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1 | # Licensed to the Apache Software Foundation (ASF) under one |
2 | # or more contributor license agreements. See the NOTICE file | |
3 | # distributed with this work for additional information | |
4 | # regarding copyright ownership. The ASF licenses this file | |
5 | # to you under the Apache License, Version 2.0 (the | |
6 | # "License"); you may not use this file except in compliance | |
7 | # with the License. You may obtain a copy of the License at | |
8 | # | |
9 | # http://www.apache.org/licenses/LICENSE-2.0 | |
10 | # | |
11 | # Unless required by applicable law or agreed to in writing, | |
12 | # software distributed under the License is distributed on an | |
13 | # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | |
14 | # KIND, either express or implied. See the License for the | |
15 | # specific language governing permissions and limitations | |
16 | # under the License. | |
17 | ||
18 | ||
19 | from collections import defaultdict | |
20 | from concurrent import futures | |
21 | from functools import partial, reduce | |
22 | ||
23 | import json | |
24 | from collections.abc import Collection | |
25 | import numpy as np | |
26 | import os | |
27 | import re | |
28 | import operator | |
29 | import urllib.parse | |
30 | import warnings | |
31 | ||
32 | import pyarrow as pa | |
33 | import pyarrow.lib as lib | |
34 | import pyarrow._parquet as _parquet | |
35 | ||
36 | from pyarrow._parquet import (ParquetReader, Statistics, # noqa | |
37 | FileMetaData, RowGroupMetaData, | |
38 | ColumnChunkMetaData, | |
39 | ParquetSchema, ColumnSchema) | |
40 | from pyarrow.fs import (LocalFileSystem, FileSystem, | |
41 | _resolve_filesystem_and_path, _ensure_filesystem) | |
42 | from pyarrow import filesystem as legacyfs | |
43 | from pyarrow.util import guid, _is_path_like, _stringify_path | |
44 | ||
45 | _URI_STRIP_SCHEMES = ('hdfs',) | |
46 | ||
47 | ||
48 | def _parse_uri(path): | |
49 | path = _stringify_path(path) | |
50 | parsed_uri = urllib.parse.urlparse(path) | |
51 | if parsed_uri.scheme in _URI_STRIP_SCHEMES: | |
52 | return parsed_uri.path | |
53 | else: | |
54 | # ARROW-4073: On Windows returning the path with the scheme | |
55 | # stripped removes the drive letter, if any | |
56 | return path | |
57 | ||
58 | ||
59 | def _get_filesystem_and_path(passed_filesystem, path): | |
60 | if passed_filesystem is None: | |
61 | return legacyfs.resolve_filesystem_and_path(path, passed_filesystem) | |
62 | else: | |
63 | passed_filesystem = legacyfs._ensure_filesystem(passed_filesystem) | |
64 | parsed_path = _parse_uri(path) | |
65 | return passed_filesystem, parsed_path | |
66 | ||
67 | ||
68 | def _check_contains_null(val): | |
69 | if isinstance(val, bytes): | |
70 | for byte in val: | |
71 | if isinstance(byte, bytes): | |
72 | compare_to = chr(0) | |
73 | else: | |
74 | compare_to = 0 | |
75 | if byte == compare_to: | |
76 | return True | |
77 | elif isinstance(val, str): | |
78 | return '\x00' in val | |
79 | return False | |
80 | ||
81 | ||
82 | def _check_filters(filters, check_null_strings=True): | |
83 | """ | |
84 | Check if filters are well-formed. | |
85 | """ | |
86 | if filters is not None: | |
87 | if len(filters) == 0 or any(len(f) == 0 for f in filters): | |
88 | raise ValueError("Malformed filters") | |
89 | if isinstance(filters[0][0], str): | |
90 | # We have encountered the situation where we have one nesting level | |
91 | # too few: | |
92 | # We have [(,,), ..] instead of [[(,,), ..]] | |
93 | filters = [filters] | |
94 | if check_null_strings: | |
95 | for conjunction in filters: | |
96 | for col, op, val in conjunction: | |
97 | if ( | |
98 | isinstance(val, list) and | |
99 | all(_check_contains_null(v) for v in val) or | |
100 | _check_contains_null(val) | |
101 | ): | |
102 | raise NotImplementedError( | |
103 | "Null-terminated binary strings are not supported " | |
104 | "as filter values." | |
105 | ) | |
106 | return filters | |
107 | ||
108 | ||
109 | _DNF_filter_doc = """Predicates are expressed in disjunctive normal form (DNF), like | |
110 | ``[[('x', '=', 0), ...], ...]``. DNF allows arbitrary boolean logical | |
111 | combinations of single column predicates. The innermost tuples each | |
112 | describe a single column predicate. The list of inner predicates is | |
113 | interpreted as a conjunction (AND), forming a more selective and | |
114 | multiple column predicate. Finally, the most outer list combines these | |
115 | filters as a disjunction (OR). | |
116 | ||
117 | Predicates may also be passed as List[Tuple]. This form is interpreted | |
118 | as a single conjunction. To express OR in predicates, one must | |
119 | use the (preferred) List[List[Tuple]] notation. | |
120 | ||
121 | Each tuple has format: (``key``, ``op``, ``value``) and compares the | |
122 | ``key`` with the ``value``. | |
123 | The supported ``op`` are: ``=`` or ``==``, ``!=``, ``<``, ``>``, ``<=``, | |
124 | ``>=``, ``in`` and ``not in``. If the ``op`` is ``in`` or ``not in``, the | |
125 | ``value`` must be a collection such as a ``list``, a ``set`` or a | |
126 | ``tuple``. | |
127 | ||
128 | Examples: | |
129 | ||
130 | .. code-block:: python | |
131 | ||
132 | ('x', '=', 0) | |
133 | ('y', 'in', ['a', 'b', 'c']) | |
134 | ('z', 'not in', {'a','b'}) | |
135 | ||
136 | """ | |
137 | ||
138 | ||
139 | def _filters_to_expression(filters): | |
140 | """ | |
141 | Check if filters are well-formed. | |
142 | ||
143 | See _DNF_filter_doc above for more details. | |
144 | """ | |
145 | import pyarrow.dataset as ds | |
146 | ||
147 | if isinstance(filters, ds.Expression): | |
148 | return filters | |
149 | ||
150 | filters = _check_filters(filters, check_null_strings=False) | |
151 | ||
152 | def convert_single_predicate(col, op, val): | |
153 | field = ds.field(col) | |
154 | ||
155 | if op == "=" or op == "==": | |
156 | return field == val | |
157 | elif op == "!=": | |
158 | return field != val | |
159 | elif op == '<': | |
160 | return field < val | |
161 | elif op == '>': | |
162 | return field > val | |
163 | elif op == '<=': | |
164 | return field <= val | |
165 | elif op == '>=': | |
166 | return field >= val | |
167 | elif op == 'in': | |
168 | return field.isin(val) | |
169 | elif op == 'not in': | |
170 | return ~field.isin(val) | |
171 | else: | |
172 | raise ValueError( | |
173 | '"{0}" is not a valid operator in predicates.'.format( | |
174 | (col, op, val))) | |
175 | ||
176 | disjunction_members = [] | |
177 | ||
178 | for conjunction in filters: | |
179 | conjunction_members = [ | |
180 | convert_single_predicate(col, op, val) | |
181 | for col, op, val in conjunction | |
182 | ] | |
183 | ||
184 | disjunction_members.append(reduce(operator.and_, conjunction_members)) | |
185 | ||
186 | return reduce(operator.or_, disjunction_members) | |
187 | ||
188 | ||
189 | # ---------------------------------------------------------------------- | |
190 | # Reading a single Parquet file | |
191 | ||
192 | ||
193 | class ParquetFile: | |
194 | """ | |
195 | Reader interface for a single Parquet file. | |
196 | ||
197 | Parameters | |
198 | ---------- | |
199 | source : str, pathlib.Path, pyarrow.NativeFile, or file-like object | |
200 | Readable source. For passing bytes or buffer-like file containing a | |
201 | Parquet file, use pyarrow.BufferReader. | |
202 | metadata : FileMetaData, default None | |
203 | Use existing metadata object, rather than reading from file. | |
204 | common_metadata : FileMetaData, default None | |
205 | Will be used in reads for pandas schema metadata if not found in the | |
206 | main file's metadata, no other uses at the moment. | |
207 | memory_map : bool, default False | |
208 | If the source is a file path, use a memory map to read file, which can | |
209 | improve performance in some environments. | |
210 | buffer_size : int, default 0 | |
211 | If positive, perform read buffering when deserializing individual | |
212 | column chunks. Otherwise IO calls are unbuffered. | |
213 | pre_buffer : bool, default False | |
214 | Coalesce and issue file reads in parallel to improve performance on | |
215 | high-latency filesystems (e.g. S3). If True, Arrow will use a | |
216 | background I/O thread pool. | |
217 | read_dictionary : list | |
218 | List of column names to read directly as DictionaryArray. | |
219 | coerce_int96_timestamp_unit : str, default None. | |
220 | Cast timestamps that are stored in INT96 format to a particular | |
221 | resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' | |
222 | and therefore INT96 timestamps will be infered as timestamps | |
223 | in nanoseconds. | |
224 | """ | |
225 | ||
226 | def __init__(self, source, metadata=None, common_metadata=None, | |
227 | read_dictionary=None, memory_map=False, buffer_size=0, | |
228 | pre_buffer=False, coerce_int96_timestamp_unit=None): | |
229 | self.reader = ParquetReader() | |
230 | self.reader.open( | |
231 | source, use_memory_map=memory_map, | |
232 | buffer_size=buffer_size, pre_buffer=pre_buffer, | |
233 | read_dictionary=read_dictionary, metadata=metadata, | |
234 | coerce_int96_timestamp_unit=coerce_int96_timestamp_unit | |
235 | ) | |
236 | self.common_metadata = common_metadata | |
237 | self._nested_paths_by_prefix = self._build_nested_paths() | |
238 | ||
239 | def _build_nested_paths(self): | |
240 | paths = self.reader.column_paths | |
241 | ||
242 | result = defaultdict(list) | |
243 | ||
244 | for i, path in enumerate(paths): | |
245 | key = path[0] | |
246 | rest = path[1:] | |
247 | while True: | |
248 | result[key].append(i) | |
249 | ||
250 | if not rest: | |
251 | break | |
252 | ||
253 | key = '.'.join((key, rest[0])) | |
254 | rest = rest[1:] | |
255 | ||
256 | return result | |
257 | ||
258 | @property | |
259 | def metadata(self): | |
260 | return self.reader.metadata | |
261 | ||
262 | @property | |
263 | def schema(self): | |
264 | """ | |
265 | Return the Parquet schema, unconverted to Arrow types | |
266 | """ | |
267 | return self.metadata.schema | |
268 | ||
269 | @property | |
270 | def schema_arrow(self): | |
271 | """ | |
272 | Return the inferred Arrow schema, converted from the whole Parquet | |
273 | file's schema | |
274 | """ | |
275 | return self.reader.schema_arrow | |
276 | ||
277 | @property | |
278 | def num_row_groups(self): | |
279 | return self.reader.num_row_groups | |
280 | ||
281 | def read_row_group(self, i, columns=None, use_threads=True, | |
282 | use_pandas_metadata=False): | |
283 | """ | |
284 | Read a single row group from a Parquet file. | |
285 | ||
286 | Parameters | |
287 | ---------- | |
288 | i : int | |
289 | Index of the individual row group that we want to read. | |
290 | columns : list | |
291 | If not None, only these columns will be read from the row group. A | |
292 | column name may be a prefix of a nested field, e.g. 'a' will select | |
293 | 'a.b', 'a.c', and 'a.d.e'. | |
294 | use_threads : bool, default True | |
295 | Perform multi-threaded column reads. | |
296 | use_pandas_metadata : bool, default False | |
297 | If True and file has custom pandas schema metadata, ensure that | |
298 | index columns are also loaded. | |
299 | ||
300 | Returns | |
301 | ------- | |
302 | pyarrow.table.Table | |
303 | Content of the row group as a table (of columns) | |
304 | """ | |
305 | column_indices = self._get_column_indices( | |
306 | columns, use_pandas_metadata=use_pandas_metadata) | |
307 | return self.reader.read_row_group(i, column_indices=column_indices, | |
308 | use_threads=use_threads) | |
