#include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include #include namespace DB { namespace ErrorCodes { extern const int NOT_IMPLEMENTED; extern const int NUMBER_OF_ARGUMENTS_DOESNT_MATCH; extern const int TOO_LARGE_ARRAY_SIZE; extern const int TOO_LARGE_STRING_SIZE; } namespace { void fillBufferWithRandomData(char * __restrict data, size_t limit, size_t size_of_type, pcg64 & rng, [[maybe_unused]] bool flip_bytes = false) { size_t size = limit * size_of_type; char * __restrict end = data + size; while (data < end) { /// The loop can be further optimized. UInt64 number = rng(); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ unalignedStoreLittleEndian(data, number); #else unalignedStore(data, number); #endif data += sizeof(UInt64); /// We assume that data has at least 7-byte padding (see PaddedPODArray) } #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ if (flip_bytes) { data = end - size; while (data < end) { char * rev_end = data + size_of_type; std::reverse(data, rev_end); data += size_of_type; } } #endif } size_t estimateValueSize( const DataTypePtr type, UInt64 max_array_length, UInt64 max_string_length) { if (type->haveMaximumSizeOfValue()) return type->getMaximumSizeOfValueInMemory(); TypeIndex idx = type->getTypeId(); switch (idx) { case TypeIndex::String: { return max_string_length + sizeof(size_t) + 1; } /// The logic in this function should reflect the logic of fillColumnWithRandomData. case TypeIndex::Array: { auto nested_type = typeid_cast(*type).getNestedType(); return sizeof(size_t) + estimateValueSize(nested_type, max_array_length / 2, max_string_length); } case TypeIndex::Map: { const DataTypePtr & nested_type = typeid_cast(*type).getNestedType(); return sizeof(size_t) + estimateValueSize(nested_type, max_array_length / 2, max_string_length); } case TypeIndex::Tuple: { auto elements = typeid_cast(type.get())->getElements(); const size_t tuple_size = elements.size(); size_t res = 0; for (size_t i = 0; i < tuple_size; ++i) res += estimateValueSize(elements[i], max_array_length, max_string_length); return res; } case TypeIndex::Nullable: { auto nested_type = typeid_cast(*type).getNestedType(); return 1 + estimateValueSize(nested_type, max_array_length, max_string_length); } case TypeIndex::LowCardinality: { auto nested_type = typeid_cast(*type).getDictionaryType(); return sizeof(size_t) + estimateValueSize(nested_type, max_array_length, max_string_length); } default: throw Exception(ErrorCodes::NOT_IMPLEMENTED, "The 'GenerateRandom' is not implemented for type {}", type->getName()); } } ColumnPtr fillColumnWithRandomData( const DataTypePtr type, UInt64 limit, UInt64 max_array_length, UInt64 max_string_length, pcg64 & rng, ContextPtr context) { TypeIndex idx = type->getTypeId(); switch (idx) { case TypeIndex::String: { /// Mostly the same as the implementation of randomPrintableASCII function. auto column = ColumnString::create(); ColumnString::Chars & data_to = column->getChars(); ColumnString::Offsets & offsets_to = column->getOffsets(); offsets_to.resize(limit); IColumn::Offset offset = 0; for (size_t row_num = 0; row_num < limit; ++row_num) { size_t length = rng() % (max_string_length + 1); /// Slow IColumn::Offset next_offset = offset + length + 1; data_to.resize(next_offset); offsets_to[row_num] = next_offset; auto * data_to_ptr = data_to.data(); /// avoid assert on array indexing after end for (size_t pos = offset, end = offset + length; pos < end; pos += 4) /// We have padding in column buffers that we can overwrite. { UInt64 rand = rng(); UInt16 rand1 = rand; UInt16 rand2 = rand >> 16; UInt16 rand3 = rand >> 32; UInt16 rand4 = rand >> 48; /// Printable characters are from range [32; 126]. /// https://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/ data_to_ptr[pos + 0] = 32 + ((rand1 * 95) >> 16); data_to_ptr[pos + 1] = 32 + ((rand2 * 95) >> 16); data_to_ptr[pos + 2] = 32 + ((rand3 * 95) >> 16); data_to_ptr[pos + 3] = 32 + ((rand4 * 95) >> 16); /// NOTE gcc failed to vectorize this code (aliasing of char?) /// TODO Implement SIMD optimizations from Danila Kutenin. } data_to[offset + length] = 0; offset = next_offset; } return column; } case TypeIndex::Enum8: { auto column = ColumnVector::create(); auto values = typeid_cast *>(type.get())->getValues(); auto & data = column->getData(); data.resize(limit); UInt8 size = values.size(); UInt8 off; for (UInt64 i = 0; i < limit; ++i) { off = static_cast(rng()) % size; data[i] = values[off].second; } return column; } case