be/src/format/parquet/vparquet_column_reader.cpp
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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 | | #include "format/parquet/vparquet_column_reader.h" |
19 | | |
20 | | #include <cctz/time_zone.h> |
21 | | #include <gen_cpp/parquet_types.h> |
22 | | #include <rapidjson/document.h> |
23 | | #include <sys/types.h> |
24 | | |
25 | | #include <algorithm> |
26 | | #include <cmath> |
27 | | #include <limits> |
28 | | #include <map> |
29 | | #include <string_view> |
30 | | #include <utility> |
31 | | #include <vector> |
32 | | |
33 | | #include "common/exception.h" |
34 | | #include "common/status.h" |
35 | | #include "core/column/column.h" |
36 | | #include "core/column/column_array.h" |
37 | | #include "core/column/column_map.h" |
38 | | #include "core/column/column_nullable.h" |
39 | | #include "core/column/column_string.h" |
40 | | #include "core/column/column_struct.h" |
41 | | #include "core/column/column_variant.h" |
42 | | #include "core/data_type/data_type_array.h" |
43 | | #include "core/data_type/data_type_factory.hpp" |
44 | | #include "core/data_type/data_type_map.h" |
45 | | #include "core/data_type/data_type_nullable.h" |
46 | | #include "core/data_type/data_type_number.h" |
47 | | #include "core/data_type/data_type_struct.h" |
48 | | #include "core/data_type/data_type_variant.h" |
49 | | #include "core/data_type/define_primitive_type.h" |
50 | | #include "core/data_type_serde/data_type_serde.h" |
51 | | #include "core/string_buffer.hpp" |
52 | | #include "core/value/jsonb_value.h" |
53 | | #include "core/value/timestamptz_value.h" |
54 | | #include "core/value/vdatetime_value.h" |
55 | | #include "exec/common/variant_util.h" |
56 | | #include "format/parquet/level_decoder.h" |
57 | | #include "format/parquet/parquet_variant_reader.h" |
58 | | #include "format/parquet/schema_desc.h" |
59 | | #include "format/parquet/vparquet_column_chunk_reader.h" |
60 | | #include "io/fs/tracing_file_reader.h" |
61 | | #include "runtime/runtime_profile.h" |
62 | | #include "util/jsonb_document.h" |
63 | | |
64 | | namespace doris { |
65 | | static void fill_struct_null_map(FieldSchema* field, NullMap& null_map, |
66 | | const std::vector<level_t>& rep_levels, |
67 | 11 | const std::vector<level_t>& def_levels) { |
68 | 11 | size_t num_levels = def_levels.size(); |
69 | 11 | DCHECK_EQ(num_levels, rep_levels.size()); |
70 | 11 | size_t origin_size = null_map.size(); |
71 | 11 | null_map.resize(origin_size + num_levels); |
72 | 11 | size_t pos = origin_size; |
73 | 26 | for (size_t i = 0; i < num_levels; ++i) { |
74 | | // skip the levels affect its ancestor or its descendants |
75 | 15 | if (def_levels[i] < field->repeated_parent_def_level || |
76 | 15 | rep_levels[i] > field->repetition_level) { |
77 | 0 | continue; |
78 | 0 | } |
79 | 15 | if (def_levels[i] >= field->definition_level) { |
80 | 15 | null_map[pos++] = 0; |
81 | 15 | } else { |
82 | 0 | null_map[pos++] = 1; |
83 | 0 | } |
84 | 15 | } |
85 | 11 | null_map.resize(pos); |
86 | 11 | } |
87 | | |
88 | | static void fill_array_offset(FieldSchema* field, ColumnArray::Offsets64& offsets_data, |
89 | | NullMap* null_map_ptr, const std::vector<level_t>& rep_levels, |
90 | 2 | const std::vector<level_t>& def_levels) { |
91 | 2 | size_t num_levels = rep_levels.size(); |
92 | 2 | DCHECK_EQ(num_levels, def_levels.size()); |
93 | 2 | size_t origin_size = offsets_data.size(); |
94 | 2 | offsets_data.resize(origin_size + num_levels); |
95 | 2 | if (null_map_ptr != nullptr) { |
96 | 2 | null_map_ptr->resize(origin_size + num_levels); |
97 | 2 | } |
98 | 2 | size_t offset_pos = origin_size - 1; |
99 | 8 | for (size_t i = 0; i < num_levels; ++i) { |
100 | | // skip the levels affect its ancestor or its descendants |
101 | 6 | if (def_levels[i] < field->repeated_parent_def_level || |
102 | 6 | rep_levels[i] > field->repetition_level) { |
103 | 0 | continue; |
104 | 0 | } |
105 | 6 | if (rep_levels[i] == field->repetition_level) { |
106 | 4 | offsets_data[offset_pos]++; |
107 | 4 | continue; |
108 | 4 | } |
109 | 2 | offset_pos++; |
110 | 2 | offsets_data[offset_pos] = offsets_data[offset_pos - 1]; |
111 | 2 | if (def_levels[i] >= field->definition_level) { |
112 | 2 | offsets_data[offset_pos]++; |
113 | 2 | } |
114 | 2 | if (def_levels[i] >= field->definition_level - 1) { |
115 | 2 | (*null_map_ptr)[offset_pos] = 0; |
116 | 2 | } else { |
117 | 0 | (*null_map_ptr)[offset_pos] = 1; |
118 | 0 | } |
119 | 2 | } |
120 | 2 | offsets_data.resize(offset_pos + 1); |
121 | 2 | if (null_map_ptr != nullptr) { |
122 | 2 | null_map_ptr->resize(offset_pos + 1); |
123 | 2 | } |
124 | 2 | } |
125 | | |
126 | | static constexpr int64_t UNIX_EPOCH_DAYNR = 719528; |
127 | | static constexpr int64_t MICROS_PER_SECOND = 1000000; |
128 | | |
129 | 0 | static int64_t variant_date_value(const VecDateTimeValue& value) { |
130 | 0 | return value.daynr() - UNIX_EPOCH_DAYNR; |
131 | 0 | } |
132 | | |
133 | 1 | static int64_t variant_date_value(const DateV2Value<DateV2ValueType>& value) { |
134 | 1 | return value.daynr() - UNIX_EPOCH_DAYNR; |
135 | 1 | } |
136 | | |
137 | 0 | static int64_t variant_datetime_value(const VecDateTimeValue& value) { |
138 | 0 | int64_t timestamp = 0; |
139 | 0 | value.unix_timestamp(×tamp, cctz::utc_time_zone()); |
140 | 0 | return timestamp * MICROS_PER_SECOND; |
141 | 0 | } |
142 | | |
143 | 1 | static int64_t variant_datetime_value(const DateV2Value<DateTimeV2ValueType>& value) { |
144 | 1 | int64_t timestamp = 0; |
145 | 1 | value.unix_timestamp(×tamp, cctz::utc_time_zone()); |
146 | 1 | return timestamp * MICROS_PER_SECOND + value.microsecond(); |
147 | 1 | } |
148 | | |
149 | 0 | static int64_t variant_datetime_value(const TimestampTzValue& value) { |
150 | 0 | int64_t timestamp = 0; |
151 | 0 | value.unix_timestamp(×tamp, cctz::utc_time_zone()); |
152 | 0 | return timestamp * MICROS_PER_SECOND + value.microsecond(); |
153 | 0 | } |
154 | | |
155 | 52 | static int find_child_idx(const FieldSchema& field, std::string_view name) { |
156 | 108 | for (int i = 0; i < field.children.size(); ++i) { |
157 | 97 | if (field.children[i].lower_case_name == name) { |
158 | 41 | return i; |
159 | 41 | } |
160 | 97 | } |
161 | 11 | return -1; |
162 | 52 | } |
163 | | |
164 | 0 | static bool is_variant_wrapper_typed_value_child(const FieldSchema& field) { |
165 | 0 | auto type = remove_nullable(field.data_type); |
166 | 0 | return type->get_primitive_type() == TYPE_STRUCT || type->get_primitive_type() == TYPE_ARRAY; |
167 | 0 | } |
168 | | |
169 | | static bool is_variant_wrapper_field(const FieldSchema& field, |
170 | | bool allow_scalar_typed_value_only_wrapper, |
171 | 36 | bool allow_value_only_wrapper = false) { |
172 | 36 | auto type = remove_nullable(field.data_type); |
173 | 36 | if (type->get_primitive_type() != TYPE_STRUCT && type->get_primitive_type() != TYPE_VARIANT) { |
174 | 30 | return false; |
175 | 30 | } |
176 | | |
177 | 6 | bool has_metadata = false; |
178 | 6 | bool has_value = false; |
179 | 6 | const FieldSchema* typed_value = nullptr; |
180 | 10 | for (const auto& child : field.children) { |
181 | 10 | if (child.lower_case_name == "metadata") { |
182 | 1 | if (child.physical_type != tparquet::Type::BYTE_ARRAY) { |
183 | 0 | return false; |
184 | 0 | } |
185 | 1 | has_metadata = true; |
186 | 1 | continue; |
187 | 1 | } |
188 | 9 | if (child.lower_case_name == "value") { |
189 | 5 | if (child.physical_type != tparquet::Type::BYTE_ARRAY) { |
190 | 2 | return false; |
191 | 2 | } |
192 | 3 | has_value = true; |
193 | 3 | continue; |
194 | 5 | } |
195 | 4 | if (child.lower_case_name == "typed_value") { |
196 | 3 | typed_value = &child; |
197 | 3 | continue; |
198 | 3 | } |
199 | 1 | return false; |
200 | 4 | } |
201 | 3 | if (has_metadata && has_value) { |
202 | 1 | return type->get_primitive_type() == TYPE_VARIANT || typed_value != nullptr; |
203 | 1 | } |
204 | 2 | if (has_value) { |
205 | 2 | return typed_value != nullptr || allow_value_only_wrapper; |
206 | 2 | } |
207 | 0 | return typed_value != nullptr && (allow_scalar_typed_value_only_wrapper || |
208 | 0 | is_variant_wrapper_typed_value_child(*typed_value)); |
209 | 2 | } |
210 | | |
211 | 22 | static Status get_binary_field(const Field& field, std::string* value, bool* present) { |
212 | 22 | if (field.is_null()) { |
213 | 3 | *present = false; |
214 | 3 | return Status::OK(); |
215 | 3 | } |
216 | 19 | *present = true; |
217 | 19 | switch (field.get_type()) { |
218 | 19 | case TYPE_STRING: |
219 | 19 | *value = field.get<TYPE_STRING>(); |
220 | 19 | return Status::OK(); |
221 | 0 | case TYPE_CHAR: |
222 | 0 | *value = field.get<TYPE_CHAR>(); |
223 | 0 | return Status::OK(); |
224 | 0 | case TYPE_VARCHAR: |
225 | 0 | *value = field.get<TYPE_VARCHAR>(); |
226 | 0 | return Status::OK(); |
227 | 0 | case TYPE_VARBINARY: { |
228 | 0 | auto ref = field.get<TYPE_VARBINARY>().to_string_ref(); |
229 | 0 | value->assign(ref.data, ref.size); |
230 | 0 | return Status::OK(); |
231 | 0 | } |
232 | 0 | default: |
233 | 0 | return Status::Corruption("Parquet VARIANT binary field has unexpected Doris type {}", |
234 | 0 | field.get_type_name()); |
235 | 19 | } |
236 | 19 | } |
237 | | |
238 | 8 | static PathInData append_path(const PathInData& prefix, const PathInData& suffix) { |
239 | 8 | if (prefix.empty()) { |
240 | 7 | return suffix; |
241 | 7 | } |
242 | 1 | if (suffix.empty()) { |
243 | 0 | return prefix; |
244 | 0 | } |
245 | 1 | PathInDataBuilder builder; |
246 | 1 | builder.append(prefix.get_parts(), false); |
247 | 1 | builder.append(suffix.get_parts(), false); |
248 | 1 | return builder.build(); |
249 | 1 | } |
250 | | |
251 | 7 | static Status insert_empty_object_marker(const PathInData& path, VariantMap* values) { |
252 | 7 | JsonBinaryValue empty_object; |
253 | 7 | RETURN_IF_ERROR(empty_object.from_json_string("{}")); |
254 | 7 | (*values)[path] = FieldWithDataType {.field = Field::create_field<TYPE_JSONB>(JsonbField( |
255 | 7 | empty_object.value(), empty_object.size())), |
256 | 7 | .base_scalar_type_id = TYPE_JSONB}; |
257 | 7 | return Status::OK(); |
258 | 7 | } |
259 | | |
260 | 25 | static bool is_empty_object_marker(const FieldWithDataType& value) { |
261 | 25 | if (value.field.get_type() != TYPE_JSONB) { |
262 | 15 | return false; |
263 | 15 | } |
264 | 10 | const auto& jsonb = value.field.get<TYPE_JSONB>(); |
265 | 10 | const JsonbDocument* document = nullptr; |
266 | 10 | Status st = |
267 | 10 | JsonbDocument::checkAndCreateDocument(jsonb.get_value(), jsonb.get_size(), &document); |
268 | 10 | if (!st.ok() || document == nullptr || document->getValue() == nullptr || |
269 | 10 | !document->getValue()->isObject()) { |
270 | 0 | return false; |
271 | 0 | } |
272 | 10 | return document->getValue()->unpack<ObjectVal>()->numElem() == 0; |
273 | 10 | } |
274 | | |
275 | | static Status collect_empty_object_markers(const rapidjson::Value& value, PathInDataBuilder* path, |
276 | 5 | VariantMap* values) { |
277 | 5 | if (!value.IsObject()) { |
278 | 0 | return Status::OK(); |
279 | 0 | } |
280 | 5 | if (value.MemberCount() == 0) { |
281 | 4 | return insert_empty_object_marker(path->build(), values); |
282 | 4 | } |
283 | 2 | for (auto it = value.MemberBegin(); it != value.MemberEnd(); ++it) { |
284 | 1 | if (it->value.IsObject()) { |
285 | 1 | path->append(std::string_view(it->name.GetString(), it->name.GetStringLength()), false); |
286 | 1 | RETURN_IF_ERROR(collect_empty_object_markers(it->value, path, values)); |
287 | 1 | path->pop_back(); |
288 | 1 | } |
289 | 1 | } |
290 | 1 | return Status::OK(); |
291 | 1 | } |
292 | | |
293 | | static Status add_empty_object_markers_from_json(const std::string& json, const PathInData& prefix, |
294 | 12 | VariantMap* values) { |
295 | 12 | if (json.find("{}") == std::string::npos) { |
296 | 8 | return Status::OK(); |
297 | 8 | } |
298 | 4 | rapidjson::Document document; |
299 | 4 | document.Parse(json.data(), json.size()); |
300 | 4 | if (document.HasParseError()) { |
301 | 0 | return Status::Corruption("Invalid Parquet VARIANT decoded JSON"); |
302 | 0 | } |
303 | 4 | PathInDataBuilder path; |
304 | 4 | path.append(prefix.get_parts(), false); |
305 | 4 | return collect_empty_object_markers(document, &path, values); |
306 | 4 | } |
307 | | |
308 | | static Status parse_json_to_variant_map(const std::string& json, const PathInData& prefix, |
309 | 12 | VariantMap* values) { |
310 | 12 | auto parsed_column = ColumnVariant::create(0, false); |
311 | 12 | ParseConfig parse_config; |
312 | 12 | StringRef json_ref(json.data(), json.size()); |
313 | 12 | RETURN_IF_CATCH_EXCEPTION( |
314 | 12 | variant_util::parse_json_to_variant(*parsed_column, json_ref, nullptr, parse_config)); |
315 | 12 | Field parsed = (*parsed_column)[0]; |
316 | 12 | if (!parsed.is_null()) { |
317 | 8 | auto& parsed_values = parsed.get<TYPE_VARIANT>(); |
318 | 8 | for (auto& [path, value] : parsed_values) { |
319 | 8 | (*values)[append_path(prefix, path)] = std::move(value); |
320 | 8 | } |
321 | 8 | } |
322 | 12 | RETURN_IF_ERROR(add_empty_object_markers_from_json(json, prefix, values)); |
323 | 12 | return Status::OK(); |
324 | 12 | } |
325 | | |
326 | 2 | static Status variant_map_to_json(VariantMap values, std::string* json) { |
327 | 2 | auto variant_column = ColumnVariant::create(0, false); |
328 | 2 | RETURN_IF_CATCH_EXCEPTION( |
329 | 2 | variant_column->insert(Field::create_field<TYPE_VARIANT>(std::move(values)))); |
330 | 2 | DataTypeSerDe::FormatOptions options; |
331 | 2 | variant_column->serialize_one_row_to_string(0, json, options); |
332 | 2 | return Status::OK(); |
333 | 2 | } |
334 | | |
335 | 14 | static bool path_has_prefix(const PathInData& path, const PathInData& prefix) { |
336 | 14 | const auto& parts = path.get_parts(); |
337 | 14 | const auto& prefix_parts = prefix.get_parts(); |
338 | 14 | if (parts.size() < prefix_parts.size()) { |
339 | 0 | return false; |
340 | 0 | } |
341 | 15 | for (size_t i = 0; i < prefix_parts.size(); ++i) { |
342 | 1 | if (parts[i] != prefix_parts[i]) { |
343 | 0 | return false; |
344 | 0 | } |
345 | 1 | } |
346 | 14 | return true; |
347 | 14 | } |
348 | | |
349 | 15 | static bool has_descendant_path(const VariantMap& values, const PathInData& prefix) { |
350 | 15 | const size_t prefix_size = prefix.get_parts().size(); |
351 | 15 | return std::ranges::any_of(values, [&](const auto& entry) { |
352 | 12 | const auto& path = entry.first; |
353 | 12 | return path.get_parts().size() > prefix_size && path_has_prefix(path, prefix); |
354 | 12 | }); |
355 | 15 | } |
356 | | |
357 | | static void erase_shadowed_empty_object_markers(VariantMap* values, |
358 | 26 | const VariantMap& shadowing_values) { |
359 | 46 | for (auto it = values->begin(); it != values->end();) { |
360 | 20 | if (is_empty_object_marker(it->second) && |
361 | 20 | (has_descendant_path(*values, it->first) || |
362 | 7 | has_descendant_path(shadowing_values, it->first))) { |
363 | 3 | it = values->erase(it); |
364 | 3 | continue; |
365 | 3 | } |
366 | 17 | ++it; |
367 | 17 | } |
368 | 26 | } |
369 | | |
370 | | static void erase_shadowed_empty_object_markers(VariantMap* value_values, |
371 | 13 | VariantMap* typed_values) { |
372 | 13 | erase_shadowed_empty_object_markers(value_values, *typed_values); |
373 | 13 | erase_shadowed_empty_object_markers(typed_values, *value_values); |
374 | 13 | } |
375 | | |
376 | | static Status check_no_shredded_value_typed_duplicates(const VariantMap& value_values, |
377 | | const VariantMap& typed_values, |
378 | 13 | const PathInData& prefix) { |
379 | 13 | const size_t prefix_size = prefix.get_parts().size(); |
380 | 13 | for (const auto& value_entry : value_values) { |
381 | 7 | const auto& value_path = value_entry.first; |
382 | 7 | if (!path_has_prefix(value_path, prefix)) { |
383 | 0 | continue; |
384 | 0 | } |
385 | 7 | if (value_path.get_parts().size() == prefix_size) { |
386 | 1 | if (is_empty_object_marker(value_entry.second) && |
387 | 1 | !has_descendant_path(typed_values, value_path)) { |
388 | 1 | continue; |
389 | 1 | } |
390 | 0 | if (!typed_values.empty()) { |
391 | 0 | return Status::Corruption( |
392 | 0 | "Parquet VARIANT residual value conflicts with typed_value at path {}", |
393 | 0 | value_path.get_path()); |
394 | 0 | } |
395 | 0 | continue; |
396 | 0 | } |
397 | 6 | for (const auto& typed_entry : typed_values) { |
398 | 4 | const auto& typed_path = typed_entry.first; |
399 | 4 | if (!path_has_prefix(typed_path, prefix)) { |
400 | 0 | continue; |
401 | 0 | } |
402 | 4 | if (typed_path.get_parts().size() == prefix_size) { |
403 | 0 | if (is_empty_object_marker(typed_entry.second) && |
404 | 0 | !has_descendant_path(value_values, typed_path)) { |
405 | 0 | continue; |
406 | 0 | } |
407 | 0 | return Status::Corruption( |
408 | 0 | "Parquet VARIANT residual value and typed_value contain duplicate field {}", |
409 | 0 | value_path.get_parts()[prefix_size].key); |
410 | 0 | } |
411 | 4 | if (value_path.get_parts()[prefix_size] == typed_path.get_parts()[prefix_size]) { |
412 | 3 | if (value_path == typed_path && is_empty_object_marker(value_entry.second) && |
413 | 3 | is_empty_object_marker(typed_entry.second)) { |
414 | 1 | continue; |
415 | 1 | } |
416 | 2 | return Status::Corruption( |
417 | 2 | "Parquet VARIANT residual value and typed_value contain duplicate field {}", |
418 | 2 | value_path.get_parts()[prefix_size].key); |
419 | 3 | } |
420 | 4 | } |
421 | 6 | } |
422 | 11 | return Status::OK(); |
423 | 13 | } |
424 | | |
425 | 9 | static bool has_direct_typed_parent_null(const std::vector<const NullMap*>& null_maps, size_t row) { |
426 | 18 | return std::ranges::any_of(null_maps, [&](const NullMap* null_map) { |
427 | 18 | DCHECK_LT(row, null_map->size()); |
428 | 18 | return (*null_map)[row]; |
429 | 18 | }); |
430 | 9 | } |
431 | | |
432 | | static void insert_direct_typed_leaf_range(const IColumn& column, size_t start, size_t rows, |
