YoloV8-NuGet Performance X64 CPU

When checking the dme-compunet, YoloDotNet, and sstainba and NuGets I noticed YoloDotNet readme.md detailed some performance enhancements…

What’s new in YoloDotNet v2.0?

YoloDotNet 2.0 is a Speed Demon release where the main focus has been on supercharging performance to bring you the fastest and most efficient version yet. With major code optimizations, a switch to SkiaSharp for lightning-fast image processing, and added support for Yolov10 as a little extra 😉 this release is set to redefine your YoloDotNet experience:

Changing the implementation to use SkiaSharp caught my attention because in previous testing manipulating images with the Sixlabors.ImageSharp library took longer than expected.

I built a test rig for comparing the performance of the different NuGets using standard images and ONNX Models.

I started with the dme-compunet YoloV8 NuGet which found all the tennis balls and the results were consistent with earlier tests.

dme-compunet test harness image bounding boxes

The YoloDotNet by NickSwardh NuGet update had some “breaking changes” so I built “old” and “updated” test harnesses. The V1 version found all the tennis balls and the results were consistent with earlier tests.

NickSwardh V1 test harness image bounding boxes

The YoloDotNet by NickSwardh NuGet update had some “breaking changes” so there were some code changes but the V1 and V2 results were slightly different.

NickSwardh V2 test harness image bounding boxes

Even though the YoloV8 by sstainba NuGet hadn’t been updated I ran the test harness just in case and the results were consistent with previous tests.

sstainba test harness image bounding boxes

The dme-compunet YoloV8 and NickSwardh YoloDotNet V1 versions produce the same results, but the NickSwardh YoloDotNet V2 results were slightly different. The YoloV8 by sstainba results were unchanged.

  • dme-Compunet 71 mSec
  • NickSwardV1 76 mSec
  • NickSwardV2 33 mSecs
  • SStainba 82mSec

The NickSwardV2 implementation was significantly faster, but I need to investigate the slight difference in the bounding boxes. It looks like Sixlabors.ImageSharp might be the issue.

Azure Event Grid YoloV8- Basic MQTT Client Pose Estimation

The Azure.EventGrid.Image.YoloV8.Pose application downloads images from a security camera, processes them with the default YoloV8(by Ultralytics) Pose Estimation model then publishes the results to an Azure Event Grid MQTT broker topic.

private async void ImageUpdateTimerCallback(object? state)
{
   DateTime requestAtUtc = DateTime.UtcNow;

   // Just incase - stop code being called while photo or prediction already in progress
   if (_ImageProcessing)
   {
      return;
   }
   _ImageProcessing = true;

   try
   {
      _logger.LogDebug("Camera request start");

      PoseResult result;

      using (Stream cameraStream = await _httpClient.GetStreamAsync(_applicationSettings.CameraUrl))
      {
         result = await _predictor.PoseAsync(cameraStream);
      }

      _logger.LogInformation("Speed Preprocess:{Preprocess} Postprocess:{Postprocess}", result.Speed.Preprocess, result.Speed.Postprocess);


      if (_logger.IsEnabled(LogLevel.Debug))
      {
         _logger.LogDebug("Pose results");

         foreach (var box in result.Boxes)
         {
            _logger.LogDebug(" Class:{box.Class} Confidence:{Confidence:f1}% X:{X} Y:{Y} Width:{Width} Height:{Height}", box.Class.Name, box.Confidence * 100.0, box.Bounds.X, box.Bounds.Y, box.Bounds.Width, box.Bounds.Height);

            foreach (var keypoint in box.Keypoints)
            {
               Model.PoseMarker poseMarker = (Model.PoseMarker)keypoint.Index;

               _logger.LogDebug("  Class:{Class} Confidence:{Confidence:f1}% X:{X} Y:{Y}", Enum.GetName(poseMarker), keypoint.Confidence * 100.0, keypoint.Point.X, keypoint.Point.Y);
            }
         }
      }

      var message = new MQTT5PublishMessage
      {
         Topic = string.Format(_applicationSettings.PublishTopic, _applicationSettings.UserName),
         Payload = Encoding.ASCII.GetBytes(JsonSerializer.Serialize(new
         {
            result.Boxes
         })),
         QoS = _applicationSettings.PublishQualityOfService,
      };

      _logger.LogDebug("HiveMQ.Publish start");

      var resultPublish = await _mqttclient.PublishAsync(message);

      _logger.LogDebug("HiveMQ.Publish done");
   }
   catch (Exception ex)
   {
      _logger.LogError(ex, "Camera image download, processing, or telemetry failed");
   }
   finally
   {
      _ImageProcessing = false;
   }

   TimeSpan duration = DateTime.UtcNow - requestAtUtc;

   _logger.LogDebug("Camera Image download, processing and telemetry done {TotalSeconds:f2} sec", duration.TotalSeconds);
}

The application uses a Timer(with configurable Due and Period times) to poll the security camera, detect objects in the image then publish a JavaScript Object Notation(JSON) representation of the results to Azure Event Grid MQTT broker topic using a HiveMQ client.

Utralytics Pose Model input image

The Unv ADZK-10 camera used in this sample has a Hypertext Transfer Protocol (HTTP) Uniform Resource Locator(URL) for downloading the current image. Like the YoloV8.Detect.SecurityCamera.Stream sample the image “streamed” using the HttpClient.GetStreamAsync to the YoloV8 PoseAsync method.

Azure.EventGrid.Image.YoloV8.Pose application console output

The same approach as the YoloV8.Detect.SecurityCamera.Stream sample is used because the image doesn’t have to be saved on the local filesystem.

Utralytics Pose Model marked-up image

To check the results, I put a breakpoint in the timer just after PoseAsync method is called and then used the Visual Studio 2022 Debugger QuickWatch functionality to inspect the contents of the PoseResult object.

Visual Studio 2022 Debugger PoseResult Quickwatch

For testing I configured a single Azure Event Grid custom topic subscription an Azure Storage Queue.

Azure Event Grid Topic Metrics

An Azure Storage Queue is an easy way to store messages while debugging/testing an application.

Azure Storage Explorer messages list

Azure Storage Explorer is a good tool for listing recent messages, then inspecting their payloads.

Azure Storage Explorer Message Details

The Azure Event Grid custom topic message text(in data_base64) contains the JavaScript Object Notation(JSON) of the pose detection result.