TypeIndex::Enum16: { auto column = ColumnVector::create(); auto values = typeid_cast *>(type.get())->getValues(); auto & data = column->getData(); data.resize(limit); UInt16 size = values.size(); UInt8 off; for (UInt64 i = 0; i < limit; ++i) { off = static_cast(rng()) % size; data[i] = values[off].second; } return column; } case TypeIndex::Array: { auto nested_type = typeid_cast(*type).getNestedType(); auto offsets_column = ColumnVector::create(); auto & offsets = offsets_column->getData(); UInt64 offset = 0; offsets.resize(limit); for (UInt64 i = 0; i < limit; ++i) { offset += static_cast(rng()) % (max_array_length + 1); offsets[i] = offset; } /// This division by two makes the size growth subexponential on depth. auto data_column = fillColumnWithRandomData(nested_type, offset, max_array_length / 2, max_string_length, rng, context); return ColumnArray::create(data_column, std::move(offsets_column)); } case TypeIndex::Map: { const DataTypePtr & nested_type = typeid_cast(*type).getNestedType(); auto nested_column = fillColumnWithRandomData(nested_type, limit, max_array_length / 2, max_string_length, rng, context); return ColumnMap::create(nested_column); } case TypeIndex::Tuple: { auto elements = typeid_cast(type.get())->getElements(); const size_t tuple_size = elements.size(); Columns tuple_columns(tuple_size); for (size_t i = 0; i < tuple_size; ++i) tuple_columns[i] = fillColumnWithRandomData(elements[i], limit, max_array_length, max_string_length, rng, context); return ColumnTuple::create(std::move(tuple_columns)); } case TypeIndex::Nullable: { auto nested_type = typeid_cast(*type).getNestedType(); auto nested_column = fillColumnWithRandomData(nested_type, limit, max_array_length, max_string_length, rng, context); auto null_map_column = ColumnUInt8::create(); auto & null_map = null_map_column->getData(); null_map.resize(limit); for (UInt64 i = 0; i < limit; ++i) null_map[i] = rng() % 16 == 0; /// No real motivation for this. return ColumnNullable::create(nested_column, std::move(null_map_column)); } case TypeIndex::UInt8: { auto column = ColumnUInt8::create(); auto & data = column->getData(); data.resize(limit); if (isBool(type)) { for (size_t i = 0; i < limit; ++i) data[i] = rng() % 2; } else { fillBufferWithRandomData(reinterpret_cast(data.data()), limit, sizeof(UInt8), rng); } return column; } case TypeIndex::UInt16: [[fallthrough]]; case TypeIndex::Date: { auto column = ColumnUInt16::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(UInt16), rng, true); return column; } case TypeIndex::Date32: { auto column = ColumnInt32::create(); column->getData().resize(limit); for (size_t i = 0; i < limit; ++i) column->getData()[i] = (rng() % static_cast(DATE_LUT_SIZE)) - DAYNUM_OFFSET_EPOCH; return column; } case TypeIndex::UInt32: [[fallthrough]]; case TypeIndex::DateTime: { auto column = ColumnUInt32::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(UInt32), rng, true); return column; } case TypeIndex::UInt64: { auto column = ColumnUInt64::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(UInt64), rng, true); return column; } case TypeIndex::UInt128: { auto column = ColumnUInt128::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(UInt128), rng, true); return column; } case TypeIndex::UInt256: { auto column = ColumnUInt256::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(UInt256), rng, true); return column; } case TypeIndex::UUID: { auto column = ColumnUUID::create(); column->getData().resize(limit); /// NOTE This is slightly incorrect as random UUIDs should have fixed version 4. fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(UUID), rng, true); return column; } case TypeIndex::Int8: { auto column = ColumnInt8::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Int8), rng); return column; } case TypeIndex::Int16: { auto column = ColumnInt16::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Int16), rng, true); return column; } case TypeIndex::Int32: { auto column = ColumnInt32::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Int32), rng, true); return column; } case TypeIndex::Int64: { auto column = ColumnInt64::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Int64), rng, true); return column; } case TypeIndex::Int128: { auto column = ColumnInt128::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Int128), rng, true); return column; } case TypeIndex::Int256: { auto column = ColumnInt256::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Int256), rng, true); return