433 | | const std::vector<const NullMap*>& parent_null_maps, |
434 | 5 | IColumn* variant_leaf) { |
435 | 5 | auto& nullable_leaf = assert_cast<ColumnNullable&>(*variant_leaf); |
436 | 5 | const IColumn* value_column = &column; |
437 | 5 | const NullMap* leaf_null_map = nullptr; |
438 | 5 | if (const auto* nullable_column = check_and_get_column<ColumnNullable>(&column)) { |
439 | 0 | value_column = &nullable_column->get_nested_column(); |
440 | 0 | leaf_null_map = &nullable_column->get_null_map_data(); |
441 | 0 | } |
442 | | |
443 | 5 | nullable_leaf.get_nested_column().insert_range_from(*value_column, start, rows); |
444 | 5 | auto& null_map = nullable_leaf.get_null_map_data(); |
445 | 5 | null_map.reserve(null_map.size() + rows); |
446 | 11 | for (size_t i = 0; i < rows; ++i) { |
447 | 6 | const size_t row = start + i; |
448 | 6 | const bool leaf_is_null = leaf_null_map != nullptr && (*leaf_null_map)[row]; |
449 | 6 | null_map.push_back(leaf_is_null || has_direct_typed_parent_null(parent_null_maps, row)); |
450 | 6 | } |
451 | 5 | } |
452 | | |
453 | 16 | static bool is_temporal_variant_leaf_type(PrimitiveType type) { |
454 | 16 | switch (type) { |
455 | 2 | case TYPE_TIMEV2: |
456 | 2 | case TYPE_DATE: |
457 | 2 | case TYPE_DATETIME: |
458 | 4 | case TYPE_DATEV2: |
459 | 6 | case TYPE_DATETIMEV2: |
460 | 6 | case TYPE_TIMESTAMPTZ: |
461 | 6 | return true; |
462 | 10 | default: |
463 | 10 | return false; |
464 | 16 | } |
465 | 16 | } |
466 | | |
467 | 8 | static DataTypePtr direct_variant_leaf_type(const DataTypePtr& data_type) { |
468 | 8 | const auto& type = remove_nullable(data_type); |
469 | 8 | if (is_temporal_variant_leaf_type(type->get_primitive_type())) { |
470 | 3 | return std::make_shared<DataTypeInt64>(); |
471 | 3 | } |
472 | 5 | return type; |
473 | 8 | } |
474 | | |
475 | 0 | static bool contains_temporal_variant_leaf_type(const DataTypePtr& data_type) { |
476 | 0 | const auto& type = remove_nullable(data_type); |
477 | 0 | if (is_temporal_variant_leaf_type(type->get_primitive_type())) { |
478 | 0 | return true; |
479 | 0 | } |
480 | 0 | if (type->get_primitive_type() == TYPE_ARRAY) { |
481 | 0 | return contains_temporal_variant_leaf_type( |
482 | 0 | assert_cast<const DataTypeArray*>(type.get())->get_nested_type()); |
483 | 0 | } |
484 | 0 | return false; |
485 | 0 | } |
486 | | |
487 | | static int64_t direct_temporal_variant_value(PrimitiveType type, const IColumn& column, |
488 | 3 | size_t row) { |
489 | 3 | switch (type) { |
490 | 1 | case TYPE_TIMEV2: |
491 | 1 | return static_cast<int64_t>( |
492 | 1 | std::llround(assert_cast<const ColumnTimeV2&>(column).get_data()[row])); |
493 | 0 | case TYPE_DATE: |
494 | 0 | return variant_date_value(assert_cast<const ColumnDate&>(column).get_data()[row]); |
495 | 0 | case TYPE_DATETIME: |
496 | 0 | return variant_datetime_value(assert_cast<const ColumnDateTime&>(column).get_data()[row]); |
497 | 1 | case TYPE_DATEV2: |
498 | 1 | return variant_date_value(assert_cast<const ColumnDateV2&>(column).get_data()[row]); |
499 | 1 | case TYPE_DATETIMEV2: |
500 | 1 | return variant_datetime_value(assert_cast<const ColumnDateTimeV2&>(column).get_data()[row]); |
501 | 0 | case TYPE_TIMESTAMPTZ: |
502 | 0 | return variant_datetime_value( |
503 | 0 | assert_cast<const ColumnTimeStampTz&>(column).get_data()[row]); |
504 | 0 | default: |
505 | 0 | DORIS_CHECK(false); |
506 | 0 | return 0; |
507 | 3 | } |
508 | 3 | } |
509 | | |
510 | | static void insert_direct_typed_temporal_leaf_range( |
511 | | PrimitiveType type, const IColumn& column, size_t start, size_t rows, |
512 | 3 | const std::vector<const NullMap*>& parent_null_maps, IColumn* variant_leaf) { |
513 | 3 | auto& nullable_leaf = assert_cast<ColumnNullable&>(*variant_leaf); |
514 | 3 | const IColumn* value_column = &column; |
515 | 3 | const NullMap* leaf_null_map = nullptr; |
516 | 3 | if (const auto* nullable_column = check_and_get_column<ColumnNullable>(&column)) { |
517 | 0 | value_column = &nullable_column->get_nested_column(); |
518 | 0 | leaf_null_map = &nullable_column->get_null_map_data(); |
519 | 0 | } |
520 | | |
521 | 3 | auto& data = assert_cast<ColumnInt64&>(nullable_leaf.get_nested_column()).get_data(); |
522 | 3 | data.reserve(data.size() + rows); |
523 | 3 | auto& null_map = nullable_leaf.get_null_map_data(); |
524 | 3 | null_map.reserve(null_map.size() + rows); |
525 | 6 | for (size_t i = 0; i < rows; ++i) { |
526 | 3 | const size_t row = start + i; |
527 | 3 | data.push_back(direct_temporal_variant_value(type, *value_column, row)); |
528 | 3 | const bool leaf_is_null = leaf_null_map != nullptr && (*leaf_null_map)[row]; |
529 | 3 | null_map.push_back(leaf_is_null || has_direct_typed_parent_null(parent_null_maps, row)); |
530 | 3 | } |
531 | 3 | } |
532 | | |
533 | 3 | static void append_json_string(std::string_view value, std::string* json) { |
534 | 3 | auto column = ColumnString::create(); |
535 | 3 | VectorBufferWriter writer(*column); |
536 | 3 | writer.write_json_string(value); |
537 | 3 | writer.commit(); |
538 | 3 | json->append(column->get_data_at(0).data, column->get_data_at(0).size); |
539 | 3 | } |
540 | | |
541 | | static bool is_column_selected(const FieldSchema& field_schema, |
542 | 53 | const std::set<uint64_t>& column_ids) { |
543 | 53 | return column_ids.empty() || column_ids.find(field_schema.get_column_id()) != column_ids.end(); |
544 | 53 | } |
545 | | |
546 | | static bool has_selected_column(const FieldSchema& field_schema, |
547 | 53 | const std::set<uint64_t>& column_ids) { |
548 | 53 | if (is_column_selected(field_schema, column_ids)) { |
549 | 45 | return true; |
550 | 45 | } |
551 | 8 | return std::any_of(field_schema.children.begin(), field_schema.children.end(), |
552 | 8 | [&column_ids](const FieldSchema& child) { |
553 | 5 | return has_selected_column(child, column_ids); |
554 | 5 | }); |
555 | 53 | } |
556 | | |
557 | 12 | static bool is_direct_variant_leaf_type(const DataTypePtr& data_type) { |
558 | 12 | const auto& type = remove_nullable(data_type); |
559 | 12 | switch (type->get_primitive_type()) { |
560 | 0 | case TYPE_BOOLEAN: |
561 | 0 | case TYPE_TINYINT: |
562 | 0 | case TYPE_SMALLINT: |
563 | 4 | case TYPE_INT: |
564 | 9 | case TYPE_BIGINT: |
565 | 9 | case TYPE_LARGEINT: |
566 | 9 | case TYPE_DECIMALV2: |
567 | 9 | case TYPE_DECIMAL32: |
568 | 9 | case TYPE_DECIMAL64: |
569 | 9 | case TYPE_DECIMAL128I: |
570 | 9 | case TYPE_DECIMAL256: |
571 | 9 | case TYPE_STRING: |
572 | 9 | case TYPE_CHAR: |
573 | 9 | case TYPE_VARCHAR: |
574 | 9 | return true; |
575 | 1 | case TYPE_TIMEV2: |
576 | 1 | case TYPE_DATE: |
577 | 1 | case TYPE_DATETIME: |
578 | 2 | case TYPE_DATEV2: |
579 | 3 | case TYPE_DATETIMEV2: |
580 | 3 | case TYPE_TIMESTAMPTZ: |
581 | 3 | return true; |
582 | 0 | case TYPE_ARRAY: { |
583 | 0 | const auto* array_type = assert_cast<const DataTypeArray*>(type.get()); |
584 | 0 | return !contains_temporal_variant_leaf_type(array_type->get_nested_type()) && |
585 | 0 | is_direct_variant_leaf_type(array_type->get_nested_type()); |
586 | 3 | } |
587 | 0 | default: |
588 | 0 | return false; |
589 | 12 | } |
590 | 12 | } |
591 | | |
592 | | static bool can_direct_read_typed_value(const FieldSchema& field_schema, bool allow_variant_wrapper, |
593 | 17 | const std::set<uint64_t>& column_ids) { |
594 | 17 | if (!has_selected_column(field_schema, column_ids)) { |
595 | 0 | return true; |
596 | 0 | } |
597 | 17 | if (allow_variant_wrapper && is_variant_wrapper_field(field_schema, false)) { |
598 | 0 | const int value_idx = find_child_idx(field_schema, "value"); |
599 | 0 | const int typed_value_idx = find_child_idx(field_schema, "typed_value"); |
600 | 0 | return (value_idx < 0 || |
601 | 0 | !has_selected_column(field_schema.children[value_idx], column_ids)) && |
602 | 0 | typed_value_idx >= 0 && |
603 | 0 | can_direct_read_typed_value(field_schema.children[typed_value_idx], false, |
604 | 0 | column_ids); |
605 | 0 | } |
606 | | |
607 | 17 | const auto& type = remove_nullable(field_schema.data_type); |
608 | 17 | if (type->get_primitive_type() == TYPE_STRUCT) { |
609 | 7 | return std::all_of(field_schema.children.begin(), field_schema.children.end(), |
610 | 11 | [&column_ids](const FieldSchema& child) { |
611 | 11 | return can_direct_read_typed_value(child, true, column_ids); |
612 | 11 | }); |
613 | 7 | } |
614 | 10 | return is_direct_variant_leaf_type(field_schema.data_type); |
615 | 17 | } |
616 | | |
617 | | static bool has_selected_direct_typed_leaf(const FieldSchema& field_schema, |
618 | | bool allow_variant_wrapper, |
619 | 7 | const std::set<uint64_t>& column_ids) { |
620 | 7 | if (!has_selected_column(field_schema, column_ids)) { |
621 | 2 | return false; |
622 | 2 | } |
623 | 5 | if (allow_variant_wrapper && is_variant_wrapper_field(field_schema, false)) { |
624 | 0 | const int typed_value_idx = find_child_idx(field_schema, "typed_value"); |
625 | 0 | DCHECK_GE(typed_value_idx, 0); |
626 | 0 | return has_selected_direct_typed_leaf(field_schema.children[typed_value_idx], false, |
627 | 0 | column_ids); |
628 | 0 | } |
629 | | |
630 | 5 | const auto& type = remove_nullable(field_schema.data_type); |
631 | 5 | if (type->get_primitive_type() == TYPE_STRUCT) { |
632 | 3 | return std::any_of(field_schema.children.begin(), field_schema.children.end(), |
633 | 3 | [&column_ids](const FieldSchema& child) { |
634 | 3 | return has_selected_direct_typed_leaf(child, true, column_ids); |
635 | 3 | }); |
636 | 3 | } |
637 | 2 | return is_direct_variant_leaf_type(field_schema.data_type); |
638 | 5 | } |
639 | | |
640 | | static bool can_use_direct_typed_only_value(const FieldSchema& variant_field, |
641 | 5 | const std::set<uint64_t>& column_ids) { |
642 | 5 | const int value_idx = find_child_idx(variant_field, "value"); |
643 | 5 | const int typed_value_idx = find_child_idx(variant_field, "typed_value"); |
644 | 5 | return (value_idx < 0 || !has_selected_column(variant_field.children[value_idx], column_ids)) && |
645 | 5 | typed_value_idx >= 0 && |
646 | 5 | has_selected_direct_typed_leaf(variant_field.children[typed_value_idx], false, |
647 | 4 | column_ids) && |
648 | 5 | can_direct_read_typed_value(variant_field.children[typed_value_idx], false, column_ids); |
649 | 5 | } |
650 | | |
651 | 7 | static void fill_variant_field_info(FieldWithDataType* value) { |
652 | 7 | FieldInfo info; |
653 | 7 | variant_util::get_field_info(value->field, &info); |
654 | 7 | DCHECK_LE(info.num_dimensions, std::numeric_limits<uint8_t>::max()); |
655 | 7 | value->base_scalar_type_id = info.scalar_type_id; |
656 | 7 | value->num_dimensions = static_cast<uint8_t>(info.num_dimensions); |
657 | 7 | } |
658 | | |
659 | | static Status field_to_variant_field(const FieldSchema& field_schema, const Field& field, |
660 | 7 | FieldWithDataType* value, bool* present) { |
661 | 7 | if (field.is_null()) { |
662 | 0 | *present = false; |
663 | 0 | return Status::OK(); |
664 | 0 | } |
665 | 7 | *present = true; |
666 | 7 | const DataTypePtr& type = remove_nullable(field_schema.data_type); |
667 | 7 | switch (type->get_primitive_type()) { |
668 | 0 | case TYPE_BOOLEAN: |
669 | 0 | case TYPE_TINYINT: |
670 | 0 | case TYPE_SMALLINT: |
671 | 5 | case TYPE_INT: |
672 | 5 | case TYPE_BIGINT: |
673 | 5 | case TYPE_LARGEINT: |
674 | 5 | case TYPE_DECIMALV2: |
675 | 5 | case TYPE_DECIMAL32: |
676 | 5 | case TYPE_DECIMAL64: |
677 | 5 | case TYPE_DECIMAL128I: |
678 | 5 | case TYPE_DECIMAL256: |
679 | 7 | case TYPE_STRING: |
680 | 7 | case TYPE_CHAR: |
681 | 7 | case TYPE_VARCHAR: |
682 | 7 | case TYPE_ARRAY: |
683 | 7 | value->field = field; |
684 | 7 | fill_variant_field_info(value); |
685 | 7 | value->precision = type->get_precision(); |
686 | 7 | value->scale = type->get_scale(); |
687 | 7 | return Status::OK(); |
688 | 0 | case TYPE_FLOAT: { |
689 | 0 | const auto float_value = field.get<TYPE_FLOAT>(); |
690 | 0 | if (!std::isfinite(float_value)) { |
691 | 0 | return Status::NotSupported( |
692 | 0 | "Parquet VARIANT non-finite floating point typed_value is not supported"); |
693 | 0 | } |
694 | 0 | value->field = field; |
695 | 0 | fill_variant_field_info(value); |
696 | 0 | return Status::OK(); |
697 | 0 | } |
698 | 0 | case TYPE_DOUBLE: { |
699 | 0 | const auto double_value = field.get<TYPE_DOUBLE>(); |
700 | 0 | if (!std::isfinite(double_value)) { |
701 | 0 | return Status::NotSupported( |
702 | 0 | "Parquet VARIANT non-finite floating point typed_value is not supported"); |
703 | 0 | } |
704 | 0 | value->field = field; |
705 | 0 | fill_variant_field_info(value); |
706 | 0 | return Status::OK(); |
707 | 0 | } |
708 | 0 | case TYPE_TIMEV2: |
709 | 0 | value->field = Field::create_field<TYPE_BIGINT>( |
710 | 0 | static_cast<int64_t>(std::llround(field.get<TYPE_TIMEV2>()))); |
711 | 0 | value->base_scalar_type_id = TYPE_BIGINT; |
712 | 0 | return Status::OK(); |
713 | 0 | case TYPE_DATE: |
714 | 0 | value->field = Field::create_field<TYPE_BIGINT>(variant_date_value(field.get<TYPE_DATE>())); |
715 | 0 | value->base_scalar_type_id = TYPE_BIGINT; |
716 | 0 | return Status::OK(); |
717 | 0 | case TYPE_DATETIME: |
718 | 0 | value->field = Field::create_field<TYPE_BIGINT>( |
719 | 0 | variant_datetime_value(field.get<TYPE_DATETIME>())); |
720 | 0 | value->base_scalar_type_id = TYPE_BIGINT; |
721 | 0 | return Status::OK(); |
722 | 0 | case TYPE_DATEV2: |
723 | 0 | value->field = |
724 | 0 | Field::create_field<TYPE_BIGINT>(variant_date_value(field.get<TYPE_DATEV2>())); |
725 | 0 | value->base_scalar_type_id = TYPE_BIGINT; |
726 | 0 | return Status::OK(); |
727 | 0 | case TYPE_DATETIMEV2: |
728 | 0 | value->field = Field::create_field<TYPE_BIGINT>( |
729 | 0 | variant_datetime_value(field.get<TYPE_DATETIMEV2>())); |
730 | 0 | value->base_scalar_type_id = TYPE_BIGINT; |
731 | 0 | return Status::OK(); |
732 | 0 | case TYPE_TIMESTAMPTZ: |
733 | 0 | value->field = Field::create_field<TYPE_BIGINT>( |
734 | 0 | variant_datetime_value(field.get<TYPE_TIMESTAMPTZ>())); |
735 | 0 | value->base_scalar_type_id = TYPE_BIGINT; |
736 | 0 | return Status::OK(); |
737 | 0 | case TYPE_VARBINARY: |
738 | 0 | return Status::NotSupported("Parquet VARIANT binary typed_value is not supported"); |
739 | 0 | default: |
740 | 0 | return Status::Corruption("Unsupported Parquet VARIANT typed_value Doris type {}", |
741 | 0 | type->get_name()); |
742 | 7 | } |
743 | 7 | } |
744 | | |
745 | | static Status typed_value_to_json(const FieldSchema& typed_value_field, const Field& field, |
746 | | const std::string& metadata, std::string* json, bool* present); |
747 | | |
748 | | static Status serialize_field_to_json(const DataTypePtr& data_type, const Field& field, |
749 | 3 | std::string* json) { |
750 | 3 | MutableColumnPtr column = data_type->create_column(); |
751 | 3 | column->insert(field); |
752 | | |
753 | 3 | auto json_column = ColumnString::create(); |
754 | 3 | VectorBufferWriter writer(*json_column); |
755 | 3 | auto serde = data_type->get_serde(); |
756 | 3 | DataTypeSerDe::FormatOptions options; |
757 | 3 | RETURN_IF_ERROR(serde->serialize_one_cell_to_json(*column, 0, writer, options)); |
758 | 3 | writer.commit(); |
759 | 3 | *json = json_column->get_data_at(0).to_string(); |
760 | 3 | return Status::OK(); |
761 | 3 | } |
762 | | |
763 | | static Status scalar_typed_value_to_json(const FieldSchema& field_schema, const Field& field, |
764 | 3 | std::string* json, bool* present) { |
765 | 3 | FieldWithDataType value; |
766 | 3 | RETURN_IF_ERROR(field_to_variant_field(field_schema, field, &value, present)); |
767 | 3 | if (!*present) { |
768 | 0 | return Status::OK(); |
769 | 0 | } |
770 | 3 | if (value.field.is_null()) { |
771 | 0 | *json = "null"; |
772 | 0 | return Status::OK(); |
773 | 0 | } |
774 | | |
775 | 3 | DataTypePtr json_type; |
776 | 3 | if (value.base_scalar_type_id != PrimitiveType::INVALID_TYPE) { |
777 | 3 | json_type = DataTypeFactory::instance().create_data_type(value.base_scalar_type_id, false, |
778 | 3 | value.precision, value.scale); |
779 | 3 | } else { |
780 | 0 | json_type = remove_nullable(field_schema.data_type); |
781 | 0 | } |
782 | 3 | return serialize_field_to_json(json_type, value.field, json); |
783 | 3 | } |
784 | | |
785 | | static Status variant_to_json(const FieldSchema& variant_field, const Field& field, |
786 | | const std::string* inherited_metadata, std::string* json, |
787 | 4 | bool* present) { |
788 | 4 | if (field.is_null()) { |
789 | 1 | *present = false; |
790 | 1 | return Status::OK(); |
791 | 1 | } |
792 | | |
793 | 3 | const auto& fields = field.get<TYPE_STRUCT>(); |
794 | 3 | const int metadata_idx = find_child_idx(variant_field, "metadata"); |
795 | 3 | const int value_idx = find_child_idx(variant_field, "value"); |
796 | 3 | const int typed_value_idx = find_child_idx(variant_field, "typed_value"); |
797 | | |
798 | 3 | std::string metadata; |
799 | 3 | bool has_metadata = false; |
800 | 3 | if (inherited_metadata != nullptr) { |
801 | 3 | metadata = *inherited_metadata; |
802 | 3 | has_metadata = true; |
803 | 3 | } |
804 | 3 | if (metadata_idx >= 0) { |
805 | 0 | bool metadata_present = false; |
806 | 0 | RETURN_IF_ERROR(get_binary_field(fields[metadata_idx], &metadata, &metadata_present)); |
807 | 0 | has_metadata = metadata_present; |
808 | 0 | } |
809 | | |
810 | 3 | std::string typed_json; |
811 | 3 | bool typed_present = false; |
812 | 3 | if (typed_value_idx >= 0) { |
813 | 3 | RETURN_IF_ERROR(typed_value_to_json(variant_field.children[typed_value_idx], |
814 | 3 | fields[typed_value_idx], metadata, &typed_json, |
815 | 3 | &typed_present)); |
816 | 3 | } |
817 | | |
818 | 3 | std::string value_json; |
819 | 3 | bool value_present = false; |
820 | 3 | if (value_idx >= 0) { |
821 | 3 | std::string value; |
822 | 3 | RETURN_IF_ERROR(get_binary_field(fields[value_idx], &value, &value_present)); |
823 | 3 | if (value_present) { |
824 | 3 | if (!has_metadata) { |
825 | 0 | return Status::Corruption("Parquet VARIANT value is present without metadata"); |
