/** * @file quickstart_sparse_heter.cc * * @section LICENSE * * The MIT License * * @copyright Copyright (c) 2018-2020 TileDB, Inc. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to deal * in the Software without restriction, including without limitation the rights * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell * copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in * all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN * THE SOFTWARE. * * @section DESCRIPTION * * When run, this program will create a 2D sparse array with each dimension * having separate datatypes, similar to a dataframe. It will write some data to * it, and read a slice of the data back. */ #include #include using namespace tiledb; // Name of array. std::string array_name("quickstart_sparse_heter_array"); void create_array() { // Create a TileDB context. Context ctx; // The array will be 4x4 with dimensions "rows" and "cols", with domain [1,4]. Domain domain(ctx); domain.add_dimension(Dimension::create(ctx, "rows", {{1, 4}}, 4)) .add_dimension(Dimension::create(ctx, "cols", {{1, 4}}, 4)); // The array will be sparse. ArraySchema schema(ctx, TILEDB_SPARSE); schema.set_domain(domain).set_order({{TILEDB_ROW_MAJOR, TILEDB_ROW_MAJOR}}); // Add a single attribute "a" so each (i,j) cell can store an integer. schema.add_attribute(Attribute::create(ctx, "a")); // Create the (empty) array on disk. Array::create(array_name, schema); } void write_array() { Context ctx; // Write some simple data to cells (1, 1.1), (2, 1.2) and (2, 1.3). std::vector rows = {1, 2, 2}; std::vector cols = {1.1, 1.2, 1.3}; std::vector data = {1, 2, 3}; // Open the array for writing and create the query. Array array(ctx, array_name, TILEDB_WRITE); Query query(ctx, array, TILEDB_WRITE); query.set_layout(TILEDB_UNORDERED) .set_buffer("a", data) .set_buffer("rows", rows) .set_buffer("cols", cols); // Perform the write and close the array. query.submit(); array.close(); } void read_array() { Context ctx; // Prepare the array for reading Array array(ctx, array_name, TILEDB_READ); // Prepare the query Query query(ctx, array, TILEDB_READ); // Slice only rows 1, 2 and cols 1.1, 1.2, 1.3 query.add_range(0, 1, 2); query.add_range(1, 1, 2); // Prepare the vector that will hold the result. // We take an upper bound on the result size, as we do not // know a priori how big it is (since the array is sparse) std::vector data(3); std::vector rows(3); std::vector cols(3); query.set_layout(TILEDB_ROW_MAJOR) .set_buffer("a", data) .set_buffer("rows", rows) .set_buffer("cols", cols); // Submit the query and close the array. query.submit(); array.close(); // Print out the results. auto result_num = query.result_buffer_elements()["a"].second; for (uint64_t r = 0; r < result_num; r++) { int32_t i = rows[r]; float j = cols[r]; int32_t a = data[r]; std::cout << "Cell (" << i << ", " << j << ") has data " << a << "\n"; } } int main() { Context ctx; if (Object::object(ctx, array_name).type() != Object::Type::Array) { create_array(); write_array(); } read_array(); return 0; }