309 | ||
310 | def read_row_groups(self, row_groups, columns=None, use_threads=True, | |
311 | use_pandas_metadata=False): | |
312 | """ | |
313 | Read a multiple row groups from a Parquet file. | |
314 | ||
315 | Parameters | |
316 | ---------- | |
317 | row_groups : list | |
318 | Only these row groups will be read from the file. | |
319 | columns : list | |
320 | If not None, only these columns will be read from the row group. A | |
321 | column name may be a prefix of a nested field, e.g. 'a' will select | |
322 | 'a.b', 'a.c', and 'a.d.e'. | |
323 | use_threads : bool, default True | |
324 | Perform multi-threaded column reads. | |
325 | use_pandas_metadata : bool, default False | |
326 | If True and file has custom pandas schema metadata, ensure that | |
327 | index columns are also loaded. | |
328 | ||
329 | Returns | |
330 | ------- | |
331 | pyarrow.table.Table | |
332 | Content of the row groups as a table (of columns). | |
333 | """ | |
334 | column_indices = self._get_column_indices( | |
335 | columns, use_pandas_metadata=use_pandas_metadata) | |
336 | return self.reader.read_row_groups(row_groups, | |
337 | column_indices=column_indices, | |
338 | use_threads=use_threads) | |
339 | ||
340 | def iter_batches(self, batch_size=65536, row_groups=None, columns=None, | |
341 | use_threads=True, use_pandas_metadata=False): | |
342 | """ | |
343 | Read streaming batches from a Parquet file | |
344 | ||
345 | Parameters | |
346 | ---------- | |
347 | batch_size : int, default 64K | |
348 | Maximum number of records to yield per batch. Batches may be | |
349 | smaller if there aren't enough rows in the file. | |
350 | row_groups : list | |
351 | Only these row groups will be read from the file. | |
352 | columns : list | |
353 | If not None, only these columns will be read from the file. A | |
354 | column name may be a prefix of a nested field, e.g. 'a' will select | |
355 | 'a.b', 'a.c', and 'a.d.e'. | |
356 | use_threads : boolean, default True | |
357 | Perform multi-threaded column reads. | |
358 | use_pandas_metadata : boolean, default False | |
359 | If True and file has custom pandas schema metadata, ensure that | |
360 | index columns are also loaded. | |
361 | ||
362 | Returns | |
363 | ------- | |
364 | iterator of pyarrow.RecordBatch | |
365 | Contents of each batch as a record batch | |
366 | """ | |
367 | if row_groups is None: | |
368 | row_groups = range(0, self.metadata.num_row_groups) | |
369 | column_indices = self._get_column_indices( | |
370 | columns, use_pandas_metadata=use_pandas_metadata) | |
371 | ||
372 | batches = self.reader.iter_batches(batch_size, | |
373 | row_groups=row_groups, | |
374 | column_indices=column_indices, | |
375 | use_threads=use_threads) | |
376 | return batches | |
377 | ||
378 | def read(self, columns=None, use_threads=True, use_pandas_metadata=False): | |
379 | """ | |
380 | Read a Table from Parquet format, | |
381 | ||
382 | Parameters | |
383 | ---------- | |
384 | columns : list | |
385 | If not None, only these columns will be read from the file. A | |
386 | column name may be a prefix of a nested field, e.g. 'a' will select | |
387 | 'a.b', 'a.c', and 'a.d.e'. | |
388 | use_threads : bool, default True | |
389 | Perform multi-threaded column reads. | |
390 | use_pandas_metadata : bool, default False | |
391 | If True and file has custom pandas schema metadata, ensure that | |
392 | index columns are also loaded. | |
393 | ||
394 | Returns | |
395 | ------- | |
396 | pyarrow.table.Table | |
397 | Content of the file as a table (of columns). | |
398 | """ | |
399 | column_indices = self._get_column_indices( | |
400 | columns, use_pandas_metadata=use_pandas_metadata) | |
401 | return self.reader.read_all(column_indices=column_indices, | |
402 | use_threads=use_threads) | |
403 | ||
404 | def scan_contents(self, columns=None, batch_size=65536): | |
405 | """ | |
406 | Read contents of file for the given columns and batch size. | |
407 | ||
408 | Notes | |
409 | ----- | |
410 | This function's primary purpose is benchmarking. | |
411 | The scan is executed on a single thread. | |
412 | ||
413 | Parameters | |
414 | ---------- | |
415 | columns : list of integers, default None | |
416 | Select columns to read, if None scan all columns. | |
417 | batch_size : int, default 64K | |
418 | Number of rows to read at a time internally. | |
419 | ||
420 | Returns | |
421 | ------- | |
422 | num_rows : number of rows in file | |
423 | """ | |
424 | column_indices = self._get_column_indices(columns) | |
425 | return self.reader.scan_contents(column_indices, | |
426 | batch_size=batch_size) | |
427 | ||
428 | def _get_column_indices(self, column_names, use_pandas_metadata=False): | |
429 | if column_names is None: | |
430 | return None | |
431 | ||
432 | indices = [] | |
433 | ||
434 | for name in column_names: | |
435 | if name in self._nested_paths_by_prefix: | |
436 | indices.extend(self._nested_paths_by_prefix[name]) | |
437 | ||
438 | if use_pandas_metadata: | |
439 | file_keyvalues = self.metadata.metadata | |
440 | common_keyvalues = (self.common_metadata.metadata | |
441 | if self.common_metadata is not None | |
442 | else None) | |
443 | ||
444 | if file_keyvalues and b'pandas' in file_keyvalues: | |
445 | index_columns = _get_pandas_index_columns(file_keyvalues) | |
446 | elif common_keyvalues and b'pandas' in common_keyvalues: | |
447 | index_columns = _get_pandas_index_columns(common_keyvalues) | |
448 | else: | |
449 | index_columns = [] | |
450 | ||
451 | if indices is not None and index_columns: | |
452 | indices += [self.reader.column_name_idx(descr) | |
453 | for descr in index_columns | |
454 | if not isinstance(descr, dict)] | |
455 | ||
456 | return indices | |
457 | ||
458 | ||
459 | _SPARK_DISALLOWED_CHARS = re.compile('[ ,;{}()\n\t=]') | |
460 | ||
461 | ||
462 | def _sanitized_spark_field_name(name): | |
463 | return _SPARK_DISALLOWED_CHARS.sub('_', name) | |
464 | ||
465 | ||
466 | def _sanitize_schema(schema, flavor): | |
467 | if 'spark' in flavor: | |
468 | sanitized_fields = [] | |
469 | ||
470 | schema_changed = False | |
471 | ||
472 | for field in schema: | |
473 | name = field.name | |
474 | sanitized_name = _sanitized_spark_field_name(name) | |
475 | ||
476 | if sanitized_name != name: | |
477 | schema_changed = True | |
478 | sanitized_field = pa.field(sanitized_name, field.type, | |
479 | field.nullable, field.metadata) | |
480 | sanitized_fields.append(sanitized_field) | |
481 | else: | |
482 | sanitized_fields.append(field) | |
483 | ||
484 | new_schema = pa.schema(sanitized_fields, metadata=schema.metadata) | |
485 | return new_schema, schema_changed | |
486 | else: | |
487 | return schema, False | |
488 | ||
489 | ||
490 | def _sanitize_table(table, new_schema, flavor): | |
491 | # TODO: This will not handle prohibited characters in nested field names | |
492 | if 'spark' in flavor: | |
493 | column_data = [table[i] for i in range(table.num_columns)] | |
494 | return pa.Table.from_arrays(column_data, schema=new_schema) | |
495 | else: | |
496 | return table | |
497 | ||
498 | ||
499 | _parquet_writer_arg_docs = """version : {"1.0", "2.4", "2.6"}, default "1.0" | |
500 | Determine which Parquet logical types are available for use, whether the | |
501 | reduced set from the Parquet 1.x.x format or the expanded logical types | |
502 | added in later format versions. | |
503 | Files written with version='2.4' or '2.6' may not be readable in all | |
504 | Parquet implementations, so version='1.0' is likely the choice that | |
505 | maximizes file compatibility. | |
506 | UINT32 and some logical types are only available with version '2.4'. | |
507 | Nanosecond timestamps are only available with version '2.6'. | |
508 | Other features such as compression algorithms or the new serialized | |
509 | data page format must be enabled separately (see 'compression' and | |
510 | 'data_page_version'). | |
511 | use_dictionary : bool or list | |
512 | Specify if we should use dictionary encoding in general or only for | |
513 | some columns. | |
514 | use_deprecated_int96_timestamps : bool, default None | |
515 | Write timestamps to INT96 Parquet format. Defaults to False unless enabled | |
516 | by flavor argument. This take priority over the coerce_timestamps option. | |
517 | coerce_timestamps : str, default None | |
518 | Cast timestamps to a particular resolution. If omitted, defaults are chosen | |
519 | depending on `version`. By default, for ``version='1.0'`` (the default) | |
520 | and ``version='2.4'``, nanoseconds are cast to microseconds ('us'), while | |
521 | for other `version` values, they are written natively without loss | |
522 | of resolution. Seconds are always cast to milliseconds ('ms') by default, | |
523 | as Parquet does not have any temporal type with seconds resolution. | |
524 | If the casting results in loss of data, it will raise an exception | |
525 | unless ``allow_truncated_timestamps=True`` is given. | |
526 | Valid values: {None, 'ms', 'us'} | |
527 | data_page_size : int, default None | |
528 | Set a target threshold for the approximate encoded size of data | |
529 | pages within a column chunk (in bytes). If None, use the default data page | |
530 | size of 1MByte. | |
531 | allow_truncated_timestamps : bool, default False | |
532 | Allow loss of data when coercing timestamps to a particular | |
533 | resolution. E.g. if microsecond or nanosecond data is lost when coercing to | |
534 | 'ms', do not raise an exception. Passing ``allow_truncated_timestamp=True`` | |
535 | will NOT result in the truncation exception being ignored unless | |
536 | ``coerce_timestamps`` is not None. | |
537 | compression : str or dict | |
538 | Specify the compression codec, either on a general basis or per-column. | |
539 | Valid values: {'NONE', 'SNAPPY', 'GZIP', 'BROTLI', 'LZ4', 'ZSTD'}. | |
540 | write_statistics : bool or list | |
541 | Specify if we should write statistics in general (default is True) or only | |
542 | for some columns. | |
543 | flavor : {'spark'}, default None | |
544 | Sanitize schema or set other compatibility options to work with | |
545 | various target systems. | |
546 | filesystem : FileSystem, default None | |
547 | If nothing passed, will be inferred from `where` if path-like, else | |
548 | `where` is already a file-like object so no filesystem is needed. | |
549 | compression_level : int or dict, default None | |
550 | Specify the compression level for a codec, either on a general basis or | |
551 | per-column. If None is passed, arrow selects the compression level for | |
552 | the compression codec in use. The compression level has a different | |
553 | meaning for each codec, so you have to read the documentation of the | |
554 | codec you are using. | |
555 | An exception is thrown if the compression codec does not allow specifying | |
556 | a compression level. | |
557 | use_byte_stream_split : bool or list, default False | |
558 | Specify if the byte_stream_split encoding should be used in general or | |
559 | only for some columns. If both dictionary and byte_stream_stream are | |
560 | enabled, then dictionary is preferred. | |
561 | The byte_stream_split encoding is valid only for floating-point data types | |
562 | and should be combined with a compression codec. | |
563 | data_page_version : {"1.0", "2.0"}, default "1.0" | |
564 | The serialized Parquet data page format version to write, defaults to | |
565 | 1.0. This does not impact the file schema logical types and Arrow to | |