826 | 0 | } |
827 | 3 | RETURN_IF_ERROR(parquet::decode_variant_to_json( |
828 | 3 | StringRef(metadata.data(), metadata.size()), |
829 | 3 | StringRef(value.data(), value.size()), &value_json)); |
830 | 3 | } |
831 | 3 | } |
832 | | |
833 | 3 | if (value_present && typed_present) { |
834 | 3 | VariantMap value_values; |
835 | 3 | RETURN_IF_ERROR(parse_json_to_variant_map(value_json, PathInData(), &value_values)); |
836 | 3 | VariantMap typed_values; |
837 | 3 | RETURN_IF_ERROR(parse_json_to_variant_map(typed_json, PathInData(), &typed_values)); |
838 | 3 | erase_shadowed_empty_object_markers(&value_values, &typed_values); |
839 | 3 | auto root_value = value_values.find(PathInData()); |
840 | 3 | if (root_value != value_values.end() && !is_empty_object_marker(root_value->second)) { |
841 | 0 | return Status::Corruption( |
842 | 0 | "Parquet VARIANT has conflicting non-object value and typed_value"); |
843 | 0 | } |
844 | 3 | RETURN_IF_ERROR( |
845 | 3 | check_no_shredded_value_typed_duplicates(value_values, typed_values, PathInData())); |
846 | 2 | value_values.merge(std::move(typed_values)); |
847 | 2 | RETURN_IF_ERROR(variant_map_to_json(std::move(value_values), json)); |
848 | 2 | *present = true; |
849 | 2 | return Status::OK(); |
850 | 2 | } |
851 | | |
852 | 0 | if (typed_present) { |
853 | 0 | *json = std::move(typed_json); |
854 | 0 | *present = true; |
855 | 0 | return Status::OK(); |
856 | 0 | } |
857 | 0 | if (value_present) { |
858 | 0 | *json = std::move(value_json); |
859 | 0 | *present = true; |
860 | 0 | return Status::OK(); |
861 | 0 | } |
862 | | |
863 | 0 | *present = false; |
864 | 0 | return Status::OK(); |
865 | 0 | } |
866 | | |
867 | | static Status shredded_field_to_json(const FieldSchema& field_schema, const Field& field, |
868 | | const std::string& metadata, std::string* json, bool* present, |
869 | 4 | bool allow_scalar_typed_value_only_wrapper) { |
870 | 4 | if (is_variant_wrapper_field(field_schema, allow_scalar_typed_value_only_wrapper, true)) { |
871 | 1 | return variant_to_json(field_schema, field, &metadata, json, present); |
872 | 1 | } |
873 | 3 | return typed_value_to_json(field_schema, field, metadata, json, present); |
874 | 4 | } |
875 | | |
876 | | static Status typed_array_to_json(const FieldSchema& typed_value_field, const Field& field, |
877 | 1 | const std::string& metadata, std::string* json, bool* present) { |
878 | 1 | if (field.is_null()) { |
879 | 0 | *present = false; |
880 | 0 | return Status::OK(); |
881 | 0 | } |
882 | 1 | if (typed_value_field.children.empty()) { |
883 | 0 | return Status::Corruption("Parquet VARIANT array typed_value has no element schema"); |
884 | 0 | } |
885 | | |
886 | 1 | const auto& elements = field.get<TYPE_ARRAY>(); |
887 | 1 | const auto& element_schema = typed_value_field.children[0]; |
888 | 1 | json->clear(); |
889 | 1 | json->push_back('['); |
890 | 1 | for (size_t i = 0; i < elements.size(); ++i) { |
891 | 1 | if (i != 0) { |
892 | 0 | json->push_back(','); |
893 | 0 | } |
894 | 1 | std::string element_json; |
895 | 1 | bool element_present = false; |
896 | 1 | RETURN_IF_ERROR(shredded_field_to_json(element_schema, elements[i], metadata, &element_json, |
897 | 1 | &element_present, true)); |
898 | 1 | if (element_present) { |
899 | 0 | json->append(element_json); |
900 | 1 | } else { |
901 | 1 | return Status::Corruption("Parquet VARIANT array typed_value element is missing"); |
902 | 1 | } |
903 | 1 | } |
904 | 0 | json->push_back(']'); |
905 | 0 | *present = true; |
906 | 0 | return Status::OK(); |
907 | 1 | } |
908 | | |
909 | | static Status typed_struct_to_json(const FieldSchema& typed_value_field, const Field& field, |
910 | 3 | const std::string& metadata, std::string* json, bool* present) { |
911 | 3 | if (field.is_null()) { |
912 | 0 | *present = false; |
913 | 0 | return Status::OK(); |
914 | 0 | } |
915 | | |
916 | 3 | const auto& fields = field.get<TYPE_STRUCT>(); |
917 | 3 | json->clear(); |
918 | 3 | json->push_back('{'); |
919 | 3 | bool first = true; |
920 | 6 | for (int i = 0; i < typed_value_field.children.size(); ++i) { |
921 | 3 | std::string child_json; |
922 | 3 | bool child_present = false; |
923 | 3 | RETURN_IF_ERROR(shredded_field_to_json(typed_value_field.children[i], fields[i], metadata, |
924 | 3 | &child_json, &child_present, false)); |
925 | 3 | if (!child_present) { |
926 | 0 | continue; |
927 | 0 | } |
928 | 3 | if (!first) { |
929 | 0 | json->push_back(','); |
930 | 0 | } |
931 | 3 | append_json_string(typed_value_field.children[i].name, json); |
932 | 3 | json->push_back(':'); |
933 | 3 | json->append(child_json); |
934 | 3 | first = false; |
935 | 3 | } |
936 | 3 | json->push_back('}'); |
937 | 3 | *present = true; |
938 | 3 | return Status::OK(); |
939 | 3 | } |
940 | | |
941 | | static Status typed_value_to_json(const FieldSchema& typed_value_field, const Field& field, |
942 | 7 | const std::string& metadata, std::string* json, bool* present) { |
943 | 7 | const DataTypePtr& typed_type = remove_nullable(typed_value_field.data_type); |
944 | 7 | switch (typed_type->get_primitive_type()) { |
945 | 3 | case TYPE_STRUCT: |
946 | 3 | return typed_struct_to_json(typed_value_field, field, metadata, json, present); |
947 | 1 | case TYPE_ARRAY: |
948 | 1 | return typed_array_to_json(typed_value_field, field, metadata, json, present); |
949 | 3 | default: |
950 | 3 | return scalar_typed_value_to_json(typed_value_field, field, json, present); |
951 | 7 | } |
952 | 7 | } |
953 | | |
954 | | static Status typed_value_to_variant_map(const FieldSchema& typed_value_field, const Field& field, |
955 | | const std::string& metadata, PathInDataBuilder* path, |
956 | | VariantMap* values, bool* present); |
957 | | |
958 | | static Status variant_to_variant_map(const FieldSchema& variant_field, const Field& field, |
959 | | const std::string* inherited_metadata, PathInDataBuilder* path, |
960 | 11 | VariantMap* values, bool* present) { |
961 | 11 | if (field.is_null()) { |
962 | 0 | *present = false; |
963 | 0 | return Status::OK(); |
964 | 0 | } |
965 | 11 | const auto& fields = field.get<TYPE_STRUCT>(); |
966 | 11 | const int metadata_idx = find_child_idx(variant_field, "metadata"); |
967 | 11 | const int value_idx = find_child_idx(variant_field, "value"); |
968 | 11 | const int typed_value_idx = find_child_idx(variant_field, "typed_value"); |
969 | | |
970 | 11 | std::string metadata; |
971 | 11 | bool has_metadata = false; |
972 | 11 | if (inherited_metadata != nullptr) { |
973 | 1 | metadata = *inherited_metadata; |
974 | 1 | has_metadata = true; |
975 | 1 | } |
976 | 11 | if (metadata_idx >= 0) { |
977 | 10 | bool metadata_present = false; |
978 | 10 | RETURN_IF_ERROR(get_binary_field(fields[metadata_idx], &metadata, &metadata_present)); |
979 | 10 | has_metadata = metadata_present; |
980 | 10 | } |
981 | | |
982 | 11 | VariantMap value_values; |
983 | 11 | bool value_present = false; |
984 | 11 | const PathInData current_path = path->build(); |
985 | 11 | if (value_idx >= 0) { |
986 | 9 | std::string value; |
987 | 9 | RETURN_IF_ERROR(get_binary_field(fields[value_idx], &value, &value_present)); |
988 | 9 | if (value_present) { |
989 | 6 | if (!has_metadata) { |
990 | 0 | return Status::Corruption("Parquet VARIANT value is present without metadata"); |
991 | 0 | } |
992 | 6 | std::string value_json; |
993 | 6 | RETURN_IF_ERROR(parquet::decode_variant_to_json( |
994 | 6 | StringRef(metadata.data(), metadata.size()), |
995 | 6 | StringRef(value.data(), value.size()), &value_json)); |
996 | 6 | RETURN_IF_ERROR(parse_json_to_variant_map(value_json, current_path, &value_values)); |
997 | 6 | } |
998 | 9 | } |
999 | | |
1000 | 11 | VariantMap typed_values; |
1001 | 11 | bool typed_present = false; |
1002 | 11 | if (typed_value_idx >= 0) { |
1003 | 9 | RETURN_IF_ERROR(typed_value_to_variant_map(variant_field.children[typed_value_idx], |
1004 | 9 | fields[typed_value_idx], metadata, path, |
1005 | 9 | &typed_values, &typed_present)); |
1006 | 9 | } |
1007 | | |
1008 | 10 | erase_shadowed_empty_object_markers(&value_values, &typed_values); |
1009 | 10 | auto current_value = value_values.find(current_path); |
1010 | 10 | if (value_present && typed_present && current_value != value_values.end() && |
1011 | 10 | !is_empty_object_marker(current_value->second)) { |
1012 | 0 | return Status::Corruption( |
1013 | 0 | "Parquet VARIANT has conflicting non-object value and typed_value"); |
1014 | 0 | } |
1015 | 10 | RETURN_IF_ERROR( |
1016 | 10 | check_no_shredded_value_typed_duplicates(value_values, typed_values, current_path)); |
1017 | 9 | values->merge(std::move(value_values)); |
1018 | 9 | values->merge(std::move(typed_values)); |
1019 | 9 | *present = value_present || typed_present; |
1020 | 9 | return Status::OK(); |
1021 | 10 | } |
1022 | | |
1023 | | static Status shredded_field_to_variant_map(const FieldSchema& field_schema, const Field& field, |
1024 | | const std::string& metadata, PathInDataBuilder* path, |
1025 | 10 | VariantMap* values, bool* present) { |
1026 | 10 | if (is_variant_wrapper_field(field_schema, false, true)) { |
1027 | 1 | return variant_to_variant_map(field_schema, field, &metadata, path, values, present); |
1028 | 1 | } |
1029 | 9 | return typed_value_to_variant_map(field_schema, field, metadata, path, values, present); |
1030 | 10 | } |
1031 | | |
1032 | | static Status typed_value_to_variant_map(const FieldSchema& typed_value_field, const Field& field, |
1033 | | const std::string& metadata, PathInDataBuilder* path, |
1034 | 18 | VariantMap* values, bool* present) { |
1035 | 18 | if (field.is_null()) { |
1036 | 4 | *present = false; |
1037 | 4 | return Status::OK(); |
1038 | 4 | } |
1039 | 14 | const DataTypePtr& typed_type = remove_nullable(typed_value_field.data_type); |
1040 | 14 | if (typed_type->get_primitive_type() == TYPE_STRUCT) { |
1041 | 9 | const auto& fields = field.get<TYPE_STRUCT>(); |
1042 | 9 | *present = true; |
1043 | 9 | bool has_present_child = false; |
1044 | 19 | for (int i = 0; i < typed_value_field.children.size(); ++i) { |
1045 | 10 | path->append(typed_value_field.children[i].name, false); |
1046 | 10 | bool child_present = false; |
1047 | 10 | RETURN_IF_ERROR(shredded_field_to_variant_map(typed_value_field.children[i], fields[i], |
1048 | 10 | metadata, path, values, &child_present)); |
1049 | 10 | has_present_child |= child_present; |
1050 | 10 | path->pop_back(); |
1051 | 10 | } |
1052 | 9 | if (!has_present_child) { |
1053 | 3 | RETURN_IF_ERROR(insert_empty_object_marker(path->build(), values)); |
1054 | 3 | } |
1055 | 9 | return Status::OK(); |
1056 | 9 | } |
1057 | 5 | if (typed_type->get_primitive_type() == TYPE_ARRAY) { |
1058 | 1 | std::string value_json; |
1059 | 1 | RETURN_IF_ERROR( |
1060 | 1 | typed_value_to_json(typed_value_field, field, metadata, &value_json, present)); |
1061 | 0 | if (*present) { |
1062 | 0 | RETURN_IF_ERROR(parse_json_to_variant_map(value_json, path->build(), values)); |
1063 | 0 | } |
1064 | 0 | return Status::OK(); |
1065 | 0 | } |
1066 | | |
1067 | 4 | FieldWithDataType value; |
1068 | 4 | RETURN_IF_ERROR(field_to_variant_field(typed_value_field, field, &value, present)); |
1069 | 4 | if (*present) { |
1070 | 4 | (*values)[path->build()] = std::move(value); |
1071 | 4 | } |
1072 | 4 | return Status::OK(); |
1073 | 4 | } |
1074 | | |
1075 | | static Status append_direct_typed_column_to_batch(const FieldSchema& field_schema, |
1076 | | const IColumn& column, size_t start, size_t rows, |
1077 | | PathInDataBuilder* path, ColumnVariant* batch, |
1078 | | bool allow_variant_wrapper, |
1079 | | const std::set<uint64_t>& column_ids, |
1080 | 13 | std::vector<const NullMap*> parent_null_maps) { |
1081 | 13 | if (!has_selected_column(field_schema, column_ids)) { |
1082 | 0 | return Status::OK(); |
1083 | 0 | } |
1084 | | |
1085 | 13 | const IColumn* value_column = &column; |
1086 | 13 | if (const auto* nullable_column = check_and_get_column<ColumnNullable>(&column)) { |
1087 | 12 | parent_null_maps.push_back(&nullable_column->get_null_map_data()); |
1088 | 12 | value_column = &nullable_column->get_nested_column(); |
1089 | 12 | } |
1090 | | |
1091 | 13 | if (allow_variant_wrapper && is_variant_wrapper_field(field_schema, false)) { |
1092 | 0 | const int typed_value_idx = find_child_idx(field_schema, "typed_value"); |
1093 | 0 | DCHECK_GE(typed_value_idx, 0); |
1094 | 0 | const auto& typed_struct = assert_cast<const ColumnStruct&>(*value_column); |
1095 | 0 | return append_direct_typed_column_to_batch( |
1096 | 0 | field_schema.children[typed_value_idx], typed_struct.get_column(typed_value_idx), |
1097 | 0 | start, rows, path, batch, false, column_ids, parent_null_maps); |
1098 | 0 | } |
1099 | | |
1100 | 13 | const auto& type = remove_nullable(field_schema.data_type); |
1101 | 13 | if (type->get_primitive_type() == TYPE_STRUCT) { |
1102 | 5 | const auto& struct_column = assert_cast<const ColumnStruct&>(*value_column); |
1103 | 14 | for (int i = 0; i < field_schema.children.size(); ++i) { |
1104 | 9 | if (!has_selected_column(field_schema.children[i], column_ids)) { |
1105 | 0 | continue; |
1106 | 0 | } |
1107 | 9 | path->append(field_schema.children[i].name, false); |
1108 | 9 | RETURN_IF_ERROR(append_direct_typed_column_to_batch( |
1109 | 9 | field_schema.children[i], struct_column.get_column(i), start, rows, path, batch, |
1110 | 9 | true, column_ids, parent_null_maps)); |
1111 | 9 | path->pop_back(); |
1112 | 9 | } |
1113 | 5 | return Status::OK(); |
1114 | 5 | } |
1115 | | |
1116 | 8 | DataTypePtr variant_leaf_type = make_nullable(direct_variant_leaf_type(field_schema.data_type)); |
1117 | 8 | MutableColumnPtr variant_leaf = variant_leaf_type->create_column(); |
1118 | 8 | variant_leaf->insert_default(); |
1119 | 8 | if (is_temporal_variant_leaf_type(type->get_primitive_type())) { |
1120 | 3 | insert_direct_typed_temporal_leaf_range(type->get_primitive_type(), *value_column, start, |
1121 | 3 | rows, parent_null_maps, variant_leaf.get()); |
1122 | 5 | } else { |
1123 | 5 | insert_direct_typed_leaf_range(*value_column, start, rows, parent_null_maps, |
1124 | 5 | variant_leaf.get()); |
1125 | 5 | } |
1126 | 8 | if (!batch->add_sub_column(path->build(), std::move(variant_leaf), variant_leaf_type)) { |
1127 | 0 | return Status::Corruption("Failed to add Parquet VARIANT typed subcolumn {}", |
1128 | 0 | path->build().get_path()); |
1129 | 0 | } |
1130 | 8 | return Status::OK(); |
1131 | 8 | } |
1132 | | |
1133 | | #ifdef BE_TEST |
1134 | | namespace parquet_variant_reader_test { |
1135 | 4 | bool can_direct_read_typed_value_for_test(const FieldSchema& typed_value_field) { |
1136 | 4 | const std::set<uint64_t> column_ids; |
1137 | 4 | return can_direct_read_typed_value(typed_value_field, false, column_ids); |
1138 | 4 | } |
1139 | | |
1140 | | bool can_use_direct_typed_only_value_for_test(const FieldSchema& variant_field, |
1141 | 5 | const std::set<uint64_t>& column_ids) { |
1142 | 5 | return can_use_direct_typed_only_value(variant_field, column_ids); |
1143 | 5 | } |
1144 | | |
1145 | | Status append_direct_typed_column_to_batch_for_test(const FieldSchema& typed_value_field, |
1146 | | const IColumn& typed_value_column, size_t start, |
1147 | 4 | size_t rows, ColumnVariant* batch) { |
1148 | 4 | PathInDataBuilder path; |
1149 | 4 | const std::set<uint64_t> column_ids; |
1150 | 4 | return append_direct_typed_column_to_batch(typed_value_field, typed_value_column, start, rows, |
1151 | 4 | &path, batch, false, column_ids, {}); |
1152 | 4 | } |
1153 | | |
1154 | | Status read_variant_row_for_test(const FieldSchema& variant_field, const Field& field, |
1155 | 11 | bool output_nullable, Field* result, bool* sql_null) { |
1156 | 11 | if (field.is_null()) { |
1157 | 1 | if (!output_nullable) { |
1158 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
1159 | 0 | } |
1160 | 1 | *sql_null = true; |
1161 | 1 | return Status::OK(); |
1162 | 1 | } |
1163 | | |
1164 | 10 | VariantMap values; |
1165 | 10 | bool present = false; |
1166 | 10 | PathInDataBuilder path; |
1167 | 10 | RETURN_IF_ERROR( |
1168 | 10 | variant_to_variant_map(variant_field, field, nullptr, &path, &values, &present)); |
1169 | 8 | if (!present) { |
1170 | 1 | values[PathInData()] = FieldWithDataType {.field = Field()}; |
1171 | 1 | } |
1172 | | |
1173 | 8 | auto variant_column = ColumnVariant::create(0, false); |
1174 | 8 | RETURN_IF_CATCH_EXCEPTION( |
1175 | 8 | variant_column->insert(Field::create_field<TYPE_VARIANT>(std::move(values)))); |
1176 | 8 | variant_column->get(0, *result); |
1177 | 8 | *sql_null = false; |
1178 | 8 | return Status::OK(); |
1179 | 8 | } |
1180 | | |
1181 | | Status variant_to_json_for_test(const FieldSchema& variant_field, const Field& field, |
1182 | | const std::string& inherited_metadata, std::string* json, |
1183 | 3 | bool* present) { |
1184 | 3 | return variant_to_json(variant_field, field, &inherited_metadata, json, present); |
1185 | 3 | } |
1186 | | } // namespace parquet_variant_reader_test |
1187 | | #endif |
1188 | | |
1189 | | Status ParquetColumnReader::create(io::FileReaderSPtr file, FieldSchema* field, |
1190 | | const tparquet::RowGroup& row_group, const RowRanges& row_ranges, |
1191 | | const cctz::time_zone* ctz, io::IOContext* io_ctx, |
1192 | | std::unique_ptr<ParquetColumnReader>& reader, |
1193 | | size_t max_buf_size, |
1194 | | std::unordered_map<int, tparquet::OffsetIndex>& col_offsets, |
1195 | | RuntimeState* state, bool in_collection, |
1196 | | const std::set<uint64_t>& column_ids, |
1197 | 132 | const std::set<uint64_t>& filter_column_ids) { |
1198 | 132 | size_t total_rows = row_group.num_rows; |
1199 | 132 | if (field->data_type->get_primitive_type() == TYPE_ARRAY) { |
1200 | 2 | std::unique_ptr<ParquetColumnReader> element_reader; |
1201 | 2 | RETURN_IF_ERROR(create(file, &field->children[0], row_group, row_ranges, ctz, io_ctx, |