{"Boxes":[{"Keypoints":[{"Index":0,"Point":{"X":744,"Y":58,"IsEmpty":false},"Confidence":0.6334442},{"Index":1,"Point":{"X":746,"Y":33,"IsEmpty":false},"Confidence":0.759928},{"Index":2,"Point":{"X":739,"Y":46,"IsEmpty":false},"Confidence":0.19036674},{"Index":3,"Point":{"X":784,"Y":8,"IsEmpty":false},"Confidence":0.8745915},{"Index":4,"Point":{"X":766,"Y":45,"IsEmpty":false},"Confidence":0.086735755},{"Index":5,"Point":{"X":852,"Y":50,"IsEmpty":false},"Confidence":0.9166329},{"Index":6,"Point":{"X":837,"Y":121,"IsEmpty":false},"Confidence":0.85815763},{"Index":7,"Point":{"X":888,"Y":31,"IsEmpty":false},"Confidence":0.6234426},{"Index":8,"Point":{"X":871,"Y":205,"IsEmpty":false},"Confidence":0.37670398},{"Index":9,"Point":{"X":799,"Y":21,"IsEmpty":false},"Confidence":0.3686208},{"Index":10,"Point":{"X":768,"Y":205,"IsEmpty":false},"Confidence":0.21734264},{"Index":11,"Point":{"X":912,"Y":364,"IsEmpty":false},"Confidence":0.98523325},{"Index":12,"Point":{"X":896,"Y":382,"IsEmpty":false},"Confidence":0.98377174},{"Index":13,"Point":{"X":888,"Y":637,"IsEmpty":false},"Confidence":0.985927},{"Index":14,"Point":{"X":849,"Y":645,"IsEmpty":false},"Confidence":0.9834709},{"Index":15,"Point":{"X":951,"Y":909,"IsEmpty":false},"Confidence":0.96191007},{"Index":16,"Point":{"X":921,"Y":894,"IsEmpty":false},"Confidence":0.9618156}],"Class":{"Id":0,"Name":"person"},"Bounds":{"X":690,"Y":3,"Width":315,"Height":1001,"Location":{"X":690,"Y":3,"IsEmpty":false},"Size":{"Width":315,"Height":1001,"IsEmpty":false},"IsEmpty":false,"Top":3,"Right":1005,"Bottom":1004,"Left":690},"Confidence":0.8341071}]}

Myriota Connector – Azure IoT Central Downlink Methods

This post is about Azure IoT Central downlink methods and should be read in conjunction with the Myriota Connector – Azure IoT Central Downlink Methods post. My Myriota Sense and Locate template has 4 commands and in this post, I have focused on the fan speed command.

Sense and Locate Azure IoT Central Template

The Myriota Connector only supports Direct Methods which provide immediate confirmation of the result being queued by the Myriota Cloud API. The Myriota (API) control message send method responds with 400 Bad Request if there is already a message being sent to a device.

Myriota Azure Function Environment Variable configuration

The fan speed downlink payload formatter is specified in the Azure Function Environment Variables.

Sense and Locate Azure IoT Central Template Fan Speed Enumeration

The fan speed value in the message payload is configured in the fan speed enumeration.

Sense and Locate Azure IoT Central Command Fan Speed Selection

The FanSpeed.cs payload formatter extracts the FanSpeed value from the Javascript Object Notation(JSON) payload and returns a two-byte array containing the message type and speed of the fan.

using System;
using System.Collections.Generic;

using Newtonsoft.Json;
using Newtonsoft.Json.Linq;

public class FormatterDownlink : PayloadFormatter.IFormatterDownlink
{
   public byte[] Evaluate(string terminalId, string methodName, JObject payloadJson, byte[] payloadBytes)
   {
      byte? status = payloadJson.Value<byte?>("FanSpeed");

      if (!status.HasValue)
      {
         return new byte[] { };
      }

      return new byte[] { 1, status.Value };
   }
}

Sense and Locate Azure IoT Central Command Fan Speed History

Each Azure Application Insights log entry starts with the TerminalID (to simplify searching for all the messages related to device) and the requestId a Globally Unique Identifier (GUID) to simplify searching for all the “steps” associated with sending/receiving a message) with the rest of the logging message containing “step” specific diagnostic information.

Sense and Locate Azure IoT Central Command Fan Speed Application Insights

In the Myriota Device Manager the status of Control Messages can be tracked and they can be cancelled if in the “pending” state.

Myriota Control Message status Pending

A Control Message can take up to 24hrs to be delivered and confirmation of delivery has to be implemented by the application developer.

Azure Event Grid YoloV8- Basic MQTT Client Object Detection

The Azure.EventGrid.Image.Detect application downloads images from a security camera, processes them with the default YoloV8(by Ultralytics) object detection model, then publishes the results to an Azure Event Grid MQTT broker topic.

The Unv ADZK-10 camera used in this sample has a Hypertext Transfer Protocol (HTTP) Uniform Resource Locator(URL) for downloading the current image. Like the YoloV8.Detect.SecurityCamera.Stream sample the image “streamed” using the HttpClient.GetStreamAsync to the YoloV8 DetectAsync method.

private async void ImageUpdateTimerCallback(object? state)
{
   DateTime requestAtUtc = DateTime.UtcNow;

   // Just incase - stop code being called while photo or prediction already in progress
   if (_ImageProcessing)
   {
      return;
   }
   _ImageProcessing = true;

   try
   {
      _logger.LogDebug("Camera request start");

      DetectionResult result;

      using (Stream cameraStream = await _httpClient.GetStreamAsync(_applicationSettings.CameraUrl))
      {
         result = await _predictor.DetectAsync(cameraStream);
      }

      _logger.LogInformation("Speed Preprocess:{Preprocess} Postprocess:{Postprocess}", result.Speed.Preprocess, result.Speed.Postprocess);

      if (_logger.IsEnabled(LogLevel.Debug))
      {
         _logger.LogDebug("Detection results");

         foreach (var box in result.Boxes)
         {
            _logger.LogDebug(" Class {box.Class} {Confidence:f1}% X:{box.Bounds.X} Y:{box.Bounds.Y} Width:{box.Bounds.Width} Height:{box.Bounds.Height}", box.Class, box.Confidence * 100.0, box.Bounds.X, box.Bounds.Y, box.Bounds.Width, box.Bounds.Height);
         }
      }

      var message = new MQTT5PublishMessage
      {
         Topic = string.Format(_applicationSettings.PublishTopic, _applicationSettings.UserName),
         Payload = Encoding.ASCII.GetBytes(JsonSerializer.Serialize(new
         {
            result.Boxes,
         })),
         QoS = _applicationSettings.PublishQualityOfService,
      };

      _logger.LogDebug("HiveMQ.Publish start");

      var resultPublish = await _mqttclient.PublishAsync(message);

      _logger.LogDebug("HiveMQ.Publish done");
   }
   catch (Exception ex)
   {
      _logger.LogError(ex, "Camera image download, processing, or telemetry failed");
   }
   finally
   {
      _ImageProcessing = false;
   }

   TimeSpan duration = DateTime.UtcNow - requestAtUtc;

   _logger.LogDebug("Camera Image download, processing and telemetry done {TotalSeconds:f2} sec", duration.TotalSeconds);
}

The application uses a Timer(with configurable Due and Period times) to poll the security camera, detect objects in the image then publish a JavaScript Object Notation(JSON) representation of the results to Azure Event Grid MQTT broker topic using a HiveMQ client.

