| 1 | #include "duckdb/function/table/arrow.hpp" |
| 2 | #include "duckdb/common/limits.hpp" |
| 3 | #include "duckdb/common/operator/multiply.hpp" |
| 4 | #include "duckdb/common/types/hugeint.hpp" |
| 5 | #include "duckdb/common/types/arrow_aux_data.hpp" |
| 6 | #include "duckdb/function/scalar/nested_functions.hpp" |
| 7 | |
| 8 | namespace { |
| 9 | using duckdb::idx_t; |
| 10 | struct ArrowConvertDataIndices { |
| 11 | //! The index that refers to 'variable_sz_type' in ArrowConvertData |
| 12 | idx_t variable_sized_index; |
| 13 | //! The index that refers to 'date_time_precision' in ArrowConvertData |
| 14 | idx_t datetime_precision_index; |
| 15 | }; |
| 16 | } // namespace |
| 17 | |
| 18 | namespace duckdb { |
| 19 | |
| 20 | static void ShiftRight(unsigned char *ar, int size, int shift) { |
| 21 | int carry = 0; |
| 22 | while (shift--) { |
| 23 | for (int i = size - 1; i >= 0; --i) { |
| 24 | int next = (ar[i] & 1) ? 0x80 : 0; |
| 25 | ar[i] = carry | (ar[i] >> 1); |
| 26 | carry = next; |
| 27 | } |
| 28 | } |
| 29 | } |
| 30 | |
| 31 | template <class T> |
| 32 | T *ArrowBufferData(ArrowArray &array, idx_t buffer_idx) { |
| 33 | return (T *)array.buffers[buffer_idx]; // NOLINT |
| 34 | } |
| 35 | |
| 36 | static void GetValidityMask(ValidityMask &mask, ArrowArray &array, ArrowScanLocalState &scan_state, idx_t size, |
| 37 | int64_t nested_offset = -1, bool add_null = false) { |
| 38 | // In certains we don't need to or cannot copy arrow's validity mask to duckdb. |
| 39 | // |
| 40 | // The conditions where we do want to copy arrow's mask to duckdb are: |
| 41 | // 1. nulls exist |
| 42 | // 2. n_buffers > 0, meaning the array's arrow type is not `null` |
| 43 | // 3. the validity buffer (the first buffer) is not a nullptr |
| 44 | if (array.null_count != 0 && array.n_buffers > 0 && array.buffers[0]) { |
| 45 | auto bit_offset = scan_state.chunk_offset + array.offset; |
| 46 | if (nested_offset != -1) { |
| 47 | bit_offset = nested_offset; |
| 48 | } |
| 49 | mask.EnsureWritable(); |
| 50 | #if STANDARD_VECTOR_SIZE > 64 |
| 51 | auto n_bitmask_bytes = (size + 8 - 1) / 8; |
| 52 | if (bit_offset % 8 == 0) { |
| 53 | //! just memcpy nullmask |
| 54 | memcpy(dest: (void *)mask.GetData(), src: ArrowBufferData<uint8_t>(array, buffer_idx: 0) + bit_offset / 8, n: n_bitmask_bytes); |
| 55 | } else { |
| 56 | //! need to re-align nullmask |
| 57 | vector<uint8_t> temp_nullmask(n_bitmask_bytes + 1); |
| 58 | memcpy(dest: temp_nullmask.data(), src: ArrowBufferData<uint8_t>(array, buffer_idx: 0) + bit_offset / 8, n: n_bitmask_bytes + 1); |
| 59 | ShiftRight(ar: temp_nullmask.data(), size: n_bitmask_bytes + 1, |
| 60 | shift: bit_offset % 8); //! why this has to be a right shift is a mystery to me |
| 61 | memcpy(dest: (void *)mask.GetData(), src: data_ptr_cast(src: temp_nullmask.data()), n: n_bitmask_bytes); |
| 62 | } |
| 63 | #else |
| 64 | auto byte_offset = bit_offset / 8; |
| 65 | auto source_data = ArrowBufferData<uint8_t>(array, 0); |
| 66 | bit_offset %= 8; |
| 67 | for (idx_t i = 0; i < size; i++) { |
| 68 | mask.Set(i, source_data[byte_offset] & (1 << bit_offset)); |
| 69 | bit_offset++; |
| 70 | if (bit_offset == 8) { |
| 71 | bit_offset = 0; |
| 72 | byte_offset++; |
| 73 | } |
| 74 | } |
| 75 | #endif |
| 76 | } |
| 77 | if (add_null) { |
| 78 | //! We are setting a validity mask of the data part of dictionary vector |
| 79 | //! For some reason, Nulls are allowed to be indexes, hence we need to set the last element here to be null |
| 80 | //! We might have to resize the mask |
| 81 | mask.Resize(old_size: size, new_size: size + 1); |
| 82 | mask.SetInvalid(size); |
| 83 | } |
| 84 | } |
| 85 | |
| 86 | static void SetValidityMask(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, idx_t size, |
| 87 | int64_t nested_offset, bool add_null = false) { |
| 88 | D_ASSERT(vector.GetVectorType() == VectorType::FLAT_VECTOR); |
| 89 | auto &mask = FlatVector::Validity(vector); |
| 90 | GetValidityMask(mask, array, scan_state, size, nested_offset, add_null); |
| 91 | } |
| 92 | |
| 93 | static void ColumnArrowToDuckDB(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, idx_t size, |
| 94 | std::unordered_map<idx_t, unique_ptr<ArrowConvertData>> &arrow_convert_data, |
| 95 | idx_t col_idx, ArrowConvertDataIndices &arrow_convert_idx, int64_t nested_offset = -1, |
| 96 | ValidityMask *parent_mask = nullptr); |
| 97 | |
| 98 | static void ArrowToDuckDBList(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, idx_t size, |
| 99 | std::unordered_map<idx_t, unique_ptr<ArrowConvertData>> &arrow_convert_data, |
| 100 | idx_t col_idx, ArrowConvertDataIndices &arrow_convert_idx, int64_t nested_offset, |
| 101 | ValidityMask *parent_mask) { |
| 102 | auto original_type = arrow_convert_data[col_idx]->variable_sz_type[arrow_convert_idx.variable_sized_index++]; |
| 103 | idx_t list_size = 0; |
| 104 | SetValidityMask(vector, array, scan_state, size, nested_offset); |
| 105 | idx_t start_offset = 0; |