1202 | 2 | element_reader, max_buf_size, col_offsets, state, true, column_ids, |
1203 | 2 | filter_column_ids)); |
1204 | 2 | auto array_reader = ArrayColumnReader::create_unique(row_ranges, total_rows, ctz, io_ctx); |
1205 | 2 | element_reader->set_column_in_nested(); |
1206 | 2 | RETURN_IF_ERROR(array_reader->init(std::move(element_reader), field)); |
1207 | 2 | array_reader->_filter_column_ids = filter_column_ids; |
1208 | 2 | reader.reset(array_reader.release()); |
1209 | 130 | } else if (field->data_type->get_primitive_type() == TYPE_MAP) { |
1210 | 0 | std::unique_ptr<ParquetColumnReader> key_reader; |
1211 | 0 | std::unique_ptr<ParquetColumnReader> value_reader; |
1212 | |
|
1213 | 0 | if (column_ids.empty() || |
1214 | 0 | column_ids.find(field->children[0].get_column_id()) != column_ids.end()) { |
1215 | | // Create key reader |
1216 | 0 | RETURN_IF_ERROR(create(file, &field->children[0], row_group, row_ranges, ctz, io_ctx, |
1217 | 0 | key_reader, max_buf_size, col_offsets, state, true, column_ids, |
1218 | 0 | filter_column_ids)); |
1219 | 0 | } else { |
1220 | 0 | auto skip_reader = std::make_unique<SkipReadingReader>(row_ranges, total_rows, ctz, |
1221 | 0 | io_ctx, &field->children[0]); |
1222 | 0 | key_reader = std::move(skip_reader); |
1223 | 0 | } |
1224 | | |
1225 | 0 | if (column_ids.empty() || |
1226 | 0 | column_ids.find(field->children[1].get_column_id()) != column_ids.end()) { |
1227 | | // Create value reader |
1228 | 0 | RETURN_IF_ERROR(create(file, &field->children[1], row_group, row_ranges, ctz, io_ctx, |
1229 | 0 | value_reader, max_buf_size, col_offsets, state, true, column_ids, |
1230 | 0 | filter_column_ids)); |
1231 | 0 | } else { |
1232 | 0 | auto skip_reader = std::make_unique<SkipReadingReader>(row_ranges, total_rows, ctz, |
1233 | 0 | io_ctx, &field->children[0]); |
1234 | 0 | value_reader = std::move(skip_reader); |
1235 | 0 | } |
1236 | | |
1237 | 0 | auto map_reader = MapColumnReader::create_unique(row_ranges, total_rows, ctz, io_ctx); |
1238 | 0 | key_reader->set_column_in_nested(); |
1239 | 0 | value_reader->set_column_in_nested(); |
1240 | 0 | RETURN_IF_ERROR(map_reader->init(std::move(key_reader), std::move(value_reader), field)); |
1241 | 0 | map_reader->_filter_column_ids = filter_column_ids; |
1242 | 0 | reader.reset(map_reader.release()); |
1243 | 130 | } else if (field->data_type->get_primitive_type() == TYPE_STRUCT) { |
1244 | 11 | std::unordered_map<std::string, std::unique_ptr<ParquetColumnReader>> child_readers; |
1245 | 11 | child_readers.reserve(field->children.size()); |
1246 | 11 | int non_skip_reader_idx = -1; |
1247 | 37 | for (int i = 0; i < field->children.size(); ++i) { |
1248 | 26 | auto& child = field->children[i]; |
1249 | 26 | std::unique_ptr<ParquetColumnReader> child_reader; |
1250 | 26 | if (column_ids.empty() || column_ids.find(child.get_column_id()) != column_ids.end()) { |
1251 | 22 | RETURN_IF_ERROR(create(file, &child, row_group, row_ranges, ctz, io_ctx, |
1252 | 22 | child_reader, max_buf_size, col_offsets, state, |
1253 | 22 | in_collection, column_ids, filter_column_ids)); |
1254 | 22 | child_readers[child.name] = std::move(child_reader); |
1255 | | // Record the first non-SkippingReader |
1256 | 22 | if (non_skip_reader_idx == -1) { |
1257 | 11 | non_skip_reader_idx = i; |
1258 | 11 | } |
1259 | 22 | } else { |
1260 | 4 | auto skip_reader = std::make_unique<SkipReadingReader>(row_ranges, total_rows, ctz, |
1261 | 4 | io_ctx, &child); |
1262 | 4 | skip_reader->_filter_column_ids = filter_column_ids; |
1263 | 4 | child_readers[child.name] = std::move(skip_reader); |
1264 | 4 | } |
1265 | 26 | child_readers[child.name]->set_column_in_nested(); |
1266 | 26 | } |
1267 | | // If all children are SkipReadingReader, force the first child to call create |
1268 | 11 | if (non_skip_reader_idx == -1) { |
1269 | 0 | std::unique_ptr<ParquetColumnReader> child_reader; |
1270 | 0 | RETURN_IF_ERROR(create(file, &field->children[0], row_group, row_ranges, ctz, io_ctx, |
1271 | 0 | child_reader, max_buf_size, col_offsets, state, in_collection, |
1272 | 0 | column_ids, filter_column_ids)); |
1273 | 0 | child_reader->set_column_in_nested(); |
1274 | 0 | child_readers[field->children[0].name] = std::move(child_reader); |
1275 | 0 | } |
1276 | 11 | auto struct_reader = StructColumnReader::create_unique(row_ranges, total_rows, ctz, io_ctx); |
1277 | 11 | RETURN_IF_ERROR(struct_reader->init(std::move(child_readers), field)); |
1278 | 11 | struct_reader->_filter_column_ids = filter_column_ids; |
1279 | 11 | reader.reset(struct_reader.release()); |
1280 | 119 | } else if (remove_nullable(field->data_type)->get_primitive_type() == TYPE_VARIANT) { |
1281 | 0 | auto variant_reader = |
1282 | 0 | VariantColumnReader::create_unique(row_ranges, total_rows, ctz, io_ctx); |
1283 | 0 | RETURN_IF_ERROR(variant_reader->init(file, field, row_group, max_buf_size, col_offsets, |
1284 | 0 | state, in_collection, column_ids, filter_column_ids)); |
1285 | 0 | variant_reader->_filter_column_ids = filter_column_ids; |
1286 | 0 | reader.reset(variant_reader.release()); |
1287 | 119 | } else { |
1288 | 119 | auto physical_index = field->physical_column_index; |
1289 | 119 | const tparquet::OffsetIndex* offset_index = |
1290 | 119 | col_offsets.find(physical_index) != col_offsets.end() ? &col_offsets[physical_index] |
1291 | 119 | : nullptr; |
1292 | | |
1293 | 119 | const tparquet::ColumnChunk& chunk = row_group.columns[physical_index]; |
1294 | 119 | if (in_collection) { |
1295 | 3 | if (offset_index == nullptr) { |
1296 | 3 | auto scalar_reader = ScalarColumnReader<true, false>::create_unique( |
1297 | 3 | row_ranges, total_rows, chunk, offset_index, ctz, io_ctx); |
1298 | | |
1299 | 3 | RETURN_IF_ERROR(scalar_reader->init(file, field, max_buf_size, state)); |
1300 | 3 | scalar_reader->_filter_column_ids = filter_column_ids; |
1301 | 3 | reader.reset(scalar_reader.release()); |
1302 | 3 | } else { |
1303 | 0 | auto scalar_reader = ScalarColumnReader<true, true>::create_unique( |
1304 | 0 | row_ranges, total_rows, chunk, offset_index, ctz, io_ctx); |
1305 | |
|
1306 | 0 | RETURN_IF_ERROR(scalar_reader->init(file, field, max_buf_size, state)); |
1307 | 0 | scalar_reader->_filter_column_ids = filter_column_ids; |
1308 | 0 | reader.reset(scalar_reader.release()); |
1309 | 0 | } |
1310 | 116 | } else { |
1311 | 116 | if (offset_index == nullptr) { |
1312 | 116 | auto scalar_reader = ScalarColumnReader<false, false>::create_unique( |
1313 | 116 | row_ranges, total_rows, chunk, offset_index, ctz, io_ctx); |
1314 | | |
1315 | 116 | RETURN_IF_ERROR(scalar_reader->init(file, field, max_buf_size, state)); |
1316 | 116 | scalar_reader->_filter_column_ids = filter_column_ids; |
1317 | 116 | reader.reset(scalar_reader.release()); |
1318 | 116 | } else { |
1319 | 0 | auto scalar_reader = ScalarColumnReader<false, true>::create_unique( |
1320 | 0 | row_ranges, total_rows, chunk, offset_index, ctz, io_ctx); |
1321 | |
|
1322 | 0 | RETURN_IF_ERROR(scalar_reader->init(file, field, max_buf_size, state)); |
1323 | 0 | scalar_reader->_filter_column_ids = filter_column_ids; |
1324 | 0 | reader.reset(scalar_reader.release()); |
1325 | 0 | } |
1326 | 116 | } |
1327 | 119 | } |
1328 | 132 | return Status::OK(); |
1329 | 132 | } |
1330 | | |
1331 | | void ParquetColumnReader::_generate_read_ranges(RowRange page_row_range, |
1332 | 274 | RowRanges* result_ranges) const { |
1333 | 274 | result_ranges->add(page_row_range); |
1334 | 274 | RowRanges::ranges_intersection(*result_ranges, _row_ranges, result_ranges); |
1335 | 274 | } |
1336 | | |
1337 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1338 | | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::init(io::FileReaderSPtr file, |
1339 | | FieldSchema* field, |
1340 | | size_t max_buf_size, |
1341 | 119 | RuntimeState* state) { |
1342 | 119 | _field_schema = field; |
1343 | 119 | auto& chunk_meta = _chunk_meta.meta_data; |
1344 | 119 | int64_t chunk_start = has_dict_page(chunk_meta) ? chunk_meta.dictionary_page_offset |
1345 | 119 | : chunk_meta.data_page_offset; |
1346 | 119 | size_t chunk_len = chunk_meta.total_compressed_size; |
1347 | 119 | size_t prefetch_buffer_size = std::min(chunk_len, max_buf_size); |
1348 | 119 | if ((typeid_cast<doris::io::TracingFileReader*>(file.get()) && |
1349 | 119 | typeid_cast<io::MergeRangeFileReader*>( |
1350 | 53 | ((doris::io::TracingFileReader*)(file.get()))->inner_reader().get())) || |
1351 | 119 | typeid_cast<io::MergeRangeFileReader*>(file.get())) { |
1352 | | // turn off prefetch data when using MergeRangeFileReader |
1353 | 119 | prefetch_buffer_size = 0; |
1354 | 119 | } |
1355 | 119 | _stream_reader = std::make_unique<io::BufferedFileStreamReader>(file, chunk_start, chunk_len, |
1356 | 119 | prefetch_buffer_size); |
1357 | 119 | ParquetPageReadContext ctx( |
1358 | 119 | (state == nullptr) ? true : state->query_options().enable_parquet_file_page_cache); |
1359 | | |
1360 | 119 | _chunk_reader = std::make_unique<ColumnChunkReader<IN_COLLECTION, OFFSET_INDEX>>( |
1361 | 119 | _stream_reader.get(), &_chunk_meta, field, _offset_index, _total_rows, _io_ctx, ctx); |
1362 | 119 | RETURN_IF_ERROR(_chunk_reader->init()); |
1363 | 119 | return Status::OK(); |
1364 | 119 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE4initESt10shared_ptrINS_2io10FileReaderEEPNS_11FieldSchemaEmPNS_12RuntimeStateE _ZN5doris18ScalarColumnReaderILb1ELb0EE4initESt10shared_ptrINS_2io10FileReaderEEPNS_11FieldSchemaEmPNS_12RuntimeStateE Line | Count | Source | 1341 | 3 | RuntimeState* state) { | 1342 | 3 | _field_schema = field; | 1343 | 3 | auto& chunk_meta = _chunk_meta.meta_data; | 1344 | 3 | int64_t chunk_start = has_dict_page(chunk_meta) ? chunk_meta.dictionary_page_offset | 1345 | 3 | : chunk_meta.data_page_offset; | 1346 | 3 | size_t chunk_len = chunk_meta.total_compressed_size; | 1347 | 3 | size_t prefetch_buffer_size = std::min(chunk_len, max_buf_size); | 1348 | 3 | if ((typeid_cast<doris::io::TracingFileReader*>(file.get()) && | 1349 | 3 | typeid_cast<io::MergeRangeFileReader*>( | 1350 | 0 | ((doris::io::TracingFileReader*)(file.get()))->inner_reader().get())) || | 1351 | 3 | typeid_cast<io::MergeRangeFileReader*>(file.get())) { | 1352 | | // turn off prefetch data when using MergeRangeFileReader | 1353 | 3 | prefetch_buffer_size = 0; | 1354 | 3 | } | 1355 | 3 | _stream_reader = std::make_unique<io::BufferedFileStreamReader>(file, chunk_start, chunk_len, | 1356 | 3 | prefetch_buffer_size); | 1357 | 3 | ParquetPageReadContext ctx( | 1358 | 3 | (state == nullptr) ? true : state->query_options().enable_parquet_file_page_cache); | 1359 | | | 1360 | 3 | _chunk_reader = std::make_unique<ColumnChunkReader<IN_COLLECTION, OFFSET_INDEX>>( | 1361 | 3 | _stream_reader.get(), &_chunk_meta, field, _offset_index, _total_rows, _io_ctx, ctx); | 1362 | 3 | RETURN_IF_ERROR(_chunk_reader->init()); | 1363 | 3 | return Status::OK(); | 1364 | 3 | } |
Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE4initESt10shared_ptrINS_2io10FileReaderEEPNS_11FieldSchemaEmPNS_12RuntimeStateE _ZN5doris18ScalarColumnReaderILb0ELb0EE4initESt10shared_ptrINS_2io10FileReaderEEPNS_11FieldSchemaEmPNS_12RuntimeStateE Line | Count | Source | 1341 | 116 | RuntimeState* state) { | 1342 | 116 | _field_schema = field; | 1343 | 116 | auto& chunk_meta = _chunk_meta.meta_data; | 1344 | 116 | int64_t chunk_start = has_dict_page(chunk_meta) ? chunk_meta.dictionary_page_offset | 1345 | 116 | : chunk_meta.data_page_offset; | 1346 | 116 | size_t chunk_len = chunk_meta.total_compressed_size; | 1347 | 116 | size_t prefetch_buffer_size = std::min(chunk_len, max_buf_size); | 1348 | 116 | if ((typeid_cast<doris::io::TracingFileReader*>(file.get()) && | 1349 | 116 | typeid_cast<io::MergeRangeFileReader*>( | 1350 | 53 | ((doris::io::TracingFileReader*)(file.get()))->inner_reader().get())) || | 1351 | 116 | typeid_cast<io::MergeRangeFileReader*>(file.get())) { | 1352 | | // turn off prefetch data when using MergeRangeFileReader | 1353 | 116 | prefetch_buffer_size = 0; | 1354 | 116 | } | 1355 | 116 | _stream_reader = std::make_unique<io::BufferedFileStreamReader>(file, chunk_start, chunk_len, | 1356 | 116 | prefetch_buffer_size); | 1357 | 116 | ParquetPageReadContext ctx( | 1358 | 116 | (state == nullptr) ? true : state->query_options().enable_parquet_file_page_cache); | 1359 | | | 1360 | 116 | _chunk_reader = std::make_unique<ColumnChunkReader<IN_COLLECTION, OFFSET_INDEX>>( | 1361 | 116 | _stream_reader.get(), &_chunk_meta, field, _offset_index, _total_rows, _io_ctx, ctx); | 1362 | 116 | RETURN_IF_ERROR(_chunk_reader->init()); | 1363 | 116 | return Status::OK(); | 1364 | 116 | } |
|
1365 | | |
1366 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1367 | 244 | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::_skip_values(size_t num_values) { |
1368 | 244 | if (num_values == 0) { |
1369 | 142 | return Status::OK(); |
1370 | 142 | } |
1371 | 102 | if (_chunk_reader->max_def_level() > 0) { |
1372 | 102 | LevelDecoder& def_decoder = _chunk_reader->def_level_decoder(); |
1373 | 102 | size_t skipped = 0; |
1374 | 102 | size_t null_size = 0; |
1375 | 102 | size_t nonnull_size = 0; |
1376 | 217 | while (skipped < num_values) { |
1377 | 115 | level_t def_level = -1; |
1378 | 115 | size_t loop_skip = def_decoder.get_next_run(&def_level, num_values - skipped); |
1379 | 115 | if (loop_skip == 0) { |
1380 | 0 | std::stringstream ss; |
1381 | 0 | auto& bit_reader = def_decoder.rle_decoder().bit_reader(); |
1382 | 0 | ss << "def_decoder buffer (hex): "; |
1383 | 0 | for (size_t i = 0; i < bit_reader.max_bytes(); ++i) { |
1384 | 0 | ss << std::hex << std::setw(2) << std::setfill('0') |
1385 | 0 | << static_cast<int>(bit_reader.buffer()[i]) << " "; |
1386 | 0 | } |
1387 | 0 | LOG(WARNING) << ss.str(); |
1388 | 0 | return Status::InternalError("Failed to decode definition level."); |
1389 | 0 | } |
1390 | 115 | if (def_level < _field_schema->definition_level) { |
1391 | 8 | null_size += loop_skip; |
1392 | 107 | } else { |
1393 | 107 | nonnull_size += loop_skip; |
1394 | 107 | } |
1395 | 115 | skipped += loop_skip; |
1396 | 115 | } |
1397 | 102 | if (null_size > 0) { |
1398 | 5 | RETURN_IF_ERROR(_chunk_reader->skip_values(null_size, false)); |
1399 | 5 | } |
1400 | 102 | if (nonnull_size > 0) { |
1401 | 101 | RETURN_IF_ERROR(_chunk_reader->skip_values(nonnull_size, true)); |
1402 | 101 | } |
1403 | 102 | } else { |
1404 | 0 | RETURN_IF_ERROR(_chunk_reader->skip_values(num_values)); |
1405 | 0 | } |
1406 | 102 | return Status::OK(); |
1407 | 102 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE12_skip_valuesEm Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb0EE12_skip_valuesEm Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE12_skip_valuesEm _ZN5doris18ScalarColumnReaderILb0ELb0EE12_skip_valuesEm Line | Count | Source | 1367 | 244 | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::_skip_values(size_t num_values) { | 1368 | 244 | if (num_values == 0) { | 1369 | 142 | return Status::OK(); | 1370 | 142 | } | 1371 | 102 | if (_chunk_reader->max_def_level() > 0) { | 1372 | 102 | LevelDecoder& def_decoder = _chunk_reader->def_level_decoder(); | 1373 | 102 | size_t skipped = 0; | 1374 | 102 | size_t null_size = 0; | 1375 | 102 | size_t nonnull_size = 0; | 1376 | 217 | while (skipped < num_values) { | 1377 | 115 | level_t def_level = -1; | 1378 | 115 | size_t loop_skip = def_decoder.get_next_run(&def_level, num_values - skipped); | 1379 | 115 | if (loop_skip == 0) { | 1380 | 0 | std::stringstream ss; | 1381 | 0 | auto& bit_reader = def_decoder.rle_decoder().bit_reader(); | 1382 | 0 | ss << "def_decoder buffer (hex): "; | 1383 | 0 | for (size_t i = 0; i < bit_reader.max_bytes(); ++i) { | 1384 | 0 | ss << std::hex << std::setw(2) << std::setfill('0') | 1385 | 0 | << static_cast<int>(bit_reader.buffer()[i]) << " "; | 1386 | 0 | } | 1387 | 0 | LOG(WARNING) << ss.str(); | 1388 | 0 | return Status::InternalError("Failed to decode definition level."); | 1389 | 0 | } | 1390 | 115 | if (def_level < _field_schema->definition_level) { | 1391 | 8 | null_size += loop_skip; | 1392 | 107 | } else { | 1393 | 107 | nonnull_size += loop_skip; | 1394 | 107 | } | 1395 | 115 | skipped += loop_skip; | 1396 | 115 | } | 1397 | 102 | if (null_size > 0) { | 1398 | 5 | RETURN_IF_ERROR(_chunk_reader->skip_values(null_size, false)); | 1399 | 5 | } | 1400 | 102 | if (nonnull_size > 0) { | 1401 | 101 | RETURN_IF_ERROR(_chunk_reader->skip_values(nonnull_size, true)); | 1402 | 101 | } | 1403 | 102 | } else { | 1404 | 0 | RETURN_IF_ERROR(_chunk_reader->skip_values(num_values)); | 1405 | 0 | } | 1406 | 102 | return Status::OK(); | 1407 | 102 | } |
|
1408 | | |
1409 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1410 | | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::_read_values(size_t num_values, |
1411 | | ColumnPtr& doris_column, |
1412 | | DataTypePtr& type, |
1413 | | FilterMap& filter_map, |
1414 | 244 | bool is_dict_filter) { |
1415 | 244 | if (num_values == 0) { |
1416 | 0 | return Status::OK(); |
1417 | 0 | } |
1418 | 244 | MutableColumnPtr data_column; |
1419 | 244 | std::vector<uint16_t> null_map; |
1420 | 244 | NullMap* map_data_column = nullptr; |
1421 | 244 | if (doris_column->is_nullable()) { |
1422 | 242 | SCOPED_RAW_TIMER(&_decode_null_map_time); |
1423 | | // doris_column either originates from a mutable block in vparquet_group_reader |
1424 | | // or is a newly created ColumnPtr, and therefore can be modified. |
1425 | 242 | auto* nullable_column = |
1426 | 242 | assert_cast<ColumnNullable*>(const_cast<IColumn*>(doris_column.get())); |
1427 | | |
1428 | 242 | data_column = nullable_column->get_nested_column_ptr(); |
1429 | 242 | map_data_column = &(nullable_column->get_null_map_data()); |
1430 | 242 | if (_chunk_reader->max_def_level() > 0) { |
1431 | 174 | LevelDecoder& def_decoder = _chunk_reader->def_level_decoder(); |
1432 | 174 | size_t has_read = 0; |
