# ZipponData ZipponData is a library developped in the context of [ZipponDB](https://github.com/MrBounty/ZipponDB/tree/v0.1.3). The library intent to create a simple way to store and parse data from a file in the most efficient and fast way possible. There is 6 data type available in ZipponData: | Type | Zig type | Bytes in file | | --- | --- | --- | | int | i32 | 4 | | float | f64 | 8 | | bool | bool | 1 | | str | []u8 | 4 + len | | uuid | [16]u8 | 16 | | unix | u64 | 8 | Each type have its array equivalent. ## Quickstart 1. Create a file with `createFile` 2. Create some `Data` 3. Create a `DataWriter` 4. Write the data 5. Create a schema 6. Create an iterator with `DataIterator` 7. Iterate over all value 8. Delete the file with `deleteFile` Here an example of how to use it: ``` zig const std = @import("std"); pub fn main() !void { const allocator = std.testing.allocator; // 0. Make a temporary directory try std.fs.cwd().makeDir("tmp"); const dir = try std.fs.cwd().openDir("tmp", .{}); // 1. Create a file try createFile("test", dir); // 2. Create some Data const data = [_]Data{ Data.initInt(1), Data.initFloat(3.14159), Data.initInt(-5), Data.initStr("Hello world"), Data.initBool(true), Data.initUnix(2021), }; // 3. Create a DataWriter var dwriter = try DataWriter.init("test", dir); defer dwriter.deinit(); // This just close the file // 4. Write some data try dwriter.write(&data); try dwriter.write(&data); try dwriter.write(&data); try dwriter.write(&data); try dwriter.write(&data); try dwriter.write(&data); try dwriter.flush(); // Dont forget to flush ! // 5. Create a schema // A schema is how the iterator will parse the file. // If you are wrong here, it will return wrong/random data // And most likely an error when iterating in the while loop const schema = &[_]DType{ .Int, .Float, .Int, .Str, .Bool, .Unix, }; // 6. Create a DataIterator var iter = try DataIterator.init(allocator, "test", dir, schema); defer iter.deinit(); // 7. Iterate over data while (try iter.next()) |row| { std.debug.print("Row: {any}\n", .{ row }); } // 8. Delete the file (Optional ofc) try deleteFile("test", dir); try std.fs.cwd().deleteDir("tmp"); } ``` ***Note: The dir can be null and it will use cwd.*** ## Array All data type have an array equivalent. To write an array, you need to first encode it using `allocEncodArray` before writing it. This use an allocator so you need to free what it return. When read, an array is just the raw bytes. To get the data itself, you need to create an `ArrayIterator`. Here an example: ```zig pub fn main() !void { const allocator = std.testing.allocator; // 0. Make a temporary directory try std.fs.cwd().makeDir("array_tmp"); const dir = try std.fs.cwd().openDir("array_tmp", .{}); // 1. Create a file try createFile("test", dir); // 2. Create and encode some Data const int_array = [4]i32{ 32, 11, 15, 99 }; const data = [_]Data{ Data.initIntArray(try allocEncodArray.Int(allocator, &int_array)), // Encode }; defer allocator.free(data[0].IntArray); // DOnt forget to free it // 3. Create a DataWriter var dwriter = try DataWriter.init("test", dir); defer dwriter.deinit(); // 4. Write some data try dwriter.write(&data); try dwriter.flush(); // 5. Create a schema const schema = &[_]DType{ .IntArray, }; // 6. Create a DataIterator var iter = try DataIterator.init(allocator, "test", dir, schema); defer iter.deinit(); // 7. Iterate over data var i: usize = 0; if (try iter.next()) |row| { // 8. Iterate over array var array_iter = ArrayIterator.init(&row[0]); // Sub array iterator while (array_iter.next()) |d| { try std.testing.expectEqual(int_array[i], d.Int); i += 1; } } try deleteFile("test", dir); try std.fs.cwd().deleteDir("array_tmp"); } ``` ## Benchmark Done on a AMD Ryzen 7 7800X3D with a Samsung SSD 980 PRO 2TB (up to 7,000/5,100MB/s for read/write speed) on one thread. Data use: ```zig const schema = &[_]DType{ .Int, .Float, .Int, .Str, .Bool, .Unix, }; const data = &[_]Data{ Data.initInt(1), Data.initFloat(3.14159), Data.initInt(-5), Data.initStr("Hello world"), Data.initBool(true), Data.initUnix(2021), }; ``` Result: | Number of Entity | Total Write Time (ms) | Average Write Time / entity (μs) | Total Read Time (ms) | Average Read Time / entity (μs) | File Size (kB) | | --- | --- | --- | --- | --- | --- | | 1 | 0.01 | 13.63 | 0.025 | 25.0 | 0.04 | | 10 | 0.01 | 1.69 | 0.03 | 3.28 | 0.4 | | 100 | 0.04 | 0.49 | 0.07 | 0.67 | 4.0 | | 1_000 | 0.36 | 0.36 | 0.48 | 0.48 | 40 | | 10_000 | 3.42 | 0.34 | 4.67 | 0.47 | 400 | | 100_000 | 36.39 | 0.36 | 48.00 | 0.49 | 4_000 | | 1_000_000 | 361.41 | 0.36 | 481.00 | 0.48 | 40_000 | ***Note: You can check [benchmark](/ZipponDB/Benchmark) to see performance of the real database using multi-threading. Was able to parse 1_000_000 users in less than 100ms***