be/src/format/parquet/parquet_predicate.h
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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 | | #pragma once |
19 | | |
20 | | #include <gen_cpp/parquet_types.h> |
21 | | |
22 | | #include <cmath> |
23 | | #include <cstring> |
24 | | #include <vector> |
25 | | |
26 | | #include "cctz/time_zone.h" |
27 | | #include "core/data_type/data_type_decimal.h" |
28 | | #include "core/data_type/primitive_type.h" |
29 | | #include "exec/common/endian.h" |
30 | | #include "format/format_common.h" |
31 | | #include "format/parquet/parquet_block_split_bloom_filter.h" |
32 | | #include "format/parquet/parquet_column_convert.h" |
33 | | #include "format/parquet/parquet_common.h" |
34 | | #include "format/parquet/schema_desc.h" |
35 | | #include "storage/olap_scan_common.h" |
36 | | #include "storage/segment/row_ranges.h" |
37 | | #include "util/timezone_utils.h" |
38 | | |
39 | | namespace doris { |
40 | | class ParquetPredicate { |
41 | | private: |
42 | 51 | static inline bool _is_ascii(uint8_t byte) { return byte < 128; } |
43 | | |
44 | 14 | static int _common_prefix(const std::string& encoding_min, const std::string& encoding_max) { |
45 | 14 | size_t min_length = std::min(encoding_min.size(), encoding_max.size()); |
46 | 14 | int common_length = 0; |
47 | 49 | while (common_length < min_length && |
48 | 49 | encoding_min[common_length] == encoding_max[common_length]) { |
49 | 35 | common_length++; |
50 | 35 | } |
51 | 14 | return common_length; |
52 | 14 | } |
53 | | |
54 | 15 | static bool _try_read_old_utf8_stats(std::string& encoding_min, std::string& encoding_max) { |
55 | 15 | if (encoding_min == encoding_max) { |
56 | | // If min = max, then there is a single value only |
57 | | // No need to modify, just use min |
58 | 1 | encoding_max = encoding_min; |
59 | 1 | return true; |
60 | 14 | } else { |
61 | 14 | int common_prefix_length = _common_prefix(encoding_min, encoding_max); |
62 | | |
63 | | // For min we can retain all-ASCII, because this produces a strictly lower value. |
64 | 14 | int min_good_length = common_prefix_length; |
65 | 28 | while (min_good_length < encoding_min.size() && |
66 | 28 | _is_ascii(static_cast<uint8_t>(encoding_min[min_good_length]))) { |
67 | 14 | min_good_length++; |
68 | 14 | } |
69 | | |
70 | | // For max we can be sure only of the part matching the min. When they differ, we can consider only one next, and only if both are ASCII |
71 | 14 | int max_good_length = common_prefix_length; |
72 | 14 | if (max_good_length < encoding_max.size() && max_good_length < encoding_min.size() && |
73 | 14 | _is_ascii(static_cast<uint8_t>(encoding_min[max_good_length])) && |
74 | 14 | _is_ascii(static_cast<uint8_t>(encoding_max[max_good_length]))) { |
75 | 7 | max_good_length++; |
76 | 7 | } |
77 | | // Incrementing 127 would overflow. Incrementing within non-ASCII can have side-effects. |
78 | 19 | while (max_good_length > 0 && |
79 | 19 | (static_cast<uint8_t>(encoding_max[max_good_length - 1]) == 127 || |
80 | 15 | !_is_ascii(static_cast<uint8_t>(encoding_max[max_good_length - 1])))) { |
81 | 5 | max_good_length--; |
82 | 5 | } |
83 | 14 | if (max_good_length == 0) { |