1433 | 174 | bool prev_is_null = true; |
1434 | 348 | while (has_read < num_values) { |
1435 | 174 | level_t def_level; |
1436 | 174 | size_t loop_read = def_decoder.get_next_run(&def_level, num_values - has_read); |
1437 | 174 | if (loop_read == 0) { |
1438 | 0 | std::stringstream ss; |
1439 | 0 | auto& bit_reader = def_decoder.rle_decoder().bit_reader(); |
1440 | 0 | ss << "def_decoder buffer (hex): "; |
1441 | 0 | for (size_t i = 0; i < bit_reader.max_bytes(); ++i) { |
1442 | 0 | ss << std::hex << std::setw(2) << std::setfill('0') |
1443 | 0 | << static_cast<int>(bit_reader.buffer()[i]) << " "; |
1444 | 0 | } |
1445 | 0 | LOG(WARNING) << ss.str(); |
1446 | 0 | return Status::InternalError("Failed to decode definition level."); |
1447 | 0 | } |
1448 | | |
1449 | 174 | bool is_null = def_level < _field_schema->definition_level; |
1450 | 174 | if (!(prev_is_null ^ is_null)) { |
1451 | 57 | null_map.emplace_back(0); |
1452 | 57 | } |
1453 | 174 | size_t remaining = loop_read; |
1454 | 174 | while (remaining > USHRT_MAX) { |
1455 | 0 | null_map.emplace_back(USHRT_MAX); |
1456 | 0 | null_map.emplace_back(0); |
1457 | 0 | remaining -= USHRT_MAX; |
1458 | 0 | } |
1459 | 174 | null_map.emplace_back((u_short)remaining); |
1460 | 174 | prev_is_null = is_null; |
1461 | 174 | has_read += loop_read; |
1462 | 174 | } |
1463 | 174 | } |
1464 | 242 | } else { |
1465 | 2 | if (_chunk_reader->max_def_level() > 0) { |
1466 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
1467 | 0 | } |
1468 | 2 | data_column = doris_column->assume_mutable(); |
1469 | 2 | } |
1470 | 244 | if (null_map.size() == 0) { |
1471 | 70 | size_t remaining = num_values; |
1472 | 70 | while (remaining > USHRT_MAX) { |
1473 | 0 | null_map.emplace_back(USHRT_MAX); |
1474 | 0 | null_map.emplace_back(0); |
1475 | 0 | remaining -= USHRT_MAX; |
1476 | 0 | } |
1477 | 70 | null_map.emplace_back((u_short)remaining); |
1478 | 70 | } |
1479 | 244 | ColumnSelectVector select_vector; |
1480 | 244 | { |
1481 | 244 | SCOPED_RAW_TIMER(&_decode_null_map_time); |
1482 | 244 | RETURN_IF_ERROR(select_vector.init(null_map, num_values, map_data_column, &filter_map, |
1483 | 244 | _filter_map_index)); |
1484 | 244 | _filter_map_index += num_values; |
1485 | 244 | } |
1486 | 0 | return _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter); |
1487 | 244 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE12_read_valuesEmRNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEb Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb0EE12_read_valuesEmRNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEb Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE12_read_valuesEmRNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEb _ZN5doris18ScalarColumnReaderILb0ELb0EE12_read_valuesEmRNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEb Line | Count | Source | 1414 | 244 | bool is_dict_filter) { | 1415 | 244 | if (num_values == 0) { | 1416 | 0 | return Status::OK(); | 1417 | 0 | } | 1418 | 244 | MutableColumnPtr data_column; | 1419 | 244 | std::vector<uint16_t> null_map; | 1420 | 244 | NullMap* map_data_column = nullptr; | 1421 | 244 | if (doris_column->is_nullable()) { | 1422 | 242 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1423 | | // doris_column either originates from a mutable block in vparquet_group_reader | 1424 | | // or is a newly created ColumnPtr, and therefore can be modified. | 1425 | 242 | auto* nullable_column = | 1426 | 242 | assert_cast<ColumnNullable*>(const_cast<IColumn*>(doris_column.get())); | 1427 | | | 1428 | 242 | data_column = nullable_column->get_nested_column_ptr(); | 1429 | 242 | map_data_column = &(nullable_column->get_null_map_data()); | 1430 | 242 | if (_chunk_reader->max_def_level() > 0) { | 1431 | 174 | LevelDecoder& def_decoder = _chunk_reader->def_level_decoder(); | 1432 | 174 | size_t has_read = 0; | 1433 | 174 | bool prev_is_null = true; | 1434 | 348 | while (has_read < num_values) { | 1435 | 174 | level_t def_level; | 1436 | 174 | size_t loop_read = def_decoder.get_next_run(&def_level, num_values - has_read); | 1437 | 174 | if (loop_read == 0) { | 1438 | 0 | std::stringstream ss; | 1439 | 0 | auto& bit_reader = def_decoder.rle_decoder().bit_reader(); | 1440 | 0 | ss << "def_decoder buffer (hex): "; | 1441 | 0 | for (size_t i = 0; i < bit_reader.max_bytes(); ++i) { | 1442 | 0 | ss << std::hex << std::setw(2) << std::setfill('0') | 1443 | 0 | << static_cast<int>(bit_reader.buffer()[i]) << " "; | 1444 | 0 | } | 1445 | 0 | LOG(WARNING) << ss.str(); | 1446 | 0 | return Status::InternalError("Failed to decode definition level."); | 1447 | 0 | } | 1448 | | | 1449 | 174 | bool is_null = def_level < _field_schema->definition_level; | 1450 | 174 | if (!(prev_is_null ^ is_null)) { | 1451 | 57 | null_map.emplace_back(0); | 1452 | 57 | } | 1453 | 174 | size_t remaining = loop_read; | 1454 | 174 | while (remaining > USHRT_MAX) { | 1455 | 0 | null_map.emplace_back(USHRT_MAX); | 1456 | 0 | null_map.emplace_back(0); | 1457 | 0 | remaining -= USHRT_MAX; | 1458 | 0 | } | 1459 | 174 | null_map.emplace_back((u_short)remaining); | 1460 | 174 | prev_is_null = is_null; | 1461 | 174 | has_read += loop_read; | 1462 | 174 | } | 1463 | 174 | } | 1464 | 242 | } else { | 1465 | 2 | if (_chunk_reader->max_def_level() > 0) { | 1466 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); | 1467 | 0 | } | 1468 | 2 | data_column = doris_column->assume_mutable(); | 1469 | 2 | } | 1470 | 244 | if (null_map.size() == 0) { | 1471 | 70 | size_t remaining = num_values; | 1472 | 70 | while (remaining > USHRT_MAX) { | 1473 | 0 | null_map.emplace_back(USHRT_MAX); | 1474 | 0 | null_map.emplace_back(0); | 1475 | 0 | remaining -= USHRT_MAX; | 1476 | 0 | } | 1477 | 70 | null_map.emplace_back((u_short)remaining); | 1478 | 70 | } | 1479 | 244 | ColumnSelectVector select_vector; | 1480 | 244 | { | 1481 | 244 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1482 | 244 | RETURN_IF_ERROR(select_vector.init(null_map, num_values, map_data_column, &filter_map, | 1483 | 244 | _filter_map_index)); | 1484 | 244 | _filter_map_index += num_values; | 1485 | 244 | } | 1486 | 0 | return _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter); | 1487 | 244 | } |
|
1488 | | |
1489 | | /** |
1490 | | * Load the nested column data of complex type. |
1491 | | * A row of complex type may be stored across two(or more) pages, and the parameter `align_rows` indicates that |
1492 | | * whether the reader should read the remaining value of the last row in previous page. |
1493 | | */ |
1494 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1495 | | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::_read_nested_column( |
1496 | | ColumnPtr& doris_column, DataTypePtr& type, FilterMap& filter_map, size_t batch_size, |
1497 | 13 | size_t* read_rows, bool* eof, bool is_dict_filter) { |
1498 | 13 | _rep_levels.clear(); |
1499 | 13 | _def_levels.clear(); |
1500 | | |
1501 | | // Handle nullable columns |
1502 | 13 | MutableColumnPtr data_column; |
1503 | 13 | NullMap* map_data_column = nullptr; |
1504 | 13 | if (doris_column->is_nullable()) { |
1505 | 13 | SCOPED_RAW_TIMER(&_decode_null_map_time); |
1506 | | // doris_column either originates from a mutable block in vparquet_group_reader |
1507 | | // or is a newly created ColumnPtr, and therefore can be modified. |
1508 | 13 | auto* nullable_column = |
1509 | 13 | const_cast<ColumnNullable*>(assert_cast<const ColumnNullable*>(doris_column.get())); |
1510 | 13 | data_column = nullable_column->get_nested_column_ptr(); |
1511 | 13 | map_data_column = &(nullable_column->get_null_map_data()); |
1512 | 13 | } else { |
1513 | 0 | if (_field_schema->data_type->is_nullable()) { |
1514 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
1515 | 0 | } |
1516 | 0 | data_column = doris_column->assume_mutable(); |
1517 | 0 | } |
1518 | | |
1519 | 13 | std::vector<uint16_t> null_map; |
1520 | 13 | std::unordered_set<size_t> ancestor_null_indices; |
1521 | 13 | std::vector<uint8_t> nested_filter_map_data; |
1522 | | |
1523 | 13 | auto read_and_fill_data = [&](size_t before_rep_level_sz, size_t filter_map_index) { |
1524 | 13 | RETURN_IF_ERROR(_chunk_reader->fill_def(_def_levels)); |
1525 | 13 | std::unique_ptr<FilterMap> nested_filter_map = std::make_unique<FilterMap>(); |
1526 | 13 | if (filter_map.has_filter()) { |
1527 | 0 | RETURN_IF_ERROR(gen_filter_map(filter_map, filter_map_index, before_rep_level_sz, |
1528 | 0 | _rep_levels.size(), nested_filter_map_data, |
1529 | 0 | &nested_filter_map)); |
1530 | 0 | } |
1531 | | |
1532 | 13 | null_map.clear(); |
1533 | 13 | ancestor_null_indices.clear(); |
1534 | 13 | RETURN_IF_ERROR(gen_nested_null_map(before_rep_level_sz, _rep_levels.size(), null_map, |
1535 | 13 | ancestor_null_indices)); |
1536 | | |
1537 | 13 | ColumnSelectVector select_vector; |
1538 | 13 | { |
1539 | 13 | SCOPED_RAW_TIMER(&_decode_null_map_time); |
1540 | 13 | RETURN_IF_ERROR(select_vector.init( |
1541 | 13 | null_map, |
1542 | 13 | _rep_levels.size() - before_rep_level_sz - ancestor_null_indices.size(), |
1543 | 13 | map_data_column, nested_filter_map.get(), 0, &ancestor_null_indices)); |
1544 | 13 | } |
1545 | | |
1546 | 13 | RETURN_IF_ERROR( |
1547 | 13 | _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter)); |
1548 | 13 | if (ancestor_null_indices.size() != 0) { |
1549 | 0 | RETURN_IF_ERROR(_chunk_reader->skip_values(ancestor_null_indices.size(), false)); |
1550 | 0 | } |
1551 | 13 | if (filter_map.has_filter()) { |
1552 | 0 | auto new_rep_sz = before_rep_level_sz; |
1553 | 0 | for (size_t idx = before_rep_level_sz; idx < _rep_levels.size(); idx++) { |
1554 | 0 | if (nested_filter_map_data[idx - before_rep_level_sz]) { |
1555 | 0 | _rep_levels[new_rep_sz] = _rep_levels[idx]; |
1556 | 0 | _def_levels[new_rep_sz] = _def_levels[idx]; |
1557 | 0 | new_rep_sz++; |
1558 | 0 | } |
1559 | 0 | } |
1560 | 0 | _rep_levels.resize(new_rep_sz); |
1561 | 0 | _def_levels.resize(new_rep_sz); |
1562 | 0 | } |
1563 | 13 | return Status::OK(); |
1564 | 13 | }; Unexecuted instantiation: _ZZN5doris18ScalarColumnReaderILb1ELb1EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbbENKUlmmE_clEmm _ZZN5doris18ScalarColumnReaderILb1ELb0EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbbENKUlmmE_clEmm Line | Count | Source | 1523 | 3 | auto read_and_fill_data = [&](size_t before_rep_level_sz, size_t filter_map_index) { | 1524 | 3 | RETURN_IF_ERROR(_chunk_reader->fill_def(_def_levels)); | 1525 | 3 | std::unique_ptr<FilterMap> nested_filter_map = std::make_unique<FilterMap>(); | 1526 | 3 | if (filter_map.has_filter()) { | 1527 | 0 | RETURN_IF_ERROR(gen_filter_map(filter_map, filter_map_index, before_rep_level_sz, | 1528 | 0 | _rep_levels.size(), nested_filter_map_data, | 1529 | 0 | &nested_filter_map)); | 1530 | 0 | } | 1531 | | | 1532 | 3 | null_map.clear(); | 1533 | 3 | ancestor_null_indices.clear(); | 1534 | 3 | RETURN_IF_ERROR(gen_nested_null_map(before_rep_level_sz, _rep_levels.size(), null_map, | 1535 | 3 | ancestor_null_indices)); | 1536 | | | 1537 | 3 | ColumnSelectVector select_vector; | 1538 | 3 | { | 1539 | 3 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1540 | 3 | RETURN_IF_ERROR(select_vector.init( | 1541 | 3 | null_map, | 1542 | 3 | _rep_levels.size() - before_rep_level_sz - ancestor_null_indices.size(), | 1543 | 3 | map_data_column, nested_filter_map.get(), 0, &ancestor_null_indices)); | 1544 | 3 | } | 1545 | | | 1546 | 3 | RETURN_IF_ERROR( | 1547 | 3 | _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter)); | 1548 | 3 | if (ancestor_null_indices.size() != 0) { | 1549 | 0 | RETURN_IF_ERROR(_chunk_reader->skip_values(ancestor_null_indices.size(), false)); | 1550 | 0 | } | 1551 | 3 | if (filter_map.has_filter()) { | 1552 | 0 | auto new_rep_sz = before_rep_level_sz; | 1553 | 0 | for (size_t idx = before_rep_level_sz; idx < _rep_levels.size(); idx++) { | 1554 | 0 | if (nested_filter_map_data[idx - before_rep_level_sz]) { | 1555 | 0 | _rep_levels[new_rep_sz] = _rep_levels[idx]; | 1556 | 0 | _def_levels[new_rep_sz] = _def_levels[idx]; | 1557 | 0 | new_rep_sz++; | 1558 | 0 | } | 1559 | 0 | } | 1560 | 0 | _rep_levels.resize(new_rep_sz); | 1561 | 0 | _def_levels.resize(new_rep_sz); | 1562 | 0 | } | 1563 | 3 | return Status::OK(); | 1564 | 3 | }; |
Unexecuted instantiation: _ZZN5doris18ScalarColumnReaderILb0ELb1EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbbENKUlmmE_clEmm _ZZN5doris18ScalarColumnReaderILb0ELb0EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbbENKUlmmE_clEmm Line | Count | Source | 1523 | 10 | auto read_and_fill_data = [&](size_t before_rep_level_sz, size_t filter_map_index) { | 1524 | 10 | RETURN_IF_ERROR(_chunk_reader->fill_def(_def_levels)); | 1525 | 10 | std::unique_ptr<FilterMap> nested_filter_map = std::make_unique<FilterMap>(); | 1526 | 10 | if (filter_map.has_filter()) { | 1527 | 0 | RETURN_IF_ERROR(gen_filter_map(filter_map, filter_map_index, before_rep_level_sz, | 1528 | 0 | _rep_levels.size(), nested_filter_map_data, | 1529 | 0 | &nested_filter_map)); | 1530 | 0 | } | 1531 | | | 1532 | 10 | null_map.clear(); | 1533 | 10 | ancestor_null_indices.clear(); | 1534 | 10 | RETURN_IF_ERROR(gen_nested_null_map(before_rep_level_sz, _rep_levels.size(), null_map, | 1535 | 10 | ancestor_null_indices)); | 1536 | | | 1537 | 10 | ColumnSelectVector select_vector; | 1538 | 10 | { | 1539 | 10 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1540 | 10 | RETURN_IF_ERROR(select_vector.init( | 1541 | 10 | null_map, | 1542 | 10 | _rep_levels.size() - before_rep_level_sz - ancestor_null_indices.size(), | 1543 | 10 | map_data_column, nested_filter_map.get(), 0, &ancestor_null_indices)); | 1544 | 10 | } | 1545 | | | 1546 | 10 | RETURN_IF_ERROR( | 1547 | 10 | _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter)); | 1548 | 10 | if (ancestor_null_indices.size() != 0) { | 1549 | 0 | RETURN_IF_ERROR(_chunk_reader->skip_values(ancestor_null_indices.size(), false)); | 1550 | 0 | } | 1551 | 10 | if (filter_map.has_filter()) { | 1552 | 0 | auto new_rep_sz = before_rep_level_sz; | 1553 | 0 | for (size_t idx = before_rep_level_sz; idx < _rep_levels.size(); idx++) { | 1554 | 0 | if (nested_filter_map_data[idx - before_rep_level_sz]) { | 1555 | 0 | _rep_levels[new_rep_sz] = _rep_levels[idx]; | 1556 | 0 | _def_levels[new_rep_sz] = _def_levels[idx]; | 1557 | 0 | new_rep_sz++; | 1558 | 0 | } | 1559 | 0 | } | 1560 | 0 | _rep_levels.resize(new_rep_sz); | 1561 | 0 | _def_levels.resize(new_rep_sz); | 1562 | 0 | } | 1563 | 10 | return Status::OK(); | 1564 | 10 | }; |
|
1565 | | |
1566 | 15 | while (_current_range_idx < _row_ranges.range_size()) { |
1567 | 13 | size_t left_row = |
1568 | 13 | std::max(_current_row_index, _row_ranges.get_range_from(_current_range_idx)); |
1569 | 13 | size_t right_row = std::min(left_row + batch_size - *read_rows, |
1570 | 13 | (size_t)_row_ranges.get_range_to(_current_range_idx)); |
1571 | 13 | _current_row_index = left_row; |
1572 | 13 | RETURN_IF_ERROR(_chunk_reader->seek_to_nested_row(left_row)); |
1573 | 13 | size_t load_rows = 0; |
1574 | 13 | bool cross_page = false; |
1575 | 13 | size_t before_rep_level_sz = _rep_levels.size(); |
1576 | 13 | RETURN_IF_ERROR(_chunk_reader->load_page_nested_rows(_rep_levels, right_row - left_row, |
1577 | 13 | &load_rows, &cross_page)); |
1578 | 13 | RETURN_IF_ERROR(read_and_fill_data(before_rep_level_sz, _filter_map_index)); |
1579 | 13 | _filter_map_index += load_rows; |
1580 | 13 | while (cross_page) { |
1581 | 0 | before_rep_level_sz = _rep_levels.size(); |
1582 | 0 | RETURN_IF_ERROR(_chunk_reader->load_cross_page_nested_row(_rep_levels, &cross_page)); |
1583 | 0 | RETURN_IF_ERROR(read_and_fill_data(before_rep_level_sz, _filter_map_index - 1)); |
1584 | 0 | } |
1585 | 13 | *read_rows += load_rows; |
1586 | 13 | _current_row_index += load_rows; |
1587 | 13 | _current_range_idx += (_current_row_index == _row_ranges.get_range_to(_current_range_idx)); |
1588 | 13 | if (*read_rows == batch_size) { |
1589 | 11 | break; |
1590 | 11 | } |
1591 | 13 | } |
1592 | 13 | *eof = _current_range_idx == _row_ranges.range_size(); |
1593 | 13 | return Status::OK(); |
