition fields, yield dictionary + encoded types instead of plain types. This can be more efficient + when materializing virtual columns, and Expressions parsed by the + finished Partitioning will include dictionaries of all unique + inspected values for each field. + schema : Schema, default None + Use this schema instead of inferring a schema from partition + values. Partition values will be validated against this schema + before accumulation into the Partitioning's dictionary. + segment_encoding : str, default "uri" + After splitting paths into segments, decode the segments. Valid + values are "uri" (URI-decode segments) and "none" (leave as-is). + + Returns + ------- + PartitioningFactory + To be used in the FileSystemFactoryOptions. + """ + cdef: + CPartitioningFactoryOptions c_options + vector[c_string] c_field_names + + if infer_dictionary: + c_options.infer_dictionary = True + + if schema: + c_options.schema = pyarrow_unwrap_schema(schema) + c_field_names = [tobytes(f.name) for f in schema] + elif not field_names: + raise TypeError( + "Neither field_names nor schema was passed; " + "cannot infer field_names") + else: + c_field_names = [tobytes(s) for s in field_names] + + c_options.segment_encoding = _get_segment_encoding(segment_encoding) + + return PartitioningFactory.wrap( + CFilenamePartitioning.MakeFactory(c_field_names, c_options), + _constructor_filename_partitioning_factory, + (field_names, infer_dictionary, schema, segment_encoding) + ) + + +cdef class DatasetFactory(_Weakrefable): + """ + DatasetFactory is used to create a Dataset, inspect the Schema + of the fragments contained in it, and declare a partitioning. + """ + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef init(self, const shared_ptr[CDatasetFactory]& sp): + self.wrapped = sp + self.factory = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CDatasetFactory]& sp): + cdef DatasetFactory self = \ + DatasetFactory.__new__(DatasetFactory) + self.init(sp) + return self + + cdef inline shared_ptr[CDatasetFactory] unwrap(self) nogil: + return self.wrapped + + @property + def root_partition(self): + return Expression.wrap(self.factory.root_partition()) + + @root_partition.setter + def root_partition(self, Expression expr): + check_status(self.factory.SetRootPartition(expr.unwrap())) + + def inspect_schemas(self): + cdef CResult[vector[shared_ptr[CSchema]]] result + cdef CInspectOptions options + with nogil: + result = self.factory.InspectSchemas(options) + + schemas = [] + for s in GetResultValue(result): + schemas.append(pyarrow_wrap_schema(s)) + return schemas + + def inspect(self): + """ + Inspect all data fragments and return a common Schema. + + Returns + ------- + Schema + """ + cdef: + CInspectOptions options + CResult[shared_ptr[CSchema]] result + with nogil: + result = self.factory.Inspect(options) + return pyarrow_wrap_schema(GetResultValue(result)) + + def finish(self, Schema schema=None): + """ + Create a Dataset using the inspected schema or an explicit schema + (if given). + + Parameters + ---------- + schema : Schema, default None + The schema to conform the source to. If None, the inspected + schema is used. + + Returns + ------- + Dataset + """ + cdef: + shared_ptr[CSchema] sp_schema + CResult[shared_ptr[CDataset]] result + + if schema is not None: + sp_schema = pyarrow_unwrap_schema(schema) + with nogil: + result = self.factory.FinishWithSchema(sp_schema) + else: + with nogil: + result = self.factory.Finish() + + return Dataset.wrap(GetResultValue(result)) + + +cdef class FileSystemFactoryOptions(_Weakrefable): + """ + Influences the discovery of filesystem paths. + + Parameters + ---------- + partition_base_dir : str, optional + For the purposes of applying the partitioning, paths will be + stripped of the partition_base_dir. Files not matching the + partition_base_dir prefix will be skipped for partitioning discovery. + The ignored files will still be part of the Dataset, but will not + have partition information. + partitioning : Partitioning/PartitioningFactory, optional + Apply the Partitioning to every discovered Fragment. See Partitioning or + PartitioningFactory documentation. + exclude_invalid_files : bool, optional (default True) + If True, invalid files will be excluded (file format specific check). + This will incur IO for each files in a