column; } case TypeIndex::Float32: { auto column = ColumnFloat32::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Float32), rng, true); return column; } case TypeIndex::Float64: { auto column = ColumnFloat64::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(Float64), rng, true); return column; } case TypeIndex::Decimal32: { const auto & decimal_type = assert_cast &>(*type); auto column = decimal_type.createColumn(); auto & column_concrete = typeid_cast &>(*column); auto & data = column_concrete.getData(); data.resize(limit); /// Generate numbers from range [-10^P + 1, 10^P - 1] Int32 range = common::exp10_i32(decimal_type.getPrecision()); for (size_t i = 0; i != limit; ++i) data[i] = static_cast(rng()) % range; return column; } case TypeIndex::Decimal64: { const auto & decimal_type = assert_cast &>(*type); auto column = type->createColumn(); auto & column_concrete = typeid_cast &>(*column); auto & data = column_concrete.getData(); data.resize(limit); /// Generate numbers from range [-10^P + 1, 10^P - 1] Int64 range = common::exp10_i64(decimal_type.getPrecision()); for (size_t i = 0; i != limit; ++i) data[i] = static_cast(rng()) % range; return column; } case TypeIndex::Decimal128: { const auto & decimal_type = assert_cast &>(*type); auto column = type->createColumn(); auto & column_concrete = typeid_cast &>(*column); auto & data = column_concrete.getData(); data.resize(limit); /// Generate numbers from range [-10^P + 1, 10^P - 1] Int128 range = common::exp10_i128(decimal_type.getPrecision()); for (size_t i = 0; i != limit; ++i) data[i] = Int128({rng(), rng()}) % range; return column; } case TypeIndex::Decimal256: { const auto & decimal_type = assert_cast &>(*type); auto column = type->createColumn(); auto & column_concrete = typeid_cast &>(*column); auto & data = column_concrete.getData(); data.resize(limit); /// Generate numbers from range [-10^P + 1, 10^P - 1] Int256 range = common::exp10_i256(decimal_type.getPrecision()); for (size_t i = 0; i != limit; ++i) data[i] = Int256({rng(), rng(), rng(), rng()}) % range; return column; } case TypeIndex::FixedString: { size_t n = typeid_cast(*type).getN(); auto column = ColumnFixedString::create(n); column->getChars().resize(limit * n); fillBufferWithRandomData(reinterpret_cast(column->getChars().data()), limit, n, rng); return column; } case TypeIndex::DateTime64: { auto column = type->createColumn(); auto & column_concrete = typeid_cast &>(*column); column_concrete.getData().resize(limit); UInt64 range = (1ULL << 32) * intExp10(typeid_cast(*type).getScale()); for (size_t i = 0; i < limit; ++i) column_concrete.getData()[i] = rng() % range; /// Slow return column; } case TypeIndex::LowCardinality: { /// We are generating the values using the same random distribution as for full columns /// so it's not in fact "low cardinality", /// but it's ok for testing purposes, because the LowCardinality data type supports high cardinality data as well. auto nested_type = typeid_cast(*type).getDictionaryType(); auto nested_column = fillColumnWithRandomData(nested_type, limit, max_array_length, max_string_length, rng, context); auto column = type->createColumn(); typeid_cast(*column).insertRangeFromFullColumn(*nested_column, 0, limit); return column; } case TypeIndex::IPv4: { auto column = ColumnIPv4::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(IPv4), rng, true); return column; } case TypeIndex::IPv6: { auto column = ColumnIPv6::create(); column->getData().resize(limit); fillBufferWithRandomData(reinterpret_cast(column->getData().data()), limit, sizeof(IPv6), rng); return column; } default: throw Exception(ErrorCodes::NOT_IMPLEMENTED, "The 'GenerateRandom' is not implemented for type {}", type->getName()); } } class GenerateSource : public ISource { public: GenerateSource(UInt64 block_size_, UInt64 max_array_length_, UInt64 max_string_length_, UInt64 random_seed_, Block block_header_, ContextPtr context_) : ISource(Nested::flattenNested(prepareBlockToFill(block_header_))) , block_size(block_size_), max_array_length(max_array_length_), max_string_length(max_string_length_) , block_to_fill(std::move(block_header_)), rng(random_seed_), context(context_) {} String getName() const override { return "GenerateRandom"; } protected: Chunk generate() override { Columns columns; columns.reserve(block_to_fill.columns()); for (const auto & elem : block_to_fill) columns.emplace_back(fillColumnWithRandomData(elem.type, block_size, max_array_length, max_string_length, rng, context)); columns = Nested::flattenNested(block_to_fill.cloneWithColumns(columns)).getColumns(); return {std::move(columns), block_size}; } private: UInt64 block_size; UInt64 max_array_length; UInt64 max_string_length; Block block_to_fill; pcg64 rng; ContextPtr context; static Block & prepareBlockToFill(Block & block) { /// To support Nested types, we will collect them to single Array of Tuple. auto names_and_types = Nested::collect(block.getNamesAndTypesList()); block.clear(); for (auto & column : names_and_types) block.insert(ColumnWithTypeAndName(column.type, column.name)); return block; } }; } StorageGenerateRandom::StorageGenerateRandom( const StorageID & table_id_, const ColumnsDescription & columns_, const String & comment, UInt64 max_array_length_, UInt64 max_string_length_, const std::optional & random_seed_) : IStorage(table_id_), max_array_length(max_array_length_), max_string_length(max_string_length_) { static constexpr size_t MAX_ARRAY_SIZE = 1 << 30; static constexpr size_t MAX_STRING_SIZE = 1 << 30; if (max_array_length > MAX_ARRAY_SIZE) throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Too large array size in GenerateRandom: {}, maximum: {}", max_array_length, MAX_ARRAY_SIZE); if (max_string_length > MAX_STRING_SIZE) throw Exception(ErrorCodes::TOO_LARGE_STRING_SIZE, "Too large string size in GenerateRandom: {}, maximum: {}", max_string_length, MAX_STRING_SIZE); random_seed = random_seed_ ? sipHash64(*random_seed_) : randomSeed(); StorageInMemoryMetadata storage_metadata; storage_metadata.setColumns(columns_); storage_metadata.setComment(comment); setInMemoryMetadata(storage_metadata); } void registerStorageGenerateRandom(StorageFactory & factory) { factory.registerStorage("GenerateRandom", [](const StorageFactory::Arguments & args) { ASTs & engine_args = args.engine_args; if (engine_args.size() > 3) throw Exception(ErrorCodes::NUMBER_OF_ARGUMENTS_DOESNT_MATCH, "Storage GenerateRandom requires at most three arguments: " "random_seed, max_string_length, max_array_length."); std::optional random_seed; UInt64 max_string_length = 10; UInt64 max_array_length = 10; if (!engine_args.empty()) { const auto & ast_literal = engine_args[0]->as(); if (!ast_literal.value.isNull()) random_seed = checkAndGetLiteralArgument(ast_literal, "random_seed"); } if (engine_args.size() >= 2) max_string_length = checkAndGetLiteralArgument(engine_args[1], "max_string_length"); if (engine_args.size() == 3) max_array_length = checkAndGetLiteralArgument(engine_args[2], "max_array_length"); return std::make_shared(args.table_id, args.columns, args.comment, max_array_length, max_string_length, random_seed); }); } Pipe StorageGenerateRandom::read( const Names & column_names, const StorageSnapshotPtr & storage_snapshot, SelectQueryInfo & /*query_info*/, ContextPtr context, QueryProcessingStage::Enum /*processed_stage*/, size_t max_block_size, size_t num_streams) { storage_snapshot->check(column_names); Pipes pipes; pipes.reserve(num_streams); const ColumnsDescription & our_columns = storage_snapshot->metadata->getColumns(); Block block_header; for (const auto & name : column_names) { const auto & name_type = our_columns.get(name); MutableColumnPtr column = name_type.type->createColumn(); block_header.insert({std::move(column), name_type.type, name_type.name}); } /// Correction of block size for wide tables. size_t preferred_block_size_bytes = context->getSettingsRef().preferred_block_size_bytes; if (preferred_block_size_bytes) { size_t estimated_row_size_bytes = estimateValueSize(std::make_shared(block_header.getDataTypes()), max_array_length, max_string_length); size_t estimated_block_size_bytes = 0; if (common::mulOverflow(max_block_size, estimated_row_size_bytes, estimated_block_size_bytes)) throw Exception(ErrorCodes::TOO_LARGE_ARRAY_SIZE, "Too large estimated block size in GenerateRandom table: its estimation leads to 64bit overflow"); chassert(estimated_block_size_bytes != 0); if (estimated_block_size_bytes > preferred_block_size_bytes) { max_block_size = static_cast(max_block_size * (static_cast(preferred_block_size_bytes) / estimated_block_size_bytes)); if (max_block_size == 0) max_block_size = 1; } } /// Will create more seed values for each source from initial seed. pcg64 generate(random_seed); for (UInt64 i = 0; i < num_streams; ++i) pipes.emplace_back(std::make_shared(max_block_size, max_array_length, max_string_length, generate(), block_header, context)); return Pipe::unitePipes(std::move(pipes)); } }