566 | Parquet type casting behavior; for that use the "version" option. | |
567 | use_compliant_nested_type : bool, default False | |
568 | Whether to write compliant Parquet nested type (lists) as defined | |
569 | `here <https://github.com/apache/parquet-format/blob/master/ | |
570 | LogicalTypes.md#nested-types>`_, defaults to ``False``. | |
571 | For ``use_compliant_nested_type=True``, this will write into a list | |
572 | with 3-level structure where the middle level, named ``list``, | |
573 | is a repeated group with a single field named ``element``:: | |
574 | ||
575 | <list-repetition> group <name> (LIST) { | |
576 | repeated group list { | |
577 | <element-repetition> <element-type> element; | |
578 | } | |
579 | } | |
580 | ||
581 | For ``use_compliant_nested_type=False``, this will also write into a list | |
582 | with 3-level structure, where the name of the single field of the middle | |
583 | level ``list`` is taken from the element name for nested columns in Arrow, | |
584 | which defaults to ``item``:: | |
585 | ||
586 | <list-repetition> group <name> (LIST) { | |
587 | repeated group list { | |
588 | <element-repetition> <element-type> item; | |
589 | } | |
590 | } | |
591 | """ | |
592 | ||
593 | ||
594 | class ParquetWriter: | |
595 | ||
596 | __doc__ = """ | |
597 | Class for incrementally building a Parquet file for Arrow tables. | |
598 | ||
599 | Parameters | |
600 | ---------- | |
601 | where : path or file-like object | |
602 | schema : arrow Schema | |
603 | {} | |
604 | writer_engine_version : unused | |
605 | **options : dict | |
606 | If options contains a key `metadata_collector` then the | |
607 | corresponding value is assumed to be a list (or any object with | |
608 | `.append` method) that will be filled with the file metadata instance | |
609 | of the written file. | |
610 | """.format(_parquet_writer_arg_docs) | |
611 | ||
612 | def __init__(self, where, schema, filesystem=None, | |
613 | flavor=None, | |
614 | version='1.0', | |
615 | use_dictionary=True, | |
616 | compression='snappy', | |
617 | write_statistics=True, | |
618 | use_deprecated_int96_timestamps=None, | |
619 | compression_level=None, | |
620 | use_byte_stream_split=False, | |
621 | writer_engine_version=None, | |
622 | data_page_version='1.0', | |
623 | use_compliant_nested_type=False, | |
624 | **options): | |
625 | if use_deprecated_int96_timestamps is None: | |
626 | # Use int96 timestamps for Spark | |
627 | if flavor is not None and 'spark' in flavor: | |
628 | use_deprecated_int96_timestamps = True | |
629 | else: | |
630 | use_deprecated_int96_timestamps = False | |
631 | ||
632 | self.flavor = flavor | |
633 | if flavor is not None: | |
634 | schema, self.schema_changed = _sanitize_schema(schema, flavor) | |
635 | else: | |
636 | self.schema_changed = False | |
637 | ||
638 | self.schema = schema | |
639 | self.where = where | |
640 | ||
641 | # If we open a file using a filesystem, store file handle so we can be | |
642 | # sure to close it when `self.close` is called. | |
643 | self.file_handle = None | |
644 | ||
645 | filesystem, path = _resolve_filesystem_and_path( | |
646 | where, filesystem, allow_legacy_filesystem=True | |
647 | ) | |
648 | if filesystem is not None: | |
649 | if isinstance(filesystem, legacyfs.FileSystem): | |
650 | # legacy filesystem (eg custom subclass) | |
651 | # TODO deprecate | |
652 | sink = self.file_handle = filesystem.open(path, 'wb') | |
653 | else: | |
654 | # ARROW-10480: do not auto-detect compression. While | |
655 | # a filename like foo.parquet.gz is nonconforming, it | |
656 | # shouldn't implicitly apply compression. | |
657 | sink = self.file_handle = filesystem.open_output_stream( | |
658 | path, compression=None) | |
659 | else: | |
660 | sink = where | |
661 | self._metadata_collector = options.pop('metadata_collector', None) | |
662 | engine_version = 'V2' | |
663 | self.writer = _parquet.ParquetWriter( | |
664 | sink, schema, | |
665 | version=version, | |
666 | compression=compression, | |
667 | use_dictionary=use_dictionary, | |
668 | write_statistics=write_statistics, | |
669 | use_deprecated_int96_timestamps=use_deprecated_int96_timestamps, | |
670 | compression_level=compression_level, | |
671 | use_byte_stream_split=use_byte_stream_split, | |
672 | writer_engine_version=engine_version, | |
673 | data_page_version=data_page_version, | |
674 | use_compliant_nested_type=use_compliant_nested_type, | |
675 | **options) | |
676 | self.is_open = True | |
677 | ||
678 | def __del__(self): | |
679 | if getattr(self, 'is_open', False): | |
680 | self.close() | |
681 | ||
682 | def __enter__(self): | |
683 | return self | |
684 | ||
685 | def __exit__(self, *args, **kwargs): | |
686 | self.close() | |
687 | # return false since we want to propagate exceptions | |
688 | return False | |
689 | ||
690 | def write_table(self, table, row_group_size=None): | |
691 | if self.schema_changed: | |
692 | table = _sanitize_table(table, self.schema, self.flavor) | |
693 | assert self.is_open | |
694 | ||
695 | if not table.schema.equals(self.schema, check_metadata=False): | |
696 | msg = ('Table schema does not match schema used to create file: ' | |
697 | '\ntable:\n{!s} vs. \nfile:\n{!s}' | |
698 | .format(table.schema, self.schema)) | |
699 | raise ValueError(msg) | |
700 | ||
701 | self.writer.write_table(table, row_group_size=row_group_size) | |
702 | ||
703 | def close(self): | |
704 | if self.is_open: | |
705 | self.writer.close() | |
706 | self.is_open = False | |
707 | if self._metadata_collector is not None: | |
708 | self._metadata_collector.append(self.writer.metadata) | |
709 | if self.file_handle is not None: | |
710 | self.file_handle.close() | |
711 | ||
712 | ||
713 | def _get_pandas_index_columns(keyvalues): | |
714 | return (json.loads(keyvalues[b'pandas'].decode('utf8')) | |
715 | ['index_columns']) | |
716 | ||
717 | ||
718 | # ---------------------------------------------------------------------- | |
719 | # Metadata container providing instructions about reading a single Parquet | |
720 | # file, possibly part of a partitioned dataset | |
721 | ||
722 | ||
723 | class ParquetDatasetPiece: | |
724 | """ | |
725 | DEPRECATED: A single chunk of a potentially larger Parquet dataset to read. | |
726 | ||
727 | The arguments will indicate to read either a single row group or all row | |
728 | groups, and whether to add partition keys to the resulting pyarrow.Table. | |
729 | ||
730 | .. deprecated:: 5.0 | |
731 | Directly constructing a ``ParquetDatasetPiece`` is deprecated, as well | |
732 | as accessing the pieces of a ``ParquetDataset`` object. Specify | |
733 | ``use_legacy_dataset=False`` when constructing the ``ParquetDataset`` | |
734 | and use the ``ParquetDataset.fragments`` attribute instead. | |
735 | ||
736 | Parameters | |
737 | ---------- | |
738 | path : str or pathlib.Path | |
739 | Path to file in the file system where this piece is located. | |
740 | open_file_func : callable | |
741 | Function to use for obtaining file handle to dataset piece. | |
742 | partition_keys : list of tuples | |
743 | Two-element tuples of ``(column name, ordinal index)``. | |
744 | row_group : int, default None | |
745 | Row group to load. By default, reads all row groups. | |
746 | file_options : dict | |
747 | Options | |
748 | """ | |
749 | ||
750 | def __init__(self, path, open_file_func=partial(open, mode='rb'), | |
751 | file_options=None, row_group=None, partition_keys=None): | |
752 | warnings.warn( | |
753 | "ParquetDatasetPiece is deprecated as of pyarrow 5.0.0 and will " | |
754 | "be removed in a future version.", | |
755 | DeprecationWarning, stacklevel=2) | |
756 | self._init( | |
757 | path, open_file_func, file_options, row_group, partition_keys) | |
758 | ||
759 | @staticmethod | |
760 | def _create(path, open_file_func=partial(open, mode='rb'), | |
761 | file_options=None, row_group=None, partition_keys=None): | |
762 | self = ParquetDatasetPiece.__new__(ParquetDatasetPiece) | |
763 | self._init( | |
764 | path, open_file_func, file_options, row_group, partition_keys) | |
765 | return self | |
766 | ||
767 | def _init(self, path, open_file_func, file_options, row_group, | |
768 | partition_keys): | |
769 | self.path = _stringify_path(path) | |
770 | self.open_file_func = open_file_func | |
771 | self.row_group = row_group | |
772 | self.partition_keys = partition_keys or [] | |
773 | self.file_options = file_options or {} | |
774 | ||
775 | def __eq__(self, other): | |
776 | if not isinstance(other, ParquetDatasetPiece): | |
777 | return False | |
778 | return (self.path == other.path and | |
779 | self.row_group == other.row_group and | |
780 | self.partition_keys == other.partition_keys) | |
781 | ||
782 | def __repr__(self): | |
783 | return ('{}({!r}, row_group={!r}, partition_keys={!r})' | |
784 | .format(type(self).__name__, self.path, | |
785 | self.row_group, | |
786 | self.partition_keys)) | |
787 | ||
788 | def __str__(self): | |
789 | result = '' | |
790 | ||
791 | if len(self.partition_keys) > 0: | |
792 | partition_str = ', '.join('{}={}'.format(name, index) | |
793 | for name, index in self.partition_keys) | |
794 | result += 'partition[{}] '.format(partition_str) | |
795 | ||
796 | result += self.path | |
797 | ||
798 | if self.row_group is not None: | |
799 | result += ' | row_group={}'.format(self.row_group) | |
800 | ||
801 | return result | |
802 | ||
803 | def get_metadata(self): | |
804 | """ | |
805 | Return the file's metadata. | |
806 | ||
807 | Returns | |
808 | ------- | |
809 | metadata : FileMetaData | |
810 | """ | |
811 | f = self.open() | |
812 | return f.metadata | |
813 | ||
814 | def open(self): | |
815 | """ | |
816 | Return instance of ParquetFile. | |
817 | """ | |
818 | reader = self.open_file_func(self.path) | |
819 | if not isinstance(reader, ParquetFile): | |
820 | reader = ParquetFile(reader, **self.file_options) | |
821 | return reader | |
822 | ||
823 | def read(self, columns=None, use_threads=True, partitions=None, | |
824 | file=None, use_pandas_metadata=False): | |
825 | """ | |
826 | Read this piece as a pyarrow.Table. | |
827 | ||
828 | Parameters | |
829 | ---------- | |
830 | columns : list of column names, default None | |
831 | use_threads : bool, default True | |
832 | Perform multi-threaded column reads. | |
833 | partitions : ParquetPartitions, default None | |
834 | file : file-like object | |
835 | Passed to ParquetFile. | |
836 | use_pandas_metadata : bool | |
837 | If pandas metadata should be used or not. | |
838 | ||
839 | Returns | |
840 | ------- | |
841 | table : pyarrow.Table | |
842 | """ | |
843 | if self.open_file_func is not None: | |
844 | reader = self.open() | |
845 | elif file is not None: | |
846 | reader = ParquetFile(file, **self.file_options) | |
847 | else: | |
848 | # try to read the local path | |
849 | reader = ParquetFile(self.path, **self.file_options) | |
850 | ||
851 | options = dict(columns=columns, | |
852 | use_threads=use_threads, | |
853 | use_pandas_metadata=use_pandas_metadata) | |
854 | ||
855 | if self.row_group is not None: | |
856 | table = reader.read_row_group(self.row_group, **options) | |
857 | else: | |
858 | table = reader.read(**options) | |
859 | ||
860 | if len(self.partition_keys) > 0: | |
861 | if partitions is None: | |
862 | raise ValueError('Must pass partition sets') | |
863 | ||
864 | # Here, the index is the categorical code of the partition where | |
865 | # this piece is located. Suppose we had | |
866 | # | |
867 | # /foo=a/0.parq | |
868 | # /foo=b/0.parq | |
869 | # /foo=c/0.parq | |