1594 | 13 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbb _ZN5doris18ScalarColumnReaderILb1ELb0EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbb Line | Count | Source | 1497 | 3 | size_t* read_rows, bool* eof, bool is_dict_filter) { | 1498 | 3 | _rep_levels.clear(); | 1499 | 3 | _def_levels.clear(); | 1500 | | | 1501 | | // Handle nullable columns | 1502 | 3 | MutableColumnPtr data_column; | 1503 | 3 | NullMap* map_data_column = nullptr; | 1504 | 3 | if (doris_column->is_nullable()) { | 1505 | 3 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1506 | | // doris_column either originates from a mutable block in vparquet_group_reader | 1507 | | // or is a newly created ColumnPtr, and therefore can be modified. | 1508 | 3 | auto* nullable_column = | 1509 | 3 | const_cast<ColumnNullable*>(assert_cast<const ColumnNullable*>(doris_column.get())); | 1510 | 3 | data_column = nullable_column->get_nested_column_ptr(); | 1511 | 3 | map_data_column = &(nullable_column->get_null_map_data()); | 1512 | 3 | } else { | 1513 | 0 | if (_field_schema->data_type->is_nullable()) { | 1514 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); | 1515 | 0 | } | 1516 | 0 | data_column = doris_column->assume_mutable(); | 1517 | 0 | } | 1518 | | | 1519 | 3 | std::vector<uint16_t> null_map; | 1520 | 3 | std::unordered_set<size_t> ancestor_null_indices; | 1521 | 3 | std::vector<uint8_t> nested_filter_map_data; | 1522 | | | 1523 | 3 | auto read_and_fill_data = [&](size_t before_rep_level_sz, size_t filter_map_index) { | 1524 | 3 | RETURN_IF_ERROR(_chunk_reader->fill_def(_def_levels)); | 1525 | 3 | std::unique_ptr<FilterMap> nested_filter_map = std::make_unique<FilterMap>(); | 1526 | 3 | if (filter_map.has_filter()) { | 1527 | 3 | RETURN_IF_ERROR(gen_filter_map(filter_map, filter_map_index, before_rep_level_sz, | 1528 | 3 | _rep_levels.size(), nested_filter_map_data, | 1529 | 3 | &nested_filter_map)); | 1530 | 3 | } | 1531 | | | 1532 | 3 | null_map.clear(); | 1533 | 3 | ancestor_null_indices.clear(); | 1534 | 3 | RETURN_IF_ERROR(gen_nested_null_map(before_rep_level_sz, _rep_levels.size(), null_map, | 1535 | 3 | ancestor_null_indices)); | 1536 | | | 1537 | 3 | ColumnSelectVector select_vector; | 1538 | 3 | { | 1539 | 3 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1540 | 3 | RETURN_IF_ERROR(select_vector.init( | 1541 | 3 | null_map, | 1542 | 3 | _rep_levels.size() - before_rep_level_sz - ancestor_null_indices.size(), | 1543 | 3 | map_data_column, nested_filter_map.get(), 0, &ancestor_null_indices)); | 1544 | 3 | } | 1545 | | | 1546 | 3 | RETURN_IF_ERROR( | 1547 | 3 | _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter)); | 1548 | 3 | if (ancestor_null_indices.size() != 0) { | 1549 | 3 | RETURN_IF_ERROR(_chunk_reader->skip_values(ancestor_null_indices.size(), false)); | 1550 | 3 | } | 1551 | 3 | if (filter_map.has_filter()) { | 1552 | 3 | auto new_rep_sz = before_rep_level_sz; | 1553 | 3 | for (size_t idx = before_rep_level_sz; idx < _rep_levels.size(); idx++) { | 1554 | 3 | if (nested_filter_map_data[idx - before_rep_level_sz]) { | 1555 | 3 | _rep_levels[new_rep_sz] = _rep_levels[idx]; | 1556 | 3 | _def_levels[new_rep_sz] = _def_levels[idx]; | 1557 | 3 | new_rep_sz++; | 1558 | 3 | } | 1559 | 3 | } | 1560 | 3 | _rep_levels.resize(new_rep_sz); | 1561 | 3 | _def_levels.resize(new_rep_sz); | 1562 | 3 | } | 1563 | 3 | return Status::OK(); | 1564 | 3 | }; | 1565 | | | 1566 | 3 | while (_current_range_idx < _row_ranges.range_size()) { | 1567 | 3 | size_t left_row = | 1568 | 3 | std::max(_current_row_index, _row_ranges.get_range_from(_current_range_idx)); | 1569 | 3 | size_t right_row = std::min(left_row + batch_size - *read_rows, | 1570 | 3 | (size_t)_row_ranges.get_range_to(_current_range_idx)); | 1571 | 3 | _current_row_index = left_row; | 1572 | 3 | RETURN_IF_ERROR(_chunk_reader->seek_to_nested_row(left_row)); | 1573 | 3 | size_t load_rows = 0; | 1574 | 3 | bool cross_page = false; | 1575 | 3 | size_t before_rep_level_sz = _rep_levels.size(); | 1576 | 3 | RETURN_IF_ERROR(_chunk_reader->load_page_nested_rows(_rep_levels, right_row - left_row, | 1577 | 3 | &load_rows, &cross_page)); | 1578 | 3 | RETURN_IF_ERROR(read_and_fill_data(before_rep_level_sz, _filter_map_index)); | 1579 | 3 | _filter_map_index += load_rows; | 1580 | 3 | while (cross_page) { | 1581 | 0 | before_rep_level_sz = _rep_levels.size(); | 1582 | 0 | RETURN_IF_ERROR(_chunk_reader->load_cross_page_nested_row(_rep_levels, &cross_page)); | 1583 | 0 | RETURN_IF_ERROR(read_and_fill_data(before_rep_level_sz, _filter_map_index - 1)); | 1584 | 0 | } | 1585 | 3 | *read_rows += load_rows; | 1586 | 3 | _current_row_index += load_rows; | 1587 | 3 | _current_range_idx += (_current_row_index == _row_ranges.get_range_to(_current_range_idx)); | 1588 | 3 | if (*read_rows == batch_size) { | 1589 | 3 | break; | 1590 | 3 | } | 1591 | 3 | } | 1592 | 3 | *eof = _current_range_idx == _row_ranges.range_size(); | 1593 | 3 | return Status::OK(); | 1594 | 3 | } |
Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbb _ZN5doris18ScalarColumnReaderILb0ELb0EE19_read_nested_columnERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERSt10shared_ptrIKNS_9IDataTypeEERNS_9FilterMapEmPmPbb Line | Count | Source | 1497 | 10 | size_t* read_rows, bool* eof, bool is_dict_filter) { | 1498 | 10 | _rep_levels.clear(); | 1499 | 10 | _def_levels.clear(); | 1500 | | | 1501 | | // Handle nullable columns | 1502 | 10 | MutableColumnPtr data_column; | 1503 | 10 | NullMap* map_data_column = nullptr; | 1504 | 10 | if (doris_column->is_nullable()) { | 1505 | 10 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1506 | | // doris_column either originates from a mutable block in vparquet_group_reader | 1507 | | // or is a newly created ColumnPtr, and therefore can be modified. | 1508 | 10 | auto* nullable_column = | 1509 | 10 | const_cast<ColumnNullable*>(assert_cast<const ColumnNullable*>(doris_column.get())); | 1510 | 10 | data_column = nullable_column->get_nested_column_ptr(); | 1511 | 10 | map_data_column = &(nullable_column->get_null_map_data()); | 1512 | 10 | } else { | 1513 | 0 | if (_field_schema->data_type->is_nullable()) { | 1514 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); | 1515 | 0 | } | 1516 | 0 | data_column = doris_column->assume_mutable(); | 1517 | 0 | } | 1518 | | | 1519 | 10 | std::vector<uint16_t> null_map; | 1520 | 10 | std::unordered_set<size_t> ancestor_null_indices; | 1521 | 10 | std::vector<uint8_t> nested_filter_map_data; | 1522 | | | 1523 | 10 | auto read_and_fill_data = [&](size_t before_rep_level_sz, size_t filter_map_index) { | 1524 | 10 | RETURN_IF_ERROR(_chunk_reader->fill_def(_def_levels)); | 1525 | 10 | std::unique_ptr<FilterMap> nested_filter_map = std::make_unique<FilterMap>(); | 1526 | 10 | if (filter_map.has_filter()) { | 1527 | 10 | RETURN_IF_ERROR(gen_filter_map(filter_map, filter_map_index, before_rep_level_sz, | 1528 | 10 | _rep_levels.size(), nested_filter_map_data, | 1529 | 10 | &nested_filter_map)); | 1530 | 10 | } | 1531 | | | 1532 | 10 | null_map.clear(); | 1533 | 10 | ancestor_null_indices.clear(); | 1534 | 10 | RETURN_IF_ERROR(gen_nested_null_map(before_rep_level_sz, _rep_levels.size(), null_map, | 1535 | 10 | ancestor_null_indices)); | 1536 | | | 1537 | 10 | ColumnSelectVector select_vector; | 1538 | 10 | { | 1539 | 10 | SCOPED_RAW_TIMER(&_decode_null_map_time); | 1540 | 10 | RETURN_IF_ERROR(select_vector.init( | 1541 | 10 | null_map, | 1542 | 10 | _rep_levels.size() - before_rep_level_sz - ancestor_null_indices.size(), | 1543 | 10 | map_data_column, nested_filter_map.get(), 0, &ancestor_null_indices)); | 1544 | 10 | } | 1545 | | | 1546 | 10 | RETURN_IF_ERROR( | 1547 | 10 | _chunk_reader->decode_values(data_column, type, select_vector, is_dict_filter)); | 1548 | 10 | if (ancestor_null_indices.size() != 0) { | 1549 | 10 | RETURN_IF_ERROR(_chunk_reader->skip_values(ancestor_null_indices.size(), false)); | 1550 | 10 | } | 1551 | 10 | if (filter_map.has_filter()) { | 1552 | 10 | auto new_rep_sz = before_rep_level_sz; | 1553 | 10 | for (size_t idx = before_rep_level_sz; idx < _rep_levels.size(); idx++) { | 1554 | 10 | if (nested_filter_map_data[idx - before_rep_level_sz]) { | 1555 | 10 | _rep_levels[new_rep_sz] = _rep_levels[idx]; | 1556 | 10 | _def_levels[new_rep_sz] = _def_levels[idx]; | 1557 | 10 | new_rep_sz++; | 1558 | 10 | } | 1559 | 10 | } | 1560 | 10 | _rep_levels.resize(new_rep_sz); | 1561 | 10 | _def_levels.resize(new_rep_sz); | 1562 | 10 | } | 1563 | 10 | return Status::OK(); | 1564 | 10 | }; | 1565 | | | 1566 | 12 | while (_current_range_idx < _row_ranges.range_size()) { | 1567 | 10 | size_t left_row = | 1568 | 10 | std::max(_current_row_index, _row_ranges.get_range_from(_current_range_idx)); | 1569 | 10 | size_t right_row = std::min(left_row + batch_size - *read_rows, | 1570 | 10 | (size_t)_row_ranges.get_range_to(_current_range_idx)); | 1571 | 10 | _current_row_index = left_row; | 1572 | 10 | RETURN_IF_ERROR(_chunk_reader->seek_to_nested_row(left_row)); | 1573 | 10 | size_t load_rows = 0; | 1574 | 10 | bool cross_page = false; | 1575 | 10 | size_t before_rep_level_sz = _rep_levels.size(); | 1576 | 10 | RETURN_IF_ERROR(_chunk_reader->load_page_nested_rows(_rep_levels, right_row - left_row, | 1577 | 10 | &load_rows, &cross_page)); | 1578 | 10 | RETURN_IF_ERROR(read_and_fill_data(before_rep_level_sz, _filter_map_index)); | 1579 | 10 | _filter_map_index += load_rows; | 1580 | 10 | while (cross_page) { | 1581 | 0 | before_rep_level_sz = _rep_levels.size(); | 1582 | 0 | RETURN_IF_ERROR(_chunk_reader->load_cross_page_nested_row(_rep_levels, &cross_page)); | 1583 | 0 | RETURN_IF_ERROR(read_and_fill_data(before_rep_level_sz, _filter_map_index - 1)); | 1584 | 0 | } | 1585 | 10 | *read_rows += load_rows; | 1586 | 10 | _current_row_index += load_rows; | 1587 | 10 | _current_range_idx += (_current_row_index == _row_ranges.get_range_to(_current_range_idx)); | 1588 | 10 | if (*read_rows == batch_size) { | 1589 | 8 | break; | 1590 | 8 | } | 1591 | 10 | } | 1592 | 10 | *eof = _current_range_idx == _row_ranges.range_size(); | 1593 | 10 | return Status::OK(); | 1594 | 10 | } |
|
1595 | | |
1596 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1597 | | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::read_dict_values_to_column( |
1598 | 2 | MutableColumnPtr& doris_column, bool* has_dict) { |
1599 | 2 | bool loaded; |
1600 | 2 | RETURN_IF_ERROR(_try_load_dict_page(&loaded, has_dict)); |
1601 | 2 | if (loaded && *has_dict) { |
1602 | 2 | return _chunk_reader->read_dict_values_to_column(doris_column); |
1603 | 2 | } |
1604 | 0 | return Status::OK(); |
1605 | 2 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE26read_dict_values_to_columnERNS_3COWINS_7IColumnEE11mutable_ptrIS3_EEPb Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb0EE26read_dict_values_to_columnERNS_3COWINS_7IColumnEE11mutable_ptrIS3_EEPb Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE26read_dict_values_to_columnERNS_3COWINS_7IColumnEE11mutable_ptrIS3_EEPb _ZN5doris18ScalarColumnReaderILb0ELb0EE26read_dict_values_to_columnERNS_3COWINS_7IColumnEE11mutable_ptrIS3_EEPb Line | Count | Source | 1598 | 2 | MutableColumnPtr& doris_column, bool* has_dict) { | 1599 | 2 | bool loaded; | 1600 | 2 | RETURN_IF_ERROR(_try_load_dict_page(&loaded, has_dict)); | 1601 | 2 | if (loaded && *has_dict) { | 1602 | 2 | return _chunk_reader->read_dict_values_to_column(doris_column); | 1603 | 2 | } | 1604 | 0 | return Status::OK(); | 1605 | 2 | } |
|
1606 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1607 | | Result<MutableColumnPtr> |
1608 | | ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::convert_dict_column_to_string_column( |
1609 | 0 | const ColumnInt32* dict_column) { |
1610 | 0 | return _chunk_reader->convert_dict_column_to_string_column(dict_column); |
1611 | 0 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE36convert_dict_column_to_string_columnEPKNS_12ColumnVectorILNS_13PrimitiveTypeE5EEE Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb0EE36convert_dict_column_to_string_columnEPKNS_12ColumnVectorILNS_13PrimitiveTypeE5EEE Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE36convert_dict_column_to_string_columnEPKNS_12ColumnVectorILNS_13PrimitiveTypeE5EEE Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb0EE36convert_dict_column_to_string_columnEPKNS_12ColumnVectorILNS_13PrimitiveTypeE5EEE |
1612 | | |
1613 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1614 | | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::_try_load_dict_page(bool* loaded, |
1615 | 2 | bool* has_dict) { |
1616 | | // _chunk_reader init will load first page header to check whether has dict page |
1617 | 2 | *loaded = true; |
1618 | 2 | *has_dict = _chunk_reader->has_dict(); |
1619 | 2 | return Status::OK(); |
1620 | 2 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE19_try_load_dict_pageEPbS2_ Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb0EE19_try_load_dict_pageEPbS2_ Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE19_try_load_dict_pageEPbS2_ _ZN5doris18ScalarColumnReaderILb0ELb0EE19_try_load_dict_pageEPbS2_ Line | Count | Source | 1615 | 2 | bool* has_dict) { | 1616 | | // _chunk_reader init will load first page header to check whether has dict page | 1617 | 2 | *loaded = true; | 1618 | 2 | *has_dict = _chunk_reader->has_dict(); | 1619 | 2 | return Status::OK(); | 1620 | 2 | } |
|
1621 | | |
1622 | | template <bool IN_COLLECTION, bool OFFSET_INDEX> |
1623 | | Status ScalarColumnReader<IN_COLLECTION, OFFSET_INDEX>::read_column_data( |
1624 | | ColumnPtr& doris_column, const DataTypePtr& type, |
1625 | | const std::shared_ptr<TableSchemaChangeHelper::Node>& root_node, FilterMap& filter_map, |
1626 | | size_t batch_size, size_t* read_rows, bool* eof, bool is_dict_filter, |
1627 | 287 | int64_t real_column_size) { |
1628 | 287 | if (_converter == nullptr) { |
1629 | 114 | _converter = parquet::PhysicalToLogicalConverter::get_converter( |
1630 | 114 | _field_schema, _field_schema->data_type, type, _ctz, is_dict_filter); |
1631 | 114 | if (!_converter->support()) { |
1632 | 0 | return Status::InternalError( |
1633 | 0 | "The column type of '{}' is not supported: {}, is_dict_filter: {}, " |
1634 | 0 | "src_logical_type: {}, dst_logical_type: {}", |
1635 | 0 | _field_schema->name, _converter->get_error_msg(), is_dict_filter, |
1636 | 0 | _field_schema->data_type->get_name(), type->get_name()); |
1637 | 0 | } |
1638 | 114 | } |
1639 | | // !FIXME: We should verify whether the get_physical_column logic is correct, why do we return a doris_column? |
1640 | 287 | ColumnPtr resolved_column = |
1641 | 287 | _converter->get_physical_column(_field_schema->physical_type, _field_schema->data_type, |
1642 | 287 | doris_column, type, is_dict_filter); |
1643 | 287 | DataTypePtr& resolved_type = _converter->get_physical_type(); |
1644 | | |
1645 | 287 | _def_levels.clear(); |
1646 | 287 | _rep_levels.clear(); |
1647 | 287 | *read_rows = 0; |
1648 | | |
1649 | 287 | if (_in_nested) { |
1650 | 13 | RETURN_IF_ERROR(_read_nested_column(resolved_column, resolved_type, filter_map, batch_size, |
1651 | 13 | read_rows, eof, is_dict_filter)); |
1652 | 13 | return _converter->convert(resolved_column, _field_schema->data_type, type, doris_column, |
1653 | 13 | is_dict_filter); |
1654 | 13 | } |
1655 | | |
1656 | 274 | int64_t right_row = 0; |
1657 | 274 | if constexpr (OFFSET_INDEX == false) { |
1658 | 274 | RETURN_IF_ERROR(_chunk_reader->parse_page_header()); |
1659 | 274 | right_row = _chunk_reader->page_end_row(); |
1660 | 274 | } else { |
1661 | 0 | right_row = _chunk_reader->page_end_row(); |
1662 | 0 | } |
1663 | | |
1664 | 274 | do { |
1665 | | // generate the row ranges that should be read |
1666 | 274 | RowRanges read_ranges; |
1667 | 274 | _generate_read_ranges(RowRange {_current_row_index, right_row}, &read_ranges); |
1668 | 274 | if (read_ranges.count() == 0) { |
1669 | | // skip the whole page |
1670 | 63 | _current_row_index = right_row; |
1671 | 211 | } else { |
1672 | 211 | bool skip_whole_batch = false; |
1673 | | // Determining whether to skip page or batch will increase the calculation time. |
1674 | | // When the filtering effect is greater than 60%, it is possible to skip the page or batch. |
1675 | 211 | if (filter_map.has_filter() && filter_map.filter_ratio() > 0.6) { |
1676 | | // lazy read |
1677 | 0 | size_t remaining_num_values = read_ranges.count(); |
1678 | 0 | if (batch_size >= remaining_num_values && |
1679 | 0 | filter_map.can_filter_all(remaining_num_values, _filter_map_index)) { |
1680 | | // We can skip the whole page if the remaining values are filtered by predicate columns |
1681 | 0 | _filter_map_index += remaining_num_values; |
1682 | 0 | _current_row_index = right_row; |
1683 | 0 | *read_rows = remaining_num_values; |
1684 | 0 | break; |
1685 | 0 | } |
1686 | 0 | skip_whole_batch = batch_size <= remaining_num_values && |
1687 | 0 | filter_map.can_filter_all(batch_size, _filter_map_index); |
1688 | 0 | if (skip_whole_batch) { |
1689 | 0 | _filter_map_index += batch_size; |
1690 | 0 | } |
1691 | 0 | } |
1692 | | // load page data to decode or skip values |
1693 | 211 | RETURN_IF_ERROR(_chunk_reader->parse_page_header()); |
1694 | 211 | RETURN_IF_ERROR(_chunk_reader->load_page_data_idempotent()); |
1695 | 211 | size_t has_read = 0; |
1696 | 344 | for (size_t idx = 0; idx < read_ranges.range_size(); idx++) { |
1697 | 244 | auto range = read_ranges.get_range(idx); |
1698 | | // generate the skipped values |
1699 | 244 | size_t skip_values = range.from() - _current_row_index; |
1700 | 244 | RETURN_IF_ERROR(_skip_values(skip_values)); |
1701 | 244 | _current_row_index += skip_values; |
1702 | | // generate the read values |
1703 | 244 | size_t read_values = |
1704 | 244 | std::min((size_t)(range.to() - range.from()), batch_size - has_read); |
1705 | 244 | if (skip_whole_batch) { |
1706 | 0 | RETURN_IF_ERROR(_skip_values(read_values)); |
1707 | 244 | } else { |
1708 | 244 | RETURN_IF_ERROR(_read_values(read_values, resolved_column, resolved_type, |
1709 | 244 | filter_map, is_dict_filter)); |
1710 | 244 | } |
1711 | 244 | has_read += read_values; |
1712 | 244 | *read_rows += read_values; |
1713 | 244 | _current_row_index += read_values; |
1714 | 244 | if (has_read == batch_size) { |
1715 | 111 | break; |
1716 | 111 | } |
1717 | 244 | } |
1718 | 211 | } |
1719 | 274 | } while (false); |
1720 | | |
1721 | 274 | if (right_row == _current_row_index) { |
1722 | 101 | if (!_chunk_reader->has_next_page()) { |
1723 | 101 | *eof = true; |
1724 | 101 | } else { |
1725 | 0 | RETURN_IF_ERROR(_chunk_reader->next_page()); |
1726 | 0 | } |
1727 | 101 | } |
1728 | | |
1729 | 274 | { |
1730 | 274 | SCOPED_RAW_TIMER(&_convert_time); |
1731 | 274 | RETURN_IF_ERROR(_converter->convert(resolved_column, _field_schema->data_type, type, |
1732 | 274 | doris_column, is_dict_filter)); |
1733 | 274 | } |
1734 | 274 | return Status::OK(); |