serial and single threaded + fashion. Disabling this feature will skip the IO, but unsupported + files may be present in the Dataset (resulting in an error at scan + time). + selector_ignore_prefixes : list, optional + When discovering from a Selector (and not from an explicit file list), + ignore files and directories matching any of these prefixes. + By default this is ['.', '_']. + """ + + cdef: + CFileSystemFactoryOptions options + + __slots__ = () # avoid mistakingly creating attributes + + def __init__(self, partition_base_dir=None, partitioning=None, + exclude_invalid_files=None, + list selector_ignore_prefixes=None): + if isinstance(partitioning, PartitioningFactory): + self.partitioning_factory = partitioning + elif isinstance(partitioning, Partitioning): + self.partitioning = partitioning + + if partition_base_dir is not None: + self.partition_base_dir = partition_base_dir + if exclude_invalid_files is not None: + self.exclude_invalid_files = exclude_invalid_files + if selector_ignore_prefixes is not None: + self.selector_ignore_prefixes = selector_ignore_prefixes + + cdef inline CFileSystemFactoryOptions unwrap(self): + return self.options + + @property + def partitioning(self): + """Partitioning to apply to discovered files. + + NOTE: setting this property will overwrite partitioning_factory. + """ + c_partitioning = self.options.partitioning.partitioning() + if c_partitioning.get() == nullptr: + return None + return Partitioning.wrap(c_partitioning) + + @partitioning.setter + def partitioning(self, Partitioning value): + self.options.partitioning = ( value).unwrap() + + @property + def partitioning_factory(self): + """PartitioningFactory to apply to discovered files and + discover a Partitioning. + + NOTE: setting this property will overwrite partitioning. + """ + c_factory = self.options.partitioning.factory() + if c_factory.get() == nullptr: + return None + return PartitioningFactory.wrap(c_factory, None, None) + + @partitioning_factory.setter + def partitioning_factory(self, PartitioningFactory value): + self.options.partitioning = ( value).unwrap() + + @property + def partition_base_dir(self): + """ + Base directory to strip paths before applying the partitioning. + """ + return frombytes(self.options.partition_base_dir) + + @partition_base_dir.setter + def partition_base_dir(self, value): + self.options.partition_base_dir = tobytes(value) + + @property + def exclude_invalid_files(self): + """Whether to exclude invalid files.""" + return self.options.exclude_invalid_files + + @exclude_invalid_files.setter + def exclude_invalid_files(self, bint value): + self.options.exclude_invalid_files = value + + @property + def selector_ignore_prefixes(self): + """ + List of prefixes. Files matching one of those prefixes will be + ignored by the discovery process. + """ + return [frombytes(p) for p in self.options.selector_ignore_prefixes] + + @selector_ignore_prefixes.setter + def selector_ignore_prefixes(self, values): + self.options.selector_ignore_prefixes = [tobytes(v) for v in values] + + +cdef vector[CFileInfo] unwrap_finfos(finfos): + cdef vector[CFileInfo] o_vect + for fi in finfos: + o_vect.push_back(( fi).unwrap()) + return o_vect + + +cdef class FileSystemDatasetFactory(DatasetFactory): + """ + Create a DatasetFactory from a list of paths with schema inspection. + + Parameters + ---------- + filesystem : pyarrow.fs.FileSystem + Filesystem to discover. + paths_or_selector : pyarrow.fs.FileSelector or list of path-likes + Either a Selector object or a list of path-like objects. + format : FileFormat + Currently only ParquetFileFormat and IpcFileFormat are supported. + options : FileSystemFactoryOptions, optional + Various flags influencing the discovery of filesystem paths. + """ + + cdef: + CFileSystemDatasetFactory* filesystem_factory + + def __init__(self, FileSystem filesystem not None, paths_or_selector, + FileFormat format not None, + FileSystemFactoryOptions options=None): + cdef: + vector[c_string] paths + vector[CFileInfo] finfos + CFileSelector c_selector + CResult[shared_ptr[CDatasetFactory]] result + shared_ptr[CFileSystem] c_filesystem + shared_ptr[CFileFormat] c_format + CFileSystemFactoryOptions c_options + + options = options or FileSystemFactoryOptions() + c_options = options.unwrap() + c_filesystem = filesystem.unwrap() + c_format = format.unwrap() + + if isinstance(paths_or_selector, FileSelector): + with nogil: + c_selector = ( paths_or_selector).selector + result = CFileSystemDatasetFactory.MakeFromSelector( + c_filesystem, + c_selector, + c_format, + c_options + ) + elif isinstance(paths_or_selector, (list, tuple)): + if len(paths_or_selector) > 0 and isinstance(paths_or_selector[0], FileInfo): + finfos = unwrap_finfos(paths_or_selector) + with nogil: + result = CFileSystemDatasetFactory.MakeFromFileInfos( + c_filesystem, + finfos, + c_format, + c_options + ) + else: + paths = [tobytes(s) for s in paths_or_selector] + with nogil: + result = CFileSystemDatasetFactory.MakeFromPaths( + c_filesystem, + paths, + c_format, + c_options + ) + else: + raise TypeError('Must pass either paths or a FileSelector, but ' + 'passed {}'.format(type(paths_or_selector))) + + self.init(GetResultValue(result)) + + cdef init(self, shared_ptr[CDatasetFactory]& sp): + DatasetFactory.init(self, sp) + self.filesystem_factory = sp.get() + + +cdef class UnionDatasetFactory(DatasetFactory): + """ + Provides a way to inspect/discover a Dataset's expected schema before + materialization. + + Parameters + ---------- + factories : list of DatasetFactory + """ + + cdef: + CUnionDatasetFactory* union_factory + + def __init__(self, list factories): + cdef: + DatasetFactory factory + vector[shared_ptr[CDatasetFactory]] c_factories + for factory in factories: + c_factories.push_back(factory.unwrap()) + self.init(GetResultValue(CUnionDatasetFactory.Make(c_factories))) + + cdef init(self, const shared_ptr[CDatasetFactory]& sp): + DatasetFactory.init(self, sp) + self.union_factory = sp.get() + + +cdef class RecordBatchIterator(_Weakrefable): + """An iterator over a sequence of record batches.""" + cdef: + # An object that must be kept alive with the iterator. + object iterator_owner + # Iterator is a non-POD type and Cython uses offsetof, leading + # to a compiler warning unless wrapped like so + SharedPtrNoGIL[CRecordBatchIterator] iterator + + def __init__(self): + _forbid_instantiation(self.__class__, subclasses_instead=False) + + @staticmethod + cdef wrap(object owner, CRecordBatchIterator iterator): + cdef RecordBatchIterator self = \ + RecordBatchIterator.__new__(RecordBatchIterator) + self.iterator_owner = owner + self.iterator = make_shared[CRecordBatchIterator](move(iterator)) + return self + + cdef inline shared_ptr[CRecordBatchIterator] unwrap(self) nogil: + return self.iterator + + def __iter__(self): + return self + + def __next__(self): + cdef shared_ptr[CRecordBatch] record_batch + with nogil: + record_batch = GetResultValue(move(self.iterator.get().Next())) + if record_batch == NULL: + raise StopIteration + return pyarrow_wrap_batch(record_batch) + + +class TaggedRecordBatch(collections.namedtuple( + "TaggedRecordBatch", ["record_batch", "fragment"])): + """ + A combination of a record batch and the fragment it came from. + + Parameters + ---------- + record_batch : RecordBatch + The record batch. + fragment : Fragment + Fragment of the record batch. + """ + + +cdef class TaggedRecordBatchIterator(_Weakrefable): + """An iterator over a sequence of record batches with fragments.""" + cdef: + object iterator_owner + SharedPtrNoGIL[CTaggedRecordBatchIterator] iterator + + def __init__(self): + _forbid_instantiation(self.__class__, subclasses_instead=False) + + @staticmethod + cdef wrap(object owner, CTaggedRecordBatchIterator iterator): + cdef TaggedRecordBatchIterator self = \ + TaggedRecordBatchIterator.__new__(TaggedRecordBatchIterator) + self.iterator_owner = owner + self.iterator = make_shared[CTaggedRecordBatchIterator]( + move(iterator)) + return self + + def __iter__(self): + return self + + def __next__(self): + cdef CTaggedRecordBatch batch + with nogil: + batch = GetResultValue(move(self.iterator.get().Next())) + if batch.record_batch == NULL: + raise StopIteration + return TaggedRecordBatch( + record_batch=pyarrow_wrap_batch(batch.record_batch), + fragment=Fragment.wrap(batch.fragment)) + + +cdef void _populate_builder(const shared_ptr[CScannerBuilder]& ptr, + object columns=None, Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + bint use_threads=True, MemoryPool memory_pool=None, + FragmentScanOptions fragment_scan_options=None)\ + except *: + cdef: + CScannerBuilder *builder + vector[CExpression] c_exprs + + builder = ptr.get() + + check_status(builder.Filter(_bind( + filter, pyarrow_wrap_schema(builder.schema())))) + + if columns is not None: + if isinstance(columns, dict): + for expr in columns.values(): + if not isinstance(expr, Expression): + raise TypeError( + "Expected an Expression for a 'column' dictionary " + "value, got {} instead".format(type(expr)) + ) + c_exprs.push_back(( expr).unwrap()) + + check_status( + builder.Project(c_exprs, [tobytes(c) for c in columns.keys()]) + ) + elif isinstance(columns, list): + check_status(builder.ProjectColumns([tobytes(c) for c in columns])) + else: + raise ValueError( + "Expected a list or a dict for 'columns', " + "got {} instead.".format(type(columns)) + ) + + check_status(builder.BatchSize(batch_size)) + check_status(builder.BatchReadahead(batch_readahead)) + check_status(builder.FragmentReadahead(fragment_readahead)) + check_status(builder.UseThreads(use_threads)) + check_status(builder.Pool(maybe_unbox_memory_pool(memory_pool))) + if fragment_scan_options: + check_status( + builder.FragmentScanOptions(fragment_scan_options.wrapped)) + + +cdef class Scanner(_Weakrefable): + """A materialized scan operation with context and options bound. + + A scanner is the class that glues the scan tasks, data fragments and data + sources together. + """ + + def __init__(self): + _forbid_instantiation(self.__class__) + + cdef void init(self, const shared_ptr[CScanner]& sp): + self.wrapped = sp + self.scanner = sp.get() + + @staticmethod + cdef wrap(const shared_ptr[CScanner]& sp): + cdef Scanner self = Scanner.__new__(Scanner) + self.init(sp) + return self + + cdef inline shared_ptr[CScanner] unwrap(self): + return self.wrapped + + @staticmethod + cdef shared_ptr[CScanOptions] _make_scan_options(Dataset dataset, dict py_scanoptions) except *: + cdef: + shared_ptr[CScannerBuilder] builder = make_shared[CScannerBuilder](dataset.unwrap()) + + py_scanoptions = dataset._scanner_options(py_scanoptions) + + # Need to explicitly expand the arguments as Cython doesn't support + # keyword expansion in cdef functions. + _populate_builder( + builder, + columns=py_scanoptions.get("columns"), + filter=py_scanoptions.get("filter"), + batch_size=py_scanoptions.get("batch_size", _DEFAULT_BATCH_SIZE), + batch_readahead=py_scanoptions.get( + "batch_readahead", _DEFAULT_BATCH_READAHEAD), + fragment_readahead=py_scanoptions.get( + "fragment_readahead", _DEFAULT_FRAGMENT_READAHEAD), + use_threads=py_scanoptions.get("use_threads", True), + memory_pool=py_scanoptions.get("memory_pool"), + fragment_scan_options=py_scanoptions.get("fragment_scan_options")) + + return GetResultValue(deref(builder).GetScanOptions()) + + @staticmethod + def from_dataset(Dataset dataset not None, *, + object columns=None, + Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, MemoryPool memory_pool=None): + """ + Create Scanner from Dataset, + + Parameters + ---------- + dataset : Dataset + Dataset to scan. + columns : list[str] or dict[str, Expression], default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + """ + cdef: + shared_ptr[CScanOptions] options + shared_ptr[CScannerBuilder] builder + shared_ptr[CScanner] scanner + + options = Scanner._make_scan_options( + dataset, + dict(columns=columns, filter=filter, batch_size=batch_size, + batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, use_threads=use_threads, + memory_pool=memory_pool, fragment_scan_options=fragment_scan_options) + ) + builder = make_shared[CScannerBuilder](dataset.unwrap(), options) + scanner = GetResultValue(builder.get().Finish()) + return Scanner.wrap(scanner) + + @staticmethod + def from_fragment(Fragment fragment not None, *, Schema schema=None, + object columns=None, Expression filter=None, + int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, MemoryPool memory_pool=None): + """ + Create Scanner from Fragment, + + Parameters + ---------- + fragment : Fragment + fragment to scan. + schema : Schema, optional + The schema of the fragment. + columns : list[str] or dict[str, Expression], default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + """ + cdef: + shared_ptr[CScanOptions] options = make_shared[CScanOptions]() + shared_ptr[CScannerBuilder] builder + shared_ptr[CScanner] scanner + + schema = schema or fragment.physical_schema + + builder = make_shared[CScannerBuilder](pyarrow_unwrap_schema(schema), + fragment.unwrap(), options) + _populate_builder(builder, columns=columns, filter=filter, + batch_size=batch_size, batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, + use_threads=use_threads, + memory_pool=memory_pool, + fragment_scan_options=fragment_scan_options) + + scanner = GetResultValue(builder.get().Finish()) + return Scanner.wrap(scanner) + + @staticmethod + def from_batches(source, *, Schema schema=None, object columns=None, + Expression filter=None, int batch_size=_DEFAULT_BATCH_SIZE, + int batch_readahead=_DEFAULT_BATCH_READAHEAD, + int fragment_readahead=_DEFAULT_FRAGMENT_READAHEAD, + FragmentScanOptions fragment_scan_options=None, + bint use_threads=True, MemoryPool memory_pool=None): + """ + Create a Scanner from an iterator of batches. + + This creates a scanner which can be used only once. It is + intended to support writing a dataset (which takes a scanner) + from a source which can be read only once (e.g. a + RecordBatchReader or generator). + + Parameters + ---------- + source : Iterator + The iterator of Batches. + schema : Schema + The schema of the batches. + columns : list[str] or dict[str, Expression], default None + The columns to project. This can be a list of column names to + include (order and duplicates will be preserved), or a dictionary + with {new_column_name: expression} values for more advanced + projections. + + The list of columns or expressions may use the special fields + `__batch_index` (the index of the batch within the fragment), + `__fragment_index` (the index of the fragment within the dataset), + `__last_in_fragment` (whether the batch is last in fragment), and + `__filename` (the name of the source file or a description of the + source fragment). + + The columns will be passed down to Datasets and corresponding data + fragments to avoid loading, copying, and deserializing columns + that will not be required further down the compute chain. + By default all of the available columns are projected. Raises + an exception if any of the referenced column names does not exist + in the dataset's Schema. + filter : Expression, default None + Scan will return only the rows matching the filter. + If possible the predicate will be pushed down to exploit the + partition information or internal metadata found in the data + source, e.g. Parquet statistics. Otherwise filters the loaded + RecordBatches before yielding them. + batch_size : int, default 131_072 + The maximum row count for scanned record batches. If scanned + record batches are overflowing memory then this method can be + called to reduce their size. + batch_readahead : int, default 16 + The number of batches to read ahead in a file. This might not work + for all file formats. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_readahead : int, default 4 + The number of files to read ahead. Increasing this number will increase + RAM usage but could also improve IO utilization. + fragment_scan_options : FragmentScanOptions, default None + Options specific to a particular scan and fragment type, which + can change between different scans of the same dataset. + use_threads : bool, default True + If enabled, then maximum parallelism will be used determined by + the number of available CPU cores. + memory_pool : MemoryPool, default None + For memory allocations, if required. If not specified, uses the + default pool. + """ + cdef: + shared_ptr[CScannerBuilder] builder + shared_ptr[CScanner] scanner + RecordBatchReader reader + if isinstance(source, pa.ipc.RecordBatchReader): + if schema: + raise ValueError('Cannot specify a schema when providing ' + 'a RecordBatchReader') + reader = source + elif _is_iterable(source): + if schema is None: + raise ValueError('Must provide schema to construct scanner ' + 'from an iterable') + reader = pa.ipc.RecordBatchReader.from_batches(schema, source) + else: + raise TypeError('Expected a RecordBatchReader or an iterable of ' + 'batches instead of the given type: ' + + type(source).