870 | # | |
871 | # Then we assign a=0, b=1, c=2. And the resulting Table pieces will | |
872 | # have a DictionaryArray column named foo having the constant index | |
873 | # value as indicated. The distinct categories of the partition have | |
874 | # been computed in the ParquetManifest | |
875 | for i, (name, index) in enumerate(self.partition_keys): | |
876 | # The partition code is the same for all values in this piece | |
877 | indices = np.full(len(table), index, dtype='i4') | |
878 | ||
879 | # This is set of all partition values, computed as part of the | |
880 | # manifest, so ['a', 'b', 'c'] as in our example above. | |
881 | dictionary = partitions.levels[i].dictionary | |
882 | ||
883 | arr = pa.DictionaryArray.from_arrays(indices, dictionary) | |
884 | table = table.append_column(name, arr) | |
885 | ||
886 | return table | |
887 | ||
888 | ||
889 | class PartitionSet: | |
890 | """ | |
891 | A data structure for cataloguing the observed Parquet partitions at a | |
892 | particular level. So if we have | |
893 | ||
894 | /foo=a/bar=0 | |
895 | /foo=a/bar=1 | |
896 | /foo=a/bar=2 | |
897 | /foo=b/bar=0 | |
898 | /foo=b/bar=1 | |
899 | /foo=b/bar=2 | |
900 | ||
901 | Then we have two partition sets, one for foo, another for bar. As we visit | |
902 | levels of the partition hierarchy, a PartitionSet tracks the distinct | |
903 | values and assigns categorical codes to use when reading the pieces | |
904 | ||
905 | Parameters | |
906 | ---------- | |
907 | name : str | |
908 | Name of the partition set. Under which key to collect all values. | |
909 | keys : list | |
910 | All possible values that have been collected for that partition set. | |
911 | """ | |
912 | ||
913 | def __init__(self, name, keys=None): | |
914 | self.name = name | |
915 | self.keys = keys or [] | |
916 | self.key_indices = {k: i for i, k in enumerate(self.keys)} | |
917 | self._dictionary = None | |
918 | ||
919 | def get_index(self, key): | |
920 | """ | |
921 | Get the index of the partition value if it is known, otherwise assign | |
922 | one | |
923 | ||
924 | Parameters | |
925 | ---------- | |
926 | key : The value for which we want to known the index. | |
927 | """ | |
928 | if key in self.key_indices: | |
929 | return self.key_indices[key] | |
930 | else: | |
931 | index = len(self.key_indices) | |
932 | self.keys.append(key) | |
933 | self.key_indices[key] = index | |
934 | return index | |
935 | ||
936 | @property | |
937 | def dictionary(self): | |
938 | if self._dictionary is not None: | |
939 | return self._dictionary | |
940 | ||
941 | if len(self.keys) == 0: | |
942 | raise ValueError('No known partition keys') | |
943 | ||
944 | # Only integer and string partition types are supported right now | |
945 | try: | |
946 | integer_keys = [int(x) for x in self.keys] | |
947 | dictionary = lib.array(integer_keys) | |
948 | except ValueError: | |
949 | dictionary = lib.array(self.keys) | |
950 | ||
951 | self._dictionary = dictionary | |
952 | return dictionary | |
953 | ||
954 | @property | |
955 | def is_sorted(self): | |
956 | return list(self.keys) == sorted(self.keys) | |
957 | ||
958 | ||
959 | class ParquetPartitions: | |
960 | ||
961 | def __init__(self): | |
962 | self.levels = [] | |
963 | self.partition_names = set() | |
964 | ||
965 | def __len__(self): | |
966 | return len(self.levels) | |
967 | ||
968 | def __getitem__(self, i): | |
969 | return self.levels[i] | |
970 | ||
971 | def equals(self, other): | |
972 | if not isinstance(other, ParquetPartitions): | |
973 | raise TypeError('`other` must be an instance of ParquetPartitions') | |
974 | ||
975 | return (self.levels == other.levels and | |
976 | self.partition_names == other.partition_names) | |
977 | ||
978 | def __eq__(self, other): | |
979 | try: | |
980 | return self.equals(other) | |
981 | except TypeError: | |
982 | return NotImplemented | |
983 | ||
984 | def get_index(self, level, name, key): | |
985 | """ | |
986 | Record a partition value at a particular level, returning the distinct | |
987 | code for that value at that level. | |
988 | ||
989 | Example: | |
990 | ||
991 | partitions.get_index(1, 'foo', 'a') returns 0 | |
992 | partitions.get_index(1, 'foo', 'b') returns 1 | |
993 | partitions.get_index(1, 'foo', 'c') returns 2 | |
994 | partitions.get_index(1, 'foo', 'a') returns 0 | |
995 | ||
996 | Parameters | |
997 | ---------- | |
998 | level : int | |
999 | The nesting level of the partition we are observing | |
1000 | name : str | |
1001 | The partition name | |
1002 | key : str or int | |
1003 | The partition value | |
1004 | """ | |
1005 | if level == len(self.levels): | |
1006 | if name in self.partition_names: | |
1007 | raise ValueError('{} was the name of the partition in ' | |
1008 | 'another level'.format(name)) | |
1009 | ||
1010 | part_set = PartitionSet(name) | |
1011 | self.levels.append(part_set) | |
1012 | self.partition_names.add(name) | |
1013 | ||
1014 | return self.levels[level].get_index(key) | |
1015 | ||
1016 | def filter_accepts_partition(self, part_key, filter, level): | |
1017 | p_column, p_value_index = part_key | |
1018 | f_column, op, f_value = filter | |
1019 | if p_column != f_column: | |
1020 | return True | |
1021 | ||
1022 | f_type = type(f_value) | |
1023 | ||
1024 | if op in {'in', 'not in'}: | |
1025 | if not isinstance(f_value, Collection): | |
1026 | raise TypeError( | |
1027 | "'%s' object is not a collection", f_type.__name__) | |
1028 | if not f_value: | |
1029 | raise ValueError("Cannot use empty collection as filter value") | |
1030 | if len({type(item) for item in f_value}) != 1: | |
1031 | raise ValueError("All elements of the collection '%s' must be" | |
1032 | " of same type", f_value) | |
1033 | f_type = type(next(iter(f_value))) | |
1034 | ||
1035 | elif not isinstance(f_value, str) and isinstance(f_value, Collection): | |
1036 | raise ValueError( | |
1037 | "Op '%s' not supported with a collection value", op) | |
1038 | ||
1039 | p_value = f_type(self.levels[level] | |
1040 | .dictionary[p_value_index].as_py()) | |
1041 | ||
1042 | if op == "=" or op == "==": | |
1043 | return p_value == f_value | |
1044 | elif op == "!=": | |
1045 | return p_value != f_value | |
1046 | elif op == '<': | |
1047 | return p_value < f_value | |
1048 | elif op == '>': | |
1049 | return p_value > f_value | |
1050 | elif op == '<=': | |
1051 | return p_value <= f_value | |
1052 | elif op == '>=': | |
1053 | return p_value >= f_value | |
1054 | elif op == 'in': | |
1055 | return p_value in f_value | |
1056 | elif op == 'not in': | |
1057 | return p_value not in f_value | |
1058 | else: | |
1059 | raise ValueError("'%s' is not a valid operator in predicates.", | |
1060 | filter[1]) | |
1061 | ||
1062 | ||
1063 | class ParquetManifest: | |
1064 | ||
1065 | def __init__(self, dirpath, open_file_func=None, filesystem=None, | |
1066 | pathsep='/', partition_scheme='hive', metadata_nthreads=1): | |
1067 | filesystem, dirpath = _get_filesystem_and_path(filesystem, dirpath) | |
1068 | self.filesystem = filesystem | |
1069 | self.open_file_func = open_file_func | |
1070 | self.pathsep = pathsep | |
1071 | self.dirpath = _stringify_path(dirpath) | |
1072 | self.partition_scheme = partition_scheme | |
1073 | self.partitions = ParquetPartitions() | |
1074 | self.pieces = [] | |
1075 | self._metadata_nthreads = metadata_nthreads | |
1076 | self._thread_pool = futures.ThreadPoolExecutor( | |
1077 | max_workers=metadata_nthreads) | |
1078 | ||
1079 | self.common_metadata_path = None | |
1080 | self.metadata_path = None | |
1081 | ||
1082 | self._visit_level(0, self.dirpath, []) | |
1083 | ||
1084 | # Due to concurrency, pieces will potentially by out of order if the | |
1085 | # dataset is partitioned so we sort them to yield stable results | |
1086 | self.pieces.sort(key=lambda piece: piece.path) | |
1087 | ||
1088 | if self.common_metadata_path is None: | |
1089 | # _common_metadata is a subset of _metadata | |
1090 | self.common_metadata_path = self.metadata_path | |
1091 | ||
1092 | self._thread_pool.shutdown() | |
1093 | ||
1094 | def _visit_level(self, level, base_path, part_keys): | |
1095 | fs = self.filesystem | |
1096 | ||
1097 | _, directories, files = next(fs.walk(base_path)) | |
1098 | ||
1099 | filtered_files = [] | |
1100 | for path in files: | |
1101 | full_path = self.pathsep.join((base_path, path)) | |
1102 | if path.endswith('_common_metadata'): | |
1103 | self.common_metadata_path = full_path | |
1104 | elif path.endswith('_metadata'): | |
1105 | self.metadata_path = full_path | |
1106 | elif self._should_silently_exclude(path): | |
1107 | continue | |
1108 | else: | |
1109 | filtered_files.append(full_path) | |
1110 | ||
1111 | # ARROW-1079: Filter out "private" directories starting with underscore | |
1112 | filtered_directories = [self.pathsep.join((base_path, x)) | |
1113 | for x in directories | |
1114 | if not _is_private_directory(x)] | |
1115 | ||
1116 | filtered_files.sort() | |
1117 | filtered_directories.sort() | |
1118 | ||
1119 | if len(filtered_files) > 0 and len(filtered_directories) > 0: | |
1120 | raise ValueError('Found files in an intermediate ' | |
1121 | 'directory: {}'.format(base_path)) | |
1122 | elif len(filtered_directories) > 0: | |
1123 | self._visit_directories(level, filtered_directories, part_keys) | |
1124 | else: | |
1125 | self._push_pieces(filtered_files, part_keys) | |
1126 | ||
1127 | def _should_silently_exclude(self, file_name): | |
1128 | return (file_name.endswith('.crc') or # Checksums | |
1129 | file_name.endswith('_$folder$') or # HDFS directories in S3 | |
1130 | file_name.startswith('.') or # Hidden files starting with . | |
1131 | file_name.startswith('_') or # Hidden files starting with _ | |
1132 | file_name in EXCLUDED_PARQUET_PATHS) | |
1133 | ||
1134 | def _visit_directories(self, level, directories, part_keys): | |
1135 | futures_list = [] | |
1136 | for path in directories: | |
1137 | head, tail = _path_split(path, self.pathsep) | |
1138 | name, key = _parse_hive_partition(tail) | |
1139 | ||
1140 | index = self.partitions.get_index(level, name, key) | |
1141 | dir_part_keys = part_keys + [(name, index)] | |
1142 | # If you have less threads than levels, the wait call will block | |
1143 | # indefinitely due to multiple waits within a thread. | |
1144 | if level < self._metadata_nthreads: | |
1145 | future = self._thread_pool.submit(self._visit_level, | |
1146 | level + 1, | |
1147 | path, | |
1148 | dir_part_keys) | |
1149 | futures_list.append(future) | |
1150 | else: | |
1151 | self._visit_level(level + 1, path, dir_part_keys) | |
1152 | if futures_list: | |
1153 | futures.wait(futures_list) | |
1154 | ||
1155 | def _parse_partition(self, dirname): | |
1156 | if self.partition_scheme == 'hive': | |
1157 | return _parse_hive_partition(dirname) | |
1158 | else: | |
1159 | raise NotImplementedError('partition schema: {}' | |
1160 | .format(self.partition_scheme)) | |
1161 | ||
1162 | def _push_pieces(self, files, part_keys): | |
1163 | self.pieces.extend([ | |
1164 | ParquetDatasetPiece._create(path, partition_keys=part_keys, | |
1165 | open_file_func=self.open_file_func) | |
1166 | for path in files | |
1167 | ]) | |
1168 | ||
1169 | ||
1170 | def _parse_hive_partition(value): | |
1171 | if '=' not in value: | |
1172 | raise ValueError('Directory name did not appear to be a ' | |
1173 | 'partition: {}'.format(value)) | |
1174 | return value.split('=', 1) | |
1175 | ||
1176 | ||
1177 | def _is_private_directory(x): | |
1178 | _, tail = os.path.split(x) | |