Console application displaying object detection results

The uses the Microsoft.Extensions.Logging library to publish diagnostic information to the console while debugging the application.

Visual Studio 2022 QuickWatch displaying object detection results.

To check the results I put a breakpoint in the timer just after DetectAsync method is called and then used the Visual Studio 2022 Debugger QuickWatch functionality to inspect the contents of the DetectionResult object.

Visual Studio 2022 JSON Visualiser displaying object detection results.

To check the JSON payload of the MQTT message I put a breakpoint just before the HiveMQ PublishAsync method. I then inspected the payload using the Visual Studio 2022 JSON Visualizer.

Security Camera image for object detection photo bombed by Yarnold our Standard Apricot Poodle.

This application can also be deployed as a Linux systemd Service so it will start then run in the background. The same approach as the YoloV8.Detect.SecurityCamera.Stream sample is used because the image doesn’t have to be saved on the local filesystem.

YoloV8-File, Stream, & Byte array Camera Images

After building some proof-of-concept applications I have decided to use the YoloV8 by dme-compunet NuGet because it supports async await and code with async await is always better (yeah right).

The YoloV8.Detect.SecurityCamera.File sample downloads images from the security camera to the local file system, then calls DetectAsync with the local file path.

private static async void ImageUpdateTimerCallback(object state)
{
   //...
   try
   {
      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image File processing start");

      using (Stream cameraStream = await _httpClient.GetStreamAsync(_applicationSettings.CameraUrl))
      using (Stream fileStream = System.IO.File.Create(_applicationSettings.ImageFilepath))
      {
         await cameraStream.CopyToAsync(fileStream);
      }

      DetectionResult result = await _predictor.DetectAsync(_applicationSettings.ImageFilepath);

      Console.WriteLine($"Speed: {result.Speed}");

      foreach (var prediction in result.Boxes)
      {
         Console.WriteLine($" Class {prediction.Class} {(prediction.Confidence * 100.0):f1}% X:{prediction.Bounds.X} Y:{prediction.Bounds.Y} Width:{prediction.Bounds.Width} Height:{prediction.Bounds.Height}");
      }

      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image processing done");
   }
   catch (Exception ex)
   {
      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} YoloV8 Security camera image download or YoloV8 prediction failed {ex.Message}");
   }
//...
}
Console application using camera image saved on filesystem

The YoloV8.Detect.SecurityCamera.Bytes sample downloads images from the security camera as an array of bytes then calls DetectAsync.

private static async void ImageUpdateTimerCallback(object state)
{
   //...
   try
   {
      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image Bytes processing start");

      byte[] bytes = await _httpClient.GetByteArrayAsync(_applicationSettings.CameraUrl);

      DetectionResult result = await _predictor.DetectAsync(bytes);

      Console.WriteLine($"Speed: {result.Speed}");

      foreach (var prediction in result.Boxes)
      {
         Console.WriteLine($" Class {prediction.Class} {(prediction.Confidence * 100.0):f1}% X:{prediction.Bounds.X} Y:{prediction.Bounds.Y} Width:{prediction.Bounds.Width} Height:{prediction.Bounds.Height}");
      }

      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image processing done");
   }
   catch (Exception ex)
   {
      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} YoloV8 Security camera image download or YoloV8 prediction failed {ex.Message}");
   }
//...
}
Console application downloading camera image as an array bytes.

The YoloV8.Detect.SecurityCamera.Stream sample “streams” the image from the security camera to DetectAsync.

private static async void ImageUpdateTimerCallback(object state)
{
   // ...
   try
   {
      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image Stream processing start");

      DetectionResult result;

      using (System.IO.Stream cameraStream = await _httpClient.GetStreamAsync(_applicationSettings.CameraUrl))
      {
         result = await _predictor.DetectAsync(cameraStream);
      }

      Console.WriteLine($"Speed: {result.Speed}");

      foreach (var prediction in result.Boxes)
      {
         Console.WriteLine($" Class {prediction.Class} {(prediction.Confidence * 100.0):f1}% X:{prediction.Bounds.X} Y:{prediction.Bounds.Y} Width:{prediction.Bounds.Width} Height:{prediction.Bounds.Height}");
      }

      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss:fff} YoloV8 Security Camera Image processing done");
   }
   catch (Exception ex)
   {
      Console.WriteLine($"{DateTime.UtcNow:yy-MM-dd HH:mm:ss} YoloV8 Security camera image download or YoloV8 prediction failed {ex.Message}");
   }
//...
}
Console application streaming camera image.

The ImageSelector parameter of DetectAsync caught my attention as I hadn’t seen this approach used before. The developers who wrote the NuGet package are definitely smarter than me so I figured I might learn something useful digging deeper.

My sample object detection applications all call

public static async Task<DetectionResult> DetectAsync(this YoloV8 predictor, ImageSelector selector)
{
    return await Task.Run(() => predictor.Detect(selector));
}

Which then invokes

public static DetectionResult Detect(this YoloV8 predictor, ImageSelector selector)
{
    predictor.ValidateTask(YoloV8Task.Detect);

    return predictor.Run(selector, (outputs, image, timer) =>
    {
        var output = outputs[0].AsTensor<float>();

        var parser = new DetectionOutputParser(predictor.Metadata, predictor.Parameters);

        var boxes = parser.Parse(output, image);
        var speed = timer.Stop();

        return new DetectionResult
        {
            Boxes = boxes,
            Image = image,
            Speed = speed,
        };
    });

    public TResult Run<TResult>(ImageSelector selector, PostprocessContext<TResult> postprocess) where TResult : YoloV8Result
    {
        using var image = selector.Load(true);

        var originSize = image.Size;

        var timer = new SpeedTimer();

        timer.StartPreprocess();

        var input = Preprocess(image);

        var inputs = MapNamedOnnxValues([input]);

        timer.StartInference();

        using var outputs = Infer(inputs);

        var list = new List<NamedOnnxValue>(outputs);

        timer.StartPostprocess();

        return postprocess(list, originSize, timer);
    }
}

It looks like most of the image loading magic of ImageSelector class is implemented using the SixLabors library…

public class ImageSelector<TPixel> where TPixel : unmanaged, IPixel<TPixel>
{
    private readonly Func<Image<TPixel>> _factory;

    public ImageSelector(Image image)
    {
        _factory = image.CloneAs<TPixel>;
    }

    public ImageSelector(string path)
    {
        _factory = () => Image.Load<TPixel>(path);
    }

    public ImageSelector(byte[] data)
    {
        _factory = () => Image.Load<TPixel>(data);
    }

    public ImageSelector(Stream stream)
    {
        _factory = () => Image.Load<TPixel>(stream);
    }

    internal Image<TPixel> Load(bool autoOrient)
    {
        var image = _factory();

        if (autoOrient)
            image.Mutate(x => x.AutoOrient());

        return image;
    }

    public static implicit operator ImageSelector<TPixel>(Image image) => new(image);
    public static implicit operator ImageSelector<TPixel>(string path) => new(path);
    public static implicit operator ImageSelector<TPixel>(byte[] data) => new(data);
    public static implicit operator ImageSelector<TPixel>(Stream stream) => new(stream);
}

Learnt something new must be careful to apply it only where it adds value.