1735 | 274 | } Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb1ELb1EE16read_column_dataERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERKSt10shared_ptrIKNS_9IDataTypeEERKS8_INS_23TableSchemaChangeHelper4NodeEERNS_9FilterMapEmPmPbbl _ZN5doris18ScalarColumnReaderILb1ELb0EE16read_column_dataERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERKSt10shared_ptrIKNS_9IDataTypeEERKS8_INS_23TableSchemaChangeHelper4NodeEERNS_9FilterMapEmPmPbbl Line | Count | Source | 1627 | 3 | int64_t real_column_size) { | 1628 | 3 | if (_converter == nullptr) { | 1629 | 3 | _converter = parquet::PhysicalToLogicalConverter::get_converter( | 1630 | 3 | _field_schema, _field_schema->data_type, type, _ctz, is_dict_filter); | 1631 | 3 | if (!_converter->support()) { | 1632 | 0 | return Status::InternalError( | 1633 | 0 | "The column type of '{}' is not supported: {}, is_dict_filter: {}, " | 1634 | 0 | "src_logical_type: {}, dst_logical_type: {}", | 1635 | 0 | _field_schema->name, _converter->get_error_msg(), is_dict_filter, | 1636 | 0 | _field_schema->data_type->get_name(), type->get_name()); | 1637 | 0 | } | 1638 | 3 | } | 1639 | | // !FIXME: We should verify whether the get_physical_column logic is correct, why do we return a doris_column? | 1640 | 3 | ColumnPtr resolved_column = | 1641 | 3 | _converter->get_physical_column(_field_schema->physical_type, _field_schema->data_type, | 1642 | 3 | doris_column, type, is_dict_filter); | 1643 | 3 | DataTypePtr& resolved_type = _converter->get_physical_type(); | 1644 | | | 1645 | 3 | _def_levels.clear(); | 1646 | 3 | _rep_levels.clear(); | 1647 | 3 | *read_rows = 0; | 1648 | | | 1649 | 3 | if (_in_nested) { | 1650 | 3 | RETURN_IF_ERROR(_read_nested_column(resolved_column, resolved_type, filter_map, batch_size, | 1651 | 3 | read_rows, eof, is_dict_filter)); | 1652 | 3 | return _converter->convert(resolved_column, _field_schema->data_type, type, doris_column, | 1653 | 3 | is_dict_filter); | 1654 | 3 | } | 1655 | | | 1656 | 0 | int64_t right_row = 0; | 1657 | 0 | if constexpr (OFFSET_INDEX == false) { | 1658 | 0 | RETURN_IF_ERROR(_chunk_reader->parse_page_header()); | 1659 | 0 | right_row = _chunk_reader->page_end_row(); | 1660 | | } else { | 1661 | | right_row = _chunk_reader->page_end_row(); | 1662 | | } | 1663 | | | 1664 | 0 | do { | 1665 | | // generate the row ranges that should be read | 1666 | 0 | RowRanges read_ranges; | 1667 | 0 | _generate_read_ranges(RowRange {_current_row_index, right_row}, &read_ranges); | 1668 | 0 | if (read_ranges.count() == 0) { | 1669 | | // skip the whole page | 1670 | 0 | _current_row_index = right_row; | 1671 | 0 | } else { | 1672 | 0 | bool skip_whole_batch = false; | 1673 | | // Determining whether to skip page or batch will increase the calculation time. | 1674 | | // When the filtering effect is greater than 60%, it is possible to skip the page or batch. | 1675 | 0 | if (filter_map.has_filter() && filter_map.filter_ratio() > 0.6) { | 1676 | | // lazy read | 1677 | 0 | size_t remaining_num_values = read_ranges.count(); | 1678 | 0 | if (batch_size >= remaining_num_values && | 1679 | 0 | filter_map.can_filter_all(remaining_num_values, _filter_map_index)) { | 1680 | | // We can skip the whole page if the remaining values are filtered by predicate columns | 1681 | 0 | _filter_map_index += remaining_num_values; | 1682 | 0 | _current_row_index = right_row; | 1683 | 0 | *read_rows = remaining_num_values; | 1684 | 0 | break; | 1685 | 0 | } | 1686 | 0 | skip_whole_batch = batch_size <= remaining_num_values && | 1687 | 0 | filter_map.can_filter_all(batch_size, _filter_map_index); | 1688 | 0 | if (skip_whole_batch) { | 1689 | 0 | _filter_map_index += batch_size; | 1690 | 0 | } | 1691 | 0 | } | 1692 | | // load page data to decode or skip values | 1693 | 0 | RETURN_IF_ERROR(_chunk_reader->parse_page_header()); | 1694 | 0 | RETURN_IF_ERROR(_chunk_reader->load_page_data_idempotent()); | 1695 | 0 | size_t has_read = 0; | 1696 | 0 | for (size_t idx = 0; idx < read_ranges.range_size(); idx++) { | 1697 | 0 | auto range = read_ranges.get_range(idx); | 1698 | | // generate the skipped values | 1699 | 0 | size_t skip_values = range.from() - _current_row_index; | 1700 | 0 | RETURN_IF_ERROR(_skip_values(skip_values)); | 1701 | 0 | _current_row_index += skip_values; | 1702 | | // generate the read values | 1703 | 0 | size_t read_values = | 1704 | 0 | std::min((size_t)(range.to() - range.from()), batch_size - has_read); | 1705 | 0 | if (skip_whole_batch) { | 1706 | 0 | RETURN_IF_ERROR(_skip_values(read_values)); | 1707 | 0 | } else { | 1708 | 0 | RETURN_IF_ERROR(_read_values(read_values, resolved_column, resolved_type, | 1709 | 0 | filter_map, is_dict_filter)); | 1710 | 0 | } | 1711 | 0 | has_read += read_values; | 1712 | 0 | *read_rows += read_values; | 1713 | 0 | _current_row_index += read_values; | 1714 | 0 | if (has_read == batch_size) { | 1715 | 0 | break; | 1716 | 0 | } | 1717 | 0 | } | 1718 | 0 | } | 1719 | 0 | } while (false); | 1720 | | | 1721 | 0 | if (right_row == _current_row_index) { | 1722 | 0 | if (!_chunk_reader->has_next_page()) { | 1723 | 0 | *eof = true; | 1724 | 0 | } else { | 1725 | 0 | RETURN_IF_ERROR(_chunk_reader->next_page()); | 1726 | 0 | } | 1727 | 0 | } | 1728 | | | 1729 | 0 | { | 1730 | 0 | SCOPED_RAW_TIMER(&_convert_time); | 1731 | 0 | RETURN_IF_ERROR(_converter->convert(resolved_column, _field_schema->data_type, type, | 1732 | 0 | doris_column, is_dict_filter)); | 1733 | 0 | } | 1734 | 0 | return Status::OK(); | 1735 | 0 | } |
Unexecuted instantiation: _ZN5doris18ScalarColumnReaderILb0ELb1EE16read_column_dataERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERKSt10shared_ptrIKNS_9IDataTypeEERKS8_INS_23TableSchemaChangeHelper4NodeEERNS_9FilterMapEmPmPbbl _ZN5doris18ScalarColumnReaderILb0ELb0EE16read_column_dataERNS_3COWINS_7IColumnEE13immutable_ptrIS3_EERKSt10shared_ptrIKNS_9IDataTypeEERKS8_INS_23TableSchemaChangeHelper4NodeEERNS_9FilterMapEmPmPbbl Line | Count | Source | 1627 | 284 | int64_t real_column_size) { | 1628 | 284 | if (_converter == nullptr) { | 1629 | 111 | _converter = parquet::PhysicalToLogicalConverter::get_converter( | 1630 | 111 | _field_schema, _field_schema->data_type, type, _ctz, is_dict_filter); | 1631 | 111 | if (!_converter->support()) { | 1632 | 0 | return Status::InternalError( | 1633 | 0 | "The column type of '{}' is not supported: {}, is_dict_filter: {}, " | 1634 | 0 | "src_logical_type: {}, dst_logical_type: {}", | 1635 | 0 | _field_schema->name, _converter->get_error_msg(), is_dict_filter, | 1636 | 0 | _field_schema->data_type->get_name(), type->get_name()); | 1637 | 0 | } | 1638 | 111 | } | 1639 | | // !FIXME: We should verify whether the get_physical_column logic is correct, why do we return a doris_column? | 1640 | 284 | ColumnPtr resolved_column = | 1641 | 284 | _converter->get_physical_column(_field_schema->physical_type, _field_schema->data_type, | 1642 | 284 | doris_column, type, is_dict_filter); | 1643 | 284 | DataTypePtr& resolved_type = _converter->get_physical_type(); | 1644 | | | 1645 | 284 | _def_levels.clear(); | 1646 | 284 | _rep_levels.clear(); | 1647 | 284 | *read_rows = 0; | 1648 | | | 1649 | 284 | if (_in_nested) { | 1650 | 10 | RETURN_IF_ERROR(_read_nested_column(resolved_column, resolved_type, filter_map, batch_size, | 1651 | 10 | read_rows, eof, is_dict_filter)); | 1652 | 10 | return _converter->convert(resolved_column, _field_schema->data_type, type, doris_column, | 1653 | 10 | is_dict_filter); | 1654 | 10 | } | 1655 | | | 1656 | 274 | int64_t right_row = 0; | 1657 | 274 | if constexpr (OFFSET_INDEX == false) { | 1658 | 274 | RETURN_IF_ERROR(_chunk_reader->parse_page_header()); | 1659 | 274 | right_row = _chunk_reader->page_end_row(); | 1660 | | } else { | 1661 | | right_row = _chunk_reader->page_end_row(); | 1662 | | } | 1663 | | | 1664 | 274 | do { | 1665 | | // generate the row ranges that should be read | 1666 | 274 | RowRanges read_ranges; | 1667 | 274 | _generate_read_ranges(RowRange {_current_row_index, right_row}, &read_ranges); | 1668 | 274 | if (read_ranges.count() == 0) { | 1669 | | // skip the whole page | 1670 | 63 | _current_row_index = right_row; | 1671 | 211 | } else { | 1672 | 211 | bool skip_whole_batch = false; | 1673 | | // Determining whether to skip page or batch will increase the calculation time. | 1674 | | // When the filtering effect is greater than 60%, it is possible to skip the page or batch. | 1675 | 211 | if (filter_map.has_filter() && filter_map.filter_ratio() > 0.6) { | 1676 | | // lazy read | 1677 | 0 | size_t remaining_num_values = read_ranges.count(); | 1678 | 0 | if (batch_size >= remaining_num_values && | 1679 | 0 | filter_map.can_filter_all(remaining_num_values, _filter_map_index)) { | 1680 | | // We can skip the whole page if the remaining values are filtered by predicate columns | 1681 | 0 | _filter_map_index += remaining_num_values; | 1682 | 0 | _current_row_index = right_row; | 1683 | 0 | *read_rows = remaining_num_values; | 1684 | 0 | break; | 1685 | 0 | } | 1686 | 0 | skip_whole_batch = batch_size <= remaining_num_values && | 1687 | 0 | filter_map.can_filter_all(batch_size, _filter_map_index); | 1688 | 0 | if (skip_whole_batch) { | 1689 | 0 | _filter_map_index += batch_size; | 1690 | 0 | } | 1691 | 0 | } | 1692 | | // load page data to decode or skip values | 1693 | 211 | RETURN_IF_ERROR(_chunk_reader->parse_page_header()); | 1694 | 211 | RETURN_IF_ERROR(_chunk_reader->load_page_data_idempotent()); | 1695 | 211 | size_t has_read = 0; | 1696 | 344 | for (size_t idx = 0; idx < read_ranges.range_size(); idx++) { | 1697 | 244 | auto range = read_ranges.get_range(idx); | 1698 | | // generate the skipped values | 1699 | 244 | size_t skip_values = range.from() - _current_row_index; | 1700 | 244 | RETURN_IF_ERROR(_skip_values(skip_values)); | 1701 | 244 | _current_row_index += skip_values; | 1702 | | // generate the read values | 1703 | 244 | size_t read_values = | 1704 | 244 | std::min((size_t)(range.to() - range.from()), batch_size - has_read); | 1705 | 244 | if (skip_whole_batch) { | 1706 | 0 | RETURN_IF_ERROR(_skip_values(read_values)); | 1707 | 244 | } else { | 1708 | 244 | RETURN_IF_ERROR(_read_values(read_values, resolved_column, resolved_type, | 1709 | 244 | filter_map, is_dict_filter)); | 1710 | 244 | } | 1711 | 244 | has_read += read_values; | 1712 | 244 | *read_rows += read_values; | 1713 | 244 | _current_row_index += read_values; | 1714 | 244 | if (has_read == batch_size) { | 1715 | 111 | break; | 1716 | 111 | } | 1717 | 244 | } | 1718 | 211 | } | 1719 | 274 | } while (false); | 1720 | | | 1721 | 274 | if (right_row == _current_row_index) { | 1722 | 101 | if (!_chunk_reader->has_next_page()) { | 1723 | 101 | *eof = true; | 1724 | 101 | } else { | 1725 | 0 | RETURN_IF_ERROR(_chunk_reader->next_page()); | 1726 | 0 | } | 1727 | 101 | } | 1728 | | | 1729 | 274 | { | 1730 | 274 | SCOPED_RAW_TIMER(&_convert_time); | 1731 | 274 | RETURN_IF_ERROR(_converter->convert(resolved_column, _field_schema->data_type, type, | 1732 | 274 | doris_column, is_dict_filter)); | 1733 | 274 | } | 1734 | 274 | return Status::OK(); | 1735 | 274 | } |
|
1736 | | |
1737 | | Status ArrayColumnReader::init(std::unique_ptr<ParquetColumnReader> element_reader, |
1738 | 2 | FieldSchema* field) { |
1739 | 2 | _field_schema = field; |
1740 | 2 | _element_reader = std::move(element_reader); |
1741 | 2 | return Status::OK(); |
1742 | 2 | } |
1743 | | |
1744 | | Status ArrayColumnReader::read_column_data( |
1745 | | ColumnPtr& doris_column, const DataTypePtr& type, |
1746 | | const std::shared_ptr<TableSchemaChangeHelper::Node>& root_node, FilterMap& filter_map, |
1747 | | size_t batch_size, size_t* read_rows, bool* eof, bool is_dict_filter, |
1748 | 2 | int64_t real_column_size) { |
1749 | 2 | MutableColumnPtr data_column; |
1750 | 2 | NullMap* null_map_ptr = nullptr; |
1751 | 2 | if (doris_column->is_nullable()) { |
1752 | 2 | auto mutable_column = doris_column->assume_mutable(); |
1753 | 2 | auto* nullable_column = assert_cast<ColumnNullable*>(mutable_column.get()); |
1754 | 2 | null_map_ptr = &nullable_column->get_null_map_data(); |
1755 | 2 | data_column = nullable_column->get_nested_column_ptr(); |
1756 | 2 | } else { |
1757 | 0 | if (_field_schema->data_type->is_nullable()) { |
1758 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
1759 | 0 | } |
1760 | 0 | data_column = doris_column->assume_mutable(); |
1761 | 0 | } |
1762 | 2 | if (type->get_primitive_type() != PrimitiveType::TYPE_ARRAY) { |
1763 | 0 | return Status::Corruption( |
1764 | 0 | "Wrong data type for column '{}', expected Array type, actual type: {}.", |
1765 | 0 | _field_schema->name, type->get_name()); |
1766 | 0 | } |
1767 | | |
1768 | 2 | ColumnPtr& element_column = assert_cast<ColumnArray&>(*data_column).get_data_ptr(); |
1769 | 2 | const DataTypePtr& element_type = |
1770 | 2 | (assert_cast<const DataTypeArray*>(remove_nullable(type).get()))->get_nested_type(); |
1771 | | // read nested column |
1772 | 2 | RETURN_IF_ERROR(_element_reader->read_column_data(element_column, element_type, |
1773 | 2 | root_node->get_element_node(), filter_map, |
1774 | 2 | batch_size, read_rows, eof, is_dict_filter)); |
1775 | 2 | if (*read_rows == 0) { |
1776 | 0 | return Status::OK(); |
1777 | 0 | } |
1778 | | |
1779 | 2 | ColumnArray::Offsets64& offsets_data = assert_cast<ColumnArray&>(*data_column).get_offsets(); |
1780 | | // fill offset and null map |
1781 | 2 | fill_array_offset(_field_schema, offsets_data, null_map_ptr, _element_reader->get_rep_level(), |
1782 | 2 | _element_reader->get_def_level()); |
1783 | 2 | DCHECK_EQ(element_column->size(), offsets_data.back()); |
1784 | 2 | #ifndef NDEBUG |
1785 | 2 | doris_column->sanity_check(); |
1786 | 2 | #endif |
1787 | 2 | return Status::OK(); |
1788 | 2 | } |
1789 | | |
1790 | | Status MapColumnReader::init(std::unique_ptr<ParquetColumnReader> key_reader, |
1791 | | std::unique_ptr<ParquetColumnReader> value_reader, |
1792 | 0 | FieldSchema* field) { |
1793 | 0 | _field_schema = field; |
1794 | 0 | _key_reader = std::move(key_reader); |
1795 | 0 | _value_reader = std::move(value_reader); |
1796 | 0 | return Status::OK(); |
1797 | 0 | } |
1798 | | |
1799 | | Status MapColumnReader::read_column_data( |
1800 | | ColumnPtr& doris_column, const DataTypePtr& type, |
1801 | | const std::shared_ptr<TableSchemaChangeHelper::Node>& root_node, FilterMap& filter_map, |
1802 | | size_t batch_size, size_t* read_rows, bool* eof, bool is_dict_filter, |
1803 | 0 | int64_t real_column_size) { |
1804 | 0 | MutableColumnPtr data_column; |
1805 | 0 | NullMap* null_map_ptr = nullptr; |
1806 | 0 | if (doris_column->is_nullable()) { |
1807 | 0 | auto mutable_column = doris_column->assume_mutable(); |
1808 | 0 | auto* nullable_column = assert_cast<ColumnNullable*>(mutable_column.get()); |
1809 | 0 | null_map_ptr = &nullable_column->get_null_map_data(); |
1810 | 0 | data_column = nullable_column->get_nested_column_ptr(); |
1811 | 0 | } else { |
1812 | 0 | if (_field_schema->data_type->is_nullable()) { |
1813 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
1814 | 0 | } |
1815 | 0 | data_column = doris_column->assume_mutable(); |
1816 | 0 | } |
1817 | 0 | if (remove_nullable(type)->get_primitive_type() != PrimitiveType::TYPE_MAP) { |
1818 | 0 | return Status::Corruption( |
1819 | 0 | "Wrong data type for column '{}', expected Map type, actual type id {}.", |
1820 | 0 | _field_schema->name, type->get_name()); |
1821 | 0 | } |
1822 | | |
1823 | 0 | auto& map = assert_cast<ColumnMap&>(*data_column); |
1824 | 0 | const DataTypePtr& key_type = |
1825 | 0 | assert_cast<const DataTypeMap*>(remove_nullable(type).get())->get_key_type(); |
1826 | 0 | const DataTypePtr& value_type = |
1827 | 0 | assert_cast<const DataTypeMap*>(remove_nullable(type).get())->get_value_type(); |
1828 | 0 | ColumnPtr& key_column = map.get_keys_ptr(); |
1829 | 0 | ColumnPtr& value_column = map.get_values_ptr(); |
1830 | |
|
1831 | 0 | size_t key_rows = 0; |
1832 | 0 | size_t value_rows = 0; |
1833 | 0 | bool key_eof = false; |
1834 | 0 | bool value_eof = false; |
1835 | 0 | int64_t orig_col_column_size = key_column->size(); |
1836 | |
|
1837 | 0 | RETURN_IF_ERROR(_key_reader->read_column_data(key_column, key_type, root_node->get_key_node(), |
1838 | 0 | filter_map, batch_size, &key_rows, &key_eof, |
1839 | 0 | is_dict_filter)); |
1840 | | |
1841 | 0 | while (value_rows < key_rows && !value_eof) { |
1842 | 0 | size_t loop_rows = 0; |
1843 | 0 | RETURN_IF_ERROR(_value_reader->read_column_data( |
1844 | 0 | value_column, value_type, root_node->get_value_node(), filter_map, |
1845 | 0 | key_rows - value_rows, &loop_rows, &value_eof, is_dict_filter, |
1846 | 0 | key_column->size() - orig_col_column_size)); |
1847 | 0 | value_rows += loop_rows; |
1848 | 0 | } |
1849 | 0 | DCHECK_EQ(key_rows, value_rows); |
1850 | 0 | *read_rows = key_rows; |
1851 | 0 | *eof = key_eof; |
1852 | |
|
1853 | 0 | if (*read_rows == 0) { |
1854 | 0 | return Status::OK(); |
1855 | 0 | } |
1856 | | |
1857 | 0 | DCHECK_EQ(key_column->size(), value_column->size()); |
1858 | | // fill offset and null map |
1859 | 0 | fill_array_offset(_field_schema, map.get_offsets(), null_map_ptr, _key_reader->get_rep_level(), |
1860 | 0 | _key_reader->get_def_level()); |
1861 | 0 | DCHECK_EQ(key_column->size(), map.get_offsets().back()); |
1862 | 0 | #ifndef NDEBUG |
1863 | 0 | doris_column->sanity_check(); |
1864 | 0 | #endif |
1865 | 0 | return Status::OK(); |
1866 | 0 | } |
1867 | | |
1868 | | Status StructColumnReader::init( |
1869 | | std::unordered_map<std::string, std::unique_ptr<ParquetColumnReader>>&& child_readers, |
1870 | 11 | FieldSchema* field) { |
1871 | 11 | _field_schema = field; |
1872 | 11 | _child_readers = std::move(child_readers); |
1873 | 11 | return Status::OK(); |
1874 | 11 | } |
1875 | | Status StructColumnReader::read_column_data( |
1876 | | ColumnPtr& doris_column, const DataTypePtr& type, |
1877 | | const std::shared_ptr<TableSchemaChangeHelper::Node>& root_node, FilterMap& filter_map, |
1878 | | size_t batch_size, size_t* read_rows, bool* eof, bool is_dict_filter, |
1879 | 11 | int64_t real_column_size) { |
1880 | 11 | MutableColumnPtr data_column; |
1881 | 11 | NullMap* null_map_ptr = nullptr; |
1882 | 11 | if (doris_column->is_nullable()) { |
1883 | 11 | auto mutable_column = doris_column->assume_mutable(); |
1884 | 11 | auto* nullable_column = assert_cast<ColumnNullable*>(mutable_column.get()); |
1885 | 11 | null_map_ptr = &nullable_column->get_null_map_data(); |
1886 | 11 | data_column = nullable_column->get_nested_column_ptr(); |
1887 | 11 | } else { |
1888 | 0 | if (_field_schema->data_type->is_nullable()) { |
1889 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
1890 | 0 | } |