1179 | return (tail.startswith('_') or tail.startswith('.')) and '=' not in tail | |
1180 | ||
1181 | ||
1182 | def _path_split(path, sep): | |
1183 | i = path.rfind(sep) + 1 | |
1184 | head, tail = path[:i], path[i:] | |
1185 | head = head.rstrip(sep) | |
1186 | return head, tail | |
1187 | ||
1188 | ||
1189 | EXCLUDED_PARQUET_PATHS = {'_SUCCESS'} | |
1190 | ||
1191 | ||
1192 | class _ParquetDatasetMetadata: | |
1193 | __slots__ = ('fs', 'memory_map', 'read_dictionary', 'common_metadata', | |
1194 | 'buffer_size') | |
1195 | ||
1196 | ||
1197 | def _open_dataset_file(dataset, path, meta=None): | |
1198 | if (dataset.fs is not None and | |
1199 | not isinstance(dataset.fs, legacyfs.LocalFileSystem)): | |
1200 | path = dataset.fs.open(path, mode='rb') | |
1201 | return ParquetFile( | |
1202 | path, | |
1203 | metadata=meta, | |
1204 | memory_map=dataset.memory_map, | |
1205 | read_dictionary=dataset.read_dictionary, | |
1206 | common_metadata=dataset.common_metadata, | |
1207 | buffer_size=dataset.buffer_size | |
1208 | ) | |
1209 | ||
1210 | ||
1211 | _DEPR_MSG = ( | |
1212 | "'{}' attribute is deprecated as of pyarrow 5.0.0 and will be removed " | |
1213 | "in a future version.{}" | |
1214 | ) | |
1215 | ||
1216 | ||
1217 | _read_docstring_common = """\ | |
1218 | read_dictionary : list, default None | |
1219 | List of names or column paths (for nested types) to read directly | |
1220 | as DictionaryArray. Only supported for BYTE_ARRAY storage. To read | |
1221 | a flat column as dictionary-encoded pass the column name. For | |
1222 | nested types, you must pass the full column "path", which could be | |
1223 | something like level1.level2.list.item. Refer to the Parquet | |
1224 | file's schema to obtain the paths. | |
1225 | memory_map : bool, default False | |
1226 | If the source is a file path, use a memory map to read file, which can | |
1227 | improve performance in some environments. | |
1228 | buffer_size : int, default 0 | |
1229 | If positive, perform read buffering when deserializing individual | |
1230 | column chunks. Otherwise IO calls are unbuffered. | |
1231 | partitioning : Partitioning or str or list of str, default "hive" | |
1232 | The partitioning scheme for a partitioned dataset. The default of "hive" | |
1233 | assumes directory names with key=value pairs like "/year=2009/month=11". | |
1234 | In addition, a scheme like "/2009/11" is also supported, in which case | |
1235 | you need to specify the field names or a full schema. See the | |
1236 | ``pyarrow.dataset.partitioning()`` function for more details.""" | |
1237 | ||
1238 | ||
1239 | class ParquetDataset: | |
1240 | ||
1241 | __doc__ = """ | |
1242 | Encapsulates details of reading a complete Parquet dataset possibly | |
1243 | consisting of multiple files and partitions in subdirectories. | |
1244 | ||
1245 | Parameters | |
1246 | ---------- | |
1247 | path_or_paths : str or List[str] | |
1248 | A directory name, single file name, or list of file names. | |
1249 | filesystem : FileSystem, default None | |
1250 | If nothing passed, paths assumed to be found in the local on-disk | |
1251 | filesystem. | |
1252 | metadata : pyarrow.parquet.FileMetaData | |
1253 | Use metadata obtained elsewhere to validate file schemas. | |
1254 | schema : pyarrow.parquet.Schema | |
1255 | Use schema obtained elsewhere to validate file schemas. Alternative to | |
1256 | metadata parameter. | |
1257 | split_row_groups : bool, default False | |
1258 | Divide files into pieces for each row group in the file. | |
1259 | validate_schema : bool, default True | |
1260 | Check that individual file schemas are all the same / compatible. | |
1261 | filters : List[Tuple] or List[List[Tuple]] or None (default) | |
1262 | Rows which do not match the filter predicate will be removed from scanned | |
1263 | data. Partition keys embedded in a nested directory structure will be | |
1264 | exploited to avoid loading files at all if they contain no matching rows. | |
1265 | If `use_legacy_dataset` is True, filters can only reference partition | |
1266 | keys and only a hive-style directory structure is supported. When | |
1267 | setting `use_legacy_dataset` to False, also within-file level filtering | |
1268 | and different partitioning schemes are supported. | |
1269 | ||
1270 | {1} | |
1271 | metadata_nthreads : int, default 1 | |
1272 | How many threads to allow the thread pool which is used to read the | |
1273 | dataset metadata. Increasing this is helpful to read partitioned | |
1274 | datasets. | |
1275 | {0} | |
1276 | use_legacy_dataset : bool, default True | |
1277 | Set to False to enable the new code path (experimental, using the | |
1278 | new Arrow Dataset API). Among other things, this allows to pass | |
1279 | `filters` for all columns and not only the partition keys, enables | |
1280 | different partitioning schemes, etc. | |
1281 | pre_buffer : bool, default True | |
1282 | Coalesce and issue file reads in parallel to improve performance on | |
1283 | high-latency filesystems (e.g. S3). If True, Arrow will use a | |
1284 | background I/O thread pool. This option is only supported for | |
1285 | use_legacy_dataset=False. If using a filesystem layer that itself | |
1286 | performs readahead (e.g. fsspec's S3FS), disable readahead for best | |
1287 | results. | |
1288 | coerce_int96_timestamp_unit : str, default None. | |
1289 | Cast timestamps that are stored in INT96 format to a particular resolution | |
1290 | (e.g. 'ms'). Setting to None is equivalent to 'ns' and therefore INT96 | |
1291 | timestamps will be infered as timestamps in nanoseconds. | |
1292 | """.format(_read_docstring_common, _DNF_filter_doc) | |
1293 | ||
1294 | def __new__(cls, path_or_paths=None, filesystem=None, schema=None, | |
1295 | metadata=None, split_row_groups=False, validate_schema=True, | |
1296 | filters=None, metadata_nthreads=1, read_dictionary=None, | |
1297 | memory_map=False, buffer_size=0, partitioning="hive", | |
1298 | use_legacy_dataset=None, pre_buffer=True, | |
1299 | coerce_int96_timestamp_unit=None): | |
1300 | if use_legacy_dataset is None: | |
1301 | # if a new filesystem is passed -> default to new implementation | |
1302 | if isinstance(filesystem, FileSystem): | |
1303 | use_legacy_dataset = False | |
1304 | # otherwise the default is still True | |
1305 | else: | |
1306 | use_legacy_dataset = True | |
1307 | ||
1308 | if not use_legacy_dataset: | |
1309 | return _ParquetDatasetV2( | |
1310 | path_or_paths, filesystem=filesystem, | |
1311 | filters=filters, | |
1312 | partitioning=partitioning, | |
1313 | read_dictionary=read_dictionary, | |
1314 | memory_map=memory_map, | |
1315 | buffer_size=buffer_size, | |
1316 | pre_buffer=pre_buffer, | |
1317 | coerce_int96_timestamp_unit=coerce_int96_timestamp_unit, | |
1318 | # unsupported keywords | |
1319 | schema=schema, metadata=metadata, | |
1320 | split_row_groups=split_row_groups, | |
1321 | validate_schema=validate_schema, | |
1322 | metadata_nthreads=metadata_nthreads | |
1323 | ) | |
1324 | self = object.__new__(cls) | |
1325 | return self | |
1326 | ||
1327 | def __init__(self, path_or_paths, filesystem=None, schema=None, | |
1328 | metadata=None, split_row_groups=False, validate_schema=True, | |
1329 | filters=None, metadata_nthreads=1, read_dictionary=None, | |
1330 | memory_map=False, buffer_size=0, partitioning="hive", | |
1331 | use_legacy_dataset=True, pre_buffer=True, | |
1332 | coerce_int96_timestamp_unit=None): | |
1333 | if partitioning != "hive": | |
1334 | raise ValueError( | |
1335 | 'Only "hive" for hive-like partitioning is supported when ' | |
1336 | 'using use_legacy_dataset=True') | |
1337 | self._metadata = _ParquetDatasetMetadata() | |
1338 | a_path = path_or_paths | |
1339 | if isinstance(a_path, list): | |
1340 | a_path = a_path[0] | |
1341 | ||
1342 | self._metadata.fs, _ = _get_filesystem_and_path(filesystem, a_path) | |
1343 | if isinstance(path_or_paths, list): | |
1344 | self.paths = [_parse_uri(path) for path in path_or_paths] | |
1345 | else: | |
1346 | self.paths = _parse_uri(path_or_paths) | |
1347 | ||
1348 | self._metadata.read_dictionary = read_dictionary | |
1349 | self._metadata.memory_map = memory_map | |
1350 | self._metadata.buffer_size = buffer_size | |
1351 | ||
1352 | (self._pieces, | |
1353 | self._partitions, | |
1354 | self.common_metadata_path, | |
1355 | self.metadata_path) = _make_manifest( | |
1356 | path_or_paths, self._fs, metadata_nthreads=metadata_nthreads, | |
1357 | open_file_func=partial(_open_dataset_file, self._metadata) | |
1358 | ) | |
1359 | ||
1360 | if self.common_metadata_path is not None: | |
1361 | with self._fs.open(self.common_metadata_path) as f: | |
1362 | self._metadata.common_metadata = read_metadata( | |
1363 | f, | |
1364 | memory_map=memory_map | |
1365 | ) | |
1366 | else: | |
1367 | self._metadata.common_metadata = None | |
1368 | ||
1369 | if metadata is None and self.metadata_path is not None: | |
1370 | with self._fs.open(self.metadata_path) as f: | |
1371 | self.metadata = read_metadata(f, memory_map=memory_map) | |
1372 | else: | |
1373 | self.metadata = metadata | |
1374 | ||
1375 | self.schema = schema | |
1376 | ||
1377 | self.split_row_groups = split_row_groups | |
1378 | ||
1379 | if split_row_groups: | |
1380 | raise NotImplementedError("split_row_groups not yet implemented") | |
1381 | ||
1382 | if filters is not None: | |
1383 | filters = _check_filters(filters) | |
1384 | self._filter(filters) | |
1385 | ||
1386 | if validate_schema: | |
1387 | self.validate_schemas() | |
1388 | ||
1389 | def equals(self, other): | |
1390 | if not isinstance(other, ParquetDataset): | |
1391 | raise TypeError('`other` must be an instance of ParquetDataset') | |
1392 | ||
1393 | if self._fs.__class__ != other._fs.__class__: | |
1394 | return False | |
1395 | for prop in ('paths', '_pieces', '_partitions', | |
1396 | 'common_metadata_path', 'metadata_path', | |
1397 | 'common_metadata', 'metadata', 'schema', | |
1398 | 'split_row_groups'): | |
1399 | if getattr(self, prop) != getattr(other, prop): | |
1400 | return False | |
1401 | for prop in ('memory_map', 'buffer_size'): | |
1402 | if getattr(self._metadata, prop) != getattr(other._metadata, prop): | |
1403 | return False | |
1404 | ||
1405 | return True | |
1406 | ||
1407 | def __eq__(self, other): | |
1408 | try: | |
1409 | return self.equals(other) | |
1410 | except TypeError: | |
1411 | return NotImplemented | |
1412 | ||
1413 | def validate_schemas(self): | |
1414 | if self.metadata is None and self.schema is None: | |
1415 | if self.common_metadata is not None: | |
1416 | self.schema = self.common_metadata.schema | |
1417 | else: | |
1418 | self.schema = self._pieces[0].get_metadata().schema | |
1419 | elif self.schema is None: | |
1420 | self.schema = self.metadata.schema | |
1421 | ||
1422 | # Verify schemas are all compatible | |
1423 | dataset_schema = self.schema.to_arrow_schema() | |
1424 | # Exclude the partition columns from the schema, they are provided | |
1425 | # by the path, not the DatasetPiece | |
1426 | if self._partitions is not None: | |
1427 | for partition_name in self._partitions.partition_names: | |
1428 | if dataset_schema.get_field_index(partition_name) != -1: | |
1429 | field_idx = dataset_schema.get_field_index(partition_name) | |
1430 | dataset_schema = dataset_schema.remove(field_idx) | |
1431 | ||
1432 | for piece in self._pieces: | |
1433 | file_metadata = piece.get_metadata() | |
1434 | file_schema = file_metadata.schema.to_arrow_schema() | |
1435 | if not dataset_schema.equals(file_schema, check_metadata=False): | |