| 106 | idx_t cur_offset = 0; |
| 107 | if (original_type.first == ArrowVariableSizeType::FIXED_SIZE) { |
| 108 | //! Have to check validity mask before setting this up |
| 109 | idx_t offset = (scan_state.chunk_offset + array.offset) * original_type.second; |
| 110 | if (nested_offset != -1) { |
| 111 | offset = original_type.second * nested_offset; |
| 112 | } |
| 113 | start_offset = offset; |
| 114 | auto list_data = FlatVector::GetData<list_entry_t>(vector); |
| 115 | for (idx_t i = 0; i < size; i++) { |
| 116 | auto &le = list_data[i]; |
| 117 | le.offset = cur_offset; |
| 118 | le.length = original_type.second; |
| 119 | cur_offset += original_type.second; |
| 120 | } |
| 121 | list_size = start_offset + cur_offset; |
| 122 | } else if (original_type.first == ArrowVariableSizeType::NORMAL) { |
| 123 | auto offsets = ArrowBufferData<uint32_t>(array, buffer_idx: 1) + array.offset + scan_state.chunk_offset; |
| 124 | if (nested_offset != -1) { |
| 125 | offsets = ArrowBufferData<uint32_t>(array, buffer_idx: 1) + nested_offset; |
| 126 | } |
| 127 | start_offset = offsets[0]; |
| 128 | auto list_data = FlatVector::GetData<list_entry_t>(vector); |
| 129 | for (idx_t i = 0; i < size; i++) { |
| 130 | auto &le = list_data[i]; |
| 131 | le.offset = cur_offset; |
| 132 | le.length = offsets[i + 1] - offsets[i]; |
| 133 | cur_offset += le.length; |
| 134 | } |
| 135 | list_size = offsets[size]; |
| 136 | } else { |
| 137 | auto offsets = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + array.offset + scan_state.chunk_offset; |
| 138 | if (nested_offset != -1) { |
| 139 | offsets = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + nested_offset; |
| 140 | } |
| 141 | start_offset = offsets[0]; |
| 142 | auto list_data = FlatVector::GetData<list_entry_t>(vector); |
| 143 | for (idx_t i = 0; i < size; i++) { |
| 144 | auto &le = list_data[i]; |
| 145 | le.offset = cur_offset; |
| 146 | le.length = offsets[i + 1] - offsets[i]; |
| 147 | cur_offset += le.length; |
| 148 | } |
| 149 | list_size = offsets[size]; |
| 150 | } |
| 151 | list_size -= start_offset; |
| 152 | ListVector::Reserve(vec&: vector, required_capacity: list_size); |
| 153 | ListVector::SetListSize(vec&: vector, size: list_size); |
| 154 | auto &child_vector = ListVector::GetEntry(vector); |
| 155 | SetValidityMask(vector&: child_vector, array&: *array.children[0], scan_state, size: list_size, nested_offset: start_offset); |
| 156 | auto &list_mask = FlatVector::Validity(vector); |
| 157 | if (parent_mask) { |
| 158 | //! Since this List is owned by a struct we must guarantee their validity map matches on Null |
| 159 | if (!parent_mask->AllValid()) { |
| 160 | for (idx_t i = 0; i < size; i++) { |
| 161 | if (!parent_mask->RowIsValid(row_idx: i)) { |
| 162 | list_mask.SetInvalid(i); |
| 163 | } |
| 164 | } |
| 165 | } |
| 166 | } |
| 167 | if (list_size == 0 && start_offset == 0) { |
| 168 | ColumnArrowToDuckDB(vector&: child_vector, array&: *array.children[0], scan_state, size: list_size, arrow_convert_data, col_idx, |
| 169 | arrow_convert_idx, nested_offset: -1); |
| 170 | } else { |
| 171 | ColumnArrowToDuckDB(vector&: child_vector, array&: *array.children[0], scan_state, size: list_size, arrow_convert_data, col_idx, |
| 172 | arrow_convert_idx, nested_offset: start_offset); |
| 173 | } |
| 174 | } |
| 175 | |
| 176 | static void ArrowToDuckDBBlob(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, idx_t size, |
| 177 | std::unordered_map<idx_t, unique_ptr<ArrowConvertData>> &arrow_convert_data, |
| 178 | idx_t col_idx, ArrowConvertDataIndices &arrow_convert_idx, int64_t nested_offset) { |
| 179 | auto original_type = arrow_convert_data[col_idx]->variable_sz_type[arrow_convert_idx.variable_sized_index++]; |
| 180 | SetValidityMask(vector, array, scan_state, size, nested_offset); |
| 181 | if (original_type.first == ArrowVariableSizeType::FIXED_SIZE) { |
| 182 | //! Have to check validity mask before setting this up |
| 183 | idx_t offset = (scan_state.chunk_offset + array.offset) * original_type.second; |
| 184 | if (nested_offset != -1) { |
| 185 | offset = original_type.second * nested_offset; |
| 186 | } |
| 187 | auto cdata = ArrowBufferData<char>(array, buffer_idx: 1); |
| 188 | for (idx_t row_idx = 0; row_idx < size; row_idx++) { |
| 189 | if (FlatVector::IsNull(vector, idx: row_idx)) { |
| 190 | continue; |
| 191 | } |
| 192 | auto bptr = cdata + offset; |
| 193 | auto blob_len = original_type.second; |
| 194 | FlatVector::GetData<string_t>(vector)[row_idx] = StringVector::AddStringOrBlob(vector, data: bptr, len: blob_len); |
| 195 | offset += blob_len; |
| 196 | } |
| 197 | } else if (original_type.first == ArrowVariableSizeType::NORMAL) { |
| 198 | auto offsets = ArrowBufferData<uint32_t>(array, buffer_idx: 1) + array.offset + scan_state.chunk_offset; |
| 199 | if (nested_offset != -1) { |
| 200 | offsets = ArrowBufferData<uint32_t>(array, buffer_idx: 1) + array.offset + nested_offset; |
| 201 | } |