84 | | // We can return just min bound, but code downstream likely expects both are present or both are absent. |
85 | 4 | return false; |
86 | 4 | } |
87 | | |
88 | 10 | encoding_min.resize(min_good_length); |
89 | 10 | encoding_max.resize(max_good_length); |
90 | 10 | if (max_good_length > 0) { |
91 | 10 | encoding_max[max_good_length - 1]++; |
92 | 10 | } |
93 | 10 | return true; |
94 | 14 | } |
95 | 15 | } |
96 | | |
97 | 0 | static SortOrder _determine_sort_order(const tparquet::SchemaElement& parquet_schema) { |
98 | 0 | tparquet::Type::type physical_type = parquet_schema.type; |
99 | 0 | const tparquet::LogicalType& logical_type = parquet_schema.logicalType; |
100 | | |
101 | | // Assume string type is SortOrder::SIGNED, use ParquetPredicate::_try_read_old_utf8_stats() to handle it. |
102 | 0 | if (logical_type.__isset.STRING && |
103 | 0 | (physical_type == tparquet::Type::BYTE_ARRAY || |
104 | 0 | physical_type == tparquet::Type::FIXED_LEN_BYTE_ARRAY)) { |
105 | 0 | return SortOrder::SIGNED; |
106 | 0 | } |
107 | | |
108 | 0 | if (logical_type.__isset.INTEGER) { |
109 | 0 | if (logical_type.INTEGER.isSigned) { |
110 | 0 | return SortOrder::SIGNED; |
111 | 0 | } else { |
112 | 0 | return SortOrder::UNSIGNED; |
113 | 0 | } |
114 | 0 | } else if (logical_type.__isset.DATE) { |
115 | 0 | return SortOrder::SIGNED; |
116 | 0 | } else if (logical_type.__isset.ENUM) { |
117 | 0 | return SortOrder::UNSIGNED; |
118 | 0 | } else if (logical_type.__isset.BSON) { |
119 | 0 | return SortOrder::UNSIGNED; |
120 | 0 | } else if (logical_type.__isset.JSON) { |
121 | 0 | return SortOrder::UNSIGNED; |
122 | 0 | } else if (logical_type.__isset.STRING) { |
123 | 0 | return SortOrder::UNSIGNED; |
124 | 0 | } else if (logical_type.__isset.DECIMAL) { |
125 | 0 | return SortOrder::UNKNOWN; |
126 | 0 | } else if (logical_type.__isset.MAP) { |
127 | 0 | return SortOrder::UNKNOWN; |
128 | 0 | } else if (logical_type.__isset.LIST) { |
129 | 0 | return SortOrder::UNKNOWN; |
130 | 0 | } else if (logical_type.__isset.TIME) { |
131 | 0 | return SortOrder::SIGNED; |
132 | 0 | } else if (logical_type.__isset.TIMESTAMP) { |
133 | 0 | return SortOrder::SIGNED; |
134 | 0 | } else if (logical_type.__isset.UNKNOWN) { |
135 | 0 | return SortOrder::UNKNOWN; |
136 | 0 | } else { |
137 | 0 | switch (physical_type) { |
138 | 0 | case tparquet::Type::BOOLEAN: |
139 | 0 | case tparquet::Type::INT32: |
140 | 0 | case tparquet::Type::INT64: |
141 | 0 | case tparquet::Type::FLOAT: |
142 | 0 | case tparquet::Type::DOUBLE: |
143 | 0 | return SortOrder::SIGNED; |
144 | 0 | case tparquet::Type::BYTE_ARRAY: |
145 | 0 | case tparquet::Type::FIXED_LEN_BYTE_ARRAY: |
146 | 0 | return SortOrder::UNSIGNED; |
147 | 0 | case tparquet::Type::INT96: |
148 | 0 | return SortOrder::UNKNOWN; |
149 | 0 | default: |
150 | 0 | return SortOrder::UNKNOWN; |
151 | 0 | } |
152 | 0 | } |
153 | 0 | } |
154 | | |
155 | | public: |
156 | | static constexpr int BLOOM_FILTER_MAX_HEADER_LENGTH = 64; |
157 | | struct ColumnStat { |
158 | | std::string encoded_min_value; |
159 | | std::string encoded_max_value; |
160 | | bool has_null; |
161 | | bool is_all_null; |
162 | | const FieldSchema* col_schema; |
163 | | const cctz::time_zone* ctz; |