YoloV8-One of these NuGets is not like the others

A couple of days after my initial testing the YoloV8 by dme-compunet NuGet was updated so I reran my test harnesses.

Then the YoloDotNet by NichSwardh NuGet was also updated so I reran my all my test harnesses again.

Even though the YoloV8 by sstainba NuGet hadn’t been updated I ran the test harness just incase.

The dme-compunet YoloV8 and NickSwardh YoloDotNet NuGets results are now the same (bar the 30% cutoff) and YoloV8 by sstainba results have not changed.

YoloV8-All of these NuGets are not like the others

As part investigating which YoloV8 NuGet to use, I built three trial applications using dme-compunet YoloV8, sstainba Yolov8.Net, and NickSwardh YoloDotNet NuGets.

All of the implementations load the model, load the sample image, detect objects in the image, then markup the image with the classification, minimum bounding boxes, and confidences of each object.

Input Image

The first implementation uses YoloV8 by dme-compunet which supports asynchronous operation. The image is loaded asynchronously, the prediction is asynchronous, then marked up and saved asynchronously.

using (var predictor = new Compunet.YoloV8.YoloV8(_applicationSettings.ModelPath))
{
   Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model load done");
   Console.WriteLine();

   using (var image = await SixLabors.ImageSharp.Image.LoadAsync<Rgba32>(_applicationSettings.ImageInputPath))
   {
      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect start");

      var predictions = await predictor.DetectAsync(image);

      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect done");
      Console.WriteLine();

      Console.WriteLine($" Speed: {predictions.Speed}");

      foreach (var prediction in predictions.Boxes)
      {
         Console.WriteLine($"  Class {prediction.Class} {(prediction.Confidence * 100.0):f1}% X:{prediction.Bounds.X} Y:{prediction.Bounds.Y} Width:{prediction.Bounds.Width} Height:{prediction.Bounds.Height}");
      }
      Console.WriteLine();

      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} Plot and save : {_applicationSettings.ImageOutputPath}");

      SixLabors.ImageSharp.Image imageOutput = await predictions.PlotImageAsync(image);

      await imageOutput.SaveAsJpegAsync(_applicationSettings.ImageOutputPath);
   }
}
dme-compunet YoloV8 test application output

The second implementation uses YoloDotNet by NichSwardh which partially supports asynchronous operation. The image is loaded asynchronously, the prediction is synchronous, the markup is synchronous, and then saved asynchronously.

using (var predictor = new Yolo(_applicationSettings.ModelPath, false))
{
   Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model load done");
   Console.WriteLine();

   using (var image = await SixLabors.ImageSharp.Image.LoadAsync<Rgba32>(_applicationSettings.ImageInputPath))
   {
      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect start");

      var predictions = predictor.RunObjectDetection(image);

      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect done");
      Console.WriteLine();

      foreach (var predicition in predictions)
      {
         Console.WriteLine($"  Class {predicition.Label.Name} {(predicition.Confidence * 100.0):f1}% X:{predicition.BoundingBox.Left} Y:{predicition.BoundingBox.Y} Width:{predicition.BoundingBox.Width} Height:{predicition.BoundingBox.Height}");
      }
      Console.WriteLine();

      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} Plot and save : {_applicationSettings.ImageOutputPath}");

      image.Draw(predictions);

      await image.SaveAsJpegAsync(_applicationSettings.ImageOutputPath);
   }
}
nickswardth YoloDotNet test application output

The third implementation uses YoloV8 by sstainba which partially supports asynchronous operation. The image is loaded asynchronously, the prediction is synchronous, the markup is synchronous, and then saved asynchronously.

using (var predictor = YoloV8Predictor.Create(_applicationSettings.ModelPath))
{
   Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model load done");
   Console.WriteLine();

   using (var image = await SixLabors.ImageSharp.Image.LoadAsync<Rgba32>(_applicationSettings.ImageInputPath))
   {
      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect start");

      var predictions = predictor.Predict(image);

      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} YoloV8 Model detect done");
      Console.WriteLine();

      foreach (var prediction in predictions)
      {
         Console.WriteLine($"  Class {prediction.Label.Name} {(prediction.Score * 100.0):f1}% X:{prediction.Rectangle.X} Y:{prediction.Rectangle.Y} Width:{prediction.Rectangle.Width} Height:{prediction.Rectangle.Height}");
      }

      Console.WriteLine();

      Console.WriteLine($" {DateTime.UtcNow:yy-MM-dd HH:mm:ss.fff} Plot and save : {_applicationSettings.ImageOutputPath}");

      // This is a bit hacky should be fixed up in future release
      Font font = new Font(SystemFonts.Get(_applicationSettings.FontName), _applicationSettings.FontSize);
      foreach (var prediction in predictions)
      {
         var x = (int)Math.Max(prediction.Rectangle.X, 0);
         var y = (int)Math.Max(prediction.Rectangle.Y, 0);
         var width = (int)Math.Min(image.Width - x, prediction.Rectangle.Width);
         var height = (int)Math.Min(image.Height - y, prediction.Rectangle.Height);

         //Note that the output is already scaled to the original image height and width.

         // Bounding Box Text
         string text = $"{prediction.Label.Name} [{prediction.Score}]";
         var size = TextMeasurer.MeasureSize(text, new TextOptions(font));

         image.Mutate(d => d.Draw(Pens.Solid(Color.Yellow, 2), new Rectangle(x, y, width, height)));

         image.Mutate(d => d.DrawText(text, font, Color.Yellow, new Point(x, (int)(y - size.Height - 1))));
      }

      await image.SaveAsJpegAsync(_applicationSettings.ImageOutputPath);
   }
}
sstainba YoloV8 test application output

I don’t understand why the three NuGets produced different results which is worrying.

Airbnb Dataset – NetTopologySuite spatial searching

I have used Entity Framework Core a “full fat” Object Relational Mapper (ORM) for a couple of projects, and it supports mapping to spatial data types using the NetTopologySuite library. On Stackoverflow there was some discussion about Dapper’s spatial support so I thought I would try it out.