1436 | raise ValueError('Schema in {!s} was different. \n' | |
1437 | '{!s}\n\nvs\n\n{!s}' | |
1438 | .format(piece, file_schema, | |
1439 | dataset_schema)) | |
1440 | ||
1441 | def read(self, columns=None, use_threads=True, use_pandas_metadata=False): | |
1442 | """ | |
1443 | Read multiple Parquet files as a single pyarrow.Table. | |
1444 | ||
1445 | Parameters | |
1446 | ---------- | |
1447 | columns : List[str] | |
1448 | Names of columns to read from the file. | |
1449 | use_threads : bool, default True | |
1450 | Perform multi-threaded column reads | |
1451 | use_pandas_metadata : bool, default False | |
1452 | Passed through to each dataset piece. | |
1453 | ||
1454 | Returns | |
1455 | ------- | |
1456 | pyarrow.Table | |
1457 | Content of the file as a table (of columns). | |
1458 | """ | |
1459 | tables = [] | |
1460 | for piece in self._pieces: | |
1461 | table = piece.read(columns=columns, use_threads=use_threads, | |
1462 | partitions=self._partitions, | |
1463 | use_pandas_metadata=use_pandas_metadata) | |
1464 | tables.append(table) | |
1465 | ||
1466 | all_data = lib.concat_tables(tables) | |
1467 | ||
1468 | if use_pandas_metadata: | |
1469 | # We need to ensure that this metadata is set in the Table's schema | |
1470 | # so that Table.to_pandas will construct pandas.DataFrame with the | |
1471 | # right index | |
1472 | common_metadata = self._get_common_pandas_metadata() | |
1473 | current_metadata = all_data.schema.metadata or {} | |
1474 | ||
1475 | if common_metadata and b'pandas' not in current_metadata: | |
1476 | all_data = all_data.replace_schema_metadata({ | |
1477 | b'pandas': common_metadata}) | |
1478 | ||
1479 | return all_data | |
1480 | ||
1481 | def read_pandas(self, **kwargs): | |
1482 | """ | |
1483 | Read dataset including pandas metadata, if any. Other arguments passed | |
1484 | through to ParquetDataset.read, see docstring for further details. | |
1485 | ||
1486 | Parameters | |
1487 | ---------- | |
1488 | **kwargs : optional | |
1489 | All additional options to pass to the reader. | |
1490 | ||
1491 | Returns | |
1492 | ------- | |
1493 | pyarrow.Table | |
1494 | Content of the file as a table (of columns). | |
1495 | """ | |
1496 | return self.read(use_pandas_metadata=True, **kwargs) | |
1497 | ||
1498 | def _get_common_pandas_metadata(self): | |
1499 | if self.common_metadata is None: | |
1500 | return None | |
1501 | ||
1502 | keyvalues = self.common_metadata.metadata | |
1503 | return keyvalues.get(b'pandas', None) | |
1504 | ||
1505 | def _filter(self, filters): | |
1506 | accepts_filter = self._partitions.filter_accepts_partition | |
1507 | ||
1508 | def one_filter_accepts(piece, filter): | |
1509 | return all(accepts_filter(part_key, filter, level) | |
1510 | for level, part_key in enumerate(piece.partition_keys)) | |
1511 | ||
1512 | def all_filters_accept(piece): | |
1513 | return any(all(one_filter_accepts(piece, f) for f in conjunction) | |
1514 | for conjunction in filters) | |
1515 | ||
1516 | self._pieces = [p for p in self._pieces if all_filters_accept(p)] | |
1517 | ||
1518 | @property | |
1519 | def pieces(self): | |
1520 | warnings.warn( | |
1521 | _DEPR_MSG.format( | |
1522 | "ParquetDataset.pieces", | |
1523 | " Specify 'use_legacy_dataset=False' while constructing the " | |
1524 | "ParquetDataset, and then use the '.fragments' attribute " | |
1525 | "instead."), | |
1526 | DeprecationWarning, stacklevel=2) | |
1527 | return self._pieces | |
1528 | ||
1529 | @property | |
1530 | def partitions(self): | |
1531 | warnings.warn( | |
1532 | _DEPR_MSG.format( | |
1533 | "ParquetDataset.partitions", | |
1534 | " Specify 'use_legacy_dataset=False' while constructing the " | |
1535 | "ParquetDataset, and then use the '.partitioning' attribute " | |
1536 | "instead."), | |
1537 | DeprecationWarning, stacklevel=2) | |
1538 | return self._partitions | |
1539 | ||
1540 | @property | |
1541 | def memory_map(self): | |
1542 | warnings.warn( | |
1543 | _DEPR_MSG.format("ParquetDataset.memory_map", ""), | |
1544 | DeprecationWarning, stacklevel=2) | |
1545 | return self._metadata.memory_map | |
1546 | ||
1547 | @property | |
1548 | def read_dictionary(self): | |
1549 | warnings.warn( | |
1550 | _DEPR_MSG.format("ParquetDataset.read_dictionary", ""), | |
1551 | DeprecationWarning, stacklevel=2) | |
1552 | return self._metadata.read_dictionary | |
1553 | ||
1554 | @property | |
1555 | def buffer_size(self): | |
1556 | warnings.warn( | |
1557 | _DEPR_MSG.format("ParquetDataset.buffer_size", ""), | |
1558 | DeprecationWarning, stacklevel=2) | |
1559 | return self._metadata.buffer_size | |
1560 | ||
1561 | _fs = property( | |
1562 | operator.attrgetter('_metadata.fs') | |
1563 | ) | |
1564 | ||
1565 | @property | |
1566 | def fs(self): | |
1567 | warnings.warn( | |
1568 | _DEPR_MSG.format( | |
1569 | "ParquetDataset.fs", | |
1570 | " Specify 'use_legacy_dataset=False' while constructing the " | |
1571 | "ParquetDataset, and then use the '.filesystem' attribute " | |
1572 | "instead."), | |
1573 | DeprecationWarning, stacklevel=2) | |
1574 | return self._metadata.fs | |
1575 | ||
1576 | common_metadata = property( | |
1577 | operator.attrgetter('_metadata.common_metadata') | |
1578 | ) | |
1579 | ||
1580 | ||
1581 | def _make_manifest(path_or_paths, fs, pathsep='/', metadata_nthreads=1, | |
1582 | open_file_func=None): | |
1583 | partitions = None | |
1584 | common_metadata_path = None | |
1585 | metadata_path = None | |
1586 | ||
1587 | if isinstance(path_or_paths, list) and len(path_or_paths) == 1: | |
1588 | # Dask passes a directory as a list of length 1 | |
1589 | path_or_paths = path_or_paths[0] | |
1590 | ||
1591 | if _is_path_like(path_or_paths) and fs.isdir(path_or_paths): | |
1592 | manifest = ParquetManifest(path_or_paths, filesystem=fs, | |
1593 | open_file_func=open_file_func, | |
1594 | pathsep=getattr(fs, "pathsep", "/"), | |
1595 | metadata_nthreads=metadata_nthreads) | |
1596 | common_metadata_path = manifest.common_metadata_path | |
1597 | metadata_path = manifest.metadata_path | |
1598 | pieces = manifest.pieces | |
1599 | partitions = manifest.partitions | |
1600 | else: | |
1601 | if not isinstance(path_or_paths, list): | |
1602 | path_or_paths = [path_or_paths] | |
1603 | ||
1604 | # List of paths | |
1605 | if len(path_or_paths) == 0: | |
1606 | raise ValueError('Must pass at least one file path') | |
1607 | ||
1608 | pieces = [] | |
1609 | for path in path_or_paths: | |
1610 | if not fs.isfile(path): | |
1611 | raise OSError('Passed non-file path: {}' | |
1612 | .format(path)) | |
1613 | piece = ParquetDatasetPiece._create( | |
1614 | path, open_file_func=open_file_func) | |
1615 | pieces.append(piece) | |
1616 | ||
1617 | return pieces, partitions, common_metadata_path, metadata_path | |
1618 | ||
1619 | ||
1620 | def _is_local_file_system(fs): | |
1621 | return isinstance(fs, LocalFileSystem) or isinstance( | |
1622 | fs, legacyfs.LocalFileSystem | |
1623 | ) | |
1624 | ||
1625 | ||
1626 | class _ParquetDatasetV2: | |
1627 | """ | |
1628 | ParquetDataset shim using the Dataset API under the hood. | |
1629 | """ | |
1630 | ||
1631 | def __init__(self, path_or_paths, filesystem=None, filters=None, | |
1632 | partitioning="hive", read_dictionary=None, buffer_size=None, | |
1633 | memory_map=False, ignore_prefixes=None, pre_buffer=True, | |
1634 | coerce_int96_timestamp_unit=None, **kwargs): | |
1635 | import pyarrow.dataset as ds | |
1636 | ||
1637 | # Raise error for not supported keywords | |
1638 | for keyword, default in [ | |
1639 | ("schema", None), ("metadata", None), | |
1640 | ("split_row_groups", False), ("validate_schema", True), | |
1641 | ("metadata_nthreads", 1)]: | |
1642 | if keyword in kwargs and kwargs[keyword] is not default: | |
1643 | raise ValueError( | |
1644 | "Keyword '{0}' is not yet supported with the new " | |
1645 | "Dataset API".format(keyword)) | |
1646 | ||
1647 | # map format arguments | |
1648 | read_options = { | |
1649 | "pre_buffer": pre_buffer, | |
1650 | "coerce_int96_timestamp_unit": coerce_int96_timestamp_unit | |
1651 | } | |
1652 | if buffer_size: | |
1653 | read_options.update(use_buffered_stream=True, | |
1654 | buffer_size=buffer_size) | |
1655 | if read_dictionary is not None: | |
1656 | read_options.update(dictionary_columns=read_dictionary) | |
1657 | ||
1658 | # map filters to Expressions | |
1659 | self._filters = filters | |
1660 | self._filter_expression = filters and _filters_to_expression(filters) | |
1661 | ||
1662 | # map old filesystems to new one | |
1663 | if filesystem is not None: | |
1664 | filesystem = _ensure_filesystem( | |
1665 | filesystem, use_mmap=memory_map) | |
1666 | elif filesystem is None and memory_map: | |
1667 | # if memory_map is specified, assume local file system (string | |
1668 | # path can in principle be URI for any filesystem) | |
1669 | filesystem = LocalFileSystem(use_mmap=memory_map) | |
1670 | ||
1671 | # This needs to be checked after _ensure_filesystem, because that | |
1672 | # handles the case of an fsspec LocalFileSystem | |
1673 | if ( | |
1674 | hasattr(path_or_paths, "__fspath__") and | |
1675 | filesystem is not None and | |
1676 | not _is_local_file_system(filesystem) | |
1677 | ): | |
1678 | raise TypeError( | |
1679 | "Path-like objects with __fspath__ must only be used with " | |
1680 | f"local file systems, not {type(filesystem)}" | |
1681 | ) | |
1682 | ||
1683 | # check for single fragment dataset | |
1684 | single_file = None | |
1685 | if isinstance(path_or_paths, list): | |
1686 | if len(path_or_paths) == 1: | |
1687 | single_file = path_or_paths[0] | |
1688 | else: | |
1689 | if _is_path_like(path_or_paths): | |
1690 | path_or_paths = _stringify_path(path_or_paths) | |
1691 | if filesystem is None: | |
1692 | # path might be a URI describing the FileSystem as well | |
1693 | try: | |
1694 | filesystem, path_or_paths = FileSystem.from_uri( | |
1695 | path_or_paths) | |
1696 | except ValueError: | |
1697 | filesystem = LocalFileSystem(use_mmap=memory_map) | |
1698 | if filesystem.get_file_info(path_or_paths).is_file: | |
1699 | single_file = path_or_paths | |
1700 | else: | |
1701 | single_file = path_or_paths | |
1702 | ||
1703 | if single_file is not None: | |
1704 | self._enable_parallel_column_conversion = True | |
1705 | read_options.update(enable_parallel_column_conversion=True) | |
1706 | ||
1707 | parquet_format = ds.ParquetFileFormat(**read_options) | |
1708 | fragment = parquet_format.make_fragment(single_file, filesystem) | |
1709 | ||
1710 | self._dataset = ds.FileSystemDataset( | |
1711 | [fragment], schema=fragment.physical_schema, | |
1712 | format=parquet_format, | |
1713 | filesystem=fragment.filesystem | |
1714 | ) | |
1715 | return | |
1716 | else: | |
1717 | self._enable_parallel_column_conversion = False | |
1718 | ||
1719 | parquet_format = ds.ParquetFileFormat(**read_options) | |
1720 | ||
1721 | # check partitioning to enable dictionary encoding | |
1722 | if partitioning == "hive": | |
1723 | partitioning = ds.HivePartitioning.discover( | |
1724 | infer_dictionary=True) | |
1725 | ||
1726 | self._dataset = ds.dataset(path_or_paths, filesystem=filesystem, | |
1727 | format=parquet_format, | |
1728 | partitioning=partitioning, | |
1729 | ignore_prefixes=ignore_prefixes) | |
1730 | ||
1731 | @property | |
1732 | def schema(self): | |
1733 | return self._dataset.schema | |
1734 | ||
1735 | def read(self, columns=None, use_threads=True, use_pandas_metadata=False): | |
1736 | """ | |
1737 | Read (multiple) Parquet files as a single pyarrow.Table. | |