164 | | std::unique_ptr<ParquetBlockSplitBloomFilter> bloom_filter; |
165 | | std::function<bool(ParquetPredicate::ColumnStat*, const int)>* get_stat_func = nullptr; |
166 | | std::function<bool(ParquetPredicate::ColumnStat*, const int)>* get_bloom_filter_func = |
167 | | nullptr; |
168 | | }; |
169 | | |
170 | 8 | static bool bloom_filter_supported(PrimitiveType type) { |
171 | | // Only support types where physical type == logical type (no conversion needed) |
172 | | // For types like DATEV2, DATETIMEV2, DECIMAL, Parquet stores them in physical format |
173 | | // (INT32, INT64, etc.) but Doris uses different internal representations. |
174 | | // Bloom filter works with physical bytes, but we only have logical type values, |
175 | | // and there's no reverse conversion (logical -> physical) available. |
176 | | // TINYINT/SMALLINT also need conversion via LittleIntPhysicalConverter. |
177 | 8 | switch (type) { |
178 | 0 | case TYPE_BOOLEAN: |
179 | 8 | case TYPE_INT: |
180 | 8 | case TYPE_BIGINT: |
181 | 8 | case TYPE_FLOAT: |
182 | 8 | case TYPE_DOUBLE: |
183 | 8 | case TYPE_CHAR: |
184 | 8 | case TYPE_VARCHAR: |
185 | 8 | case TYPE_STRING: |
186 | 8 | return true; |
187 | 0 | default: |
188 | 0 | return false; |
189 | 8 | } |
190 | 8 | } |
191 | | |
192 | | struct PageIndexStat { |
193 | | // Indicates whether the page index information in this column can be used. |
194 | | bool available = false; |
195 | | int64_t num_of_pages; |
196 | | std::vector<std::string> encoded_min_value; |
197 | | std::vector<std::string> encoded_max_value; |
198 | | std::vector<bool> has_null; |
199 | | std::vector<bool> is_all_null; |
200 | | const FieldSchema* col_schema; |
201 | | |
202 | | // Record the row range corresponding to each page. |
203 | | std::vector<segment_v2::RowRange> ranges; |
204 | | }; |
205 | | |
206 | | struct CachedPageIndexStat { |
207 | | const cctz::time_zone* ctz; |
208 | | std::map<int, PageIndexStat> stats; |
209 | | std::function<bool(PageIndexStat**, int)> get_stat_func; |
210 | | RowRange row_group_range; |
211 | | }; |
212 | | |
213 | | // The encoded Parquet min-max value is parsed into `fields`; |
214 | | // Can be used in row groups and page index statistics. |
215 | | static Status parse_min_max_value(const FieldSchema* col_schema, const std::string& encoded_min, |
216 | | const std::string& encoded_max, const cctz::time_zone& ctz, |
217 | 5.91k | Field* min_field, Field* max_field) { |
218 | 5.91k | auto logical_data_type = remove_nullable(col_schema->data_type); |
219 | 5.91k | auto converter = parquet::PhysicalToLogicalConverter::get_converter( |
220 | 5.91k | col_schema, logical_data_type, logical_data_type, &ctz); |
221 | 5.91k | ColumnPtr physical_column; |
222 | 5.91k | switch (col_schema->parquet_schema.type) { |
223 | 2 | case tparquet::Type::type::BOOLEAN: { |
224 | 2 | auto physical_col = ColumnUInt8::create(); |
225 | 2 | physical_col->get_data().data(); |
226 | 2 | physical_col->resize(2); |
227 | 2 | physical_col->get_data()[0] = *reinterpret_cast<const bool*>(encoded_min.data()); |
228 | 2 | physical_col->get_data()[1] = *reinterpret_cast<const bool*>(encoded_max.data()); |
229 | 2 | physical_column = std::move(physical_col); |