| 202 | auto cdata = ArrowBufferData<char>(array, buffer_idx: 2); |
| 203 | for (idx_t row_idx = 0; row_idx < size; row_idx++) { |
| 204 | if (FlatVector::IsNull(vector, idx: row_idx)) { |
| 205 | continue; |
| 206 | } |
| 207 | auto bptr = cdata + offsets[row_idx]; |
| 208 | auto blob_len = offsets[row_idx + 1] - offsets[row_idx]; |
| 209 | FlatVector::GetData<string_t>(vector)[row_idx] = StringVector::AddStringOrBlob(vector, data: bptr, len: blob_len); |
| 210 | } |
| 211 | } else { |
| 212 | //! Check if last offset is higher than max uint32 |
| 213 | if (ArrowBufferData<uint64_t>(array, buffer_idx: 1)[array.length] > NumericLimits<uint32_t>::Maximum()) { // LCOV_EXCL_START |
| 214 | throw ConversionException("DuckDB does not support Blobs over 4GB" ); |
| 215 | } // LCOV_EXCL_STOP |
| 216 | auto offsets = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + array.offset + scan_state.chunk_offset; |
| 217 | if (nested_offset != -1) { |
| 218 | offsets = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + array.offset + nested_offset; |
| 219 | } |
| 220 | auto cdata = ArrowBufferData<char>(array, buffer_idx: 2); |
| 221 | for (idx_t row_idx = 0; row_idx < size; row_idx++) { |
| 222 | if (FlatVector::IsNull(vector, idx: row_idx)) { |
| 223 | continue; |
| 224 | } |
| 225 | auto bptr = cdata + offsets[row_idx]; |
| 226 | auto blob_len = offsets[row_idx + 1] - offsets[row_idx]; |
| 227 | FlatVector::GetData<string_t>(vector)[row_idx] = StringVector::AddStringOrBlob(vector, data: bptr, len: blob_len); |
| 228 | } |
| 229 | } |
| 230 | } |
| 231 | |
| 232 | static void ArrowToDuckDBMapVerify(Vector &vector, idx_t count) { |
| 233 | auto valid_check = MapVector::CheckMapValidity(map&: vector, count); |
| 234 | switch (valid_check) { |
| 235 | case MapInvalidReason::VALID: |
| 236 | break; |
| 237 | case MapInvalidReason::DUPLICATE_KEY: { |
| 238 | throw InvalidInputException("Arrow map contains duplicate key, which isn't supported by DuckDB map type" ); |
| 239 | } |
| 240 | case MapInvalidReason::NULL_KEY: { |
| 241 | throw InvalidInputException("Arrow map contains NULL as map key, which isn't supported by DuckDB map type" ); |
| 242 | } |
| 243 | case MapInvalidReason::NULL_KEY_LIST: { |
| 244 | throw InvalidInputException("Arrow map contains NULL as key list, which isn't supported by DuckDB map type" ); |
| 245 | } |
| 246 | default: { |
| 247 | throw InternalException("MapInvalidReason not implemented" ); |
| 248 | } |
| 249 | } |
| 250 | } |
| 251 | |
| 252 | template <class T> |
| 253 | static void SetVectorString(Vector &vector, idx_t size, char *cdata, T *offsets) { |
| 254 | auto strings = FlatVector::GetData<string_t>(vector); |
| 255 | for (idx_t row_idx = 0; row_idx < size; row_idx++) { |
| 256 | if (FlatVector::IsNull(vector, idx: row_idx)) { |
| 257 | continue; |
| 258 | } |
| 259 | auto cptr = cdata + offsets[row_idx]; |
| 260 | auto str_len = offsets[row_idx + 1] - offsets[row_idx]; |
| 261 | if (str_len > NumericLimits<uint32_t>::Maximum()) { // LCOV_EXCL_START |
| 262 | throw ConversionException("DuckDB does not support Strings over 4GB" ); |
| 263 | } // LCOV_EXCL_STOP |
| 264 | strings[row_idx] = string_t(cptr, str_len); |
| 265 | } |
| 266 | } |
| 267 | |
| 268 | static void DirectConversion(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, |
| 269 | int64_t nested_offset) { |
| 270 | auto internal_type = GetTypeIdSize(type: vector.GetType().InternalType()); |
| 271 | auto data_ptr = ArrowBufferData<data_t>(array, buffer_idx: 1) + internal_type * (scan_state.chunk_offset + array.offset); |
| 272 | if (nested_offset != -1) { |
| 273 | data_ptr = ArrowBufferData<data_t>(array, buffer_idx: 1) + internal_type * (array.offset + nested_offset); |
| 274 | } |
| 275 | FlatVector::SetData(vector, data: data_ptr); |
| 276 | } |
| 277 | |
| 278 | template <class T> |
| 279 | static void TimeConversion(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, int64_t nested_offset, |
| 280 | idx_t size, int64_t conversion) { |
| 281 | auto tgt_ptr = FlatVector::GetData<dtime_t>(vector); |
| 282 | auto &validity_mask = FlatVector::Validity(vector); |
| 283 | auto src_ptr = (T *)array.buffers[1] + scan_state.chunk_offset + array.offset; |
| 284 | if (nested_offset != -1) { |
| 285 | src_ptr = (T *)array.buffers[1] + nested_offset + array.offset; |
| 286 | } |
| 287 | for (idx_t row = 0; row < size; row++) { |
| 288 | if (!validity_mask.RowIsValid(row_idx: row)) { |
| 289 | continue; |
| 290 | } |
| 291 | if (!TryMultiplyOperator::Operation(left: (int64_t)src_ptr[row], right: conversion, result&: tgt_ptr[row].micros)) { |
| 292 | throw ConversionException("Could not convert Time to Microsecond" ); |
| 293 | } |
| 294 | } |
| 295 | } |
| 296 | |
| 297 | static void TimestampTZConversion(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, |
| 298 | int64_t nested_offset, idx_t size, int64_t conversion) { |
| 299 | auto tgt_ptr = FlatVector::GetData<timestamp_t>(vector); |
| 300 | auto &validity_mask = FlatVector::Validity(vector); |