1738 | ||
1739 | Parameters | |
1740 | ---------- | |
1741 | columns : List[str] | |
1742 | Names of columns to read from the dataset. The partition fields | |
1743 | are not automatically included (in contrast to when setting | |
1744 | ``use_legacy_dataset=True``). | |
1745 | use_threads : bool, default True | |
1746 | Perform multi-threaded column reads. | |
1747 | use_pandas_metadata : bool, default False | |
1748 | If True and file has custom pandas schema metadata, ensure that | |
1749 | index columns are also loaded. | |
1750 | ||
1751 | Returns | |
1752 | ------- | |
1753 | pyarrow.Table | |
1754 | Content of the file as a table (of columns). | |
1755 | """ | |
1756 | # if use_pandas_metadata, we need to include index columns in the | |
1757 | # column selection, to be able to restore those in the pandas DataFrame | |
1758 | metadata = self.schema.metadata | |
1759 | if columns is not None and use_pandas_metadata: | |
1760 | if metadata and b'pandas' in metadata: | |
1761 | # RangeIndex can be represented as dict instead of column name | |
1762 | index_columns = [ | |
1763 | col for col in _get_pandas_index_columns(metadata) | |
1764 | if not isinstance(col, dict) | |
1765 | ] | |
1766 | columns = ( | |
1767 | list(columns) + list(set(index_columns) - set(columns)) | |
1768 | ) | |
1769 | ||
1770 | if self._enable_parallel_column_conversion: | |
1771 | if use_threads: | |
1772 | # Allow per-column parallelism; would otherwise cause | |
1773 | # contention in the presence of per-file parallelism. | |
1774 | use_threads = False | |
1775 | ||
1776 | table = self._dataset.to_table( | |
1777 | columns=columns, filter=self._filter_expression, | |
1778 | use_threads=use_threads | |
1779 | ) | |
1780 | ||
1781 | # if use_pandas_metadata, restore the pandas metadata (which gets | |
1782 | # lost if doing a specific `columns` selection in to_table) | |
1783 | if use_pandas_metadata: | |
1784 | if metadata and b"pandas" in metadata: | |
1785 | new_metadata = table.schema.metadata or {} | |
1786 | new_metadata.update({b"pandas": metadata[b"pandas"]}) | |
1787 | table = table.replace_schema_metadata(new_metadata) | |
1788 | ||
1789 | return table | |
1790 | ||
1791 | def read_pandas(self, **kwargs): | |
1792 | """ | |
1793 | Read dataset including pandas metadata, if any. Other arguments passed | |
1794 | through to ParquetDataset.read, see docstring for further details. | |
1795 | """ | |
1796 | return self.read(use_pandas_metadata=True, **kwargs) | |
1797 | ||
1798 | @property | |
1799 | def pieces(self): | |
1800 | warnings.warn( | |
1801 | _DEPR_MSG.format("ParquetDataset.pieces", | |
1802 | " Use the '.fragments' attribute instead"), | |
1803 | DeprecationWarning, stacklevel=2) | |
1804 | return list(self._dataset.get_fragments()) | |
1805 | ||
1806 | @property | |
1807 | def fragments(self): | |
1808 | return list(self._dataset.get_fragments()) | |
1809 | ||
1810 | @property | |
1811 | def files(self): | |
1812 | return self._dataset.files | |
1813 | ||
1814 | @property | |
1815 | def filesystem(self): | |
1816 | return self._dataset.filesystem | |
1817 | ||
1818 | @property | |
1819 | def partitioning(self): | |
1820 | """ | |
1821 | The partitioning of the Dataset source, if discovered. | |
1822 | """ | |
1823 | return self._dataset.partitioning | |
1824 | ||
1825 | ||
1826 | _read_table_docstring = """ | |
1827 | {0} | |
1828 | ||
1829 | Parameters | |
1830 | ---------- | |
1831 | source : str, pyarrow.NativeFile, or file-like object | |
1832 | If a string passed, can be a single file name or directory name. For | |
1833 | file-like objects, only read a single file. Use pyarrow.BufferReader to | |
1834 | read a file contained in a bytes or buffer-like object. | |
1835 | columns : list | |
1836 | If not None, only these columns will be read from the file. A column | |
1837 | name may be a prefix of a nested field, e.g. 'a' will select 'a.b', | |
1838 | 'a.c', and 'a.d.e'. If empty, no columns will be read. Note | |
1839 | that the table will still have the correct num_rows set despite having | |
1840 | no columns. | |
1841 | use_threads : bool, default True | |
1842 | Perform multi-threaded column reads. | |
1843 | metadata : FileMetaData | |
1844 | If separately computed | |
1845 | {1} | |
1846 | use_legacy_dataset : bool, default False | |
1847 | By default, `read_table` uses the new Arrow Datasets API since | |
1848 | pyarrow 1.0.0. Among other things, this allows to pass `filters` | |
1849 | for all columns and not only the partition keys, enables | |
1850 | different partitioning schemes, etc. | |
1851 | Set to True to use the legacy behaviour. | |
1852 | ignore_prefixes : list, optional | |
1853 | Files matching any of these prefixes will be ignored by the | |
1854 | discovery process if use_legacy_dataset=False. | |
1855 | This is matched to the basename of a path. | |
1856 | By default this is ['.', '_']. | |
1857 | Note that discovery happens only if a directory is passed as source. | |
1858 | filesystem : FileSystem, default None | |
1859 | If nothing passed, paths assumed to be found in the local on-disk | |
1860 | filesystem. | |
1861 | filters : List[Tuple] or List[List[Tuple]] or None (default) | |
1862 | Rows which do not match the filter predicate will be removed from scanned | |
1863 | data. Partition keys embedded in a nested directory structure will be | |
1864 | exploited to avoid loading files at all if they contain no matching rows. | |
1865 | If `use_legacy_dataset` is True, filters can only reference partition | |
1866 | keys and only a hive-style directory structure is supported. When | |
1867 | setting `use_legacy_dataset` to False, also within-file level filtering | |
1868 | and different partitioning schemes are supported. | |
1869 | ||
1870 | {3} | |
1871 | pre_buffer : bool, default True | |
1872 | Coalesce and issue file reads in parallel to improve performance on | |
1873 | high-latency filesystems (e.g. S3). If True, Arrow will use a | |
1874 | background I/O thread pool. This option is only supported for | |
1875 | use_legacy_dataset=False. If using a filesystem layer that itself | |
1876 | performs readahead (e.g. fsspec's S3FS), disable readahead for best | |
1877 | results. | |
1878 | coerce_int96_timestamp_unit : str, default None. | |
1879 | Cast timestamps that are stored in INT96 format to a particular | |
1880 | resolution (e.g. 'ms'). Setting to None is equivalent to 'ns' | |
1881 | and therefore INT96 timestamps will be infered as timestamps | |
1882 | in nanoseconds. | |
1883 | ||
1884 | Returns | |
1885 | ------- | |
1886 | {2} | |
1887 | """ | |
1888 | ||
1889 | ||
1890 | def read_table(source, columns=None, use_threads=True, metadata=None, | |
1891 | use_pandas_metadata=False, memory_map=False, | |
1892 | read_dictionary=None, filesystem=None, filters=None, | |
1893 | buffer_size=0, partitioning="hive", use_legacy_dataset=False, | |
1894 | ignore_prefixes=None, pre_buffer=True, | |
1895 | coerce_int96_timestamp_unit=None): | |
1896 | if not use_legacy_dataset: | |
1897 | if metadata is not None: | |
1898 | raise ValueError( | |
1899 | "The 'metadata' keyword is no longer supported with the new " | |
1900 | "datasets-based implementation. Specify " | |
1901 | "'use_legacy_dataset=True' to temporarily recover the old " | |
1902 | "behaviour." | |
1903 | ) | |
1904 | try: | |
1905 | dataset = _ParquetDatasetV2( | |
1906 | source, | |
1907 | filesystem=filesystem, | |
1908 | partitioning=partitioning, | |
1909 | memory_map=memory_map, | |
1910 | read_dictionary=read_dictionary, | |
1911 | buffer_size=buffer_size, | |
1912 | filters=filters, | |
1913 | ignore_prefixes=ignore_prefixes, | |
1914 | pre_buffer=pre_buffer, | |
1915 | coerce_int96_timestamp_unit=coerce_int96_timestamp_unit | |
1916 | ) | |
1917 | except ImportError: | |
1918 | # fall back on ParquetFile for simple cases when pyarrow.dataset | |
1919 | # module is not available | |
1920 | if filters is not None: | |
1921 | raise ValueError( | |
1922 | "the 'filters' keyword is not supported when the " | |
1923 | "pyarrow.dataset module is not available" | |
1924 | ) | |
1925 | if partitioning != "hive": | |
1926 | raise ValueError( | |
1927 | "the 'partitioning' keyword is not supported when the " | |
1928 | "pyarrow.dataset module is not available" | |
1929 | ) | |
1930 | filesystem, path = _resolve_filesystem_and_path(source, filesystem) | |
1931 | if filesystem is not None: | |
1932 | source = filesystem.open_input_file(path) | |
1933 | # TODO test that source is not a directory or a list | |
1934 | dataset = ParquetFile( | |
1935 | source, metadata=metadata, read_dictionary=read_dictionary, | |
1936 | memory_map=memory_map, buffer_size=buffer_size, | |
1937 | pre_buffer=pre_buffer, | |
1938 | coerce_int96_timestamp_unit=coerce_int96_timestamp_unit | |
1939 | ) | |
1940 | ||
1941 | return dataset.read(columns=columns, use_threads=use_threads, | |
1942 | use_pandas_metadata=use_pandas_metadata) | |
1943 | ||
1944 | if ignore_prefixes is not None: | |
1945 | raise ValueError( | |
1946 | "The 'ignore_prefixes' keyword is only supported when " | |
1947 | "use_legacy_dataset=False") | |
1948 | ||
1949 | if _is_path_like(source): | |
1950 | pf = ParquetDataset( | |
1951 | source, metadata=metadata, memory_map=memory_map, | |
1952 | read_dictionary=read_dictionary, | |
1953 | buffer_size=buffer_size, | |
1954 | filesystem=filesystem, filters=filters, | |
1955 | partitioning=partitioning, | |
1956 | coerce_int96_timestamp_unit=coerce_int96_timestamp_unit | |
1957 | ) | |
1958 | else: | |
1959 | pf = ParquetFile( | |
1960 | source, metadata=metadata, | |
1961 | read_dictionary=read_dictionary, | |
1962 | memory_map=memory_map, | |
1963 | buffer_size=buffer_size, | |
1964 | coerce_int96_timestamp_unit=coerce_int96_timestamp_unit | |
1965 | ) | |
1966 | return pf.read(columns=columns, use_threads=use_threads, | |
1967 | use_pandas_metadata=use_pandas_metadata) | |
1968 | ||
1969 | ||
1970 | read_table.__doc__ = _read_table_docstring.format( | |
1971 | """Read a Table from Parquet format | |
1972 | ||
1973 | Note: starting with pyarrow 1.0, the default for `use_legacy_dataset` is | |
1974 | switched to False.""", | |
1975 | "\n".join((_read_docstring_common, | |
1976 | """use_pandas_metadata : bool, default False | |
1977 | If True and file has custom pandas schema metadata, ensure that | |
1978 | index columns are also loaded.""")), | |
1979 | """pyarrow.Table | |
1980 | Content of the file as a table (of columns)""", | |
1981 | _DNF_filter_doc) | |
1982 | ||
1983 | ||
1984 | def read_pandas(source, columns=None, **kwargs): | |
1985 | return read_table( | |
1986 | source, columns=columns, use_pandas_metadata=True, **kwargs | |
1987 | ) | |
1988 | ||
1989 | ||
1990 | read_pandas.__doc__ = _read_table_docstring.format( | |
1991 | 'Read a Table from Parquet format, also reading DataFrame\n' | |
1992 | 'index values if known in the file metadata', | |
1993 | "\n".join((_read_docstring_common, | |
1994 | """**kwargs : additional options for :func:`read_table`""")), | |
1995 | """pyarrow.Table | |
1996 | Content of the file as a Table of Columns, including DataFrame | |
1997 | indexes as columns""", | |
1998 | _DNF_filter_doc) | |
1999 | ||
2000 | ||
2001 | def write_table(table, where, row_group_size=None, version='1.0', | |
2002 | use_dictionary=True, compression='snappy', | |
2003 | write_statistics=True, | |
2004 | use_deprecated_int96_timestamps=None, | |
2005 | coerce_timestamps=None, | |
2006 | allow_truncated_timestamps=False, | |