230 | 2 | break; |
231 | 0 | } |
232 | 5.86k | case tparquet::Type::type::INT32: { |
233 | 5.86k | auto physical_col = ColumnInt32::create(); |
234 | 5.86k | physical_col->resize(2); |
235 | | |
236 | 5.86k | physical_col->get_data()[0] = *reinterpret_cast<const int32_t*>(encoded_min.data()); |
237 | 5.86k | physical_col->get_data()[1] = *reinterpret_cast<const int32_t*>(encoded_max.data()); |
238 | | |
239 | 5.86k | physical_column = std::move(physical_col); |
240 | 5.86k | break; |
241 | 0 | } |
242 | 27 | case tparquet::Type::type::INT64: { |
243 | 27 | auto physical_col = ColumnInt64::create(); |
244 | 27 | physical_col->resize(2); |
245 | 27 | physical_col->get_data()[0] = *reinterpret_cast<const int64_t*>(encoded_min.data()); |
246 | 27 | physical_col->get_data()[1] = *reinterpret_cast<const int64_t*>(encoded_max.data()); |
247 | 27 | physical_column = std::move(physical_col); |
248 | 27 | break; |
249 | 0 | } |
250 | 14 | case tparquet::Type::type::FLOAT: { |
251 | 14 | auto physical_col = ColumnFloat32::create(); |
252 | 14 | physical_col->resize(2); |
253 | 14 | physical_col->get_data()[0] = *reinterpret_cast<const float*>(encoded_min.data()); |
254 | 14 | physical_col->get_data()[1] = *reinterpret_cast<const float*>(encoded_max.data()); |
255 | 14 | physical_column = std::move(physical_col); |
256 | 14 | break; |
257 | 0 | } |
258 | 0 | case tparquet::Type::type::DOUBLE: { |
259 | 0 | auto physical_col = ColumnFloat64 ::create(); |
260 | 0 | physical_col->resize(2); |
261 | 0 | physical_col->get_data()[0] = *reinterpret_cast<const double*>(encoded_min.data()); |
262 | 0 | physical_col->get_data()[1] = *reinterpret_cast<const double*>(encoded_max.data()); |
263 | 0 | physical_column = std::move(physical_col); |
264 | 0 | break; |
265 | 0 | } |
266 | 3 | case tparquet::Type::type::BYTE_ARRAY: { |
267 | 3 | auto physical_col = ColumnString::create(); |
268 | 3 | physical_col->insert_data(encoded_min.data(), encoded_min.size()); |
269 | 3 | physical_col->insert_data(encoded_max.data(), encoded_max.size()); |
270 | 3 | physical_column = std::move(physical_col); |
271 | 3 | break; |
272 | 0 | } |
273 | 2 | case tparquet::Type::type::FIXED_LEN_BYTE_ARRAY: { |
274 | 2 | auto physical_col = ColumnUInt8::create(); |
275 | 2 | physical_col->resize(2 * col_schema->parquet_schema.type_length); |
276 | 2 | DCHECK(col_schema->parquet_schema.type_length == encoded_min.length()); |
277 | 2 | DCHECK(col_schema->parquet_schema.type_length == encoded_max.length()); |
278 | | |
279 | 2 | auto ptr = physical_col->get_data().data(); |
280 | 2 | memcpy(ptr, encoded_min.data(), encoded_min.length()); |
281 | 2 | memcpy(ptr + encoded_min.length(), encoded_max.data(), encoded_max.length()); |
282 | 2 | physical_column = std::move(physical_col); |
283 | 2 | break; |
284 | 0 | } |
285 | 0 | case tparquet::Type::type::INT96: { |
286 | 0 | auto physical_col = ColumnInt8::create(); |
287 | 0 | physical_col->resize(2 * sizeof(ParquetInt96)); |
288 | 0 | DCHECK(sizeof(ParquetInt96) == encoded_min.length()); |
289 | 0 | DCHECK(sizeof(ParquetInt96) == encoded_max.length()); |
290 | |
|
291 | 0 | auto ptr = physical_col->get_data().data(); |