1891 | 0 | data_column = doris_column->assume_mutable(); |
1892 | 0 | } |
1893 | 11 | if (type->get_primitive_type() != PrimitiveType::TYPE_STRUCT) { |
1894 | 0 | return Status::Corruption( |
1895 | 0 | "Wrong data type for column '{}', expected Struct type, actual type id {}.", |
1896 | 0 | _field_schema->name, type->get_name()); |
1897 | 0 | } |
1898 | | |
1899 | 11 | auto& doris_struct = assert_cast<ColumnStruct&>(*data_column); |
1900 | 11 | const auto* doris_struct_type = assert_cast<const DataTypeStruct*>(remove_nullable(type).get()); |
1901 | | |
1902 | 11 | int64_t not_missing_column_id = -1; |
1903 | 11 | size_t not_missing_orig_column_size = 0; |
1904 | 11 | std::vector<size_t> missing_column_idxs {}; |
1905 | 11 | std::vector<size_t> skip_reading_column_idxs {}; |
1906 | | |
1907 | 11 | _read_column_names.clear(); |
1908 | | |
1909 | 37 | for (size_t i = 0; i < doris_struct.tuple_size(); ++i) { |
1910 | 26 | ColumnPtr& doris_field = doris_struct.get_column_ptr(i); |
1911 | 26 | auto& doris_type = doris_struct_type->get_element(i); |
1912 | 26 | auto& doris_name = doris_struct_type->get_element_name(i); |
1913 | 26 | if (!root_node->children_column_exists(doris_name)) { |
1914 | 0 | missing_column_idxs.push_back(i); |
1915 | 0 | VLOG_DEBUG << "[ParquetReader] Missing column in schema: column_idx[" << i |
1916 | 0 | << "], doris_name: " << doris_name << " (column not exists in root node)"; |
1917 | 0 | continue; |
1918 | 0 | } |
1919 | 26 | auto file_name = root_node->children_file_column_name(doris_name); |
1920 | | |
1921 | | // Check if this is a SkipReadingReader - we should skip it when choosing reference column |
1922 | | // because SkipReadingReader doesn't know the actual data size in nested context |
1923 | 26 | bool is_skip_reader = |
1924 | 26 | dynamic_cast<SkipReadingReader*>(_child_readers[file_name].get()) != nullptr; |
1925 | | |
1926 | 26 | if (is_skip_reader) { |
1927 | | // Store SkipReadingReader columns to fill them later based on reference column size |
1928 | 4 | skip_reading_column_idxs.push_back(i); |
1929 | 4 | continue; |
1930 | 4 | } |
1931 | | |
1932 | | // Only add non-SkipReadingReader columns to _read_column_names |
1933 | | // This ensures get_rep_level() and get_def_level() return valid levels |
1934 | 22 | _read_column_names.emplace_back(file_name); |
1935 | | |
1936 | 22 | size_t field_rows = 0; |
1937 | 22 | bool field_eof = false; |
1938 | 22 | if (not_missing_column_id == -1) { |
1939 | 11 | not_missing_column_id = i; |
1940 | 11 | not_missing_orig_column_size = doris_field->size(); |
1941 | 11 | RETURN_IF_ERROR(_child_readers[file_name]->read_column_data( |
1942 | 11 | doris_field, doris_type, root_node->get_children_node(doris_name), filter_map, |
1943 | 11 | batch_size, &field_rows, &field_eof, is_dict_filter)); |
1944 | 11 | *read_rows = field_rows; |
1945 | 11 | *eof = field_eof; |
1946 | | /* |
1947 | | * Considering the issue in the `_read_nested_column` function where data may span across pages, leading |
1948 | | * to missing definition and repetition levels, when filling the null_map of the struct later, it is |
1949 | | * crucial to use the definition and repetition levels from the first read column |
1950 | | * (since `_read_nested_column` is not called repeatedly). |
1951 | | * |
1952 | | * It is worth mentioning that, theoretically, any sub-column can be chosen to fill the null_map, |
1953 | | * and selecting the shortest one will offer better performance |
1954 | | */ |
1955 | 11 | } else { |
1956 | 22 | while (field_rows < *read_rows && !field_eof) { |
1957 | 11 | size_t loop_rows = 0; |
1958 | 11 | RETURN_IF_ERROR(_child_readers[file_name]->read_column_data( |
1959 | 11 | doris_field, doris_type, root_node->get_children_node(doris_name), |
1960 | 11 | filter_map, *read_rows - field_rows, &loop_rows, &field_eof, |
1961 | 11 | is_dict_filter)); |
1962 | 11 | field_rows += loop_rows; |
1963 | 11 | } |
1964 | 11 | DCHECK_EQ(*read_rows, field_rows); |
1965 | | // DCHECK_EQ(*eof, field_eof); |
1966 | 11 | } |
1967 | 22 | } |
1968 | | |
1969 | 11 | int64_t missing_column_sz = -1; |
1970 | | |
1971 | 11 | if (not_missing_column_id == -1) { |
1972 | | // All queried columns are missing in the file (e.g., all added after schema change) |
1973 | | // We need to pick a column from _field_schema children that exists in the file for RL/DL reference |
1974 | 0 | std::string reference_file_column_name; |
1975 | 0 | std::unique_ptr<ParquetColumnReader>* reference_reader = nullptr; |
1976 | |
|
1977 | 0 | for (const auto& child : _field_schema->children) { |
1978 | 0 | auto it = _child_readers.find(child.name); |
1979 | 0 | if (it != _child_readers.end()) { |
1980 | | // Skip SkipReadingReader as they don't have valid RL/DL |
1981 | 0 | bool is_skip_reader = dynamic_cast<SkipReadingReader*>(it->second.get()) != nullptr; |
1982 | 0 | if (!is_skip_reader) { |
1983 | 0 | reference_file_column_name = child.name; |
1984 | 0 | reference_reader = &(it->second); |
1985 | 0 | break; |
1986 | 0 | } |
1987 | 0 | } |
1988 | 0 | } |
1989 | |
|
1990 | 0 | if (reference_reader != nullptr) { |
1991 | | // Read the reference column to get correct RL/DL information |
1992 | | // TODO: Optimize by only reading RL/DL without actual data decoding |
1993 | | |
1994 | | // We need to find the FieldSchema for the reference column from _field_schema children |
1995 | 0 | FieldSchema* ref_field_schema = nullptr; |
1996 | 0 | for (auto& child : _field_schema->children) { |
1997 | 0 | if (child.name == reference_file_column_name) { |
1998 | 0 | ref_field_schema = &child; |
1999 | 0 | break; |
2000 | 0 | } |
2001 | 0 | } |
2002 | |
|
2003 | 0 | if (ref_field_schema == nullptr) { |
2004 | 0 | return Status::InternalError( |
2005 | 0 | "Cannot find field schema for reference column '{}' in struct '{}'", |
2006 | 0 | reference_file_column_name, _field_schema->name); |
2007 | 0 | } |
2008 | | |
2009 | | // Create a temporary column to hold the data (we'll use its size for missing_column_sz) |
2010 | 0 | ColumnPtr temp_column = ref_field_schema->data_type->create_column(); |
2011 | 0 | auto temp_type = ref_field_schema->data_type; |
2012 | |
|
2013 | 0 | size_t field_rows = 0; |
2014 | 0 | bool field_eof = false; |
2015 | | |
2016 | | // Use ConstNode for the reference column instead of looking up from root_node. |
2017 | | // The reference column is only used to get RL/DL information for determining the number |
2018 | | // of elements in the struct. It may be a column that has been dropped from the table |
2019 | | // schema (e.g., 'removed' field), but still exists in older parquet files. |
2020 | | // Since we don't need schema mapping for this column (we just need its RL/DL levels), |
2021 | | // using ConstNode is safe and avoids the issue where the reference column doesn't exist |
2022 | | // in root_node (because it was dropped from table schema). |
2023 | 0 | auto ref_child_node = TableSchemaChangeHelper::ConstNode::get_instance(); |
2024 | 0 | not_missing_orig_column_size = temp_column->size(); |
2025 | |
|
2026 | 0 | RETURN_IF_ERROR((*reference_reader) |
2027 | 0 | ->read_column_data(temp_column, temp_type, ref_child_node, |
2028 | 0 | filter_map, batch_size, &field_rows, |
2029 | 0 | &field_eof, is_dict_filter)); |
2030 | | |
2031 | 0 | *read_rows = field_rows; |
2032 | 0 | *eof = field_eof; |
2033 | | |
2034 | | // Store this reference column name for get_rep_level/get_def_level to use |
2035 | 0 | _read_column_names.emplace_back(reference_file_column_name); |
2036 | |
|
2037 | 0 | missing_column_sz = temp_column->size() - not_missing_orig_column_size; |
2038 | 0 | } else { |
2039 | 0 | return Status::Corruption( |
2040 | 0 | "Cannot read struct '{}': all queried columns are missing and no reference " |
2041 | 0 | "column found in file", |
2042 | 0 | _field_schema->name); |
2043 | 0 | } |
2044 | 0 | } |
2045 | | |
2046 | | // This missing_column_sz is not *read_rows. Because read_rows returns the number of rows. |
2047 | | // For example: suppose we have a column array<struct<a:int,b:string>>, |
2048 | | // where b is a newly added column, that is, a missing column. |
2049 | | // There are two rows of data in this column, |
2050 | | // [{1,null},{2,null},{3,null}] |
2051 | | // [{4,null},{5,null}] |
2052 | | // When you first read subcolumn a, you read 5 data items and the value of *read_rows is 2. |
2053 | | // You should insert 5 records into subcolumn b instead of 2. |
2054 | 11 | if (missing_column_sz == -1) { |
2055 | 11 | missing_column_sz = doris_struct.get_column(not_missing_column_id).size() - |
2056 | 11 | not_missing_orig_column_size; |
2057 | 11 | } |
2058 | | |
2059 | | // Fill SkipReadingReader columns with the correct amount of data based on reference column |
2060 | | // Let SkipReadingReader handle the data filling through its read_column_data method |
2061 | 11 | for (auto idx : skip_reading_column_idxs) { |
2062 | 4 | auto& doris_field = doris_struct.get_column_ptr(idx); |
2063 | 4 | auto& doris_type = const_cast<DataTypePtr&>(doris_struct_type->get_element(idx)); |
2064 | 4 | auto& doris_name = const_cast<String&>(doris_struct_type->get_element_name(idx)); |
2065 | 4 | auto file_name = root_node->children_file_column_name(doris_name); |
2066 | | |
2067 | 4 | size_t field_rows = 0; |
2068 | 4 | bool field_eof = false; |
2069 | 4 | RETURN_IF_ERROR(_child_readers[file_name]->read_column_data( |
2070 | 4 | doris_field, doris_type, root_node->get_children_node(doris_name), filter_map, |
2071 | 4 | missing_column_sz, &field_rows, &field_eof, is_dict_filter, missing_column_sz)); |
2072 | 4 | } |
2073 | | |
2074 | | // Fill truly missing columns (not in root_node) with null or default value |
2075 | 11 | for (auto idx : missing_column_idxs) { |
2076 | 0 | auto& doris_field = doris_struct.get_column_ptr(idx); |
2077 | 0 | auto& doris_type = doris_struct_type->get_element(idx); |
2078 | 0 | DCHECK(doris_type->is_nullable()); |
2079 | 0 | auto mutable_column = doris_field->assume_mutable(); |
2080 | 0 | auto* nullable_column = static_cast<ColumnNullable*>(mutable_column.get()); |
2081 | 0 | nullable_column->insert_many_defaults(missing_column_sz); |
2082 | 0 | } |
2083 | | |
2084 | 11 | if (null_map_ptr != nullptr) { |
2085 | 11 | fill_struct_null_map(_field_schema, *null_map_ptr, this->get_rep_level(), |
2086 | 11 | this->get_def_level()); |
2087 | 11 | } |
2088 | 11 | #ifndef NDEBUG |
2089 | 11 | doris_column->sanity_check(); |
2090 | 11 | #endif |
2091 | 11 | return Status::OK(); |
2092 | 11 | } |
2093 | | |
2094 | | Status VariantColumnReader::init(io::FileReaderSPtr file, FieldSchema* field, |
2095 | | const tparquet::RowGroup& row_group, size_t max_buf_size, |
2096 | | std::unordered_map<int, tparquet::OffsetIndex>& col_offsets, |
2097 | | RuntimeState* state, bool in_collection, |
2098 | | const std::set<uint64_t>& column_ids, |
2099 | 0 | const std::set<uint64_t>& filter_column_ids) { |
2100 | 0 | _field_schema = field; |
2101 | 0 | _column_ids = column_ids; |
2102 | 0 | _variant_struct_field = std::make_unique<FieldSchema>(*field); |
2103 | |
|
2104 | 0 | DataTypes child_types; |
2105 | 0 | Strings child_names; |
2106 | 0 | child_types.reserve(field->children.size()); |
2107 | 0 | child_names.reserve(field->children.size()); |
2108 | 0 | for (const auto& child : field->children) { |
2109 | 0 | child_types.push_back(make_nullable(child.data_type)); |
2110 | 0 | child_names.push_back(child.name); |
2111 | 0 | } |
2112 | 0 | DataTypePtr variant_struct_type = std::make_shared<DataTypeStruct>(child_types, child_names); |
2113 | 0 | if (field->data_type->is_nullable()) { |
2114 | 0 | variant_struct_type = make_nullable(variant_struct_type); |
2115 | 0 | } |
2116 | 0 | _variant_struct_field->data_type = variant_struct_type; |
2117 | |
|
2118 | 0 | RETURN_IF_ERROR(ParquetColumnReader::create(file, _variant_struct_field.get(), row_group, |
2119 | 0 | _row_ranges, _ctz, _io_ctx, _struct_reader, |
2120 | 0 | max_buf_size, col_offsets, state, in_collection, |
2121 | 0 | column_ids, filter_column_ids)); |
2122 | 0 | _struct_reader->set_column_in_nested(); |
2123 | 0 | return Status::OK(); |
2124 | 0 | } |
2125 | | |
2126 | | // NOLINTNEXTLINE(readability-function-size): existing variant read path stays local to avoid churn. |
2127 | | Status VariantColumnReader::read_column_data( |
2128 | | ColumnPtr& doris_column, const DataTypePtr& type, |
2129 | | const std::shared_ptr<TableSchemaChangeHelper::Node>& root_node, FilterMap& filter_map, |
2130 | | size_t batch_size, size_t* read_rows, bool* eof, bool is_dict_filter, |
2131 | 0 | int64_t real_column_size) { |
2132 | 0 | (void)root_node; |
2133 | 0 | if (remove_nullable(type)->get_primitive_type() != PrimitiveType::TYPE_VARIANT) { |
2134 | 0 | return Status::Corruption( |
2135 | 0 | "Wrong data type for column '{}', expected Variant type, actual type: {}.", |
2136 | 0 | _field_schema->name, type->get_name()); |
2137 | 0 | } |
2138 | | |
2139 | 0 | const auto& variant_struct_type = _variant_struct_field->data_type; |
2140 | 0 | ColumnPtr struct_column = variant_struct_type->create_column(); |
2141 | 0 | const size_t old_struct_rows = struct_column->size(); |
2142 | 0 | auto const_node = TableSchemaChangeHelper::ConstNode::get_instance(); |
2143 | 0 | RETURN_IF_ERROR(_struct_reader->read_column_data(struct_column, variant_struct_type, const_node, |
2144 | 0 | filter_map, batch_size, read_rows, eof, |
2145 | 0 | is_dict_filter, real_column_size)); |
2146 | | |
2147 | 0 | const size_t new_struct_rows = struct_column->size() - old_struct_rows; |
2148 | 0 | if (new_struct_rows == 0) { |
2149 | 0 | return Status::OK(); |
2150 | 0 | } |
2151 | | |
2152 | 0 | MutableColumnPtr variant_column_ptr; |
2153 | 0 | NullMap* null_map_ptr = nullptr; |
2154 | 0 | auto mutable_column = doris_column->assume_mutable(); |
2155 | 0 | if (doris_column->is_nullable()) { |
2156 | 0 | auto* nullable_column = assert_cast<ColumnNullable*>(mutable_column.get()); |
2157 | 0 | variant_column_ptr = nullable_column->get_nested_column_ptr(); |
2158 | 0 | null_map_ptr = &nullable_column->get_null_map_data(); |
2159 | 0 | } else { |
2160 | 0 | if (_field_schema->data_type->is_nullable()) { |
2161 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
2162 | 0 | } |
2163 | 0 | variant_column_ptr = std::move(mutable_column); |
2164 | 0 | } |
2165 | 0 | auto* variant_column = assert_cast<ColumnVariant*>(variant_column_ptr.get()); |
2166 | |
|
2167 | 0 | const IColumn* variant_struct_source = struct_column.get(); |
2168 | 0 | const NullMap* struct_null_map = nullptr; |
2169 | 0 | if (const auto* nullable_struct = check_and_get_column<ColumnNullable>(variant_struct_source)) { |
2170 | 0 | struct_null_map = &nullable_struct->get_null_map_data(); |
2171 | 0 | variant_struct_source = &nullable_struct->get_nested_column(); |
2172 | 0 | } |
2173 | 0 | const auto& variant_struct_column = assert_cast<const ColumnStruct&>(*variant_struct_source); |
2174 | |
|
2175 | 0 | const int typed_value_idx = find_child_idx(*_field_schema, "typed_value"); |
2176 | 0 | if (can_use_direct_typed_only_value(*_field_schema, _column_ids)) { |
2177 | 0 | _variant_statistics.variant_direct_typed_value_read_rows += |
2178 | 0 | static_cast<int64_t>(new_struct_rows); |
2179 | 0 | MutableColumnPtr batch_variant_column = |
2180 | 0 | ColumnVariant::create(variant_column->max_subcolumns_count(), |
2181 | 0 | variant_column->enable_doc_mode(), new_struct_rows + 1); |
2182 | 0 | auto* batch_variant = assert_cast<ColumnVariant*>(batch_variant_column.get()); |
2183 | 0 | PathInDataBuilder path; |
2184 | 0 | RETURN_IF_ERROR(append_direct_typed_column_to_batch( |
2185 | 0 | _field_schema->children[typed_value_idx], |
2186 | 0 | variant_struct_column.get_column(typed_value_idx), old_struct_rows, new_struct_rows, |
2187 | 0 | &path, batch_variant, false, _column_ids, {})); |
2188 | 0 | variant_column->insert_range_from(*batch_variant_column, 1, new_struct_rows); |
2189 | 0 | if (null_map_ptr != nullptr) { |
2190 | 0 | for (size_t i = old_struct_rows; i < struct_column->size(); ++i) { |
2191 | 0 | null_map_ptr->push_back(struct_null_map != nullptr && (*struct_null_map)[i]); |
2192 | 0 | } |
2193 | 0 | } |
2194 | 0 | #ifndef NDEBUG |
2195 | 0 | doris_column->sanity_check(); |
2196 | 0 | #endif |
2197 | 0 | return Status::OK(); |
2198 | 0 | } |
2199 | | |
2200 | 0 | _variant_statistics.variant_rowwise_read_rows += static_cast<int64_t>(new_struct_rows); |
2201 | 0 | for (size_t i = old_struct_rows; i < struct_column->size(); ++i) { |
2202 | 0 | if (struct_null_map != nullptr && (*struct_null_map)[i]) { |
2203 | 0 | if (null_map_ptr == nullptr) { |
2204 | 0 | return Status::Corruption("Not nullable column has null values in parquet file"); |
2205 | 0 | } |
2206 | 0 | variant_column->insert_default(); |
2207 | 0 | null_map_ptr->push_back(1); |
2208 | 0 | continue; |
2209 | 0 | } |
2210 | 0 | VariantMap values; |
2211 | 0 | bool present = false; |
2212 | 0 | PathInDataBuilder path; |
2213 | 0 | RETURN_IF_ERROR(variant_to_variant_map(*_field_schema, (*struct_column)[i], nullptr, &path, |
2214 | 0 | &values, &present)); |
2215 | 0 | if (!present) { |
2216 | 0 | values[PathInData()] = FieldWithDataType {.field = Field()}; |
2217 | 0 | } |
2218 | 0 | RETURN_IF_CATCH_EXCEPTION( |
2219 | 0 | variant_column->insert(Field::create_field<TYPE_VARIANT>(std::move(values)))); |
2220 | 0 | if (null_map_ptr != nullptr) { |
2221 | 0 | null_map_ptr->push_back(0); |
2222 | 0 | } |
2223 | 0 | } |
2224 | 0 | #ifndef NDEBUG |
2225 | 0 | doris_column->sanity_check(); |
2226 | 0 | #endif |
2227 | 0 | return Status::OK(); |
2228 | 0 | } |
2229 | | |
2230 | | template class ScalarColumnReader<true, true>; |
2231 | | template class ScalarColumnReader<true, false>; |
2232 | | template class ScalarColumnReader<false, true>; |
2233 | | template class ScalarColumnReader<false, false>; |
2234 | | |
2235 | | }; // namespace doris |