The DapperSpatialNetTopologySuite project has Dapper SQLMapper TypeHandler, Microsoft SQL Server stored procedure name and ASP.NET Core Minimal API for each location column handler implementation.

app.MapGet("/Spatial/NearbyGeography", async (double latitude, double longitude, int distance, [FromServices] IDapperContext dapperContext) =>
{
   var origin = new Point(longitude, latitude) { SRID = 4326 };

   using (var connection = dapperContext.ConnectionCreate())
   {
      var results = await connection.QueryWithRetryAsync<Model.ListingNearbyListGeographyDto>("ListingsSpatialNearbyNTS_____", new { origin, distance }, commandType: CommandType.StoredProcedure);

      return results;
   }
})
.Produces<IList<Model.ListingNearbyListGeographyDto>>(StatusCodes.Status200OK)
.Produces<ProblemDetails>(StatusCodes.Status400BadRequest)
.WithOpenApi();

After adding the NetTopologySuite spatial library to the project the schemas list got a lot bigger.

NetTopology Suite additional schemas

My first attempt inspired by a really old Marc Gravell post and the NetTopologySuite.IO.SqlServerBytes documentation didn’t work.

CREATE PROCEDURE [dbo].[ListingsSpatialNearbyNTSLocation]
	@Origin AS GEOGRAPHY,
	@distance AS INTEGER
AS
BEGIN
DECLARE @Circle AS GEOGRAPHY = @Origin.STBuffer(@distance); 

SELECT TOP(50) UID AS ListingUID
	,[Name]
	,listing_url as ListingUrl
	,Listing.Location.STDistance(@Origin) as Distance
	,Listing.Location
FROM Listing
WHERE (Listing.Location.STWithin(@Circle) = 1) 
ORDER BY Distance
END
NetTopology Suite Microsoft.SqlServer.Types library load exception

I could see the Dapper SQLMapper TypeHandler for the @origin parameter getting called but the not locations

NetTopology Suite @Origin parameter typehandler in Visual Studio 2022 Debugger

Then found a Brice Lambson post about how to use the NetTopologySuite.IO.SqlServerBytes library to read and write geography and geometry columns.

CREATE PROCEDURE [dbo].[ListingsSpatialNearbyNTSSerialize]
	@Origin AS GEOGRAPHY,
	@distance AS INTEGER
AS
BEGIN
DECLARE @Circle AS GEOGRAPHY = @Origin.STBuffer(@distance); 

SELECT TOP(50) UID AS ListingUID
	,[Name]
	,listing_url as ListingUrl
	,Listing.Location.STDistance(@Origin) as Distance
	,Location.Serialize() as Location
FROM [listing] 
WHERE (Location.STWithin(@Circle) = 1) 
ORDER BY Distance
END
class PointHandlerSerialise : SqlMapper.TypeHandler<Point>
{
   public override Point Parse(object value)
   {
      var reader = new SqlServerBytesReader { IsGeography = true };

      return (Point)reader.Read((byte[])value);
   }

   public override void SetValue(IDbDataParameter parameter, Point? value)
   {
      ((SqlParameter)parameter).SqlDbType = SqlDbType.Udt;  // @Origin parameter?
      ((SqlParameter)parameter).UdtTypeName = "GEOGRAPHY";

      var writer = new SqlServerBytesWriter { IsGeography = true };

      parameter.Value = writer.Write(value);
   }
}

Once the location column serialisation was working (I could see a valid response in the debugger) the generation of the response was failing with a “System.Text.Json.JsonException: A possible object cycle was detected. This can either be due to a cycle or if the object depth is larger than the maximum allowed depth of 64″.

NetTopology Suite serialisation “possible object cycle detection” exception

After fixing that issue the response generation failed with “System.ArgumentException: .NET number values such as positive and negative infinity cannot be written as valid JSON.”

NetTopology Suite serialisation “possible object cycle detection” exception

Fixing these two issues required adjustment of two HttpJsonOptions

//...
builder.Services.ConfigureHttpJsonOptions(options =>
{
   options.SerializerOptions.ReferenceHandler = ReferenceHandler.IgnoreCycles;
   options.SerializerOptions.NumberHandling = JsonNumberHandling.AllowNamedFloatingPointLiterals;
});

var app = builder.Build();
//...
Swagger NetTopology Suite location serialisation response

After digging into the Dapper source code I wondered how ADO.Net handled loading Microsoft.SQLServer.Types library

app.MapGet("/Listing/Search/Ado", async (double latitude, double longitude, int distance, [FromServices] IDapperContext dapperContext) =>
{
   var origin = new Point(longitude, latitude) { SRID = 4326 };

   using (SqlConnection connection = (SqlConnection)dapperContext.ConnectionCreate())
   {
      await connection.OpenAsync();

      var geographyWriter = new SqlServerBytesWriter { IsGeography = true };

      using (SqlCommand command = connection.CreateCommand())
      {
         command.CommandText = "ListingsSpatialNearbyNTSLocation";
         command.CommandType = CommandType.StoredProcedure;

         var originParameter = command.CreateParameter();
         originParameter.ParameterName = "Origin";
         originParameter.Value = new SqlBytes(geographyWriter.Write(origin));
         originParameter.SqlDbType = SqlDbType.Udt;
         originParameter.UdtTypeName = "GEOGRAPHY";
         command.Parameters.Add(originParameter);

         var distanceParameter = command.CreateParameter();
         distanceParameter.ParameterName = "Distance";
         distanceParameter.Value = distance;
         distanceParameter.DbType = DbType.Int32;
         command.Parameters.Add(distanceParameter);

         var geographyReader = new SqlServerBytesReader { IsGeography = true };

         using (var dbDataReader = await command.ExecuteReaderAsync())
         {
            List<Model.ListingNearbyListGeographyDto> listings = new List<Model.ListingNearbyListGeographyDto>();

            int listingUIDColumn = dbDataReader.GetOrdinal("ListingUID");
            int nameColumn = dbDataReader.GetOrdinal("Name");
            int listingUrlColumn = dbDataReader.GetOrdinal("ListingUrl");
            int distanceColumn = dbDataReader.GetOrdinal("Distance");
            int LocationColumn = dbDataReader.GetOrdinal("Location");

            while (await dbDataReader.ReadAsync())
            {
               listings.Add(new Model.ListingNearbyListGeographyDto
               {
                  ListingUID = dbDataReader.GetGuid(listingUIDColumn),
                  Name = dbDataReader.GetString(nameColumn),
                  ListingUrl = dbDataReader.GetString(listingUrlColumn),
                  Distance = (int)dbDataReader.GetDouble(distanceColumn),
                  Location = (Point)geographyReader.Read(dbDataReader.GetSqlBytes(LocationColumn).Value)
               });
            }

            return listings;
         }
      }
   }
})
.Produces<IList<Model.ListingNearbyListGeographyDto>>(StatusCodes.Status200OK)
.Produces<ProblemDetails>(StatusCodes.Status400BadRequest)
.WithOpenApi();

The ADO.Net implementation worked and didn’t produce any exceptions.