292 | 0 | memcpy(ptr, encoded_min.data(), encoded_min.length()); |
293 | 0 | memcpy(ptr + encoded_min.length(), encoded_max.data(), encoded_max.length()); |
294 | 0 | physical_column = std::move(physical_col); |
295 | 0 | break; |
296 | 0 | } |
297 | 5.91k | } |
298 | | |
299 | 5.91k | ColumnPtr logical_column; |
300 | 5.91k | if (converter->is_consistent()) { |
301 | 5.90k | logical_column = physical_column; |
302 | 5.90k | } else { |
303 | 4 | logical_column = logical_data_type->create_column(); |
304 | 4 | RETURN_IF_ERROR(converter->physical_convert(physical_column, logical_column)); |
305 | 4 | } |
306 | | |
307 | 5.91k | DCHECK(logical_column->size() == 2); |
308 | 5.91k | *min_field = logical_column->operator[](0); |
309 | 5.91k | *max_field = logical_column->operator[](1); |
310 | | |
311 | 5.91k | auto logical_prim_type = logical_data_type->get_primitive_type(); |
312 | | |
313 | 5.91k | if (logical_prim_type == TYPE_FLOAT) { |
314 | 11 | auto& min_value = min_field->get<TYPE_FLOAT>(); |
315 | 11 | auto& max_value = max_field->get<TYPE_FLOAT>(); |
316 | | |
317 | 11 | if (std::isnan(min_value) || std::isnan(max_value)) { |
318 | 1 | return Status::DataQualityError("Can not use this parquet min/max value."); |
319 | 1 | } |
320 | | // Updating min to -0.0 and max to +0.0 to ensure that no 0.0 values would be skipped |
321 | 10 | if (std::signbit(min_value) == 0 && min_value == 0.0F) { |
322 | 0 | min_value = -0.0F; |
323 | 0 | } |
324 | 10 | if (std::signbit(max_value) != 0 && max_value == -0.0F) { |
325 | 0 | max_value = 0.0F; |
326 | 0 | } |
327 | 5.90k | } else if (logical_prim_type == TYPE_DOUBLE) { |
328 | 0 | auto& min_value = min_field->get<TYPE_DOUBLE>(); |
329 | 0 | auto& max_value = max_field->get<TYPE_DOUBLE>(); |
330 | |
|
331 | 0 | if (std::isnan(min_value) || std::isnan(max_value)) { |
332 | 0 | return Status::DataQualityError("Can not use this parquet min/max value."); |
333 | 0 | } |
334 | | // Updating min to -0.0 and max to +0.0 to ensure that no 0.0 values would be skipped |
335 | 0 | if (std::signbit(min_value) == 0 && min_value == 0.0F) { |
336 | 0 | min_value = -0.0F; |
337 | 0 | } |
338 | 0 | if (std::signbit(max_value) != 0 && max_value == -0.0F) { |
339 | 0 | max_value = 0.0F; |
340 | 0 | } |
341 | 5.90k | } else if (col_schema->parquet_schema.type == tparquet::Type::type::INT96 || |
342 | 5.90k | logical_prim_type == TYPE_DATETIMEV2) { |
343 | 1 | auto min_value = min_field->get<TYPE_DATETIMEV2>(); |
344 | 1 | auto max_value = min_field->get<TYPE_DATETIMEV2>(); |
345 | | |
346 | | // From Trino: Parquet INT96 timestamp values were compared incorrectly |
347 | | // for the purposes of producing statistics by older parquet writers, |
348 | | // so PARQUET-1065 deprecated them. The result is that any writer that produced stats |
349 | | // was producing unusable incorrect values, except the special case where min == max |
350 | | // and an incorrect ordering would not be material to the result. |
351 | | // PARQUET-1026 made binary stats available and valid in that special case. |
352 | 1 | if (min_value != max_value) { |
353 | 0 | return Status::DataQualityError("invalid min/max value"); |
354 | 0 | } |
355 | 1 | } |
356 | | |
357 | 5.91k | return Status::OK(); |