| 301 | auto src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 302 | if (nested_offset != -1) { |
| 303 | src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 304 | } |
| 305 | for (idx_t row = 0; row < size; row++) { |
| 306 | if (!validity_mask.RowIsValid(row_idx: row)) { |
| 307 | continue; |
| 308 | } |
| 309 | if (!TryMultiplyOperator::Operation(left: src_ptr[row], right: conversion, result&: tgt_ptr[row].value)) { |
| 310 | throw ConversionException("Could not convert TimestampTZ to Microsecond" ); |
| 311 | } |
| 312 | } |
| 313 | } |
| 314 | |
| 315 | static void IntervalConversionUs(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, |
| 316 | int64_t nested_offset, idx_t size, int64_t conversion) { |
| 317 | auto tgt_ptr = FlatVector::GetData<interval_t>(vector); |
| 318 | auto src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 319 | if (nested_offset != -1) { |
| 320 | src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 321 | } |
| 322 | for (idx_t row = 0; row < size; row++) { |
| 323 | tgt_ptr[row].days = 0; |
| 324 | tgt_ptr[row].months = 0; |
| 325 | if (!TryMultiplyOperator::Operation(left: src_ptr[row], right: conversion, result&: tgt_ptr[row].micros)) { |
| 326 | throw ConversionException("Could not convert Interval to Microsecond" ); |
| 327 | } |
| 328 | } |
| 329 | } |
| 330 | |
| 331 | static void IntervalConversionMonths(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, |
| 332 | int64_t nested_offset, idx_t size) { |
| 333 | auto tgt_ptr = FlatVector::GetData<interval_t>(vector); |
| 334 | auto src_ptr = ArrowBufferData<int32_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 335 | if (nested_offset != -1) { |
| 336 | src_ptr = ArrowBufferData<int32_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 337 | } |
| 338 | for (idx_t row = 0; row < size; row++) { |
| 339 | tgt_ptr[row].days = 0; |
| 340 | tgt_ptr[row].micros = 0; |
| 341 | tgt_ptr[row].months = src_ptr[row]; |
| 342 | } |
| 343 | } |
| 344 | |
| 345 | static void IntervalConversionMonthDayNanos(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, |
| 346 | int64_t nested_offset, idx_t size) { |
| 347 | auto tgt_ptr = FlatVector::GetData<interval_t>(vector); |
| 348 | auto src_ptr = ArrowBufferData<ArrowInterval>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 349 | if (nested_offset != -1) { |
| 350 | src_ptr = ArrowBufferData<ArrowInterval>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 351 | } |
| 352 | for (idx_t row = 0; row < size; row++) { |
| 353 | tgt_ptr[row].days = src_ptr[row].days; |
| 354 | tgt_ptr[row].micros = src_ptr[row].nanoseconds / Interval::NANOS_PER_MICRO; |
| 355 | tgt_ptr[row].months = src_ptr[row].months; |
| 356 | } |
| 357 | } |
| 358 | |
| 359 | static void ColumnArrowToDuckDB(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, idx_t size, |
| 360 | std::unordered_map<idx_t, unique_ptr<ArrowConvertData>> &arrow_convert_data, |
| 361 | idx_t col_idx, ArrowConvertDataIndices &arrow_convert_idx, int64_t nested_offset, |
| 362 | ValidityMask *parent_mask) { |
| 363 | switch (vector.GetType().id()) { |
| 364 | case LogicalTypeId::SQLNULL: |
| 365 | vector.Reference(value: Value()); |
| 366 | break; |
| 367 | case LogicalTypeId::BOOLEAN: { |
| 368 | //! Arrow bit-packs boolean values |
| 369 | //! Lets first figure out where we are in the source array |
| 370 | auto src_ptr = ArrowBufferData<uint8_t>(array, buffer_idx: 1) + (scan_state.chunk_offset + array.offset) / 8; |
| 371 | |
| 372 | if (nested_offset != -1) { |
| 373 | src_ptr = ArrowBufferData<uint8_t>(array, buffer_idx: 1) + (nested_offset + array.offset) / 8; |
| 374 | } |
| 375 | auto tgt_ptr = (uint8_t *)FlatVector::GetData(vector); |
| 376 | int src_pos = 0; |
| 377 | idx_t cur_bit = scan_state.chunk_offset % 8; |
| 378 | if (nested_offset != -1) { |
| 379 | cur_bit = nested_offset % 8; |
| 380 | } |
| 381 | for (idx_t row = 0; row < size; row++) { |
| 382 | if ((src_ptr[src_pos] & (1 << cur_bit)) == 0) { |
| 383 | tgt_ptr[row] = 0; |
| 384 | } else { |
| 385 | tgt_ptr[row] = 1; |
| 386 | } |
| 387 | cur_bit++; |
| 388 | if (cur_bit == 8) { |
| 389 | src_pos++; |
| 390 | cur_bit = 0; |
| 391 | } |
| 392 | } |
| 393 | break; |
| 394 | } |
| 395 | case LogicalTypeId::TINYINT: |
| 396 | case LogicalTypeId::SMALLINT: |
| 397 | case LogicalTypeId::INTEGER: |
| 398 | case LogicalTypeId::FLOAT: |
| 399 | case LogicalTypeId::DOUBLE: |
| 400 | case LogicalTypeId::UTINYINT: |
| 401 | case LogicalTypeId::USMALLINT: |
| 402 | case LogicalTypeId::UINTEGER: |
| 403 | case LogicalTypeId::UBIGINT: |
| 404 | case LogicalTypeId::BIGINT: |
| 405 | case LogicalTypeId::HUGEINT: |
| 406 | case LogicalTypeId::TIMESTAMP: |
| 407 | case LogicalTypeId::TIMESTAMP_SEC: |
| 408 | case LogicalTypeId::TIMESTAMP_MS: |
| 409 | case LogicalTypeId::TIMESTAMP_NS: { |
| 410 | DirectConversion(vector, array, scan_state, nested_offset); |
| 411 | break; |
| 412 | } |
| 413 | case LogicalTypeId::VARCHAR: { |