Swagger ADO.Net location serialisation response

In the Visual Studio 2022 debugger I could see the Microsoft.SQLServer.Types exception but this wasn’t “bubbling” up to the response generation code.

ADO.Net location serialisation Microsoft.SqlServer.Types load failure

The location columns could also be returned as Open Geospatial Consortium (OGC) Well-Known Binary (WKB) format using the STAsBinary method.

CREATE PROCEDURE [dbo].[ListingsSpatialNearbyNTSWkb]
	@Origin AS GEOGRAPHY,
	@distance AS INTEGER
AS
BEGIN
DECLARE @Circle AS GEOGRAPHY = @Origin.STBuffer(@distance); 

SELECT TOP(50) UID AS ListingUID
	,[Name]
	,listing_url as ListingUrl
	,Listing.Location.STDistance(@Origin) as Distance
	,Location.STAsBinary() as Location
FROM [listing] 
WHERE (Location.STWithin(@Circle) = 1) 
ORDER BY Distance
END

Then converted to and from NTS Point values using WKBReader and SqlServerBytesWriter

SqlMapper.AddTypeHandler(new PointHandlerWkb());
//...
class PointHandlerWkb : SqlMapper.TypeHandler<Point>
{
   public override Point Parse(object value)
   {
      var reader = new WKBReader();

      return (Point)reader.Read((byte[])value);
   }

   public override void SetValue(IDbDataParameter parameter, Point? value)
   {
      ((SqlParameter)parameter).SqlDbType = SqlDbType.Udt;  // @Origin parameter?
      ((SqlParameter)parameter).UdtTypeName = "GEOGRAPHY";

      var geometryWriter = new SqlServerBytesWriter { IsGeography = true };

      parameter.Value = geometryWriter.Write(value);
   }
}
Successful Location processing with WKBReader

The location columns could also be returned as Open Geospatial Consortium (OGC) Well Known Text(WKT) format using the STAsText and SqlServerBytesWriter;

CREATE PROCEDURE [dbo].[ListingsSpatialNearbyNTSWkt]
	@Origin AS GEOGRAPHY,
	@distance AS INTEGER
AS
BEGIN
DECLARE @Circle AS GEOGRAPHY = @Origin.STBuffer(@distance); 

SELECT TOP(50) UID AS ListingUID
	,[Name]
	,listing_url as ListingUrl
	,Listing.Location.STDistance(@Origin) as Distance
	,Location.STAsText() as Location
FROM [listing] 
WHERE (Location.STWithin(@Circle) = 1) 
ORDER BY Distance
END

Then converted to and from NTS Point values using WKTReader and SqlServerBytesWriter

class PointHandlerWkt : SqlMapper.TypeHandler<Point>
{
   public override Point Parse(object value)
   {
      WKTReader wktReader = new WKTReader();

      return (Point)wktReader.Read(value.ToString());
   }

   public override void SetValue(IDbDataParameter parameter, Point? value)
   {
      ((SqlParameter)parameter).SqlDbType = SqlDbType.Udt;  // @Origin parameter?
      ((SqlParameter)parameter).UdtTypeName = "GEOGRAPHY";

      parameter.Value = new SqlServerBytesWriter() { IsGeography = true }.Write(value);
   }
}
Successful Location processing with WKBReader

I have focused on getting the spatial queries to work and will stress/performance test my implementations in a future post. I will also revisit the /Spatial/NearbyGeography method to see if I can get it to work without “Location.Serialize() as Location”.

Downloaded Microsoft.Data.SqlClient source code and in SqlConnection.cs this doesn’t help….

  // UDT SUPPORT
  private Assembly ResolveTypeAssembly(AssemblyName asmRef, bool throwOnError)
  {
      Debug.Assert(TypeSystemAssemblyVersion != null, "TypeSystemAssembly should be set !");
      if (string.Equals(asmRef.Name, "Microsoft.SqlServer.Types", StringComparison.OrdinalIgnoreCase))
      {
          if (asmRef.Version != TypeSystemAssemblyVersion && SqlClientEventSource.Log.IsTraceEnabled())
          {
              SqlClientEventSource.Log.TryTraceEvent("SqlConnection.ResolveTypeAssembly | SQL CLR type version change: Server sent {0}, client will instantiate {1}", asmRef.Version, TypeSystemAssemblyVersion);
          }
          asmRef.Version = TypeSystemAssemblyVersion;
      }
      try
      {
          return Assembly.Load(asmRef);
      }
      catch (Exception e)
      {
          if (throwOnError || !ADP.IsCatchableExceptionType(e))
          {
              throw;
          }
          else
          {
              return null;
          }
      }
  }

Airbnb Dataset – JSON Long Integer Issue

The Inside Airbnb London dataset has 87946 listings and the id column (which is the primary key) has a minimum value of 13913 and maximum of 973895808066047620 in the database.

Back in the early 90’s I used to live next to the Ealing Lawn Tennis Club in London

I used “Ealing” as the SearchText for my initial testing and tried different page numbers and sizes

Testing the search functionality with SwaggerUI

The listings search results JSON looked good but I missed one important detail…

[
  {
    "id": 32458423,
    "name": "Bed and breakfast in Ealing  · ★5.0 · 1 bedroom · 1 bed · 1 private bath",
    "listingURL": "https://www.airbnb.com/rooms/32458423"
  },
  {
    "id": 7905935,
    "name": "Guest suite in Ealing Broadway · ★4.93 · 1 bedroom · 1 bed · 1 private bath",
    "listingURL": "https://www.airbnb.com/rooms/7905935"
  },
  {
    "id": 5262733,
    "name": "Home in Ealing · ★4.97 · 1 bedroom · 1 shared bath",
    "listingURL": "https://www.airbnb.com/rooms/5262733"
  },
  {
    "id": 685148830257321200,
    "name": "Home in Ealing London · ★5.0 · 4 bedrooms · 8 beds · 2.5 baths",
    "listingURL": "https://www.airbnb.com/rooms/685148830257321258"
  },
  {
    "id": 10704599,
    "name": "Home in ealing, london · ★4.47 · 3 bedrooms · 4 beds · 1 bath",
    "listingURL": "https://www.airbnb.com/rooms/10704599"
  }
]

To “smoke test” to the lookup functionality I tried a couple of the listing ids

Swagger user interface successful lookup listing 7905935
Swagger user interface unsuccessful lookup of listing 685148830257321200

The HTTP GET method routing parameter and the response Data Transfer Object(DTO) the Airbnb listing Id properties are long values (ulong might have been a better choice) which should have sufficient range

string LookupByIdSql = @"SELECT Id, [Name], Listing_URL AS ListingURL
                     FROM ListingsHosts
                     WHERE id = @Id";

public record ListingLookupDto
{
   public long Id { get; set; }
   public string? Name { get; set; }
   public string? ListingURL { get; set; }
};

//...

app.MapGet("/Listing/Results/{id:long}", async (long id, IDapperContext dappperContext) =>
{
   using (var connection = dappperContext.ConnectionCreate())
   {
      ListingLookupDto result = await connection.QuerySingleOrDefaultWithRetryAsync<ListingLookupDto>(LookupByIdSql, new { id });
      if (result is null)
      {
         return Results.Problem($"Listing {id} not found", statusCode: StatusCodes.Status404NotFound);
      }

      return Results.Ok(result);
   }
})
.Produces<ListingLookupDto>(StatusCodes.Status200OK)
.Produces<ProblemDetails>(StatusCodes.Status404NotFound)
.WithOpenApi();

The id values in the search response and lookup DTOs were correct

Visual Studio 2022 Debugger inspecting listing id value

I had missed the clue in the search response JSON the listing id and the listingURL id didn’t match.