358 | 5.91k | } |
359 | | |
360 | | static Status read_column_stats(const FieldSchema* col_schema, |
361 | | const tparquet::ColumnMetaData& column_meta_data, |
362 | | std::unordered_map<tparquet::Type::type, bool>* ignored_stats, |
363 | 660 | const std::string& file_created_by, ColumnStat* ans_stat) { |
364 | 660 | auto& statistic = column_meta_data.statistics; |
365 | | |
366 | 660 | if (!statistic.__isset.null_count) [[unlikely]] { |
367 | 0 | return Status::DataQualityError("This parquet Column meta no set null_count."); |
368 | 0 | } |
369 | 660 | ans_stat->has_null = statistic.null_count > 0; |
370 | 660 | ans_stat->is_all_null = statistic.null_count == column_meta_data.num_values; |
371 | 660 | if (ans_stat->is_all_null) { |
372 | 4 | return Status::OK(); |
373 | 4 | } |
374 | 656 | auto prim_type = remove_nullable(col_schema->data_type)->get_primitive_type(); |
375 | | |
376 | | // Min-max of statistic is plain-encoded value |
377 | 656 | if (statistic.__isset.min_value && statistic.__isset.max_value) { |
378 | 656 | ColumnOrderName column_order = |
379 | 656 | col_schema->physical_type == tparquet::Type::INT96 || |
380 | 656 | col_schema->parquet_schema.logicalType.__isset.UNKNOWN |
381 | 656 | ? ColumnOrderName::UNDEFINED |
382 | 656 | : ColumnOrderName::TYPE_DEFINED_ORDER; |
383 | 656 | if ((statistic.min_value != statistic.max_value) && |
384 | 656 | (column_order != ColumnOrderName::TYPE_DEFINED_ORDER)) { |
385 | 0 | return Status::DataQualityError("Can not use this parquet min/max value."); |
386 | 0 | } |
387 | 656 | ans_stat->encoded_min_value = statistic.min_value; |
388 | 656 | ans_stat->encoded_max_value = statistic.max_value; |
389 | | |
390 | 656 | if (prim_type == TYPE_VARCHAR || prim_type == TYPE_CHAR || prim_type == TYPE_STRING) { |
391 | 2 | auto encoded_min_copy = ans_stat->encoded_min_value; |
392 | 2 | auto encoded_max_copy = ans_stat->encoded_max_value; |
393 | 2 | if (!_try_read_old_utf8_stats(encoded_min_copy, encoded_max_copy)) { |
394 | 0 | return Status::DataQualityError("Can not use this parquet min/max value."); |
395 | 0 | } |
396 | 2 | ans_stat->encoded_min_value = encoded_min_copy; |
397 | 2 | ans_stat->encoded_max_value = encoded_max_copy; |
398 | 2 | } |
399 | | |
400 | 656 | } else if (statistic.__isset.min && statistic.__isset.max) { |
401 | 0 | bool max_equals_min = statistic.min == statistic.max; |
402 | |
|
403 | 0 | SortOrder sort_order = _determine_sort_order(col_schema->parquet_schema); |
404 | 0 | bool sort_orders_match = SortOrder::SIGNED == sort_order; |
405 | 0 | if (!sort_orders_match && !max_equals_min) { |
406 | 0 | return Status::NotSupported("Can not use this parquet min/max value."); |
407 | 0 | } |
408 | | |
409 | 0 | bool should_ignore_corrupted_stats = false; |
410 | 0 | if (ignored_stats != nullptr) { |
411 | 0 | if (ignored_stats->count(col_schema->physical_type) == 0) { |
412 | 0 | if (CorruptStatistics::should_ignore_statistics(file_created_by, |
413 | 0 | col_schema->physical_type)) { |
414 | 0 | ignored_stats->emplace(col_schema->physical_type, true); |
415 | 0 | should_ignore_corrupted_stats = true; |
416 | 0 | } else { |
417 | 0 | ignored_stats->emplace(col_schema->physical_type, false); |