| 414 | auto original_type = arrow_convert_data[col_idx]->variable_sz_type[arrow_convert_idx.variable_sized_index++]; |
| 415 | auto cdata = ArrowBufferData<char>(array, buffer_idx: 2); |
| 416 | if (original_type.first == ArrowVariableSizeType::SUPER_SIZE) { |
| 417 | auto offsets = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + array.offset + scan_state.chunk_offset; |
| 418 | if (nested_offset != -1) { |
| 419 | offsets = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + array.offset + nested_offset; |
| 420 | } |
| 421 | SetVectorString(vector, size, cdata, offsets); |
| 422 | } else { |
| 423 | auto offsets = ArrowBufferData<uint32_t>(array, buffer_idx: 1) + array.offset + scan_state.chunk_offset; |
| 424 | if (nested_offset != -1) { |
| 425 | offsets = ArrowBufferData<uint32_t>(array, buffer_idx: 1) + array.offset + nested_offset; |
| 426 | } |
| 427 | SetVectorString(vector, size, cdata, offsets); |
| 428 | } |
| 429 | break; |
| 430 | } |
| 431 | case LogicalTypeId::DATE: { |
| 432 | auto precision = arrow_convert_data[col_idx]->date_time_precision[arrow_convert_idx.datetime_precision_index++]; |
| 433 | switch (precision) { |
| 434 | case ArrowDateTimeType::DAYS: { |
| 435 | DirectConversion(vector, array, scan_state, nested_offset); |
| 436 | break; |
| 437 | } |
| 438 | case ArrowDateTimeType::MILLISECONDS: { |
| 439 | //! convert date from nanoseconds to days |
| 440 | auto src_ptr = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 441 | if (nested_offset != -1) { |
| 442 | src_ptr = ArrowBufferData<uint64_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 443 | } |
| 444 | auto tgt_ptr = FlatVector::GetData<date_t>(vector); |
| 445 | for (idx_t row = 0; row < size; row++) { |
| 446 | tgt_ptr[row] = date_t(int64_t(src_ptr[row]) / static_cast<int64_t>(1000 * 60 * 60 * 24)); |
| 447 | } |
| 448 | break; |
| 449 | } |
| 450 | default: |
| 451 | throw NotImplementedException("Unsupported precision for Date Type " ); |
| 452 | } |
| 453 | break; |
| 454 | } |
| 455 | case LogicalTypeId::TIME: { |
| 456 | auto precision = arrow_convert_data[col_idx]->date_time_precision[arrow_convert_idx.datetime_precision_index++]; |
| 457 | switch (precision) { |
| 458 | case ArrowDateTimeType::SECONDS: { |
| 459 | TimeConversion<int32_t>(vector, array, scan_state, nested_offset, size, conversion: 1000000); |
| 460 | break; |
| 461 | } |
| 462 | case ArrowDateTimeType::MILLISECONDS: { |
| 463 | TimeConversion<int32_t>(vector, array, scan_state, nested_offset, size, conversion: 1000); |
| 464 | break; |
| 465 | } |
| 466 | case ArrowDateTimeType::MICROSECONDS: { |
| 467 | TimeConversion<int64_t>(vector, array, scan_state, nested_offset, size, conversion: 1); |
| 468 | break; |
| 469 | } |
| 470 | case ArrowDateTimeType::NANOSECONDS: { |
| 471 | auto tgt_ptr = FlatVector::GetData<dtime_t>(vector); |
| 472 | auto src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 473 | if (nested_offset != -1) { |
| 474 | src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 475 | } |
| 476 | for (idx_t row = 0; row < size; row++) { |
| 477 | tgt_ptr[row].micros = src_ptr[row] / 1000; |
| 478 | } |
| 479 | break; |
| 480 | } |
| 481 | default: |
| 482 | throw NotImplementedException("Unsupported precision for Time Type " ); |
| 483 | } |
| 484 | break; |
| 485 | } |
| 486 | case LogicalTypeId::TIMESTAMP_TZ: { |
| 487 | auto precision = arrow_convert_data[col_idx]->date_time_precision[arrow_convert_idx.datetime_precision_index++]; |
| 488 | switch (precision) { |
| 489 | case ArrowDateTimeType::SECONDS: { |
| 490 | TimestampTZConversion(vector, array, scan_state, nested_offset, size, conversion: 1000000); |
| 491 | break; |
| 492 | } |
| 493 | case ArrowDateTimeType::MILLISECONDS: { |
| 494 | TimestampTZConversion(vector, array, scan_state, nested_offset, size, conversion: 1000); |
| 495 | break; |
| 496 | } |
| 497 | case ArrowDateTimeType::MICROSECONDS: { |
| 498 | DirectConversion(vector, array, scan_state, nested_offset); |
| 499 | break; |
| 500 | } |
| 501 | case ArrowDateTimeType::NANOSECONDS: { |
| 502 | auto tgt_ptr = FlatVector::GetData<timestamp_t>(vector); |
| 503 | auto src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 504 | if (nested_offset != -1) { |
| 505 | src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 506 | } |
| 507 | for (idx_t row = 0; row < size; row++) { |
| 508 | tgt_ptr[row].value = src_ptr[row] / 1000; |
| 509 | } |
| 510 | break; |
| 511 | } |
| 512 | default: |
| 513 | throw NotImplementedException("Unsupported precision for TimestampTZ Type " ); |
| 514 | } |
| 515 | break; |
| 516 | } |
| 517 | case LogicalTypeId::INTERVAL: { |
| 518 | auto precision = arrow_convert_data[col_idx]->date_time_precision[arrow_convert_idx.datetime_precision_index++]; |
| 519 | switch (precision) { |
| 520 | case ArrowDateTimeType::SECONDS: { |
| 521 | IntervalConversionUs(vector, array, scan_state, nested_offset, size, conversion: 1000000); |
| 522 | break; |
| 523 | } |