{
 "id": 685148830257321200,
 "name": "Home in Ealing London · ★5.0 · 4 bedrooms · 8 beds · 2.5 baths",
 "listingURL": "https://www.airbnb.com/rooms/685148830257321258"
},

The JavaScript Object Notation (JSON) Data Interchange Format(RFC7159) specification for numbers explains the discrepancy.

This specification allows implementations to set limits on the range
and precision of numbers accepted. Since software that implements
IEEE 754-2008 binary64 (double precision) numbers [IEEE754] is
generally available and widely used, good interoperability can be
achieved by implementations that expect no more precision or range
than these provide, in the sense that implementations will
approximate JSON numbers within the expected precision.
Airbnb listing from listingURL

Airbnb Dataset – Querying the Raw Listings

My initial ASP.NET Core Minimal AP exploration uses the Inside Airbnb London dataset which has 87946 listings. The data is pretty “nasty” with lots of nullable and wide columns so it took several attempts to import.

CREATE TABLE [dbo].[listingsRaw](
	[id] [bigint] NOT NULL,
	[listing_url] [nvarchar](50) NOT NULL,
	[scrape_id] [datetime2](7) NOT NULL,
	[last_scraped] [date] NOT NULL,
	[source] [nvarchar](50) NOT NULL,
	[name] [nvarchar](max) NOT NULL,
	[description] [nvarchar](max) NULL,
	[neighborhood_overview] [nvarchar](1050) NULL,
	[picture_url] [nvarchar](150) NULL,
	[host_id] [int] NOT NULL,
	[host_url] [nvarchar](50) NOT NULL,
	[host_name] [nvarchar](50) NULL,
	[host_since] [date] NULL,
	[host_location] [nvarchar](100) NULL,
	[host_about] [nvarchar](max) NULL,
	[host_response_time] [nvarchar](50) NULL,
	[host_response_rate] [nvarchar](50) NULL,
	[host_acceptance_rate] [nvarchar](50) NULL,
	[host_is_superhost] [bit] NULL,
	[host_thumbnail_url] [nvarchar](150) NULL,
	[host_picture_url] [nvarchar](150) NULL,
	[host_neighbourhood] [nvarchar](50) NULL,
	[host_listings_count] [int] NULL,
	[host_total_listings_count] [int] NULL,
	[host_verifications] [nvarchar](50) NOT NULL,
	[host_has_profile_pic] [bit] NULL,
	[host_identity_verified] [bit] NULL,
	[neighbourhood] [nvarchar](100) NULL,
	[neighbourhood_cleansed] [nvarchar](50) NOT NULL,
	[neighbourhood_group_cleansed] [nvarchar](1) NULL,
	[latitude] [float] NOT NULL,
	[longitude] [float] NOT NULL,
	[property_type] [nvarchar](50) NOT NULL,
	[room_type] [nvarchar](50) NOT NULL,
	[accommodates] [tinyint] NOT NULL,
	[bathrooms] [nvarchar](1) NULL,
	[bathrooms_text] [nvarchar](50) NULL,
	[bedrooms] [tinyint] NULL,
	[beds] [tinyint] NULL,
	[amenities] [nvarchar](max) NOT NULL,
	[price] [money] NOT NULL,
	[minimum_nights] [smallint] NOT NULL,
	[maximum_nights] [int] NOT NULL,
	[minimum_minimum_nights] [smallint] NULL,
	[maximum_minimum_nights] [int] NULL,
	[minimum_maximum_nights] [int] NULL,
	[maximum_maximum_nights] [int] NULL,
	[minimum_nights_avg_ntm] [float] NULL,
	[maximum_nights_avg_ntm] [float] NULL,
	[calendar_updated] [nvarchar](1) NULL,
	[has_availability] [bit] NOT NULL,
	[availability_30] [tinyint] NOT NULL,
	[availability_60] [tinyint] NOT NULL,
	[availability_90] [tinyint] NOT NULL,
	[availability_365] [smallint] NOT NULL,
	[calendar_last_scraped] [date] NOT NULL,
	[number_of_reviews] [smallint] NOT NULL,
	[number_of_reviews_ltm] [int] NOT NULL,
	[number_of_reviews_l30d] [tinyint] NOT NULL,
	[first_review] [date] NULL,
	[last_review] [date] NULL,
	[review_scores_rating] [float] NULL,
	[review_scores_accuracy] [float] NULL,
	[review_scores_cleanliness] [float] NULL,
	[review_scores_checkin] [float] NULL,
	[review_scores_communication] [float] NULL,
	[review_scores_location] [float] NULL,
	[review_scores_value] [float] NULL,
	[license] [nvarchar](max) NULL,
	[instant_bookable] [bit] NOT NULL,
	[calculated_host_listings_count] [int] NULL,
	[calculated_host_listings_count_entire_homes] [int] NOT NULL,
	[calculated_host_listings_count_private_rooms] [int] NOT NULL,
	[calculated_host_listings_count_shared_rooms] [int] NOT NULL,
	[reviews_per_month] [float] NULL
) ON [PRIMARY] TEXTIMAGE_ON [PRIMARY]

There are other data quality issues e.g. the host information is duplicated in each of their Listings e.g. host_id, host_name, host_since, host_* etc. which will need to be tidied up.

Swagger user interface for Raw Listings search functionality.

I have implemented basic (“incomplete”) OpenAPI support for functionality and stress testing.

Swagger user interface parameterised search functionality.

The search results are paginated and individual listings can be retrieved using the Airbnb listing “id”.

const string SearchPaginatedSql = @"SELECT Uid,Id,[Name], neighbourhood
                     FROM listings
                     WHERE[Name] LIKE N'%' + @SearchText + N'%'
                     ORDER By[Name] 
                     OFFSET @PageSize *(@PageNumber - 1) ROWS FETCH NEXT @PageSize ROWS ONLY";

public record ListingListDto
{
   public long Id { get; set; }
   public string? Name { get; set; }
   public string? Neighbourhood { get; set; }
};
Swagger user interface search functionality with untyped response.