__name__) + builder = CScannerBuilder.FromRecordBatchReader(reader.reader) + _populate_builder(builder, columns=columns, filter=filter, + batch_size=batch_size, batch_readahead=batch_readahead, + fragment_readahead=fragment_readahead, use_threads=use_threads, + memory_pool=memory_pool, + fragment_scan_options=fragment_scan_options) + scanner = GetResultValue(builder.get().Finish()) + return Scanner.wrap(scanner) + + @property + def dataset_schema(self): + """The schema with which batches will be read from fragments.""" + return pyarrow_wrap_schema( + self.scanner.options().get().dataset_schema) + + @property + def projected_schema(self): + """ + The materialized schema of the data, accounting for projections. + + This is the schema of any data returned from the scanner. + """ + return pyarrow_wrap_schema( + self.scanner.options().get().projected_schema) + + def to_batches(self): + """ + Consume a Scanner in record batches. + + Returns + ------- + record_batches : iterator of RecordBatch + """ + def _iterator(batch_iter): + for batch in batch_iter: + yield batch.record_batch + # Don't make ourselves a generator so errors are raised immediately + return _iterator(self.scan_batches()) + + def scan_batches(self): + """ + Consume a Scanner in record batches with corresponding fragments. + + Returns + ------- + record_batches : iterator of TaggedRecordBatch + """ + cdef CTaggedRecordBatchIterator iterator + with nogil: + iterator = move(GetResultValue(self.scanner.ScanBatches())) + # Don't make ourselves a generator so errors are raised immediately + return TaggedRecordBatchIterator.wrap(self, move(iterator)) + + def to_table(self): + """ + Convert a Scanner into a Table. + + Use this convenience utility with care. This will serially materialize + the Scan result in memory before creating the Table. + + Returns + ------- + Table + """ + cdef CResult[shared_ptr[CTable]] result + + with nogil: + result = self.scanner.ToTable() + + return pyarrow_wrap_table(GetResultValue(result)) + + def take(self, object indices): + """ + Select rows of data by index. + + Will only consume as many batches of the underlying dataset as + needed. Otherwise, this is equivalent to + ``to_table().take(indices)``. + + Parameters + ---------- + indices : Array or array-like + indices of rows to select in the dataset. + + Returns + ------- + Table + """ + cdef CResult[shared_ptr[CTable]] result + cdef shared_ptr[CArray] c_indices + + if not isinstance(indices, pa.Array): + indices = pa.array(indices) + c_indices = pyarrow_unwrap_array(indices) + + with nogil: + result = self.scanner.TakeRows(deref(c_indices)) + return pyarrow_wrap_table(GetResultValue(result)) + + def head(self, int num_rows): + """ + Load the first N rows of the dataset. + + Parameters + ---------- + num_rows : int + The number of rows to load. + + Returns + ------- + Table + """ + cdef CResult[shared_ptr[CTable]] result + with nogil: + result = self.scanner.Head(num_rows) + return pyarrow_wrap_table(GetResultValue(result)) + + def count_rows(self): + """ + Count rows matching the scanner filter. + + Returns + ------- + count : int + """ + cdef CResult[int64_t] result + with nogil: + result = self.scanner.CountRows() + return GetResultValue(result) + + def to_reader(self): + """Consume this scanner as a RecordBatchReader. + + Returns + ------- + RecordBatchReader + """ + cdef RecordBatchReader reader + reader = RecordBatchReader.__new__(RecordBatchReader) + reader.reader = GetResultValue(self.scanner.ToRecordBatchReader()) + return reader + + +def get_partition_keys(Expression partition_expression): + """ + Extract partition keys (equality constraints between a field and a scalar) + from an expression as a dict mapping the field's name to its value. + + NB: All expressions yielded by a HivePartitioning or DirectoryPartitioning + will be conjunctions of equality conditions and are accessible through this + function. Other subexpressions will be ignored. + + Parameters + ---------- + partition_expression : pyarrow.dataset.Expression + + Returns + ------- + dict + + Examples + -------- + + For example, an expression of + + is converted to {'part': 'A', 'year': 2016} + """ + cdef: + CExpression expr = partition_expression.unwrap() + pair[CFieldRef, CDatum] ref_val + + out = {} + for ref_val in GetResultValue(CExtractKnownFieldValues(expr)).map: + assert ref_val.first.name() != nullptr + assert ref_val.second.kind() == DatumType_SCALAR + val = pyarrow_wrap_scalar(ref_val.second.scalar()) + out[frombytes(deref(ref_val.first.name()))] = val.as_py() + return out + + +cdef class