| 524 | case ArrowDateTimeType::DAYS: |
| 525 | case ArrowDateTimeType::MILLISECONDS: { |
| 526 | IntervalConversionUs(vector, array, scan_state, nested_offset, size, conversion: 1000); |
| 527 | break; |
| 528 | } |
| 529 | case ArrowDateTimeType::MICROSECONDS: { |
| 530 | IntervalConversionUs(vector, array, scan_state, nested_offset, size, conversion: 1); |
| 531 | break; |
| 532 | } |
| 533 | case ArrowDateTimeType::NANOSECONDS: { |
| 534 | auto tgt_ptr = FlatVector::GetData<interval_t>(vector); |
| 535 | auto src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 536 | if (nested_offset != -1) { |
| 537 | src_ptr = ArrowBufferData<int64_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 538 | } |
| 539 | for (idx_t row = 0; row < size; row++) { |
| 540 | tgt_ptr[row].micros = src_ptr[row] / 1000; |
| 541 | tgt_ptr[row].days = 0; |
| 542 | tgt_ptr[row].months = 0; |
| 543 | } |
| 544 | break; |
| 545 | } |
| 546 | case ArrowDateTimeType::MONTHS: { |
| 547 | IntervalConversionMonths(vector, array, scan_state, nested_offset, size); |
| 548 | break; |
| 549 | } |
| 550 | case ArrowDateTimeType::MONTH_DAY_NANO: { |
| 551 | IntervalConversionMonthDayNanos(vector, array, scan_state, nested_offset, size); |
| 552 | break; |
| 553 | } |
| 554 | default: |
| 555 | throw NotImplementedException("Unsupported precision for Interval/Duration Type " ); |
| 556 | } |
| 557 | break; |
| 558 | } |
| 559 | case LogicalTypeId::DECIMAL: { |
| 560 | auto val_mask = FlatVector::Validity(vector); |
| 561 | //! We have to convert from INT128 |
| 562 | auto src_ptr = ArrowBufferData<hugeint_t>(array, buffer_idx: 1) + scan_state.chunk_offset + array.offset; |
| 563 | if (nested_offset != -1) { |
| 564 | src_ptr = ArrowBufferData<hugeint_t>(array, buffer_idx: 1) + nested_offset + array.offset; |
| 565 | } |
| 566 | switch (vector.GetType().InternalType()) { |
| 567 | case PhysicalType::INT16: { |
| 568 | auto tgt_ptr = FlatVector::GetData<int16_t>(vector); |
| 569 | for (idx_t row = 0; row < size; row++) { |
| 570 | if (val_mask.RowIsValid(row_idx: row)) { |
| 571 | auto result = Hugeint::TryCast(input: src_ptr[row], result&: tgt_ptr[row]); |
| 572 | D_ASSERT(result); |
| 573 | (void)result; |
| 574 | } |
| 575 | } |
| 576 | break; |
| 577 | } |
| 578 | case PhysicalType::INT32: { |
| 579 | auto tgt_ptr = FlatVector::GetData<int32_t>(vector); |
| 580 | for (idx_t row = 0; row < size; row++) { |
| 581 | if (val_mask.RowIsValid(row_idx: row)) { |
| 582 | auto result = Hugeint::TryCast(input: src_ptr[row], result&: tgt_ptr[row]); |
| 583 | D_ASSERT(result); |
| 584 | (void)result; |
| 585 | } |
| 586 | } |
| 587 | break; |
| 588 | } |
| 589 | case PhysicalType::INT64: { |
| 590 | auto tgt_ptr = FlatVector::GetData<int64_t>(vector); |
| 591 | for (idx_t row = 0; row < size; row++) { |
| 592 | if (val_mask.RowIsValid(row_idx: row)) { |
| 593 | auto result = Hugeint::TryCast(input: src_ptr[row], result&: tgt_ptr[row]); |
| 594 | D_ASSERT(result); |
| 595 | (void)result; |
| 596 | } |
| 597 | } |
| 598 | break; |
| 599 | } |
| 600 | case PhysicalType::INT128: { |
| 601 | FlatVector::SetData(vector, |
| 602 | data: ArrowBufferData<data_t>(array, buffer_idx: 1) + GetTypeIdSize(type: vector.GetType().InternalType()) * |
| 603 | (scan_state.chunk_offset + array.offset)); |
| 604 | break; |
| 605 | } |
| 606 | default: |
| 607 | throw NotImplementedException("Unsupported physical type for Decimal: %s" , |
| 608 | TypeIdToString(type: vector.GetType().InternalType())); |
| 609 | } |
| 610 | break; |
| 611 | } |
| 612 | case LogicalTypeId::BLOB: { |
| 613 | ArrowToDuckDBBlob(vector, array, scan_state, size, arrow_convert_data, col_idx, arrow_convert_idx, |
| 614 | nested_offset); |
| 615 | break; |
| 616 | } |
| 617 | case LogicalTypeId::LIST: { |
| 618 | ArrowToDuckDBList(vector, array, scan_state, size, arrow_convert_data, col_idx, arrow_convert_idx, |
| 619 | nested_offset, parent_mask); |
| 620 | break; |
| 621 | } |
| 622 | case LogicalTypeId::MAP: { |
| 623 | ArrowToDuckDBList(vector, array, scan_state, size, arrow_convert_data, col_idx, arrow_convert_idx, |
| 624 | nested_offset, parent_mask); |
| 625 | ArrowToDuckDBMapVerify(vector, count: size); |
| 626 | break; |
| 627 | } |
| 628 | case LogicalTypeId::STRUCT: { |
| 629 | //! Fill the children |
| 630 | auto &child_entries = StructVector::GetEntries(vector); |
| 631 | auto &struct_validity_mask = FlatVector::Validity(vector); |
| 632 | for (idx_t type_idx = 0; type_idx < (idx_t)array.n_children; type_idx++) { |
| 633 | SetValidityMask(vector&: *child_entries[type_idx], array&: *array.children[type_idx], scan_state, size, nested_offset); |
| 634 | if (!struct_validity_mask.AllValid()) { |
| 635 | auto &child_validity_mark = FlatVector::Validity(vector&: *child_entries[type_idx]); |
| 636 | for (idx_t i = 0; i < size; i++) { |
| 637 | if (!struct_validity_mask.RowIsValid(row_idx: i)) { |
| 638 | child_validity_mark.SetInvalid(i); |
| 639 | } |
| 640 | } |
| 641 | } |