2007 | data_page_size=None, flavor=None, | |
2008 | filesystem=None, | |
2009 | compression_level=None, | |
2010 | use_byte_stream_split=False, | |
2011 | data_page_version='1.0', | |
2012 | use_compliant_nested_type=False, | |
2013 | **kwargs): | |
2014 | row_group_size = kwargs.pop('chunk_size', row_group_size) | |
2015 | use_int96 = use_deprecated_int96_timestamps | |
2016 | try: | |
2017 | with ParquetWriter( | |
2018 | where, table.schema, | |
2019 | filesystem=filesystem, | |
2020 | version=version, | |
2021 | flavor=flavor, | |
2022 | use_dictionary=use_dictionary, | |
2023 | write_statistics=write_statistics, | |
2024 | coerce_timestamps=coerce_timestamps, | |
2025 | data_page_size=data_page_size, | |
2026 | allow_truncated_timestamps=allow_truncated_timestamps, | |
2027 | compression=compression, | |
2028 | use_deprecated_int96_timestamps=use_int96, | |
2029 | compression_level=compression_level, | |
2030 | use_byte_stream_split=use_byte_stream_split, | |
2031 | data_page_version=data_page_version, | |
2032 | use_compliant_nested_type=use_compliant_nested_type, | |
2033 | **kwargs) as writer: | |
2034 | writer.write_table(table, row_group_size=row_group_size) | |
2035 | except Exception: | |
2036 | if _is_path_like(where): | |
2037 | try: | |
2038 | os.remove(_stringify_path(where)) | |
2039 | except os.error: | |
2040 | pass | |
2041 | raise | |
2042 | ||
2043 | ||
2044 | write_table.__doc__ = """ | |
2045 | Write a Table to Parquet format. | |
2046 | ||
2047 | Parameters | |
2048 | ---------- | |
2049 | table : pyarrow.Table | |
2050 | where : string or pyarrow.NativeFile | |
2051 | row_group_size : int | |
2052 | The number of rows per rowgroup | |
2053 | {} | |
2054 | **kwargs : optional | |
2055 | Additional options for ParquetWriter | |
2056 | """.format(_parquet_writer_arg_docs) | |
2057 | ||
2058 | ||
2059 | def _mkdir_if_not_exists(fs, path): | |
2060 | if fs._isfilestore() and not fs.exists(path): | |
2061 | try: | |
2062 | fs.mkdir(path) | |
2063 | except OSError: | |
2064 | assert fs.exists(path) | |
2065 | ||
2066 | ||
2067 | def write_to_dataset(table, root_path, partition_cols=None, | |
2068 | partition_filename_cb=None, filesystem=None, | |
2069 | use_legacy_dataset=None, **kwargs): | |
2070 | """Wrapper around parquet.write_table for writing a Table to | |
2071 | Parquet format by partitions. | |
2072 | For each combination of partition columns and values, | |
2073 | a subdirectories are created in the following | |
2074 | manner: | |
2075 | ||
2076 | root_dir/ | |
2077 | group1=value1 | |
2078 | group2=value1 | |
2079 | <uuid>.parquet | |
2080 | group2=value2 | |
2081 | <uuid>.parquet | |
2082 | group1=valueN | |
2083 | group2=value1 | |
2084 | <uuid>.parquet | |
2085 | group2=valueN | |
2086 | <uuid>.parquet | |
2087 | ||
2088 | Parameters | |
2089 | ---------- | |
2090 | table : pyarrow.Table | |
2091 | root_path : str, pathlib.Path | |
2092 | The root directory of the dataset | |
2093 | filesystem : FileSystem, default None | |
2094 | If nothing passed, paths assumed to be found in the local on-disk | |
2095 | filesystem | |
2096 | partition_cols : list, | |
2097 | Column names by which to partition the dataset | |
2098 | Columns are partitioned in the order they are given | |
2099 | partition_filename_cb : callable, | |
2100 | A callback function that takes the partition key(s) as an argument | |
2101 | and allow you to override the partition filename. If nothing is | |
2102 | passed, the filename will consist of a uuid. | |
2103 | use_legacy_dataset : bool | |
2104 | Default is True unless a ``pyarrow.fs`` filesystem is passed. | |
2105 | Set to False to enable the new code path (experimental, using the | |
2106 | new Arrow Dataset API). This is more efficient when using partition | |
2107 | columns, but does not (yet) support `partition_filename_cb` and | |
2108 | `metadata_collector` keywords. | |
2109 | **kwargs : dict, | |
2110 | Additional kwargs for write_table function. See docstring for | |
2111 | `write_table` or `ParquetWriter` for more information. | |
2112 | Using `metadata_collector` in kwargs allows one to collect the | |
2113 | file metadata instances of dataset pieces. The file paths in the | |
2114 | ColumnChunkMetaData will be set relative to `root_path`. | |
2115 | """ | |
2116 | if use_legacy_dataset is None: | |
2117 | # if a new filesystem is passed -> default to new implementation | |
2118 | if isinstance(filesystem, FileSystem): | |
2119 | use_legacy_dataset = False | |
2120 | # otherwise the default is still True | |
2121 | else: | |
2122 | use_legacy_dataset = True | |
2123 | ||
2124 | if not use_legacy_dataset: | |
2125 | import pyarrow.dataset as ds | |
2126 | ||
2127 | # extract non-file format options | |
2128 | schema = kwargs.pop("schema", None) | |
2129 | use_threads = kwargs.pop("use_threads", True) | |
2130 | ||
2131 | # raise for unsupported keywords | |
2132 | msg = ( | |
2133 | "The '{}' argument is not supported with the new dataset " | |
2134 | "implementation." | |
2135 | ) | |
2136 | metadata_collector = kwargs.pop('metadata_collector', None) | |
2137 | file_visitor = None | |
2138 | if metadata_collector is not None: | |
2139 | def file_visitor(written_file): | |
2140 | metadata_collector.append(written_file.metadata) | |
2141 | if partition_filename_cb is not None: | |
2142 | raise ValueError(msg.format("partition_filename_cb")) | |
2143 | ||
2144 | # map format arguments | |
2145 | parquet_format = ds.ParquetFileFormat() | |
2146 | write_options = parquet_format.make_write_options(**kwargs) | |
2147 | ||
2148 | # map old filesystems to new one | |
2149 | if filesystem is not None: | |
2150 | filesystem = _ensure_filesystem(filesystem) | |
2151 | ||
2152 | partitioning = None | |
2153 | if partition_cols: | |
2154 | part_schema = table.select(partition_cols).schema | |
2155 | partitioning = ds.partitioning(part_schema, flavor="hive") | |
2156 | ||
2157 | ds.write_dataset( | |
2158 | table, root_path, filesystem=filesystem, | |
2159 | format=parquet_format, file_options=write_options, schema=schema, | |
2160 | partitioning=partitioning, use_threads=use_threads, | |
2161 | file_visitor=file_visitor) | |
2162 | return | |
2163 | ||
2164 | fs, root_path = legacyfs.resolve_filesystem_and_path(root_path, filesystem) | |
2165 | ||
2166 | _mkdir_if_not_exists(fs, root_path) | |
2167 | ||
2168 | metadata_collector = kwargs.pop('metadata_collector', None) | |
2169 | ||
2170 | if partition_cols is not None and len(partition_cols) > 0: | |
2171 | df = table.to_pandas() | |
2172 | partition_keys = [df[col] for col in partition_cols] | |
2173 | data_df = df.drop(partition_cols, axis='columns') | |
2174 | data_cols = df.columns.drop(partition_cols) | |
2175 | if len(data_cols) == 0: | |
2176 | raise ValueError('No data left to save outside partition columns') | |
2177 | ||
2178 | subschema = table.schema | |
2179 | ||
2180 | # ARROW-2891: Ensure the output_schema is preserved when writing a | |
2181 | # partitioned dataset | |
2182 | for col in table.schema.names: | |
2183 | if col in partition_cols: | |
2184 | subschema = subschema.remove(subschema.get_field_index(col)) | |
2185 | ||
2186 | for keys, subgroup in data_df.groupby(partition_keys): | |
2187 | if not isinstance(keys, tuple): | |
2188 | keys = (keys,) | |
2189 | subdir = '/'.join( | |
2190 | ['{colname}={value}'.format(colname=name, value=val) | |
2191 | for name, val in zip(partition_cols, keys)]) | |
2192 | subtable = pa.Table.from_pandas(subgroup, schema=subschema, | |
2193 | safe=False) | |
2194 | _mkdir_if_not_exists(fs, '/'.join([root_path, subdir])) | |
2195 | if partition_filename_cb: | |
2196 | outfile = partition_filename_cb(keys) | |
2197 | else: | |
2198 | outfile = guid() + '.parquet' | |
2199 | relative_path = '/'.join([subdir, outfile]) | |
2200 | full_path = '/'.join([root_path, relative_path]) | |
2201 | with fs.open(full_path, 'wb') as f: | |
2202 | write_table(subtable, f, metadata_collector=metadata_collector, | |
2203 | **kwargs) | |
2204 | if metadata_collector is not None: | |
2205 | metadata_collector[-1].set_file_path(relative_path) | |
2206 | else: | |
2207 | if partition_filename_cb: | |
2208 | outfile = partition_filename_cb(None) | |
2209 | else: | |
2210 | outfile = guid() + '.parquet' | |
2211 | full_path = '/'.join([root_path, outfile]) | |
2212 | with fs.open(full_path, 'wb') as f: | |
2213 | write_table(table, f, metadata_collector=metadata_collector, | |
2214 | **kwargs) | |
2215 | if metadata_collector is not None: | |
2216 | metadata_collector[-1].set_file_path(outfile) | |
2217 | ||
2218 | ||
2219 | def write_metadata(schema, where, metadata_collector=None, **kwargs): | |
2220 | """ | |
2221 | Write metadata-only Parquet file from schema. This can be used with | |
2222 | `write_to_dataset` to generate `_common_metadata` and `_metadata` sidecar | |
2223 | files. | |
2224 | ||
2225 | Parameters | |
2226 | ---------- | |
2227 | schema : pyarrow.Schema | |
2228 | where : string or pyarrow.NativeFile | |
2229 | metadata_collector : list | |
2230 | where to collect metadata information. | |
2231 | **kwargs : dict, | |
2232 | Additional kwargs for ParquetWriter class. See docstring for | |
2233 | `ParquetWriter` for more information. | |
2234 | ||
2235 | Examples | |
2236 | -------- | |
2237 | ||
2238 | Write a dataset and collect metadata information. | |
2239 | ||
2240 | >>> metadata_collector = [] | |
2241 | >>> write_to_dataset( | |
2242 | ... table, root_path, | |
2243 | ... metadata_collector=metadata_collector, **writer_kwargs) | |
2244 | ||
2245 | Write the `_common_metadata` parquet file without row groups statistics. | |
2246 | ||
2247 | >>> write_metadata( | |
2248 | ... table.schema, root_path / '_common_metadata', **writer_kwargs) | |
2249 | ||
2250 | Write the `_metadata` parquet file with row groups statistics. | |
2251 | ||
2252 | >>> write_metadata( | |
2253 | ... table.schema, root_path / '_metadata', | |
2254 | ... metadata_collector=metadata_collector, **writer_kwargs) | |
2255 | """ | |
2256 | writer = ParquetWriter(where, schema, **kwargs) | |
2257 | writer.close() | |
2258 | ||
2259 | if metadata_collector is not None: | |
2260 | # ParquetWriter doesn't expose the metadata until it's written. Write | |
2261 | # it and read it again. | |
2262 | metadata = read_metadata(where) | |
2263 | for m in metadata_collector: | |
2264 | metadata.append_row_groups(m) | |
2265 | metadata.write_metadata_file(where) | |
2266 | ||
2267 | ||
2268 | def read_metadata(where, memory_map=False): | |
2269 | """ | |
2270 | Read FileMetadata from footer of a single Parquet file. | |
2271 | ||
2272 | Parameters | |
2273 | ---------- | |
2274 | where : str (filepath) or file-like object | |
2275 | memory_map : bool, default False | |
2276 | Create memory map when the source is a file path. | |
2277 | ||
2278 | Returns | |
2279 | ------- | |
2280 | metadata : FileMetadata | |
2281 | """ | |
2282 | return ParquetFile(where, memory_map=memory_map).metadata | |
2283 | ||
2284 | ||
2285 | def read_schema(where, memory_map=False): | |
2286 | """ | |
2287 | Read effective Arrow schema from Parquet file metadata. | |
2288 | ||
2289 | Parameters | |
2290 | ---------- | |
2291 | where : str (filepath) or file-like object | |
2292 | memory_map : bool, default False | |
2293 | Create memory map when the source is a file path. | |
2294 | ||
2295 | Returns | |
2296 | ------- | |
2297 | schema : pyarrow.Schema | |
2298 | """ | |
2299 | return ParquetFile(where, memory_map=memory_map).schema.to_arrow_schema() |