WrittenFile(_Weakrefable): + """ + Metadata information about files written as + part of a dataset write operation + + Parameters + ---------- + path : str + Path to the file. + metadata : pyarrow.parquet.FileMetaData, optional + For Parquet files, the Parquet file metadata. + size : int + The size of the file in bytes. + """ + + def __init__(self, path, metadata, size): + self.path = path + self.metadata = metadata + self.size = size + + +cdef void _filesystemdataset_write_visitor( + dict visit_args, + CFileWriter* file_writer): + cdef: + str path + str base_dir + WrittenFile written_file + FileFormat file_format + + path = frombytes(deref(file_writer).destination().path) + base_dir = frombytes(visit_args['base_dir']) + file_format = FileFormat.wrap(file_writer.format()) + written_file = file_format._finish_write(path, base_dir, file_writer) + visit_args['file_visitor'](written_file) + + +def _filesystemdataset_write( + Scanner data not None, + object base_dir not None, + str basename_template not None, + FileSystem filesystem not None, + Partitioning partitioning not None, + FileWriteOptions file_options not None, + int max_partitions, + object file_visitor, + str existing_data_behavior not None, + int max_open_files, + int max_rows_per_file, + int min_rows_per_group, + int max_rows_per_group, + bool create_dir +): + """ + CFileSystemDataset.Write wrapper + """ + cdef: + CFileSystemDatasetWriteOptions c_options + shared_ptr[CScanner] c_scanner + dict visit_args + + c_options.file_write_options = file_options.unwrap() + c_options.filesystem = filesystem.unwrap() + c_options.base_dir = tobytes(_stringify_path(base_dir)) + c_options.partitioning = partitioning.unwrap() + c_options.max_partitions = max_partitions + c_options.max_open_files = max_open_files + c_options.max_rows_per_file = max_rows_per_file + c_options.max_rows_per_group = max_rows_per_group + c_options.min_rows_per_group = min_rows_per_group + c_options.basename_template = tobytes(basename_template) + if existing_data_behavior == 'error': + c_options.existing_data_behavior = ExistingDataBehavior_ERROR + elif existing_data_behavior == 'overwrite_or_ignore': + c_options.existing_data_behavior =\ + ExistingDataBehavior_OVERWRITE_OR_IGNORE + elif existing_data_behavior == 'delete_matching': + c_options.existing_data_behavior = ExistingDataBehavior_DELETE_MATCHING + else: + raise ValueError( + ("existing_data_behavior must be one of 'error', ", + "'overwrite_or_ignore' or 'delete_matching'") + ) + c_options.create_dir = create_dir + + if file_visitor is not None: + visit_args = {'base_dir': c_options.base_dir, + 'file_visitor': file_visitor} + # Need to use post_finish because parquet metadata is not available + # until after Finish has been called + c_options.writer_post_finish = BindFunction[cb_writer_finish_internal]( + &_filesystemdataset_write_visitor, visit_args) + + c_scanner = data.unwrap() + with nogil: + check_status(CFileSystemDataset.Write(c_options, c_scanner)) + + +cdef class _ScanNodeOptions(ExecNodeOptions): + + def _set_options(self, Dataset dataset, dict scan_options): + cdef: + shared_ptr[CScanOptions] c_scan_options + + c_scan_options = Scanner._make_scan_options(dataset, scan_options) + + self.wrapped.reset( + new CScanNodeOptions(dataset.unwrap(), c_scan_options) + ) + + +class ScanNodeOptions(_ScanNodeOptions): + """ + A Source node which yields batches from a Dataset scan. + + This is the option class for the "scan" node factory. + + This node is capable of applying pushdown projections or filters + to the file readers which reduce the amount of data that needs to + be read (if supported by the file format). But note that this does not + construct associated filter or project nodes to perform the final + filtering or projection. Rather, you may supply the same filter + expression or projection to the scan node that you also supply + to the filter or project node. + + Yielded batches will be augmented with fragment/batch indices to + enable stable ordering for simple ExecPlans. + + Parameters + ---------- + dataset : pyarrow.dataset.Dataset + The table which acts as the data source. + **kwargs : dict, optional + Scan options. See `Scanner.from_dataset` for possible arguments. + """ + + def __init__(self, Dataset dataset, **kwargs): + self._set_options(dataset, kwargs)