| 642 | ColumnArrowToDuckDB(vector&: *child_entries[type_idx], array&: *array.children[type_idx], scan_state, size, |
| 643 | arrow_convert_data, col_idx, arrow_convert_idx, nested_offset, parent_mask: &struct_validity_mask); |
| 644 | } |
| 645 | break; |
| 646 | } |
| 647 | default: |
| 648 | throw NotImplementedException("Unsupported type %s" , vector.GetType().ToString()); |
| 649 | } |
| 650 | } |
| 651 | |
| 652 | template <class T> |
| 653 | static void SetSelectionVectorLoop(SelectionVector &sel, data_ptr_t indices_p, idx_t size) { |
| 654 | auto indices = reinterpret_cast<T *>(indices_p); |
| 655 | for (idx_t row = 0; row < size; row++) { |
| 656 | sel.set_index(idx: row, loc: indices[row]); |
| 657 | } |
| 658 | } |
| 659 | |
| 660 | template <class T> |
| 661 | static void SetSelectionVectorLoopWithChecks(SelectionVector &sel, data_ptr_t indices_p, idx_t size) { |
| 662 | |
| 663 | auto indices = reinterpret_cast<T *>(indices_p); |
| 664 | for (idx_t row = 0; row < size; row++) { |
| 665 | if (indices[row] > NumericLimits<uint32_t>::Maximum()) { |
| 666 | throw ConversionException("DuckDB only supports indices that fit on an uint32" ); |
| 667 | } |
| 668 | sel.set_index(idx: row, loc: indices[row]); |
| 669 | } |
| 670 | } |
| 671 | |
| 672 | template <class T> |
| 673 | static void SetMaskedSelectionVectorLoop(SelectionVector &sel, data_ptr_t indices_p, idx_t size, ValidityMask &mask, |
| 674 | idx_t last_element_pos) { |
| 675 | auto indices = reinterpret_cast<T *>(indices_p); |
| 676 | for (idx_t row = 0; row < size; row++) { |
| 677 | if (mask.RowIsValid(row_idx: row)) { |
| 678 | sel.set_index(idx: row, loc: indices[row]); |
| 679 | } else { |
| 680 | //! Need to point out to last element |
| 681 | sel.set_index(idx: row, loc: last_element_pos); |
| 682 | } |
| 683 | } |
| 684 | } |
| 685 | |
| 686 | static void SetSelectionVector(SelectionVector &sel, data_ptr_t indices_p, LogicalType &logical_type, idx_t size, |
| 687 | ValidityMask *mask = nullptr, idx_t last_element_pos = 0) { |
| 688 | sel.Initialize(count: size); |
| 689 | |
| 690 | if (mask) { |
| 691 | switch (logical_type.id()) { |
| 692 | case LogicalTypeId::UTINYINT: |
| 693 | SetMaskedSelectionVectorLoop<uint8_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 694 | break; |
| 695 | case LogicalTypeId::TINYINT: |
| 696 | SetMaskedSelectionVectorLoop<int8_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 697 | break; |
| 698 | case LogicalTypeId::USMALLINT: |
| 699 | SetMaskedSelectionVectorLoop<uint16_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 700 | break; |
| 701 | case LogicalTypeId::SMALLINT: |
| 702 | SetMaskedSelectionVectorLoop<int16_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 703 | break; |
| 704 | case LogicalTypeId::UINTEGER: |
| 705 | if (last_element_pos > NumericLimits<uint32_t>::Maximum()) { |
| 706 | //! Its guaranteed that our indices will point to the last element, so just throw an error |
| 707 | throw ConversionException("DuckDB only supports indices that fit on an uint32" ); |
| 708 | } |
| 709 | SetMaskedSelectionVectorLoop<uint32_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 710 | break; |
| 711 | case LogicalTypeId::INTEGER: |
| 712 | SetMaskedSelectionVectorLoop<int32_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 713 | break; |
| 714 | case LogicalTypeId::UBIGINT: |
| 715 | if (last_element_pos > NumericLimits<uint32_t>::Maximum()) { |
| 716 | //! Its guaranteed that our indices will point to the last element, so just throw an error |
| 717 | throw ConversionException("DuckDB only supports indices that fit on an uint32" ); |
| 718 | } |
| 719 | SetMaskedSelectionVectorLoop<uint64_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 720 | break; |
| 721 | case LogicalTypeId::BIGINT: |
| 722 | if (last_element_pos > NumericLimits<uint32_t>::Maximum()) { |
| 723 | //! Its guaranteed that our indices will point to the last element, so just throw an error |
| 724 | throw ConversionException("DuckDB only supports indices that fit on an uint32" ); |
| 725 | } |
| 726 | SetMaskedSelectionVectorLoop<int64_t>(sel, indices_p, size, mask&: *mask, last_element_pos); |
| 727 | break; |
| 728 | |
| 729 | default: |
| 730 | throw NotImplementedException("(Arrow) Unsupported type for selection vectors %s" , logical_type.ToString()); |
| 731 | } |
| 732 | |
| 733 | } else { |
| 734 | switch (logical_type.id()) { |
| 735 | case LogicalTypeId::UTINYINT: |
| 736 | SetSelectionVectorLoop<uint8_t>(sel, indices_p, size); |
| 737 | break; |
| 738 | case LogicalTypeId::TINYINT: |
| 739 | SetSelectionVectorLoop<int8_t>(sel, indices_p, size); |
| 740 | break; |
| 741 | case LogicalTypeId::USMALLINT: |
| 742 | SetSelectionVectorLoop<uint16_t>(sel, indices_p, size); |
| 743 | break; |
| 744 | case LogicalTypeId::SMALLINT: |
| 745 | SetSelectionVectorLoop<int16_t>(sel, indices_p, size); |
| 746 | break; |
| 747 | case LogicalTypeId::UINTEGER: |
| 748 | SetSelectionVectorLoop<uint32_t>(sel, indices_p, size); |