The first HTTP GET implementation returns an untyped result-set which was not very helpful.

app.MapGet("/Listing/Search", async (string searchText, int pageNumber, int pageSize, [FromServices] IDapperContext dappperContext) =>
{
   using (var connection = dappperContext.ConnectionCreate())
   {
      return await connection.QueryWithRetryAsync(SearchPaginatedSql, new { searchText, pageNumber, pageSize });
   }
})
.WithOpenApi();
Swagger user interface search functionality with typed response

The second HTTP GET implementation returns a typed result-set which improved the “usability” of clients generated from the OpenAPI definition file.

app.MapGet("/Listing/Search/Typed", async (string searchText, int pageNumber, int pageSize, [FromServices] IDapperContext dappperContext) =>
{
   using (var connection = dappperContext.ConnectionCreate())
   {
      return await connection.QueryWithRetryAsync<ListingListDto>(SearchPaginatedSql, new { searchText, pageNumber, pageSize });
   }
})
.Produces<IList<ListingListDto>>(StatusCodes.Status200OK)
.WithOpenApi();

The third HTTP GET method uses the Listing id specified in a routing parameter to lookup a Listing

string LookupByIdSql = @"SELECT Id,[Name], neighbourhood
FROM ListingsHosts
WHERE id = @Id";

public record ListingLookupDto
{
public long Id { get; set; }
public string? Name { get; set; }
public string? Neighbourhood { get; set; }
};
Swagger user interface Listing lookup functionality
app.MapGet("/Listing/{id:long}", async (long id, IDapperContext dappperContext) =>
{
   using (var connection = dappperContext.ConnectionCreate())
   {
      ListingLookupDto result = await connection.QuerySingleOrDefaultWithRetryAsync<ListingLookupDto>(LookupByIdSql, new { id });
      if (result is null)
      {
         return Results.Problem($"Listing {id} not found", statusCode: StatusCodes.Status404NotFound);
      }

      return Results.Ok(result);
   }
})
.Produces<ListingLookupDto>(StatusCodes.Status200OK)
.Produces<ProblemDetails>(StatusCodes.Status404NotFound)
.WithOpenApi();

The lack of validation of the SearchText, PageSize and PageNumber parameters allow uses to enter invalid values which caused searches to fail.

Swagger user interface search functionality with an invalid page number

My initial approach was to decorate the parameters of the ValidatedQuery method with DataAnnotations to ensure only valid values were accepted.

const byte SearchTextMinimumLength = 3;
const byte SearchTextMaximumLength = 20;
const byte PageNumberMinimum = 1;
const byte PageNumberMaximum = 100;
const byte PageSizeMinimum = 5;
const byte PageSizeMaximum = 50;

app.MapGet("/Listing/Search/ValidatedQuery", async (
   [FromQuery,Required, MinLength(SearchTextMinimumLength, ErrorMessage = "SearchTextMaximumLength"), MaxLength(SearchTextMaximumLength, ErrorMessage = "SearchTextMaximumLegth")]
   string searchText,
   [FromQuery, Range(PageNumberMinimum, PageNumberMaximum, ErrorMessage = "PageNumberMinimum PageNumberMaximum")]
   int pageNumber,
   [FromQuery, Range(PageSizeMinimum, PageSizeMaximum, ErrorMessage = "PageSizeMinimum PageSizeMaximum")]
   int pageSize,
   [FromServices] IDapperContext dappperContext) =>
{
   using (var connection = dappperContext.ConnectionCreate())
   {
      return await connection.QueryWithRetryAsync<ListingListDto>(SearchPaginatedSql, new { searchText, pageNumber, pageSize });
   }
})
.Produces<IList<ListingListDto>>(StatusCodes.Status200OK)
.Produces<ProblemDetails>(StatusCodes.Status400BadRequest)
.WithOpenApi();

This wasn’t a great solution because the validation of the parameters was declared as part of the user interface and would have to be repeated everywhere listing search functionality was provided.

Swagger user interface search functionality with parameter validation

The final HTTP GET method uses DataAnnotations on the SearchParameter(DTO) and AsParameters to bind the query string values.

app.MapGet("/Listing/Search/Parameters", async ([AsParameters] SearchParameters searchParameters,
   [FromServices] IDapperContext dappperContext) =>
{
   using (var connection = dappperContext.ConnectionCreate())
   {
      return await connection.QueryWithRetryAsync<ListingListDto>(SearchPaginatedSql, new { searchText = searchParameters.SearchText, searchParameters.PageNumber, searchParameters.PageSize });
   }
})
.Produces<IList<ListingListDto>>(StatusCodes.Status200OK)
.Produces<ProblemDetails>(StatusCodes.Status400BadRequest)
.WithOpenApi();

public record SearchParameters
{
   // https://github.com/domaindrivendev/Swashbuckle.AspNetCore/issues/2658 possibly related?
   public const byte SearchTextMinimumLength = 3;
   public const byte SearchTextMaximumLength = 15;
   public const int PageNumberMinimum = 1;
   public const int PageNumberMaximum = 100;
   public const byte PageSizeMinimum = 5;
   public const byte PageSizeMaximum = 50;

   //[FromQuery, Required, MinLength(SearchTextMinimumLength, ErrorMessage = "SearchTextMinimumLegth"), MaxLength(SearchTextMaximumLength, ErrorMessage = "SearchTextMaximumLegth")]
   //[Required, MinLength(SearchTextMinimumLength, ErrorMessage = "SearchTextMinimumLegth"), MaxLength(SearchTextMaximumLength, ErrorMessage = "SearchTextMaximumLegth")]
   [MinLength(SearchTextMinimumLength, ErrorMessage = "SearchTextMinimumLegth"), MaxLength(SearchTextMaximumLength, ErrorMessage = "SearchTextMaximumLegth")]
   public string SearchText { get; set; }

   //[FromQuery, Range(PageNumberMinimum, PageNumberMaximum, ErrorMessage = "PageNumberMinimum PageNumberMaximum")]
   //[Required, Range(PageNumberMinimum, PageNumberMaximum, ErrorMessage = "PageNumberMinimum PageNumberMaximum")]
   [Range(PageNumberMinimum, PageNumberMaximum, ErrorMessage = "PageNumberMinimum PageNumberMaximum")]
   public int PageNumber { get; set; }

   [Range(PageSizeMinimum, PageSizeMaximum, ErrorMessage = "PageSizeMinimum PageSizeMaximum")]
   public int PageSize { get; set; }
}
Swagger user interface search functionality with parameter validation

This last two implementations worked though the error messages I had embedded in the code were not displayed I think this is related to this Swashbuckle Issue.

There is also an issue looking up some listings with larger listing ids which I will need some investigation.