418 | 0 | } |
419 | 0 | } else if (ignored_stats->at(col_schema->physical_type)) { |
420 | 0 | should_ignore_corrupted_stats = true; |
421 | 0 | } |
422 | 0 | } else if (CorruptStatistics::should_ignore_statistics(file_created_by, |
423 | 0 | col_schema->physical_type)) { |
424 | 0 | should_ignore_corrupted_stats = true; |
425 | 0 | } |
426 | |
|
427 | 0 | if (should_ignore_corrupted_stats) { |
428 | 0 | return Status::DataQualityError("Error statistics, should ignore."); |
429 | 0 | } |
430 | | |
431 | 0 | ans_stat->encoded_min_value = statistic.min; |
432 | 0 | ans_stat->encoded_max_value = statistic.max; |
433 | 0 | } else { |
434 | 0 | return Status::DataQualityError("This parquet file not set min/max value"); |
435 | 0 | } |
436 | | |
437 | 656 | return Status::OK(); |
438 | 656 | } |
439 | | |
440 | | static Status read_bloom_filter(const tparquet::ColumnMetaData& column_meta_data, |
441 | | io::FileReaderSPtr file_reader, io::IOContext* io_ctx, |
442 | 12 | ColumnStat* ans_stat) { |
443 | 12 | size_t size; |
444 | 12 | if (!column_meta_data.__isset.bloom_filter_offset) { |
445 | 0 | return Status::NotSupported("Can not use this parquet bloom filter."); |
446 | 0 | } |
447 | | |
448 | 12 | if (column_meta_data.__isset.bloom_filter_length && |
449 | 12 | column_meta_data.bloom_filter_length > 0) { |
450 | 12 | size = column_meta_data.bloom_filter_length; |
451 | 12 | } else { |
452 | 0 | size = BLOOM_FILTER_MAX_HEADER_LENGTH; |
453 | 0 | } |
454 | 12 | size_t bytes_read = 0; |
455 | 12 | std::vector<uint8_t> header_buffer(size); |
456 | 12 | RETURN_IF_ERROR(file_reader->read_at(column_meta_data.bloom_filter_offset, |
457 | 12 | Slice(header_buffer.data(), size), &bytes_read, |
458 | 12 | io_ctx)); |
459 | | |
460 | 12 | tparquet::BloomFilterHeader t_bloom_filter_header; |
461 | 12 | uint32_t t_bloom_filter_header_size = static_cast<uint32_t>(bytes_read); |
462 | 12 | RETURN_IF_ERROR(deserialize_thrift_msg(header_buffer.data(), &t_bloom_filter_header_size, |
463 | 12 | true, &t_bloom_filter_header)); |
464 | | |
465 | | // TODO the bloom filter could be encrypted, too, so need to double check that this is NOT the case |
466 | 12 | if (!t_bloom_filter_header.algorithm.__isset.BLOCK || |
467 | 12 | !t_bloom_filter_header.compression.__isset.UNCOMPRESSED || |
468 | 12 | !t_bloom_filter_header.hash.__isset.XXHASH) { |
469 | 0 | return Status::NotSupported("Can not use this parquet bloom filter."); |
470 | 0 | } |
471 | | |
472 | 12 | ans_stat->bloom_filter = std::make_unique<ParquetBlockSplitBloomFilter>(); |
473 | | |
474 | 12 | std::vector<uint8_t> data_buffer(t_bloom_filter_header.numBytes); |
475 | 12 | RETURN_IF_ERROR(file_reader->read_at( |
476 | 12 | column_meta_data.bloom_filter_offset + t_bloom_filter_header_size, |
477 | 12 | Slice(data_buffer.data(), t_bloom_filter_header.numBytes), &bytes_read, io_ctx)); |
478 | | |
479 | 12 | RETURN_IF_ERROR(ans_stat->bloom_filter->init( |
480 | 12 | reinterpret_cast<const char*>(data_buffer.data()), t_bloom_filter_header.numBytes, |
481 | 12 | segment_v2::HashStrategyPB::XX_HASH_64)); |
482 | | |
483 | 12 | return Status::OK(); |
484 | 12 | } |
485 | | }; |
486 | | |
487 | | } // namespace doris |