| 749 | break; |
| 750 | case LogicalTypeId::INTEGER: |
| 751 | SetSelectionVectorLoop<int32_t>(sel, indices_p, size); |
| 752 | break; |
| 753 | case LogicalTypeId::UBIGINT: |
| 754 | if (last_element_pos > NumericLimits<uint32_t>::Maximum()) { |
| 755 | //! We need to check if our indexes fit in a uint32_t |
| 756 | SetSelectionVectorLoopWithChecks<uint64_t>(sel, indices_p, size); |
| 757 | } else { |
| 758 | SetSelectionVectorLoop<uint64_t>(sel, indices_p, size); |
| 759 | } |
| 760 | break; |
| 761 | case LogicalTypeId::BIGINT: |
| 762 | if (last_element_pos > NumericLimits<uint32_t>::Maximum()) { |
| 763 | //! We need to check if our indexes fit in a uint32_t |
| 764 | SetSelectionVectorLoopWithChecks<int64_t>(sel, indices_p, size); |
| 765 | } else { |
| 766 | SetSelectionVectorLoop<int64_t>(sel, indices_p, size); |
| 767 | } |
| 768 | break; |
| 769 | default: |
| 770 | throw ConversionException("(Arrow) Unsupported type for selection vectors %s" , logical_type.ToString()); |
| 771 | } |
| 772 | } |
| 773 | } |
| 774 | |
| 775 | static void ColumnArrowToDuckDBDictionary(Vector &vector, ArrowArray &array, ArrowScanLocalState &scan_state, |
| 776 | idx_t size, |
| 777 | std::unordered_map<idx_t, unique_ptr<ArrowConvertData>> &arrow_convert_data, |
| 778 | idx_t col_idx, ArrowConvertDataIndices &arrow_convert_idx) { |
| 779 | SelectionVector sel; |
| 780 | auto &dict_vectors = scan_state.arrow_dictionary_vectors; |
| 781 | if (!dict_vectors.count(x: col_idx)) { |
| 782 | //! We need to set the dictionary data for this column |
| 783 | auto base_vector = make_uniq<Vector>(args: vector.GetType(), args&: array.dictionary->length); |
| 784 | SetValidityMask(vector&: *base_vector, array&: *array.dictionary, scan_state, size: array.dictionary->length, nested_offset: 0, add_null: array.null_count > 0); |
| 785 | ColumnArrowToDuckDB(vector&: *base_vector, array&: *array.dictionary, scan_state, size: array.dictionary->length, arrow_convert_data, |
| 786 | col_idx, arrow_convert_idx); |
| 787 | dict_vectors[col_idx] = std::move(base_vector); |
| 788 | } |
| 789 | auto dictionary_type = arrow_convert_data[col_idx]->dictionary_type; |
| 790 | //! Get Pointer to Indices of Dictionary |
| 791 | auto indices = ArrowBufferData<data_t>(array, buffer_idx: 1) + |
| 792 | GetTypeIdSize(type: dictionary_type.InternalType()) * (scan_state.chunk_offset + array.offset); |
| 793 | if (array.null_count > 0) { |
| 794 | ValidityMask indices_validity; |
| 795 | GetValidityMask(mask&: indices_validity, array, scan_state, size); |
| 796 | SetSelectionVector(sel, indices_p: indices, logical_type&: dictionary_type, size, mask: &indices_validity, last_element_pos: array.dictionary->length); |
| 797 | } else { |
| 798 | SetSelectionVector(sel, indices_p: indices, logical_type&: dictionary_type, size); |
| 799 | } |
| 800 | vector.Slice(other&: *dict_vectors[col_idx], sel, count: size); |
| 801 | } |
| 802 | |
| 803 | void ArrowTableFunction::ArrowToDuckDB(ArrowScanLocalState &scan_state, |
| 804 | unordered_map<idx_t, unique_ptr<ArrowConvertData>> &arrow_convert_data, |
| 805 | DataChunk &output, idx_t start, bool arrow_scan_is_projected) { |
| 806 | for (idx_t idx = 0; idx < output.ColumnCount(); idx++) { |
| 807 | auto col_idx = scan_state.column_ids[idx]; |
| 808 | |
| 809 | // If projection was not pushed down into the arrow scanner, but projection pushdown is enabled on the |
| 810 | // table function, we need to use original column ids here. |
| 811 | auto arrow_array_idx = arrow_scan_is_projected ? idx : col_idx; |
| 812 | |
| 813 | if (col_idx == COLUMN_IDENTIFIER_ROW_ID) { |
| 814 | // This column is skipped by the projection pushdown |
| 815 | continue; |
| 816 | } |
| 817 | |
| 818 | ArrowConvertDataIndices arrow_convert_idx {.variable_sized_index: 0, .datetime_precision_index: 0}; |
| 819 | auto &array = *scan_state.chunk->arrow_array.children[arrow_array_idx]; |
| 820 | if (!array.release) { |
| 821 | throw InvalidInputException("arrow_scan: released array passed" ); |
| 822 | } |
| 823 | if (array.length != scan_state.chunk->arrow_array.length) { |
| 824 | throw InvalidInputException("arrow_scan: array length mismatch" ); |
| 825 | } |
| 826 | // Make sure this Vector keeps the Arrow chunk alive in case we can zero-copy the data |
| 827 | output.data[idx].GetBuffer()->SetAuxiliaryData(make_uniq<ArrowAuxiliaryData>(args&: scan_state.chunk)); |
| 828 | if (array.dictionary) { |
| 829 | ColumnArrowToDuckDBDictionary(vector&: output.data[idx], array, scan_state, size: output.size(), arrow_convert_data, |
| 830 | col_idx, arrow_convert_idx); |
| 831 | } else { |
| 832 | SetValidityMask(vector&: output.data[idx], array, scan_state, size: output.size(), nested_offset: -1); |
| 833 | ColumnArrowToDuckDB(vector&: output.data[idx], array, scan_state, size: output.size(), arrow_convert_data, col_idx, |
| 834 | arrow_convert_idx); |
| 835 | } |
| 836 | } |
| 837 | } |
| 838 | |
| 839 | } // namespace duckdb |
| 840 | |